Department of Industrial Engineering and Management Indian Institute of Technology, Kharagpur

West Bengal- 721 302

ON

SUPPLY CHAIN MANAGEMENT (03.10.07 – 07.10.07)

LECTURE NOTES

Prof. S. P. Sarmah Principal Coordinator

Prof. M. Jenamani Co-coordiantor

AICTE-SPONSORED QIP SHORT-TERM COURSE

Prepared By

Contents

Part A: Introductory Topics

I Introduction to Supply Chain Management 1

II Materials Management 11

III Sourcing Decisions 17

IV Bullwhip Effect and Supply Chain Management 27

V Distribution Management Overview 39

Part B: Advanced Topics

VI Supply Chain Management and Multi-echelon Inventories 55

VII Supply Chain Contract and Coordination 65

VIII A Method for Supply Base Rationalization Considering Supply Risk 79

IX E-procurement 103

X Economic Theory of Auctions 113

XI Technologies for Supply Chain Integration 127

XII Security and Payment Issues in Integrated Supply Chain 141

XIII Automatic Data Capture using RFID and its Implications 155

XIV Dynamic Vehicle Routing with GPS and GIS 175

XV Optimization of Supply Chain Network: Simulation, Taguchi, and Psychoclonal Algorithm Embedded Approach

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PART A

INTRODUCTORY TOPICS

CHAPTER I

Introduction to Supply Chain Management

-An Overview

1.1 Introduction A Supply Chain encompasses all activities in fulfilling customer demands and requests. These activities are associated with the flow and transformation of goods from the raw materials stage, through to the end user, as well as the associated information and funds flows. There are four stages in a supply chain: the supply network, the internal supply chain (which are manufacturing plants), distribution systems, and the end users. Moving up and down the stages are the four flows: material flow, service flow, information flow and funds flow.

Different entities of the suply chain may be owned by one individual/organization or by several individuals/organizations. Most supply chains of today belong to the later category. In such supply chains, the owner of each entity attempts to maximize its benefit. Focus on individual links of the supply chain invariably leads to inefficient and high cost product/service delivery system. In the process, such a supply chain looses to supply chain that is customer focussed where the individual links orient their business processes and decisions to ensure least cost delivery of products/services to the ultimate customer.

1.2 Increasing role of supply chain management

In the last two decades, both academicians as well as practitioners have shown keen interest on the subject supply chain management (SCM). Globalization of market, increased competition, reducing gap between products in terms of quality and performance are compelling the academicians and industry to rethink about how to manage business operations more efficiently and effectively. Since, scope for improvement within the organization is decreasing, the academicians and captains of industry are looking for newer alternatives of integrating the business activities beyond the organization’s boundary. More specifically, they are trying to align and coordinate the business processes and activities of the channel members to improve the overall performance and effectiveness of supply chain. As a result, producer, vendor and buyer have started aligning their operations to make the business more focussed. The alignment and integration lead to deliver more value and satisfaction to the customer for the same price. This makes the supply chain more competitive. In the process, the channel partners increase their market share and profit.

In principle, all the steps from procurement of raw materials to final delivery of products can be included into a supply chain, connecting raw materials suppliers, manufacturers, distributors and finally customers. Thus, a supply chain can be viewed as a group of entities interacting to transform raw material into finished product and then final delivery of the product to the customer. Each member of the chain provides some activity necessary for the transformation (value addition) and interactions among the members can take place in the form of information, material and money flow.

Narasimhan and Carter (1998) in their work have mentioned that a well-integrated supply chain involves coordinating the flows of materials and information between suppliers, manufacturers, and customers. Effective supply chain management requires planning and coordination among the various channel members including manufacturers, retailers and intermediaries if any (see Thomas and Griffin, 1996). Due to severe competition in the market, to-day companies are more focusing on their core competencies and therefore, increased cooperation between the members of the channel and coordination of decisions are important. For coordination between various parties to be effective, faith and effective communication between the members are essential.

Bowersox and Closs (1996) put forward argument that to be fully effective in today’s competitive business environment, firms must expand their integrated behavior to incorporate customer and

supplier and refer this extension of integrated behaviors through external integration as supply chain management. Further, no organization has enough resources so that it can single handedly manage the entire supply chain. Therefore, to compete in the market in the present day, the obvious choice is to work in coordination with the other members of the channel.

1.3 Evolution and growth of SCM

Supply chain management passed through various stages of evolution in last two decades to reach the present position. The present supply chain management thought could be traced back to early sixties when Mallen (1963) in his theory developed within the framework of marketing, advocated for extension of the organization to include all other members of the distribution channel. Croomi et al. (2000) in their review paper have mentioned that initial development of SCM is along the lines of managing physical distribution and transport systems. It used techniques such as industrial dynamics and minimum total cost approach to distribution and logistics. However, when the focus on opportunities for competitive advantage started shifting from inside the manufacturing plant to develop relationship with supplier and then finally with customers, work on SCM research started picking up and since 1980’s, the subject is receiving attentions of both academicians and practitioners.

Maloni et al. (1997) have mentioned that evolution of intra-firm functional integration has occurred for most firms over the last few decades. SCM extends the concept of intra-firm functional integration to inter-firm integration of all the firms in the supply chain. Stevens (1989) has shown that the process of integration of supply chain undergoes following four phases.

(i) Initially, every department of business organization functions independently. In such an environment, there is no coordination to achieve an overall customer service objective.

(ii) In the second phase, focus shifts to cost reduction without any consideration of performance achievement. The business organization goes for functional integration within the organization through materials management, manufacturing management and distribution for smooth flow of goods.

(iii) Recognizing the importance of customer’s in the business, the third phase aims to integrate those aspects which are directly under the control of company including the outward goods management and thereby integrating supply and demand along the company’s own chain.

(iv) Phase four is the extension of the integration to external activities and in the process; company becomes customer oriented by linking customer’s purchasing activities with company’s own procurement, manufacturing and subsequently marketing activities.

From the above discussion, it is clear that supply chain management has evolved around a customer focused business vision. This focus has led firms to change their internal and external linkages and capture synergy of inter-functional and inter-organizational integration and coordination.

When we talk about supply chain of two or more parties linked by flows of goods, information and funds then some interpret that SCM research is essentially the same as that of multi-echelon inventory research addressed way back in sixties by Clark and Scarf (1960). But, multi-echelon inventory theory is mainly concerned with controlling the timing and quantity of material flows whereas SCM research encompasses a much broader set of issues. As for example, the issue of transportation delay in multi echelon inventory theory is studied via lead-time whereas SCM studies the alternative modes of logistics (Tsay et al., 1999).

1.4 Definition of Supply Chain

In supply chain management literature, there are numerous definitions available indicating the fact that still there is not one universally acceptable definition for supply chain. This is probably because of the fact that authors in this area use the term supply chain rather loosely to cover a wide

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range of subject areas. This has however, helped supply chain management research to grow at much faster rate.

The definition of supply chain is given in the eighth edition of APICS dictionary (1995) as “The processes from initial raw materials to the ultimate consumption of finished product linking across supplier-user companies.”

Houlihan (1985) is credited with coining the term Supply Chain and he mentioned some characteristics unique to SCM. Jones and Riley (1985) defined supply chain as an integrative approach to dealing with the planning and control of the materials flow from supplier to end-users.

Oliver and Webber (1992) state that a supply chain should be viewed as a single entity that is guided by strategic decision-making. Stevens (1989) defines it as a connected series of activities from supplier to customer. Villa (2001) has commented that in principle, all the activities from raw material supplies to the final delivery of product to the customers can be included within the purview of the supply chain.

From the large number of definitions available in the literature of supply chain, it is observed that though they are apparently different from each other, yet, they are carrying more or less the same meaning representing a system of supplier, manufacture, distributor, retailer and customer, where materials flow downstream from supplier to customer whereas information and financial flows are bi-directional.

Further, another important observation is that each definition has recognized that supply chain works beyond the boundary of one organization and as a result a clear cut demarcation of boundary in a supply chain is difficult since, where the boundary of one organization finishes, the boundary of the other member of the supply chain starts.

Though above definitions of supply chain are mostly related with manufacturing organizations, yet, there exists supply chain related with service organizations also. Some authors (e.g. Anderson et al., 2000a, 2000b) have focused their study relating to supply chain of service organization.

Supply chain management

Supply chain management presupposes that there exists a supply chain, which needs to be managed efficiently. As mentioned above, each entity of the supply chain performs a specific activity and therefore, in a supply chain, there will be number of activities. Thus management of the supply chain means coordination of activities among the entities as one system. Thomas and Griffin (1996) have mentioned that supply chain management strategy is to coordinate the various organizations’ objectives in order to increase the efficiency of the entire supply chain.

Therefore, one major challenge of supply chain management is the development of effective coordination mechanisms between the various entities of supply chain that ultimately lead to reduction of lead time, cost and uncertainty. Time, cost and uncertainty are important (though in varying degrees) to all supply chains. For improving the profit of the firm, SCM must focus in effective and efficient management of materials, information and financial flows in the supply chain.

Ellram and Cooper (1990) have mentioned that since SCM extends the concepts of functional integration beyond a firm to all firms in the supply chain, therefore, each member of supply chain should help each other to improve the competitiveness of the chain. According to Christopher (1992), the real competition is not between a company and another company, but rather between a supply chain and another supply chain. A supply chain as a whole may have its own identity and function like an independent firm.

The council of logistics management defines supply chain management as

“The systematic strategic coordination of the traditional business functions and the tactics across these business functions within a particular company and across businesses within the supply chain for the

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purposes of improving the long term performance of the individual companies and the supply chain as whole”

Lee and Billington (1992) mentioned that supply chain management focuses in the coordination of the manufacturing, logistics, and materials management functions within an organization. They have defined supply chain management

“The integration activities taking place among a network of facilities that procure raw materials, transform them into immediate goods and then final products, and deliver products to customers through distribution system.”

Mentzer (2001) has mentioned SCM as the systematic, strategic coordination of the traditional business functions within a particular company and across business within the supply chain for the purpose of improving the long-term performance of the individual companies and the supply chain as a whole.

New and Pyne (1995) have described supply chain management as the chain linking each element of the manufacturing and supply process from raw materials through to the end user, encompassing several organizational boundaries.

Further, La Londe (1997) elaborating the activities of SCM states that it is the process of managing relationships, information, and materials flow across enterprise borders to deliver enhanced customer service and economic value through synchronized management of the flow of physical goods, money and associated information from sourcing to consumption.

Table 1.1 shows the important components of a supply chain management function. These components can be grouped into two main business processes. They are: Materials Management and Physical Distribution Management.

As a whole, supply chain management is a set of approaches used to integrate various activities of suppliers, manufacturers, warehouses efficiently, so that merchandise is produced and distributed at the right locations and the right time to minimize system-wide costs while satisfying customers’ service level requirements. Successful integration of various processes depends on the accurate and timely sharing of information by all members of the supply chain. Due to various activities involved in a supply chain, there may be multiple stakeholders (e.g. suppliers, manufacturers, distributors, retailers and customers) in a supply chain.

From the above-mentioned views of various authors, it is clear that the scope of SCM is not only confined to functional activities but also organizational. The functional scope includes a broad range of traditional business functions, whereas the organizational scope is concerned with relationship issues important to the participating firms. There is a need to develop a partnership relation between the participating firms to reap the full benefit of SCM. Therefore, firms must take steps to break down both intra and inter firm barriers to smoothen uncertainty and to improve control over distribution channels. From intra-firm functional integration to external integration is the demand of the situation and supply chain partnership can bridge the gap between the buyer and the supplier.

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Table 1.1 Components of Total Supply Chain Management (Source: Monackza et al., 2002; Min and Zhou, 2002)

Inbound Production Outbound

Materials Management Physical distribution

Supply chain Management

Materials management function

• Sourcing activities

• Inbound transportation

• Receiving and warehousing

• Production planning and scheduling

• Materials control

• Intra and inter plant movement

• Quality control

Work in process inventory

Finished goods inventory

Physical distribution function

• Order receipt and processing

• Pricing

• Promotional support

• Outbound transportation

• Return product handling

• Field warehousing

• Customer service and material availability

• Finished goods delivery

• Inventory control

1.5 Characteristics of supply chain

There are some characteristics unique to supply chain management that differentiates it from earlier researches on integrated logistics. Houlihan (1985) has mentioned that inclusion of strategic decision-making aspect is one of the key characteristics that differentiate it from earlier research of integrated logistics. Ganeshan et al. (1999) while referring to characteristics of SCM have mentioned that supply chain management reaches out beyond the boundaries of cost containment and links operating decisions to strategic considerations within and beyond the company and in channel wide supply chain. Thus, SCM as a management philosophy has the following characteristics

• It is a system approach that considers the channel as a whole to manage the flow of goods from the supplier to the ultimate customer.

• A strategic orientation towards cooperative efforts to synchronize and converge intra-firm and inter-firm operational and strategic capabilities into a united whole.

• A customer focus to create unique and individualized sources of customer value, leading to customer satisfaction.

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In adopting a supply chain management philosophy, firms must establish management practices that permit them to act or behave consistently with the philosophy. Some of the activities necessary for implementing SCM philosophy can be mentioned as follows

• Integration of processes

• Mutually aggreable goal with same focus of customer service

• Mutual sharing of information

• Mutual sharing of channel risks and benefits

• Development of partnership to maintain long term relationship and cooperation.

1.6 Centralized vs. decentralized supply chain

Generally, one encounters supply chains with two different structures. In one structure of supply chain, all upstream and down stream members of the chain have same owner. In this type of supply chain, there will be perfect information regarding cost structure and demand pattern of the members. In such chains, a central planner has the power to impose a globally optimal solution that is implemented by all memebers.

On the other hand, in the other type of supply chain structure, different entities involved in the supply chain have different ownership. In such a structure, there are many independent decision makers. This supply chain is fundamentally different from the first supply chain in two aspects: First, there is information asymmetry as a party in the chain may not be willing to share information regarding his/her cost structure and demand. Secondly, the objective of a member of the chain may be different from another member.

The second kind of supply chain as mentioned above is mostly prevalent in to-days business environment. This class of supply chains is receiving attention of researcher and practitioner. Planning and coordination issues of such supply chains have attracted attention of researchers as well as practitioners (Ertogral et al., 2001). Such supply chains may be termed as decentralized chains. Differences in focus of individual channel members make such supply chain more costly and less effective. Decentralized supply chains are less efficient compared to centralized (single owner) supply chains. Much of the current researches focus on how this gap of inefficiency can be reduced by implementing novel coordination mechanisms (e.g. contracts). Tsay, Nahmias and Agarwal (1999) refer to this as system wide performance improvement objective. The most commonly used term in the literature for system wide performance improvement is the channel coordination.

1.7 Important elements of supply chain management

Supply chain management can broadly be divided into the following four elements

(i) Purchasing elements: Purchasing is an extremely important element in supply chain management since incoming material quality, delivery time and purchase price are dependent on buyer supplier relationship and capabilities of the supplier. Problems with suppliers will ultimately affect the end customers. One of the crucial issues in purchasing is supplier management. This involves assessing supplier’s current capabilities and how can it be improved. One of the key activities in supplier management is supplier evaluation. This occurs both when potential suppliers are being evaluated for a future purchase and when existing suppliers are periodically evaluated for performance purpose. Over time, careful and effective supplier management efforts allow firms to selectively screen out poor performing suppliers and build successful, trusting relationships with the remaining top performing supplier. These suppliers can provide immense benefits to the buying firm and the entire supply chain.

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(ii) Operations Elements

Once materials, components and other purchased products are delivered to the buying organization, a number of internal operations elements become important in assembling and or processing the items into finished products, ensuring that the right amount of product is produced and that finished product meets specific quality, cost and customer service requirements. When actual demand does not materialize with forecasted demand then the firm is left with either too much inventory or not enough. In both the situations, firm is incurring cost. To minimize these costs, firms rely on demand management strategies. The objective of the system is to match demand with the available capacity, either by improving production scheduling, curtailing demand, using a back order system, or increasing capacity. Further controlling or managing inventory is one of the most important aspects of operations and certainly valuable to the firm. Firms can and typically do have some sort of material requirement planning software system for managing their inventory. These systems can be linked through out the organization and its supply chain partner using enterprise resource planning systems providing real time sales data, inventory, and production information to supply chain partners.

(iii) Distribution elements

The finished good are delivered to customer a number of different nodes of transportation. Delivering products to customers at right time, right quality, and right time and in right volume require a high level of planning and cooperation between the firm, its customers, and the various distribution elements. Transportation management decisions involve a tradeoff between cost and delivery timing and customer service. In order to provide the desired level of customer service, firms must identify customer requirements and then provide the right combination of transportation, storage, packaging, and information services to successfully satisfy those requirements. Further, designing and building a distribution network is one method of ensuring successful product delivery. Again there is a trade off between the cost of the distribution system’s design and customer service.

(iv) Integration Elements

Activities in supply chain are said to be coordinated when members of the supply chain work together while making delivery, inventory, production, and purchasing decisions that impact the profit of the supply chain. Successful supply chain integration occurs when the participants realize that supply chain management must become part of all of the firms’ strategic planning processes, in which objectives and policies are jointly determined on the basis of final customers’ needs and what the supply chain as a whole does well. Finally, firms act together to maximize total supply chain profits by determining optimal purchase quantities, product availabilities, service levels, lead times and production quantities.

1.8 Partnership formation and vendor managed inventory

Focus on external integration as means to minimize rise of cost naturally led to the development of partnership relation amongst the player of a supply chain. This trend did not escape the attention of the supply chain management researchers. Parlar and Weng (1997) have mentioned that long-term relationship makes both parties of the supply chain better off. However, Boddy et al. (1998) have mentioned that a change from traditional unfavorable relationship between the customer and the supplier to one of closer partnering requires careful consideration and attention of management.

Kotler (1997) observed that as firm globalize, they realize that no matter how large they are, they lack the total resources for success of a complete supply chain that produces value. They recognize the necessity of partnering with other organizations in the supply chain. In a comprehensive review paper on supply chain partnership, Maloni and Benton (1997) have mentioned that a supply chain partnership is a relationship formed between two independent entities in the supply channel to achieve specific objectives and benefits. From this definition, one can assume that partnership relationship between the members increases level of information sharing. This ultimately helps in improving the overall performance of the supply chain through reduction in inventories and reduction in total cost of managing the supply chain. Besides, one looks ahead to form partnership with the supplier to have

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better customer service, better capacity utilization, technological innovation, and to new products. A number of authors (e.g. Ellram, 1991) are quite optimistic about the success of supply chain partnership. Ellram (1991) has mentioned that the central theme of these partnership relationships is the establishment of, and commitment to, an interactive exchange where both parties benefit from sharing of risks and resources.

Further, supply chain management to be effective, it requires mutually sharing of channel risks and rewards among partners (Ellram and Cooper, 1990). This assures competitive advantage in the long run. In a complex relationship in which performance is difficult to measure, profit or income sharing based on incentive scheme is an important cooperation mechanism. Again, how to share the extra benefit due to cooperation is an important issue. Probably a win-win approach to sharing of benefit between parties is essential for cooperation. For sharing of benefits, many a times, a negotiation process may be needed where both the parties are free to exchange views.

According to Rubin and Carter (1990), negotiation is the process of reviewing, planning and analyzing used by two parties to reach acceptable agreement or compromise. It is a process where both parties adjust their expectations during the resolution of conflict, as one party does not have absolute power over the other party. In partnership relationship, it is important to note that each party must be rational. Negotiation building is integral to successful long-term business relationship (Sharland, 2001).

In the negotiation process, it is also important what to say and how to say since; it may have significant impact on the outcome of the process. When one party inappropriately presents the things to the other party, then the later party may perceive that long-term relationship may not be feasible with the former party. Kelle et al. (2003) have recently studied buyer supplier partnership in JIT environment and developed models that can be used as quantitative tools for contract negotiation between the two parties. This could be done through either price correction or through price premium.

Another important facet of cooperation is information sharing, an essential enabler to minimize inventory in the supply chain. Information systems must be able to track and communicate production and customer requirements at different levels in the supply chain (Cooper, Lambert and Pagh, 1997).

Despite the perceived benefits of cooperation, it is seen that there is hardly perfect cooperation as dominant player tend to be opportunistic. Munson et al. (1999) have nicely elaborated about misuse of power by channel leader. Sometimes, it is noticed that a powerful manufacturer in the channel controls dependent suppliers, subcontractors and retailers.

Another new dimension to the supplier-buyer partnership is the vendor managed inventory concept. In vendor managed inventory (VMI) strategy, supplier manages inventory at customer’s premises and assumes responsibility to replenish the inventory to meet the needs of the buyer who withdraws items as per his/her requirements. In this strategy, supplier takes decision on inventory replenishment without waiting for the customer to order the product. Recently, few articles as mentioned below have cited the benefits of vendor-managed inventory or supplier owned inventory (SOI). VMI helps in supply chain coordination leading to improvement in the performance of a supply chain.

Dong and Xu (2002) in their work have stated that VMI is an effective strategy of realizing most of a fully coordinated supply chain. Piplani et al. (2003) have used the term supplier owned inventory (SOI) instead of VMI. In their study on the effect of SOI strategy on the cost of a supply chain concluded that total cost in SOI is never be more than the non-coordinated supply chain. Some other authors such as Cetinkaya and Lee (2000), Waller et al. (1999), Hung et al. (1995) have also studied the VMI strategy and its benefits. From the study of VMI literature, it is seen that by implementation of VMI strategy, a buyer reduces total inventory related cost. However, whether VMI reduces the supplier cost is still an open question.

1.9 Future trend in supply chain management

The practice of supply chain management is recent phenomenon, as many organizations are just now beginning to realize the benefits and problems that accompany an integrated supply chain. Supply

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chain management is an incredibly complex undertaking involving cultural change among most or all of the participants, investment and training in software and communication system, and realignment of the competitive strategies employed among the participating firm. In the competitive business environment products, technology and customers change and subsequently the priorities for the supply chain must also change, requiring supply chains to be ever more flexible to respond quickly to these changes. The future issues for the supply chains that need to be addressed include increasing supply chain responsiveness, creating an environment friendly supply chain, and reducing total supply chain cost.

(i) Supply chain expansion

The supply chain dynamic to day is changing and companies are now working with firms located all over the globe to coordinate purchasing, manufacturing, shipping and distribution activities. While this global expansion of the supply chain is occurring, firms are also trying to expand their control of the supply chain to include second and third-tier suppliers and customers. Thus supply chain expansion is occurring in two fronts : increasing breadth of the supply chain to include foreign manufacturing, office and retail sites, along with foreign suppliers and customers; and increasing the depth of the supply chain to include second and third tier suppliers and customers. As the firm becomes more comfortable and experienced with their supply chain relationships with immediate suppliers and customers, there is a tendency to expand the depth of the supply chain by creating relationships with second and third tier suppliers and customers. This span expansion phenomenon is just now taking place in most industries and will continue to increase as the practice of supply chain management matures.

(ii) Increasing supply chain responsiveness

Agile manufacturing, JIT, mass customization, efficient consumer response and quick response are all terms referring to concepts that are intended to make the make the firm more flexible and responsiveness to customer requirements and changes. In to-days intense competitive market environment firms and their supply chains are looking today at ways to become more responsive to their customers. To achieve greater levels of customer responsiveness, supply chains must identify the end customers’ needs and position the supply chain’s products and services to successfully compete, and then consider the impact of these requirements on the supply chain participants and the intermediate products and services they provide. Once these issues have been adequately addressed among the firms in the supply chain, additional improvement in responsiveness comes from designing more effective and faster product and service delivery systems as the products are passed through the supply chain and by continuously monitoring changes occurring the market place and using this information to reposition the supply chain to stay competitive.

To improve customer responsiveness, firms require to reevaluate their supply chain relationships, to utilize business process reengineering, to reposition warehouses, design new products and services, reduce new product design cycles, standardize processes and products, empower and train workers on multiple skills, build customer feedback into daily operations, and, finally, link together all of the supply chain participants’ information and communication systems. To day web based systems are proving to be ideal for connecting supply chain members efficiently. One such tool is Formation systems’ Optiva 4.0. a web based product life cycle management platform that provides business intelligence and collaboration from product concept through introduction to improvement. It can be integrated within a supply chain to help product gets to market faster.

(iii) Greening of supply chains

Producing, packaging, moving, storing, repackaging, and delivering products to their final destinations can pose a significant threat to the environment in terms of discarded packaging materials, carbon monoxide emission, noise, traffic congestion, and other forms of industrial pollution. As the practice of supply chain management becomes more widespread, firms and their supply chain partners will be working harder to reduce these environmental problem.

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References

[1] Bowersox, D.J. and Closs, D.J., 1996, Logistical management: The integrated supply chain process, New York, NY: Mc Graw-Hill

[2] Christopher, M., 1992, Logistics and Supply Chain Management: Strategies for reducing costs and improving services, Publisher: FT Pitman Publishing.

[3] Croomi, Simon, Roamno, P. and Giannakis, M., 2000, Supply chain management: an analytical framework for critical literature review, European Journal of Purchasing and Supply Management, Vol. 6, No. 1, pp. 67-83.

[4] Cetinkaya, S. and Lee, C.Y., 2000, Stock replenishment and shipment scheduling for vendor managed inventory systems, Management Science, Vol. 46, No. 2, pp. 217-232.

[5] Dong, Y. and Xu, K., 2002, A supply chain model of vendor managed inventory, Transportation Research Part E, Vol. 38, No. 2, pp. 75-95.

[6] Ertogral, K., and Wu, D. S., 2001, A bargaining game of supply chain contracting, Source internat website www.lehigh.edu/sdw1/ertogral3.pdf

[7] Houlihan, J.B., 1985, International supply chain management, International Journal Of Physical Distribution and Material Management, Vol. 15, No. 1, pp. 22-38.

[8] Kelle, P., Khateeb, F. and Miller. A.P., 2003, Partnership and negotiation support by joint optimal ordering/set up policies for JIT, International Journal of Production Economics, Vol. 81-82, pp. 433-443.

[9] Lee, H.L. and Billington, C., 1993, Material management in decentralized supply chains, Operations Research, Vol. 41, No. 5, pp. 835-847.

[10] Maloni, J.M. and Benton, C.W., 1997, Supply chain partnership: Opportunities for operations research, European Journal of Operational Research, Vol. 101, No. 3, pp. 419-429.

[11] Mentzer, T.J., 2001, Supply Chain Management, Sage Publisher.

[12] Min, Hockey., Zhaou, G., 2002, Supply chain modelling: past,present and future, Computers and Industrial Engineeering, Vol. 43, No. 1-2, pp. 231-249

[13] Monczka, R., Trent, R. and Handfield, R., 2002, Purchasing and Supply Chain Management, Second Edition: publisher: Thomson Asia Pte Ltd. Singapore

[14] Narasimhan, R., Carter,J.R., 1998, Linking business unit and material sourcing strategies. Journal of Business Logistics. Vol. 19, No. 2, pp. 155-171

[15] Piplani, R. and Viswanathan, S., 2003, A model for evaluating supplier owned inventory strategy, International Journal of Production Economics, Vol. 81-82, pp. 565-571.

[16] Thomas, D. J. and Grifin, P. J., 1996, Coordinated supply chain management, European Journal of Operational Research, Vol. 94, No. 1, pp. 1-15.

[17] TSay, A., Nahmias,S., & Agarwal,N., 1999, Modeling supply chain contracts: A review, In: S. Tayur, M. Magazine, R. Ganeshan, (Eds.), Quantitative models for supply chain management, Published by Kluwer academic publishers, 1999, pp. 301-336

[18] Waller, M., Johnson, M.E., Davis, T., 1999. Vendor-managed inventory in the retail supply chain. Journal of Business Logistic, Vol. 20, No. 1, pp. 183-203.

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CHAPTER II Materials Management

2.1 Introduction

Putting in the simplest terms materials management is about moving the materials within an organization. What do “materials” mean? Materials can basically be defined as those objects or things that are to be moved in order to produce goods. Material is one of the 5M’s that a manager has at his command, the other being Men, Machine, Methods and Money. Materials could be in the form of raw materials, paperwork, messages or information etc. So materials can be both tangible and intangible. You see the newspaper boy delivering the newspaper to your doorstep everyday or the mi1kman delivering the milk packets to you. These are tangible materials. There is also some material moved when you watch a movie on your television or when you receive a phone call. These are the intangible materials that are moved. So materials management is an important function of every business. The better is the materials management in a company the better is the health of that company.

2.2 Materials management and its functions

Materials can be put in three categories. First category is purchased materials like the raw materials, components, spare parts and items that are used and do not appear in the end product. The second category is of in-process materials or the materials in the semi-finished stages and lastly the finished goods that are ready for customers. One has to manage these materials. The aim of this management is to obtain the materials at the minimum possible price while maintaining quality also and to maintain the inventories in such a way that minimum cost is incurred while maintaining adequate materials for the production process.

Let us see what materials management actually means. It is defined as a function that integrates purchasing, storage, inventory control, materials handling and standardization etc in an organization to achieve its objective of reducing the costs. Every organization wishes to maximize its profit by maximizing its production and minimizing the cost of production. The average material cost in a manufacturing setup is around 50-70% of the total expenditure, which further goes up if one takes into account the inventory costs, storage, waste and other factors etc. It is therefore imperative for an organization to have a sound materials management with an objective to reduce material costs, control inventories, ensure uniform flow of materials and maintain good relations with suppliers. Materials Management has to do activities related to planning, accusation and utilization of materials.

Materials Management as a subject started picking up from early sixties and has gained importance thereafter. Since the amount of money incurred on materials is higher than the cumulative amount for machines, men and methods, one has to give high importance to the materials. It is the most feasible area that can offer opportunities for reduction of costs and improvement of profits. Materials add value to the product, as the product quality is directly dependent on the materials used. Materials Management thus can be seen as a system that assures the availability of products to the customers at minimum cost. In a nutshell, we can say that materials management is about making available the right materials in right quantity at a right price on the right time.

The functions of the materials management are materials planning and control, purchasing, inventory control, store keeping, material handling, warehousing, standardization & simplification and organization & appraisal of materials. Let us discuss them briefly.

1) Materials planning and control: Material requirement lies at the core of successful material management. This function is at the core of all the material requirements in any manufacturing process.

2) Purchasing: This function identifies the sources of supply, does market research, call tenders and select suppliers, negotiate with them and thus make available the raw materials.

3) Inventory control: This function is responsible for the location and storage of materials so that they remain available at the minimum cost and quickest time.

4) Store keeping: This function is responsible for the receipt and issue of the materials. The materials are stored in such a way that minimum handling is required and wastage is minimal.

5) Material handling: This function aims at minimizing handling and provision of equipments for handling materials. This function is crucial for minimizing space requirements, effective distribution and for providing better working space.

6) Warehousing: This function is responsible for the storage facilities for the materials, weighing facilities, materials handling equipments, material distribution facilities, fire fighting instruments etc.

7) Standardization and simplification: This function selects items of great demand and sets the standards for quality, raw material, sizes and performance of any product.

8) Organization & appraisal of materials: This function helps in effective functioning by proving smooth flow. It provides coordination and avoid delays and wastages

Management of materials embodies various costs. Since the ultimate aim of materials management is to reduce the costs of materials and hence the final product, it is worth seeing what these costs are. Let us take a glimpse of what these costs are:

Table 1.1: Costs involved in the Management of Materials

Sl. No. Costs Description

1 Cost of materials The basic cost of materials that has to be paid to suppliers

2 Purchasing cost The cost incurred in purchases e.g. cost on staff, tendering, stationary, postage, processing supplies, receiving, inspection

3 Inventory carrying costs The cost incurred on storage including buildings, costs on staff, interest on capital locked/ borrowed, obsolescence

4 Packaging cost Costs incurred on paper, plastic, metal foils, metal and wood containers etc.

5 Transportation cost Costs incurred on moving the goods to different desired locations from time to time

6 Material handling cost Costs incurred on handling equipments like cranes and conveyors

7 Wastage during production

Costs incurred on holding scrap, obsolete stock and their disposal

Source: Shah N.M. (1996), An Integrated Concept of Materials Management

An integrated materials management system helps in taking judicious decisions that in turn leads to lower cost for materials. Similarly if an organization has low inventory carrying costs, less stock outs etc., it is bound to do well.

2.3 Management of flow of materials

In any organization, the responsibility for maintaining the quality of the product and incurring less cost on its production is the responsibility of the production/ operation, deciding the price of the product and finding the customers that will buy it comes under marketing. The question arises that if

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it is so, what do the materials management function does? The answer is that from the time the materials enter the warehouse of the organization from the suppliers, the role of materials management starts and gets going till the final product is obtained. The interrelated activities that are carried out to achieve this are sequenced after each other in a systematic manner. Management of this flow of materials is called materials management. This flow of materials is met through a set of activities presented in Table 1.2 given below

Table 1.2: Set of activities for flow of materials.

Sl. No. Activity Function

1 Planning Setting the goals, indicating the sources of finance

2 Scheduling Requirements specification, quantum and delivery schedules

3 Purchasing and Procurement Vendor selection, vendor contracts

4 Inspection and Quality Control Conforming quality

5 Stores and Inventory Control Determining inventories, maintenance and upkeep

6 Materials handling and distribution logistics Controlling flows, distribution, shipments

Source: Dutta A.K (1998), Materials Management: Procedures, Text and Cases

The table above highlights the importance of integrated systems and dependence of function models for decision-making. The organizations have now become multidimensional in nature. Total materials management concept evolved to address this dimension and avoid conflicting objectives. Total material management helps in establishing accountability so that response to a problem is quick and appropriate. The material functions are accomplished in more coordinated ways with the help of this integrated approach. When this happens there is increased communication for the need of materials and hence one gets lower costs, better inventory turnover, reduce stock outs and other significant benefits. Data processing systems are designed on the basis of the integrated material function.

2.4 Materials logistics management

Materials logistics management program (MLM) started in eighties for an American university management students to meet the industry requirements. Bowersox et al. (1984) presented an overview of this philosophy.

Figure 1.1 shows the value-added activities included in MLM. There are two flows that are depicted here. One is a requirements information flow from customers to suppliers and other is a value-added materials flow from the suppliers to the customers. The whole process is directed by an integrated database. MLM covers three essential areas required for moving materials i.e. purchasing, manufacturing and physical distribution and asserts an integrated logic to ensure smooth flow of materials. As can be seen in the figure the MLM seeks the achievement of objective like controlled customer service performance, inventory reduction, minimum variance in planned operations, minimum total cost of operations and procurement and product quality control.

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Customers Physical distribution Manufacturing Purchasing Suppliers

Requirements information flow

Industrial enterprise

Value-added materials flow

* Demand management * Master schedule management * Supply management

* Scheduled distribution * JIT Scheduling * Scheduled requirements

* Postponement * Flexibility * Responsiveness

Target objectives

* Controlled customer service performance * Minimum Variance * Min total cost of operations & procurement

* Inventory reduction * Product quality control

Figure 1.1: Materials Logistics Management Process

Source: Bowersox et al. (1984)

Bowersox et al. (1984) further described the three interfaces that MLM covers. These are the physical distribution interface, the manufacturing interface and the purchasing interface. Let us see how these interfaces contribute to the MLM productivity. The Table 1.3 summarizes this.

Table 1.3: The MLM productivity and various interfaces

Interface Interfaces with Perception 1 Perception 2 Perception 3

The Physical Distribution Interface

Customers and manufacturing

Demand Management

Scheduled distribution

Postponement

The Manufacturing Interface

Physical distribution and purchasing

Master schedule management

JIT scheduling Flexibility

The Purchasing Interface

Manufacturing and external supplier network

Supply management

Schedule requirements

Responsiveness

Source: Based on Bowersox et al. (1984)

There are some perceptions attached with each interface.

From the point of view of the physical distribution interface,

• Demand management coordinates and modifies how customers order products in an effort to reduce uncertainty and simplify transactions,

• Scheduled distribution aims to fulfill customer order in a short span of time and

• Postponement carries a planned delay of an activity as long as possible until a profitable preposition is achieved.

From the point of view of the manufacturing interface,

• Master schedule management resolves the conflicts between manufacturing and marketing as it is the point where overall requirements determined by forecasts, customers orders, back orders

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and physical distribution are collated.

• Just in time (JIT) scheduling or Kanban means bringing inventories to zero level. To reduce the inventory, methods like reducing lot sizes, load leveling, quality control and preventive maintenance can be used.

• Flexibility should be achieved by using “pull” systems, computer-based planning and control systems. Achieving flexibility is important to manufacturing as it will reduce manufacturing activities unless or otherwise specifically asked for.

From the point of view of the purchasing interface,

• Supply management identifies the manufacturing trends and initiates effective purchasing for long-term competitive advantage.

• Schedule requirements expedite purchasing. They must be specified so that suppliers provide exact lead-time information and purchasers provide exact requirement information to the supply network. This can be achieved by employing a suitable integrated data-processing system.

• Responsiveness of the supply network identifies frequent changes in customer requirements and product life cycles.

2.5 Interfaces of materials management

According to Dutta (1998), “When we say that materials management contains an integrated process of materials flow, in, through and out of an organization, we give some indication that materials management has interfaces of two kinds, internal and external”.

The interfaces are shown in Table 1.4

Materials management actually does not start with purchase of materials and end with production of materials. The total financial management concept forces it to do much more. Dutta (1998) further stresses the materials management functions as follows:

1) Materials forecasting, budgeting, planning and programming

2) Scheduling, purchasing and procurement

3) Receiving and receiving inspection as to quantity and quality

4) Inventory control, storage and warehousing

5) Materials handling, movement control and traffic etc.

6) Dispatch, shipping and disposal

In addition to this materials management also needs to put attention to coordinate all the above activities and keep liaison between manufacturing, finance and marketing etc.

2.6 Materials and information flow

The aim of any organization is to manage its 5 M’s as effectively as it can. These 5 M’s as discussed earlier are Men, Machines, Money, Methods and Materials. The purpose of this coordination is production of superior goods at minimal costs. In this discussion, you have focused on the materials. If you have to exercise proper control over materials then you have to take care of your material flow as well as information flow.

There is a definitive flow of materials from vendor/supplier to the warehousing/customer and the organization is abounded in information flow. This is very important as materials and information both are extremely important and both should be readily available at a time when needed.

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Table 1.4: Various Interfaces of Materials Management S.No. Interface Description

1 Market forecasting Forecast demands to determine production on the basis of existing/ expanded facilities, equipment, processes, manpower and materials.

2 Production Materials flow begins before the production cycle, runs throughout and continues even after production, ensuring uninterrupted flow of materials to feed the production process.

3 Finance Materials budget is affected by non-availability of finance. A major chunk of finance is invested in materials and inventors.

Internal

4 Inventory control Materials management has a critical role here as availability of materials and access to physical supply has to be assured.

5 Inspection and quality control

Close liaison with materials management is required as it also runs throughout like materials.

6

Inte

rnal

Materials Handling and physical distribution logistics

Materials management ensures that materials are physically distributed at the right time with a minimum of handling.

7 Consumers/Customers Materials management interfaces with customers rarely but it happens sometimes.

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Exte

rnal

Suppliers/Other Companies

Materials Management is responsible for keeping a close liaison with outside vendors and other companies seeking trade relationships. Evaluation of supplier performances should also be done.

External

Source: Based on Dutta A.K (1998), Materials Management: Procedures, Text and Cases

The materials flow is starting from the vendor/supplier from which material is to be purchased. Once the material is purchased, it is received and inspected. After inspection stores accepts it. Production/ manufacturing and its subsystems ca1l for the materials as and when it is required and logistics take control after that. Later on warehousing and customer comes.

Information flow embodies much more than the materials flow. Be it production planning and control or sales and marketing, inventory control or purchasing and procurement activities. The effectiveness of the materials flow is thus dependent on decision-information. If an organization can control these two flows easily and effectively then it will definitely render goods products at a low cost and also would be able to offer good service.

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CHAPTER III Sourcing Decisions

3.1 Sourcing Decisions: The Make-or-Buy Decision

While the term outsourcing popularly refers to buying materials and components from suppliers instead of making them in-house, it also refers to buying materials or components that were previously made in-house. In recent years, the trend has been moving toward outsourcing combined with the creation of supply chain relationships, although traditionally firms preferred the make option by using backward integration and forward integration. Backward integration refers to acquiring sources of supply, whereas forward integration refers to acquiring customers’ operations. For example, an end-product manufacturer acquiring a supplier’s operations that supplied component parts is an example of backward integration. Acquiring a distributor or other outbound logistics providers would be an example of forward integration.

Whether to make or buy materials or components is a strategic decision that can impact an organization’s competitive position. It is obvious that most organizations buy their MRO (Maintenance, repair and operational) items and office supplies rather than make the items themselves. Similarly, seafood restaurants usually buy their fresh seafood from fish markets. However, the decision on how to acquire highly complex engineering parts that impact the firm’s competitive position is a complicated one.

Traditionally, cost has been the major driver when making sourcing decisions. However, organizations today focus more on the strategic impact of the sourcing decision on the firm’s competitive advantage. For example, Honda would not outsource the making of its engines because it considers engines to be a vital part of its automobiles’ performance and reputation. However, Honda may outsource the production of brake drums to a high-quality, low-cost supplier that specializes in brake drums. Generally, organizations outsource non-core activities while focusing on core competencies. Finally, the make-or-buy decision is not an exclusive either-or option. Firms can always choose to make some components or services in-house and buy the rest form suppliers.

3.2 Reasons for Outsourcing

Organizations buy or outsource materials, components, and /or services from suppliers for many reasons. Let us review these now:

(i) Cost advantage: For many firms, cost is an important reason for buying or outsourcing, especially for supplies and components that are non vital to the organization’s operations and competitive advantage. This is usually true for standardized or generic supplies and materials for which suppliers may have the advantage of economy of scale because they supply the same item to multiple users. In most outsourcing cases, the quantity needed is so small that it does not justify the investment in capital equipment to make the item. Some foreign suppliers may also offer a cost advantage because of lower labor and/or materials costs.

(ii) Insufficient capacity: A firm may be running at or near capacity, making it unable to produce the components in-house. This can happen when demand grows faster than anticipated or when expansion strategies fail to meet demand. The firm buys parts or components to free up capacity in the short term to focus on vital operations. Firms may even subcontract vital components and /or operations under very strict terms and conditions in order to meet demand. When managed properly, subcontracting is an effective means to expand short-term capacity.

(iii) Lack of expertise: The firm may not have the necessary technology and expertise to manufacture the item. Maintaining long-term technological and economical viability for

non core activities may be affecting the firm’s ability to focus on core competencies. Suppliers may hold the patent to the process or product in question, thus precluding the make option, or the firm may not be able to meet environmental and safety standards to manufacture the item.

(iv) Quality: Purchased components may be superior in quality because suppliers have better technology, process, skilled labor, and the advantage of economy of scale. Suppliers may be investing more in research and development. Suppliers’ superior quality may help firms stay on top of product and process technology, especially in high-technology industries with rapid innovation.

3.3 Reasons for Making

An organization also makes its own materials, components, services, and/or equipment in-house for many reasons. Some of the reasons are as follows:

(i) Protect proprietary technology: A major reason for the make option is to protect proprietary technology. A firm may have developed an equipment, product, or process that needs to be protected for the sake of competitive advantage. Firms may choose not to reveal the technology by asking suppliers to make it, even if is patented. An advantage of not revealing the technology is to be able to surprise competitors and bring new products to market ahead of competition, allowing the firm to charge a price premium. For example, Intel or Advanced Micro Devices are not likely to ask suppliers to manufacture their new central processing units.

(ii) No competent supplier: If the component does not exist, or suppliers do not have the technology or capability to produce it, the firm may have no choice but to make an item in-house, at least for the short term. The firm may use supplier development strategies to work with a new or existing supplier to produce the component in the future as a long-term strategy.

(iii) Better quality control: If the firm is capable, the make option allows for the most direct control over the design, manufacturing process, labor, and other inputs to ensure that high-quality components are built. The firm may be so experienced and efficient in manufacturing the component that suppliers are unable to meet its exact specifications and requirements. On the other hand, suppliers may have better technology and processes to produce better-quality components. Thus, the sourcing option ensuring a higher quality level is a debatable question and must be investigated thoroughly.

(iv) Use existing idle capacity: A short-term solution for a firm with excess idle capacity is to use the excess capacity to make some of its components. This strategy is valuable for firms that produce seasonal products. It avoids laying off skilled workers and, when business picks up, the capacity is readily available to meet demand.

(v) Control of lead-time transportation, and warehousing cost: The make option provides easier control of lead-time and logistical costs since management controls all phases of the design, manufacturing, and delivery processes. Although raw materials may have to be transported, finished goods can be produced near the point of use, for instance, to minimize holding cost.

(vi) Lower cost: If technology, capacity, and managerial and labor skills are available, the make option may be more economical if large quantities of the component are needed on a continuing basis. Although the make option has a higher fixed cost due to capital investment, it has a lower variable cost because it precludes suppliers’ profits.

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3.4 Roles of Supply Base

The supply base or supplier base refers to the list of supplies that a firm uses to acquire its materials, services, supplies, and equipment. Firms engaging in supply chain management emphasize long-term strategic supplier alliances by reducing the variety of purchased items and consolidating volume into one or fewer suppliers, resulting in a smaller supply base. An effective supply base that complements and contributes to a firm’s competitive advantage is critical to its success. Savvy purchasing managers develop a sound supply base to support the firm’s overall business and supply chain strategies, based on an expanded role for the supplier. It is thus vital to understand the strategic role of suppliers.

Besides supplying the obvious purchased items, preferred or top-performing suppliers also supply

(i) Product and process technology and knowledge to support the buyer’s operations, particularly in product design – termed early supplier involvement;

(ii) Information on the latest trends in materials, processes, or designs;

(iii) Information on the supply market, such as shortages, price increases, or political situations that may threaten supplies of vital materials;

(iv) Capacity for meeting unexpected demand; and

(v) Cost efficiency due to economies of scale, since the supplier is likely to produce the same item for multiple buyers.

When developing and managing the supply chain, high-performance suppliers are found or developed to provide these services and play a very important role in the success of the supply chain.

3.5 Supplier Selection

The decision of which supplier to use for office supplies or other non-critical materials is likely to be an easy one. However, the process of selecting a group of competent suppliers for important materials, which can potentially impact the firm’s competitive advantage, is a complex one and should be based on multiple criteria. In addition to cost and delivery performance, firms should also consider how suppliers can contribute to product and process technology. Factors that firms should consider while selecting suppliers include:

(i) Product and process technologies: Suppliers should have up-to-date and capable products, as well as process technologies to produce the material needed.

(ii) Willingness to share technologies and information: With the current trend that favors outsourcing to exploit suppliers’ capabilities and to focus on core competencies, it is vital that firms seek suppliers that are willing to share their technologies and information. Suppliers can assist in new product design and development through early supplier involvement to ensure cost-effective design choices, develop alternative conceptual solutions, select the best components and technologies, and help in design assessment. By increasing the involvement of the supplier in the design assessment. By increasing the involvement of the supplier in the design process, the buyer is free to focus more attention on core competencies.

(iii) Quality: Quality levels of the purchased item should be a very important factor in supplier selection. Product quality should be high and consistent since it can directly affect the quality of the finished goods.

(iv) Cost: While unit price of the material is not typically the sole criterion in supplier selection, total cost of ownership is an important factor. Total cost of ownership includes the unit price of the material, payment terms, cash discount, ordering cost, carrying cost, logistical costs, maintenance costs, and other more qualitative costs that may not be easy

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to assess. Total cost analysis demonstrates how other costs beside unit price can affect purchase decision.

(v) Reliability: Besides reliable quality levels, reliability refers to other supplier characteristics. For example, is the supplier financially stable? Otherwise, it may not be able to invest in research and development or stay in business. Is the supplier’s delivery lead-time reliable? Otherwise, production may have to be interrupted due to shortage of material.

(vi) Order system and cycle time: How easy to use is a supplier’s ordering system, and what is the normal order cycle time? Placing orders with a supplier should be easy, quick, and effective. Delivery lead-time should be short, so that small lot sizes can be ordered on a more frequent basis to reduce inventory-holding costs.

(vii) Capacity: the firm should also consider whether the supplier has the capacity to fill orders to meet requirements and the ability to fill large orders if needed.

(viii) Communication capability: Suppliers should also possess a communication capability that facilitates communication between the parties.

(ix) Location: Geographical location is another important factor in supplier selection, as it impacts delivery lead-time, transportation, and logistical costs. Some organizations require their suppliers to be located within a certain distance from their facilities.

(x) Service: Suppliers must be able to back up their products by providing good services when needed. For example, when product information or warranty service is needed, suppliers must respond on a timely basis.

There are numerous other factors, some strategic while others tactical, that a firm must consider when choosing suppliers. The days of using competitive bidding to identify the cheapest supplier for strategic items are long gone. The ability to select competent strategic suppliers directly impacts a firm’s competitive success. Strategic suppliers are trusted partners and become an integral part of the firm’s design and production efforts.

3.6 Development of successful partnership

In the last two decades, we have learnt from the Japanese that good supplier relations can provide many benefits such as flexibility in terms of delivery, better quality, better information, and better material flows between buyers and suppliers.

True partnerships are not easily created and much has to be done to get the most out of any partnership. Several key ingredients for developing successful partnerships follow.

(i) Building Trust

Trust is critical for any partnership or alliance to work. Trust enables organizations to share valuable information, devote time and resources to understand each other’s business, and achieve results beyond what could have been done individually. Jordan Lewis, in his book Trusted Partners, points out that “Trust does not imply easy harmony. Obviously, business is too complex to expect ready agreement on all issues. However, in a rusting relationship, conflicts motivate you to probe for deeper understandings and search for constructive solutions. Trust creates goodwill, which sustains the relationship when one firm does something the other dislikes.” With trust, partners are more willing to work together, find compromise solutions to problems, work toward achieving long-term benefits for both parties, and, in short, go the extra mile.

(ii) Shared Vision and Objectives

All partnerships should state the expectations of the buyer and supplier, reasons and objectives of the partnership, and plans for the dissolution of the relationship. Both partners must share the same vision and have objectives that are not only clear but mutually agreeable. Many alliances and partnerships have failed because objectives were not well aligned or were overly optimistic. The focus must move

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beyond tactical issues and toward a more strategic path to corporate success. When partners have equal decision-making control, the partnership has a higher chance of success.

(iii) Personal Relationships

Interpersonal relationships in buyer-supplier partnerships are important since it is people who communicate and make things happen.

(iv) Mutual Benefits and Needs

Partnering should result in a win-win situation, which can only be achieved if both companies have compatible needs. Mutual needs create not only an environment conducive for collaboration but opportunities for increased innovation. When both parties share in the benefits of the partnership, the relationship will be productive and long lasting. An alliance is much like a marriage, and if only one party is happy, then the marriage is not likely to last.

(v) Commitment and Top Management Support

First, it takes a lot of time and hard work to find the right partner. Having done so, both parties must dedicate their time, best people, and resources to make the partnership succeed. Commitment must start at the highest management level. Partnerships tend to be successful when top executives are actively supporting the partnership. The level of cooperation and involvement shown by the organization’s top leaders is likely to set the tone for joint problem solving further down the line.

Successful partners are committed to continuously looking for opportunities to grow their businesses together. Management must create the right kind of internal attitude needed for alliances to flourish. Since partnerships are likely to encounter bumps along the way, it is critical that management adopt a collaborative approach to conflict resolution instead of assigning blame.

(vi) Change Management

With change comes stress, which can lead to a loss of focus. As such, companies must avoid distractions from their core businesses as a result of the changes brought about by the partnerships.

(vii) Information Sharing and Lines of Communication

Both formal and informal lines of communication should be set up to facilitate free flows of information. When there is a high degree of trust, information systems can be customized to serve each other more effectively. Confidentiality of sensitive financial, product, and process information must be maintained. Any conflict that occurs can be resolved if the channels of communication are open. For instance, early communication to supplies of specification changes and new product introductions are contributing factors to the success of purchasing partnerships. Buyers and sellers should meet regularly to discuss any change of plans, evaluate results, and address issues critical to the success of the partnerships. Since there is free exchange of information, nondisclosure agreements are often used to protect proprietary information and other sensitive data from leaking out. It is not the quantity but rather the quality and accuracy of the information exchanged that indicates the success of information sharing.

(viii) Capabilities

Organizations that have a long history of using cross-functional teams to solve problems and who have shown that their employees can collaborate successfully internally have the skills to do so externally. We all know that things do not always turn out as planned. Thus, companies must be willing to accept responsibility and have the capability to correct errors effectively when they are detected. Key suppliers must have the right technology and capabilities to meet cost, quality, and delivery requirements. In addition, suppliers must have the flexibility to respond quickly to changing customer requirements. Before entering into any partnership, an organization must conduct a thorough investigation of the supplier’s capabilities and core competencies. Organizations prefer working with suppliers who have the technology and technical expertise to assist in the development of new products or services that would lead to a competitive advantage in the marketplace.

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3.7 Supplier Performance Evaluation

Performance Metrics

Measures related to quality, cost, delivery, and flexibility have traditionally been used to evaluate how well supplies are doing. Information provided by supplier performance will be used to improve efficiency in the entire supply chain. Thus, the goal of any good performance evaluation system is to provide metrics that are understandable, easy to measure, and focused on real value-added results for both the buyer and supplier.

By evaluating supplier performance, organizations hope to identify suppliers with exceptional performance or developmental needs, improve supplier communication, reduce risk, and manage the partnership based on an analysis of reported data. After all, it is not unusual that the best customers want to work with the best suppliers. Additionally, the best suppliers are commonly rewarded and recognized for their achievements.

In a survey of buyers carried out by Purchasing Magazine, although price/cost was rated the most important factor when selecting suppliers, other criteria such as technical expertise, lead times, environmental awareness, and market knowledge were also rated highly by the respondents. An earlier study on the electronics industry by Dr. Pearson and Dr. Ellram showed that quality was the most important criterion for selection, followed by cost, current technology, and design capabilities. It would appear that in the electronics industry, which pioneered the six-sigma revolution, quality is the prime selection criteria due to its strategic importance. Thus it is seen that a multi-criteria approach is needed to measure performance. Examples of broad performance metrics are shown in Table 3.1.

Over the past several years, total cost of ownership (TCO), a broad-based performance metric, has been widely discussed in the supply chain literature. TCO is defined as “all costs associated with the acquisition, use, and maintenance of a good or service” and is comprised of pre-transaction, transaction, and post-transaction costs. Explanations of these three major cost categories follow:

• Pre-transaction costs: These costs are incurred prior to order and receipt of the purchased goods. Examples are cost of certifying and training suppliers,

• Transaction costs: These costs include the cost of the goods/services and cost associated with placing and receiving the order. Examples are purchase price, preparation of orders, and delivery costs.

• Post-transaction costs: These costs are incurred after the goods are in the possession of the company, agents, or customers. Examples are field failures, company’s goodwill/reputation, maintenance costs, and warranty costs.

TCO provides a proactive approach for understanding costs and supplier performance leading to reduced costs. However, the challenge is to effectively identify the key cost drivers needed to determine the total cost of ownership. A recent exploratory study of total cost of ownership models indicates that leading-edge companies actually use such models.

Table 3.1 Examples of Performance Metrics

(i) Cost / Price • Competitive price • Availability of cost breakdowns • Productivity improvement/cost-reduction programs • Willingness to negotiate price • Inventory cost • Information cost • Transportation cost • Actual cost compared to: historical (standard) cost, target cost, cost-reduction goal,

benchmark cost • Extent of cooperation leading to improved cost

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(ii) Quality • Zero defects • Statistical process controls • Continuous process improvement • Fit for use • Corrective action program • Documented quality program such as ISO 9000 • Warranty • Actual quality compared to : historical quality, specification quality, target quality • Quality improvement compared to : historical quality, quality-improvement goal • Extent of cooperation leading to improved quality

(iii) Delivery • Fast • Reliable/on time • Defect-free deliveries • Actual delivery compared to : promised delivery, window (i.e., two days early to zero

days late) • Extent of cooperation leading to improved delivery

(iv) Responsiveness and Flexibility • Responsiveness to customers • Accuracy of record keeping • Ability to work effectively with teams • Responsiveness to changing situations • Participation/success of supplier certification program • Short-cycle changes in demand/flexible capacity • Changes in delivery schedules • Participation in new product development • Solving problems • Willingness of supplier to seek inputs regarding product/service changes • Advance notification given by supplier as a result of product/service changes

(v) Environment • Environmentally responsible • Environmental management system such as ISO 14000 • Extent of cooperation leading to improved environmental issues

(vi) Technology • Proactive improvement using proven manufacturing/service technology • Superior product/service design • Extent of cooperation leading to improved technology

(vii) Business Metrics • Reputation of supplier/leadership in the field • Long-term relationship • Quality of information sharing • Financial strength such as Dun & Bradstreet’s credit rating • Total Cash flow • Rate of return on investment • Extent of cooperation leading to improved business processes and performance

(viii) Total Cost of Ownership • Purchased products shipped cost-effectively • Cost of special handling • Additional supplier costs as the result of the buyer’s scheduling and shipment needs • Cost of defects, rework, and problem solving associated with purchases

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3.8 Supplier Evaluation and Certification

Only the best suppliers are targeted as partners. Companies want to develop partnerships with the best suppliers to leverage suppliers’ expertise and technologies to create a competitive advantage. Learning more about how an organization’s key suppliers are performing can lead to greater visibility, which can provide opportunities for further collaborative involvement in value-added activities. A supplier evaluation and certification process must be in place so that organizations can identify their best and most reliable suppliers. In addition, sourcing decisions are made based on facts and not merely on perception of a supplier’s capabilities. Providing frequent feedback on supplier performance can help organizations avoid major surprises and maintain good relationships. For example, Honeywell has a Web-based monthly reporting system for evaluating supplier performance. Suppliers can access their ratings on-line and see how they are performing with respect to the other suppliers. While it is important to evaluate the suppliers, it is equally important that suppliers be allowed to provide constructive feedback to the customer to enhance long-term partnerships.

One of the goals of evaluating suppliers is to determine if the supplier is performing according to the buyer’s requirements. An extension of supplier evaluation is supplier certification, defined by the Institute for Supply Management as “an organization’s process for evaluating the quality systems of key supplies in an effort to eliminate incoming inspections. The certification process implies a willingness on the part of customers and suppliers to share goals, commitments, and risks to improve their relationship. A supplier certification program also indicates long-term mutual commitment. For example, a certification program might provide incentives for suppliers to deliver parts directly to the point of use in the buyer firm, thus reducing costs associated with incoming inspection and storage of inventory.

Implementing an effective supplier certification is critical to reducing the supplier base, building long-term relationships, reducing time spent on incoming inspections, improving delivery and responsiveness, recognizing excellence, developing a commitment to continuous improvement, and improving overall performance. Supplier certification allows organizations to identify the suppliers who are most committed to creating and maintaining a partnership and who have the best capabilities. Table 3.2 presents criteria generally found in many certification programs.

Table 3.2 Criteria Used in Certification Programs

• No incoming product lot rejections (e.g., less than 0..5 percent defective) for a specified time period

• No incoming non-product rejections (e.g., late delivery) for a specified time period

• No significant supplier production-related negative incidents for a specified time period

• ISO 9000/Q9000 certified or successfully passing a recent, on-site quality system evaluation

• Mutually agreed-upon set of clearly specified quality performance measures

• Fully documented process and quality system with cost controls and continuous improvement capabilities

• Supplier’s processes stable and in control

3.9 The Weighted-Criteria Evaluation System for supplier

One approach of evaluating and certifying suppliers is to use the following weighted-criteria evaluation system:

(i) Select the key dimensions of performance mutually acceptable to both customer and supplier.

(ii) Monitor and collect performance data.

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(iii) Assign weights to each of the dimensions of performance based on their relative importance to the company’s objectives. The weights for all dimensions must sum to 1.

(iv) Evaluate each of the performance measures on a rating between zero (fails to meet any intended purpose or performance) and 100 (exceptional in meeting intended purpose or performance).

(v) Multiply the dimension rating by the importance weight and sum to get an overall score.

(vi) Classify vendors based on their overall score:

• Unacceptable (less than 50) : Supplier is dropped from further business.

• Conditional (between 50 and 70) : Supplier needs development work to improve performance but may be dropped if performance continues to lag.

• Certified (between 70 and 90) : Supplier meets intended purpose or performance.

• Preferred (greater than 90): Supplier will be considered for involvement in new product development and opportunities for more business.

(vii) Audit and perform ongoing certification review.

An example of the preceding evaluation and certification process is shown in Table 3.3.

Table 3.3

Supplier Scorecard Used for the XYZ Company

Performance Measure

Rating × Weight = Final Value

Technology 80 0.10 8.00

Quality 90 0.25 22.50

Responsiveness 95 0.15 14.25

Delivery 90 0.15 13.50

Cost 80 0.15 12.00

Environmental 90 0.05 4.50

Business 90 0.15 13.50

Total Score 1.00 88.25

Note: Based on the total score of 88.25, the XYZ company is considered a certified supplier

3.10 Supplier Relationship Management Software (SRM)

Many organizations are investing in SRM software modules due to the wealth of information that can be derived from these systems. SRM software can organize supplier information and provide answers to questions such as:

• Who are our vendors? Are they the right set of suppliers?

• Who are our best suppliers and what are their competitive rankings?

• What is our suppliers’ performance with respect to on-time delivery, quality, and costs?

• Can we consolidate our buying to achieve greater scale economies?

• Do we have consistency in suppliers and performance across different locations and facilities?

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• What products/services do we purchase?

• What parts can be reused in new designs?

In general, SRM software varies by vendors in terms of capabilities offered. AMR Research has identified five key tenets of an SRM system:

• Automation of transactional processes between an organization and its suppliers.

• Integration that provides a view of the supply chain that spans multiple departments, processes, and software applications for internal users and external partners.

• Visibility of information and process flows in and between organizations. Views are customized by role and aggregated via a single portal.

• Collaboration through information sharing and suppliers’ ability to input information directly into an organization’s supply chain information system.

• Optimization of processes and decision making through enhanced analytical tools such as data warehouse and Online Analytical Processing (OLAP) tools with the migration toward more dynamic optimization tools in the future.

The key benefits of SRM include the following: (i) Better internal and external communications providing visibility into various cost components; (ii) Automated creation, negotiation, execution and compliance leading to more strategic, long-term relationships; (iii) Common and consistent measurements that help focus resources, identify performance glitches, and develop strategies for supply chain improvements; and (iv) The elimination of time-intensive, costly processes of performing paper-based business transactions.

***************

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CHAPTER IV Bullwhip Effect and Supply Chain Management

4.1 Introduction

In recent years, many suppliers and retailers have observed that while customer demand for specific products does not vary much, inventory and back-order levels fluctuate considerably across their supply chain. For instance, in examining the demand for pampers disposable diapers, executives at Procter & Gamble noticed an interesting phenomenon. An expected, retail sales of the product were fairly uniform; there is no particular day or month in which the demand is significantly higher or lower than any other. However, the executives noticed that distributors’ orders placed to the factory fluctuated much more than retail sales. In addition, Procter & Gamble’s orders to its suppliers fluctuated even more. This increase in variability as we travel up in the supply chain is referred to as the bullwhip effect.

To understand the impact of the increase in variability on the supply chain, consider the second stage in our example, the wholesaler. The wholesaler receives orders from the retailer and places orders to its supplier, the distributor. To determine these order quantities, the wholesaler must forecast the retailer’s demand. If the wholesaler does not have access to the customer’s demand data, it must use orders placed by the retailer to perform the forecasting. Since variability in orders placed by the retailer is significantly higher than variability in customer demand, the wholesaler is forced to carry more safety stock than the retailer or else to maintain higher capacity than the retailer in order to meet the same service level as the retailer.

This analysis can be carried over to the distributor as well as the factory, resulting in even higher inventory levels and therefore higher costs at these facilities. Consider, for example, a simple widget supply chain. A single factory, Widget Makers, Inc., supplies a single retailer, the Widget Store. Average annual widget demand at the Widget Store is 5200 units, and shipments are made from Widget Makers to the store each week.

External Demand

Retailer

Delivery lead-time

Order lead-time Wholesaler

Delivery lead-time

DistributorOrder lead-time

Delivery lead-time

Order lead-time Factory

Production lead time

If the variability in orders placed by the Widget Store is low, such that the shipment every week is about 100 units, Widget Makers’ production capacity and weekly shipping capacity need by only about 100 units. If weekly variability is very high, such that during certain weeks Widget Makers must make and ship 400 units and some weeks no units at all, it is easy to see that production and shipping capacity must be much higher and that some weeks this capacity will be idle. Alternatively, Widget Makers could build up inventory during weeks with low demand and supply these items during weeks with high demand, thus increasing inventory-holding costs.

4.2 Causes of bullwhip effect

It is therefore, important to identify techniques and tools that will allow us to control the bullwhip effect, i.e., to control the increase in variability in the supply chain. For this purpose, we need to first understand the main factors contributing to the increase invariability in the supply chain.

(i) Demand forecasting. Traditional inventory management techniques practiced at each level of the supply chain lead to the bullwhip effect. To explain the connection between forecasting and the bullwhip effect, consider an approach that is used frequently to manage inventory, the min-max inventory management policy. Here, whenever the inventory at a facility is less than a given number, referred to as the reorder point, the facility orders a quantity that will increase its inventory to a given target level. This target level is set based on average demand and the variability of that demand. Typically, managers use standard forecast smoothing techniques to estimate average demand and demand variability. An important characteristic of all forecasting techniques is that as more data are observed, the more we modify the estimates of the average demand and demand variability. Since the order target level strongly depends on these estimates, the user is forced to change order quantities, thus increasing variability.

(ii) Lead-time. It is easy to see that the increase in variability is magnified with increasing lead-time. Indeed, the reorder level consists of two quantities; the first is the average demand during lead-time, and the second is the safety stock, which depends on lead-time, demand variability, and service level. Thus, with longer lead times, a small change in the estimate of demand variability implies a significant change in reorder level, leading to a significant change in order quantities. This, of course, leads to increase invariability.

(iii) Batch ordering. The impact of batch ordering is quite simple to understand. If the retailer orders in batches, then the wholesaler will observe a large order, followed by several periods of no orders, followed by another large order, and so on. Thus the wholesaler sees a distorted and highly variable pattern of orders.

Recall that firms use batch ordering for a number of reasons. As pointed a firm that is faced with fixed ordering costs needs to minimize these costs, which leads to batch ordering. Second, as transportation costs become more significant, retailers may order quantities that allow them to take advantage of transportation discounts (e.g., full-truckload quantities). This may lead to some weeks with large orders and some with no orders at all. Finally, the quarterly or yearly sales quotas or incentives observed in many businesses also can result in unusually large orders observed on a periodic basis.

(iv) Price fluctuation. Price fluctuation also can lead to the bullwhip effect. If prices fluctuate, retailers often attempt to stock up when prices are lower. This is accentuated by the prevailing practice in many industries of offering promotions and discounts at certain times or for certain quantities.

(v) Inflated orders. Inflated orders placed by retailers during shortage periods tend to magnify the bullwhip effect. These are common when retailers and distributors suspect that a product will be in short supply and therefore anticipate receiving supply

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proportional to the amount ordered. When the period of shortage is over, the retailer goes back to its standard orders, leading to all kinds of distortions and variations in demand estimates.

(vi) Lack of centralized information. One of the most frequent suggestions for reducing the bullwhip effect is to centralize demand information within a supply chain, i.e., to provide each sage of the supply chain with complete information on the actual customer demand. To understand why centralized demand information can reduce the bullwhip effect, note that if demand information is centralized, each stage of the supply chain can use the actual customer demand data to create more accurate forecasts rather than relying on the orders received from the previous stage, which can vary significantly more than the actual customer demand.

Now consider two types of supply chains: one with centralized demand information and a second with decentralized demand information. In the first type of supply chain, the centralized supply chain, the retailer, or the first stage in the supply chain, observes customer demand, forecasts the average demand, determines its target inventory level, and places an order to the wholesaler. The wholesaler, or the second stage of the supply chain, receives the order along with the retailer’s forecast average demand, uses this forecast to determine its target inventory level, and places an order to the distributor.

Since the wholesaler has full information on the retailer inventory levels and customer demand, the wholesaler can predict an incoming order from the retailer and hence be ready for this order, thus reducing lead-time. This lead-time reduction leads to reduction in the increase in variability. Similarly, the distributors, or the third sage of the supply chain, has information about the wholesaler and the retailer inventory levels as well as customer demand and hence can significantly reduce lead time and as a result reduce the bullwhip effect.

The second type of supply chain that we consider is the decentralized supply chain. In this case the retailer does not make its forecast average demand available to the remainder of the supply chain. Instead, each sage of the supply chain must estimate mean demand based on the orders received from its customer, without knowledge of the retailer’s forecast.

What can we conclude about the bullwhip effect in these two types of supply chains? For either type of supply chain, centralized or decentralized, the variability of the order quantities becomes larger as we move up the supply chain so that the orders placed by the wholesaler are more variable than the orders placed by the retailer, and so on. The difference in the two types of supply chains is in terms of the ability to respond to orders from down stream facilities. Centralized information allows to reduce lead-time and hence variability in the supply chain.

Indeed, the variability of orders increases dramatically more in the decentralized system. In other words, a decentralized supply chain, in which only the retailer knows the customer demand, can lead to significantly higher variability than a centralized supply chain, in which customer demand information is available at each stage of the supply chain, particularly when lead times are large. We therefore conclude that centralizing demand information can reduce the bullwhip effect significantly.

4.3 Methods for Coping with the Bullwhip Effect

Ability to identify the causes of the bullwhip effect leads to a number of suggestions for reducing the bullwhip effect or for eliminating its impact. These include reducing uncertainty, reducing the variability of the customer demand process, reducing lead times, and engaging in strategic partnerships.

(i) Reducing uncertainty. One of the most frequent suggestions for decreasing or eliminating the bullwhip effect is to reduce uncertainty throughout the supply chain by centralizing demand information, i.e., by providing each stage of the supply chain with complete information on actual customer demand.

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Note, however, that even if each stage uses the same demand data, each may still employ different forecasting methods and different buying practices, both of which may contribute to the bullwhip effect. In addition, even when each stage uses the same demand data, the same forecasting method, and the same ordering policy, the bullwhip effect will continue to exist, albeit at a significantly reduced level. Thus centralized demand information reduces the bullwhip effect but does not eliminate it.

(ii) Reducing variability. The bullwhip effect can be diminished by reducing the variability inherent in the customer demand process. For example, if we can reduce the variability of customer demand seen by the retailer, then even if the bullwhip effect occurs, the variability of demand seen by the wholesaler also will be reduced.

We can reduce the variability of customer demand through, for example, the use of an everyday low pricing (EDLP) strategy. When a retailer used EDLP, it offers a product at a single consistent price rather than offering a regular price with periodic price promotions. By eliminating price promotions, a retailer can eliminate many of the dramatic shifts in demand that occur along with these promotions. Therefore, everyday low pricing strategies can lead to much more stable – i.e., less variable – customer demand patterns.

Of course, variability of customer demand depends not only on the retailer pricing strategy but also on its competitors’ strategies. Thus, while EDLP is an important tool used to reduce demand variability, its impact can be limited.

(iii) Lead-time reduction: As we observed earlier, the longer the lead-time, the larger is the increase in variability. Therefore, lead-time reduction can reduce the bullwhip effect significantly throughout a supply chain.

Observe that lead times typically include two components: order lead times (i.e., the time it takes to produce and ship the item) and information lead times (i.e., the time it takes to process an order). This distinction is important because order lead times can be reduced through the use of cross-docking, whereas information lead times can be reduced through the use of electronic data interchange (EDI).

(iv) Strategic partnership. The bullwhip effect can be eliminated by engaging in any of a number of strategic partnerships. These strategic partnerships change the way information is shared and inventory is managed within a supply chain, possibly eliminating the impact of the bullwhip effect. For example, in vendor-managed inventory (VMI;), the manufacturer manages the inventory of its product at the retailer outlet and therefore determines for itself how much inventory to keep on hand and how much to ship to the retailer in every period. Therefore, in VMI, the manufacturer does not rely on the orders placed by a retailer, thus avoiding the bullwhip effect entirely.

Other types of partnerships are also applied to reduce the bullwhip effect. As we discussed earlier, for example, centralizing demand information can dramatically reduce the variability seen by the upstream stages in a supply chain. Therefore, it is clear that these upstream stages would benefit from a strategic partnership that provides an incentive for the retailer to make customer demand data available to the rest of the supply chain.

4.4 Matching supply and demand

The business environment today is such that we have consumers who are more demanding and discriminating. The market has evolved into a “pull” environment with customers dictating to the supplier what products they desire and when they need them delivered. If a retailer cannot get the product it wants at the right quantity, price, and time from one supplier, the retailer will look for another company that can meet its demands. Any temporary stock-out has a tremendous downside on sales, profitability, and customer relationships. Managing demand is challenging because of the difficulty in always forecasting future consumer requirements accurately.

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In order for supply chain integration to be successful, suppliers must be able to accurately forecast demand so that they can produce and deliver the right quantities demanded by their customers in a timely and cost-effective fashion. There are several ways to closely match supply and demand. One way is for a supplier to hold plenty of stock available for delivery at any time. While this approach maximizes sales revenues, it is also expensive because of the cost of carrying inventory and the possibility of write-downs at the end of the selling season. Use of flexible pricing is another approach. During heavy demand periods, prices can be raised to reduce peak demand. Price discounts can then be used to increase sales during periods with excess inventory or slow demand. This strategy can still result in lost sales, though, as well as stock-outs and thus cannot be considered an ideal or partnership-friendly approach to satisfying demand. In the short term, companies can also use overtime, subcontracting, or temporary workers to increase capacity to meet demand for their products and services. In the interim, firms will lose sales as they train workers and quality may also tend to suffer.

Thus, it is imperative that suppliers along the supply chain find ways to better match supply and demand to achieve optimal levels of cost, quality, and customer service to enable them to compete with other supply chains. Any problems that adversely affect the timely delivery of products demanded by consumers will have ramifications throughout the entire chain.

4.5 Collaborative Planning, Forecasting, and Replenishment

The American Production and Inventory Control Society (APICS) defines collaborative planning, forecasting, and replenishment (CPFR) as “a collaboration process whereby supply chain trading partners can jointly plan key supply chain activities from production and delivery of raw materials to production and delivery of final products to end customers. Collaboration encompasses business planning, sales forecasting, and all operations required to replenish raw materials and finished goods”. The objective of CPFR is to optimize the supply chain by improving demand forecast accuracy, delivering the right product at the right time to the right location, reducing inventories across the supply chain, avoiding stock-outs, and improving customer service. Basically, this can be achieved only if the trading partners are working closely together and willing to share information and risk through a common set of processes.

The real value of CPFR comes from an exchange of forecasting information rather than from more sophisticated forecasting algorithms to improve forecasting accuracy. The fact is that forecasts developed solely by the firm tend to be inaccurate. When both the buyer and seller collaborate to develop a single forecast, incorporating knowledge of base sales, promotions, store openings or closings, and new product introductions, it is possible to synchronize buyer needs with supplier production plans, thus ensuring efficient replenishment. The jointly managed forecasts can be adjusted in the event that demand or promotions have changed, thus avoiding costly corrections after the fact.

On the surface, when decisions are made with incomplete information, it may appear that companies have “optimized” their internal processes when, in reality, inventory has merely shifted along the supply chain. Without trading partners in the supply chain collaborating and exchanging information, the supply chain will always be suboptimal, resulting in less-than-maximum supply chain profits.

CPFR is an approach that addresses the requirements for good demand management. The benefits of CPFR include the following:

• Strengthens partner relationships

• Provides analysis of sales and order forecasts upstream and downstream

• Uses point-of-sale data, seasonal activity, promotions, new product introductions, and store openings or closings to improve forecast accuracy

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• Manages the demand chain by exception and proactively eliminates problems before they appear

• Allows collaboration on future requirements and plans

• Uses joint planning and management of promotions

• Integrates planning, forecasting, and logistics activities

• Provides efficient category management and understanding of consumer purchasing habits

• Provides analysis of key performance metrics (e.g., forecast accuracy, forecast exceptions, product lead times, inventory turnover, percentage stock-outs) to reduce supply chain inefficiencies, improve customer service, and increase sales and profitability

The Global Commerce Initiative (GCI) created the GCI Recommended Standard for Globalizing CPFR. GIC is a voluntary body created in USA in 1999 to “improve the performance of the international supply chain for consumer goods through the collaborative development and endorsement of recommended standards and key business processes.” A description of the CPFR process model used by GCI follows:

• Step 1: Develop Collaboration Arrangement

The buyer and seller must agree on the objective of the collaboration, ground rules for resolving disagreements, confidentiality of information to be shared, sales forecast exception criteria, review cycle, time frame, frozen time period with acceptable tolerances, resource commitments, financial incentives, and success metrics. Some examples of objectives are to improve customer service levels, reduce stock-outs, reduce inventories, increase sales, reduce costs, improve forecast service levels, reduce stock-outs, reduce inventories, increase sales, reduce costs, improve forecast accuracy, and synchronize production with the forecast.

• Step 2: Create Joint Business Plan

A joint business plan is developed by sharing the companies’ business strategies and plans. The plan typically involves developing a joint product category and promotional plan in which the appropriate category strategies, inventory policies, promotional activities, and pricing policies are specified. A product category is a manageable group of products perceived by consumers to be similar that can be substituted in meeting their needs. For each item in the product category, an item management profile is developed that includes the minimum order quantity, lead-time, and time between orders, frozen time period, and safety stock guidelines. The trading partner should be informed of changes such as store openings or closings or changes of items in each product category. It is important that trading partners be able to understand the impact of new product introductions, promotions, and marketing campaigns have on demand and ultimately on the effective management of the supply chain.

• Step 3: Create Sales Forecast

The trading partners use Web-based technologies to share data such as retailer point-of-sale information, distribution center withdrawals, manufacturing consumption, planned events including store openings or closings, and new product introductions. Using multiple inputs into the forecasting process including information about the future, as well as the past, results in the creation of a shared forecast that reflects the most accurate and real-time information available. Either partner or both partners may generate the sales forecast. The forecasting techniques used can be qualitative or quantitative. When both partners each generate a forecast, middleware is used to highlight the differences, based on predetermined exception criteria previously agreed upon the partners.

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• Step 4: Identity Exceptions for Sales Forecast

Irrespective of how the initial forecast is generated, all exceptions are identified in Step 4. Examples of sales forecast exception criteria are: retail in-stock is less than 95 per cent, sales forecast error is greater than 20 percent, the difference in sales forecast from the same period of the previous year is greater than 10 percent, or any changes that have occurred in timing of promotional activities or number of active stores. The real-time joint decision-making reduces the risk and increases the confidence in the single forecast.

• Step 5: Resolve/collaborate on Exception Items

In Step 5, all exceptions are resolved through a collaborative process to create a single consensus forecast.

• Step 6: create Order forecast

Data are analyzed – such as point-of-sale (POS) data; historical demand; shipment data; current capacity limits; minimum order quantities; lead times; time between orders; frozen time periods; safety stock rules, impact events such as new product introductions, store openings, and store closings; and current inventory positions (on hand, on order, in transit) – to generate the order forecast consistent with the sales forecast and joint business plan developed earlier. The order forecast represents detailed time-phased ordering needs with inventory objectives by product and receiving location. The order forecast enables the manufacturer to effectively schedule production capacity based on the demand and to minimize safety stock. For the retailer, there is greater confidence that orders will be met. In effect, the real-time collaborative effort minimizes the uncertainty between trading partners, leading to reduced supply chain inventories, along with improved customer service levels.

• Step 7: Identity Exceptions for Order Forecast

The items that fall outside the order forecast exception criteria such as customer service measures, order fill rate, or forecast error measures, established jointly by the buyer and seller in the collaboration agreement, are identified as exception items. Examples of order forecast exception criteria are retail in-stock less than 95 percent, order forecast errors greater than 20 percent, annual inventory turnover less than agreed-upon goal, addition of new event than affects inventory/orders, or requested emergency orders greater than 5 percent of weekly forecast.

• Step 8: Resolve/Collaborate on Exception Items

Any order forecast exceptions are investigated by examining the shared data, e-mail, telephone conversations, meetings, and other supporting information. If the analysis justifies a change in the forecast, a revised forecast is submitted.

• Step 9: Order Generation

This last step involves converting the order forecast into a committed order. The actual order is expected to consume the forecast. The committed order is generated based on the product demand in the frozen time period of the order forecast.

Common performance metrics, such as gross margin percent, return on investment, and sales growth, are developed to measure the success of the relationship. Other metrics include in-stock percent a point of sale, inventory turnover, inventory level, sales forecast accuracy, potential sales lost due to stock-out, manufacturing cycle time, order cycle time, shipping cycle time, problem resolution time, rate of emergency or cancelled orders, and percent shipped or delivered on time.

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4.6 Forecasting Techniques

Considering the importance of forecast, in this section, a brief discussion about various techniques of forecasting is presented. Both quantitative and qualitative forecasts can be improved by seeking inputs from trading partners. Qualitative forecasting methods are based on opinions and intuition, whereas quantitative forecasting methods use mathematical models and relevant historical data to generate forecasts. The quantitative methods can be divided into two groups: time series and associative models.

4.6.1 Qualitative Methods

Qualitative forecasting methods are approaches to forecasting based on intuition or judgmental evaluation and are generally used when data are limited, unavailable, or not currently relevant. While this approach can be very low cost, the effectiveness depends to a large extent on the skill and experience of the forecasters(s) and the amount of relevant information available. The qualitative techniques are often used to develop long-range projections when current data is no longer very useful, and for new product introductions when current data does not exist. Discussions of four common qualitative forecasting models follow:

• Jury of executive opinion: A group of senior management executives who are knowledgeable about the market, competitors, and the business environment collectively develop the forecast. This technique has the advantage of several individuals with considerable experience working together, but if one member’s views dominate the discussions, then the value and reliability of the outcome can be diminished. This technique is applicable for long-range planning and new product introductions.

• Delphi method: A group of internal and external experts are surveyed during several rounds in terms of future events and long-term forecasts of demand. Group members do not physically meet and thus avoid the scenario where one or a few experts could dominate a discussion. The answers from the experts are accumulated after each round of the survey and summarized. The summary of responses is then sent out to all the experts in the next round, wherein individual experts can modify their responses based on the group’s response summary. The iterative process goes on until a consensus is reached. The process can be both time-consuming and very expensive. This approach is applicable for high-risk technology forecasting; large, expensive project; or major, new product introductions. The quality of the forecast depends largely on the knowledge of the experts.

• Sales force composite: The sales force represents a good source of market information. This type of forecast is generated based on the sales force’s knowledge of the market and estimates of customer needs. Due to the proximity of the sales personnel to the consumers, the forecast tends to be reliable but individual biases could negatively impact the effectiveness of this forecast there is a tendency for the sales force to under forecast.

• Consumer survey: A questionnaire is developed that seeks input from customers on important issues such as future buying habits, new product ideas, and opinions about existing products. The survey is administered through telephone, mail, Internet, or personal interviews. The challenge is to identify a sample of respondents who are representative of the larger population and to get an acceptable response rate.

4.6.2 Quantitative Methods

Quantitative forecasting models use mathematical techniques that are based on historical data and can include causal variables to forecast demand. Time series forecasting is based on the assumption that the future is an extension of the past, thus, historical data can be used to predict future demand. Associative forecasting assumes that one or more factors (independent variables) are related to demand and, therefore, can be used to predict future demand. Since these forecasts rely

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solely on past demand data, all quantitative methods become less accurate as the forecast’s time horizon increases. Thus, for long time horizon forecasts, it is generally recommended to utilize a combination of both quantitative and qualitative techniques.

Components of Time Series Data

Time series data typically have four components: trend, cyclical, seasonal, and random variations:

• Trend variations: Trends represent either increasing or decreasing movements over many years and are due to factors such as population growth, population shifts, cultural changes, and income shifts. Common trend lines are linear, S-curve, exponential, or asymptotic.

• Cyclical variations: Cyclical variations are wavelike movements that are longer than a year and influenced by macroeconomic and political factors.

• Seasonal variations: Seasonal variations show peaks and valleys that repeat over a consistent interval such as hours, days, weeks, months, years, or seasons. Due to seasonality, many companies do well in certain months and not so well in other months.

• Random variations: Random variations are due to unexpected or unpredictable events such as natural disasters (hurricanes, tornadoes, fire), strikes, and wars.

Time Series Forecasting Models

As discussed earlier, time series forecasts are dependent on the availability of historical data. Forecasts are estimated by extrapolating the past data into the future. Time series forecasting is one of the most widely used techniques. A survey of purchasing professionals indicates that the top three quantitative forecasting techniques used are simple moving average, weighted moving average, and exponential smoothing. Some of the more common time series approaches such as simple moving average, weighted moving average, exponential smoothing, and trend-adjusted exponential smoothing are discussed next.

(i) Simple Moving Average Forecasting Model: The simple moving average forecasting method uses historical data to generate a forecast and works well when the demand is fairly stable over time. The n-period moving average forecast is

wheren

AF

tt

ntii

t 11

∑+−=

+ =

Ft+1 = forecast for Period t + 1,

n = number of periods used to calculate moving average, and

Ai = actual demand in Period i.

The average tends to be more responsive if fewer data points are used to compute the average. However, random events can also impact the average adversely. Thus the decision maker must balance the cost of responding slowly to changes versus the cost of responding to random variations. The advantage of this technique is that it is simple to use and easy to understand.

(ii) Weighted Moving Average Forecasting Model: Weighted moving average forecasting, which is based on an n-period weighted moving average, follows:

∑+−=

+ =tt

ntiiit AwF

11

35

where

Ft+1 = forecast for Period t + 1,

n = number of periods used to determining the moving average,

Ai = actual demand in Period i, and

Wi = weight assigned to Period i (with ∑ =1iW )

The weighted moving average allows greater emphasis to be placed on more recent data to reflect changes in demand patterns. Weights used also tend to be based on experience of the forecaster. Although the forecast is more responsive to underlying changes in demand, the forecast still lags demand because of the averaging effect. As such, the weighted moving average method does not do a good job of tracking trend changes in the data.

(iii) Exponential Smoothing Forecasting Model: Exponential smoothing forecasting is a sophisticated weighted moving average forecasting in which the forecast for the next period’s demand is the current period’s forecast adjusted by a fraction of the difference between the current period’s actual demand and its forecast. This approach requires less data to be kept than the weighted moving average method because only two data points are needed. Due to its simplicity and minimal data requirement, exponential smoothing forecasting is one of the more popular techniques. This model, like the other time series models, is suitable for data that show little trend or seasonal patterns. The exponential smoothing formula is

ttttttt FAForFAFF )1( )( 11 ααα −+=−+= ++

where

Ft+1 = forecast for Period t + 1,

Ft = forecast for Period t,

At = actual demand in Period t, and

α = a smoothing constant (0 ≤ α ≤ 1).

With an α value closer to 1, there is a greater emphasis on recent data, making the model more responsive to changes in the recent demand. When α has a low value, more weight is placed on past demand (which is contained in the previous period’s forecast value) and the model responds more slowly changes in demand. The impact of using a small or large value of α is similar to the effect of using a large or small number of observations in calculating the moving average. In general, the forecast will lag any trend in the actual data because only partial adjustment to the most recent forecast error can be made. The initial forecast value could be estimated using one of the qualitative methods, such as the Delphi forecast, or by simply setting the initial forecast equal to the demand for that period.

(iv) Trend-Adjusted Exponential Smoothing Forecasting Model. The exponential smoothing method can be modified to include a trend component when the time series show a systematic upward or downward trend in the data over time. This method requires two smoothing constants, one for the smoothed forecast (α ) and the other for the trend (β ). The equations for this model are

, )1()( ),( )1(

11

11

−−

−−

−+−=+−+=

tttt

tttt

TFFTTFAF

ββαα

and the trend-adjusted forecast,

TAFt +m = Ft + mTt

where

36

Ft = exponentially smoothed average in Period t,

At = actual demand in Period t,

Tt = exponentially smoothed trend in Period t,

α = smoothing constant ( 0 ≤ α ≤1), and

β = smoothing constant for trend ( 0 ≤ β ≤ 1).

A higher value of β indicates greater emphasis on recent trend changes, while a small β places less weight on recent changes and has the effect of smoothing out the current trend. The smoothing constants, α and β, are estimated using a trial and error approach, matching actual historical demand data to the forecasted demand in search of the smoothing constants that minimize the forecast errors.

Linear Trend Forecasting Model: The trend can be estimated using simple linear regression to fit a line to time series of historical data. The linear trend method minimizes the sum of squared deviations to determine the characteristics of the linear equation:

Here,

XbbY 10 +=∧

where ∧

Y = forecast or dependent variable

X = time variable,

b0 = intercept of the line, and

b1 = slope of the line.

The coefficients b0 and b1 are calculated as follows:

( )

nxby

b

andxxn

yxxynb

∑ ∑

∑ ∑∑ ∑ ∑

−=

−

−=

10

221

)(

where b1 = slope of the line,

x = independent variable values,

y = dependent variable values,

x = average of the x values

y = average of the y values, and

n = number of observations.

Associative Forecasting Models: Associative forecasting generally uses regression analysis to estimate future demand. One or several external variables are identified that are related to demand, which are hopefully easier to determine than demand. Once the relationship between the external variable and demand is determined, it can be used as a forecasting tool. The detail discussion of this type of model is avoided here.

*****************

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CHAPTER V Distribution Management Overview

5.1 Introduction

Transportation happens to be the most fundamental part of strategic logistic management and transport costs include all costs associated with movement of products from one location to another. The average transport costs ranges from 5 to 6% of the recommended retail price of the product. Transportation is the movement of products, materials and services from one area to another, both inbound and outbound. It can also be said as movement from one node of the supply chain to the other.

Indian Army is a typical example of ideal transportation mixes in our country. It uses the aerial, land, sea and rail routes to maintain its forces strewn all over the country and aboard. The logistics is enormous and the various modes of transport are aircraft, train, trucks, animals, and human beings. It transports supplies, ration, fuel, oil lubricants, arms, ammunition, clothing and personal loads over vast distances and over varied terrain and climatic conditions.

5.2 Transportation System

Transportation system has a strategic bearing on a company’s operation efficiency. Therefore, failure to identify the best transportation mode can directly affect the growth of a company. Since, higher transport costs will raise prices; it will directly affect the customer satisfaction in a negative way. The three factors as mentioned by Gattoma & Walters required to consider are

• Customer

• Environment

• Product & company

Organization, which involves physical movement of goods require transport services that varies from mode to mode. The best suitable mode is required to be identified depending upon the nature of product that has to be moved. Like if coal or carbon has to be moved use the railways from the source to the production unit directly, so as to minimize losses, time & cost factor. Therefore, in order to identify the right transport system the following points to be considered

• Impact of the transport system on the supply chain

• Factors that determine the choice of transport mode

• Feedback and reporting both from within and the environment on the choice of transport, and rectify in case you went wrong the first time.

• Your foresight, flexibility & integration of available resources in planning stage will be one of the crucial factors that will dictate the choice of transport.

• Customer communications: In order to obviate delays in transportation and handling of logistics both the supplies and distributors are relying more and more on electronic transfer systems, IT & the Internet. This will help in considerable reduction in time delays and ensure better cooperation between the chains.

• Market coverage: Transportation costs influence the size of markets covered in a big way. The characteristics are: costs, flexibility, reliability and availability. The product per se will influence the economics of the decision. A low volume and high value product will be above to support higher costs, which means extended delivery distances and increase in delivery frequency.

• Sourcing decisions: The geographical dimension of the source markets can be influenced by low cost transportation system. Companies therefore have to consider a trade off between price and quality and the costs involved in delivering to the processing point, i.e. volume and cost of transportation.

• Manufacturing operations: Cost of transporting has a direct bearing on the location of the manufacturing market center. That is hwy, extraction based units are close to the source of raw materials and the products related to customer satisfaction are closer home, i.e. near to the customer hub center.

• Pricing decision: Transportation happens to be the important component of product costs. Therefore, selection of the appropriate transportation mode will have a direct bearing on the product costs per se, with more relevance to exports. Increase in transportation costs increases the product pricing.

• Customer service decisions: Both customer service policy and transportation decisions go hand in hand and hence one cannot be considered in isolation of the considerably. Therefore, it is pertinent to overrule the cost factor while servicing the medical customers, since speed is more important than cost in selecting the transport mode.

5.3 Transport in supply chain

Transport is vital to the overall gambit of SCM operation and therefore cannot be considered in isolation. The entire transportation process is to be monitored, in order to gauge the exact location and stage of the materials being transported. Transport, is the process, which transports materials between two or more stations, and therefore, the form of transport to be used should not only be responsive and compatible to the terminal stations, but also with the operating environment through which the product moves. In order to achieve the best, it is therefore mandatory that sufficient information be made available to enable the movement to be monitored by the producer, consumer, agencies, financial institutions and relevant groups.

Transport profile

Operating characteristics dictate the transport requirements of an organization. The transport requirement depends on the different and versatile nature of tasks that are to be performed. Therefore, to generalize, an organization, which doesn’t have versatility and varieties in operating its transport for varied tasks, will operate much below the optimum level of efficiency.

Operational factors

Operational factors that determine the transport mode are:

• Environmental factors

• Characteristics of alternate transport modes

• Combination approach

The various characteristics of alternative transportation mode are:

• Useful load: physical capability and maximum load as a percentage of gross weight.

• Density: cargo density, i.e., weight per cubic unit.

• Overheads: fixed costs as a percentage of total cost.

• Productivity: calculated in tonne-miles per direct man-hour.

It is very important to establish and determine the accurate operating characteristics of each available transport mode, so that suitability of matching these to the operating factors can be established. Each type of transport offers different characteristics and as a supply chain manager you have to understand the efficacy of these aspects:

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OPERATIONAL FACTORS

CUSTOMER CHARACTERISTICS

ENVIRONMENTAL PRODUCT COMPANY

CHARACTERISTICS OF TRANSPORT MODES

ROAD RAIL AIR WATER

CHOICE OF TRANSPORT MODE CUSTOMER SERVICE LEVEL

COSTS

Figure 5.1: Operational Factors for Deciding the Transport Mode (Deshmukh and Mohanty), 2004

Transportation costs

Transport cost vary from less than 1 per cent (for machinery) to over 30 per cent (for food) of the recommended selling price of products, depending upon the nature of the product range and its market. However, the average transport costs is between 5 to 6 per cent of the recommended retail price of a product. With inflation, transport costs also rise because the major components are the workforce (labor), fuel & maintenance, spares, driver’s cost. Similarly, transport represents a direct cost added to the price of the product and any reduction in transport costs would lead to an increase in profit, with price remaining constant. Transportation rates are almost linear with distances and not with volume, be it road, rail, water or air. Transportation costs for company owned fleet is simple and is evolved by annual cots per truck, annual mileage, amount delivered and trucks effective capacity. All this information could be effectively utilized to calculate cost per mile per SKU.

5.4 Method of transport selection

The selection procedure for the transport mode could vary from the simple decision either to identify one feasible method of distribution or to follow the competitor’s procedures, to the complex decision that calculates the cost incurred and produces an optimum solution. The three potential methods are

• Judgment: Identification of the important factors affecting the transport problem, and the transport mode from a list of alternatives available, so that the important features of the transport requirements are met. The shortcomings are tremendous in such a process, since transport is considered as a service rather than a distribution system.

• Cost trade-off: It is where the impact of transport is calculated in relation to immediate terminal objectives and activities, and the total cost of distribution system is optimized. This particular approach acknowledges the existence of trade-off within the numerous alternative approaches in an attempt to assess the situation to minimize total costs.

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• Distribution models: This identifies and explains the interrelationships between the components of the distribution system at various levels of daily, weekly or monthly demands. These models could be built to examine the impact of alternative transport modes and methods, as either the demand changes or the components in the system change.

Therefore, in order to carry out a systematic selection of the transporter a framework consisting of the following stages is recommended

SYSTEMATIC SELECTION

IDENTIFICATION OF FACTORS

CATEGORIZING &

IDENTIFICATION OF POTENTIAL

DETERMINING DISTRIBUTION

NETWORKS

MEASURE & MONITOR COSTS

STAGE 1 STAGE 2 STAGE 3 STAGE 4

MATRIX ANALYSIS TO

SELECTION

STAGE 5

Figure 5.2: The Selection System

• Stage 1: Identifying the factors affecting the choice of transport selection

• Stage 2: Categorizing the significant factors and identifying the potential risks

• Stage 3: Determining the distribution network in terms of number and size of depots

• Stage 4: Applying the matrix analysis to select the most appropriate transport method

• Stage 5: Measuring and monitoring cost factors.

5.4.1 A transportation decision

Determining an organization’s transport requirement will be based on the following underlying considerations, (Figure 5.2)

• The available depots, their sizes including movement requirements of raw materials to manufacturing units and finished products to the warehouses and on to the consumers.

• The best choice of mode available depending on the distance involved.

• Product characteristics that will further dictate the type of transport for each requirement.

• The financial option that could be employed in terms of individual type of equipment.

• The operation needs in terms of usage of the equipment for maximum utilization and minimum operational costs.

We have to understand that transportation operations cannot be seen in isolation, and hence warehousing and depot locations are equally important to understand the choice of transport selection process. Warehouse location decision is discussed in next section.

The total number and sizes of depots and warehouses will also have a direct bearing on transport operations of all companies across the board. Let us se these in more details.

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DECISION FRAMEWORK

NUMBE & SIZE OF DEPOTS TO

INCLUDE MOVEMEN OF

RAW MATERIALS/FINISHED PRODUCTS

CHOICE OF TRANSPORT

MODE COINCIDENTAL

TO ECH POTENTIAL

MOVEMENT IN DISTANCE TRAVELED

THE CHOICE OF EQUIPMENT

SPECIFICATIONS ON TYPE OF TRANSPORT DEFINED BY

PRODUCT CHARACTERIS

TICS

CHOICE OF FINANCIAL

OPTIONS AVAILABLE

CHOICE OF OPERATIONAL

NEEDS AS REGARDS

EQUIPMENTS TO MAX

UTILISATION & MINIMISE COSTS

Figure 5.3: Decision Framework

Decision matrix for appropriate transport option

A decision matrix approach helps in identifying the most appropriate transport option from the substantial range available. This approach uses the following steps:

• Selection of initial decisions required based upon known alternatives; like, choice of transport mode, choice of equipment specification, choice of financial options and operational needs.

• To select two options (factors) so that a matrix can be formulated suing one in vertical axis and the other on horizontal.

• Selection of basic alternatives, which adequately cover the conditions, imposed by the vertical and horizontal axes.

• Determination of organization needs by analysis of the important factors generated to produce the matrix and use of the matrix to select the options required.

• Selection of the resources required by considering the results of the matrix analysis plus other factors of importance.

• The combination of the matrix solutions to provide an effective and efficient profile, which identifies the transport tasks and appropriate resources for the tasks.

This approach will require imagination to develop the selection of the initial decisions, to determine the important factors to use for the vertical and horizontal axes on the matrix, and to construct the matrix. Yet, the majority of the question could be answered by a combination of brainstorming, analysis and categorization of important factors, which affect the choice of transport selection.

5.5 Fleet sizing and configuration

Fleet sizing objective is to employ through ownership, hire, lease and or rental the fewest possible trucks to manage the company’s load profile/shipping requirements. This decision is akin to the decision of how much inventory is to be made available to the consumers / customers. In fleet sizing, increased availability yields fewer lost sales, shorter customer cycle times, improved customer services but higher fleet costs. Fleet sizing projections should be developed a few times during the year and at any time when a major shift in demand pattern occurs. In certain cases, the cost of vehicle

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shortages can be estimated and a cost of shortages versus cost of ownership analysis can be made to determine the optimal fleet size. Fleet size can be regulated and minimized by:

• Utilizing standard size pallets and transport containers.

• Vigorously monitoring fleet utilization levels annually.

• Maintaining total fleet visibility, including loading times, unloading, transit times and maintenance times.

• Choosing low-use periods to conduct routine maintenance.

• Monitoring and charging for demiurges for fleet detention by suppliers, customers and carriers.

• Utilizing alternative coverage means during super peak periods to avoid carrying the burden of an oversized fleet.

Therefore, it can be seen that whatever be the fleet size, the company has to use it judiciously and constantly monitor its progress for optimum utilization of the available resources and at the same time cut down cost of maintenance e and tile lag.

Fleet maintenance is one means of reducing the ownership cost of the fleet by delaying potential replacements and improving customer service through improved reliability.

5.6 Routing and scheduling

India is one of the best examples of routing and re-scheduling, wherein such activities are optioned in the shortest possible time. Delay in delivery due to routing problems increase costs of goods manifold. Therefore, to tide over this the company has to plan these activities well in advance with detailed coordination and judicious and realistic planning. India is a versatile country with equally versatile terrain and climate condition. Companies have to gear itself to such changing scenarios and terrain since the very inception. Efficient versus inefficient routing can save tremendous amount of money in fuel, labor, and capital expenditures and significantly enhance customer satisfaction. The objective should be to minimize:

• Total route costs

• Number of routes

• Distance traveled

• Route time

The constraints are:

• Customer requirements and time available

• Balancing of the route for the driver, to minimize overtaxing

• Maximum route time

• Vehicle capacity

• Start & stop points en-route

• Infrastructure constraints.

Routing problems are some of the most difficult criticalities encountered, and cannot actually be solved optimally.

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5.7 Future directions in transportation

One salient aspect that we all have to understand that with e-services, our lead time to delivery has reduced considerably, but somehow the movement of the product and raw materials performance cannot move through e-services and have to restrict movement to roads, rail, air and waterways. Yes, the order can be placed through e-services in a faster mode and so can payment be but the products cannot be physically moved through the net. A truck, rail wagon or a ship or the cargo aircraft has to move it from the place of origin to the consumer’s destination.

Transportation too, has improved considerably with the advent of technology and mechanical developments within a short span. Certain programs and organizations help in coordinating transportation in a better way and as time passes they are bound to improve transportation in a big way. They can be clubbed under as follows

• Carrier relationship management: Through carrier relationship management programs we can bind transporters and those working with it under one roof/ enterprise. These programs are designed to formalize communication, partnering, negotiating, and performance monitoring aspects of carrier management. At the heart of most carrier relationship management programs is a set of guidelines for selecting core carriers, the minority of carriers who carry a majority of the enterprise’s weight, cube and shipments.

• Corporate traffic councils: These help in bringing together all personnel working in the area of transportation within an enterprise. The traffic council sets corporate transportation policy and explores opportunities for leveraging transportation spending across the corporation.

• Training and certification: Corporations should aim at making and maintain transportation as a value added activity. For this everyone should be in one plane and therefore, such training activities are carried out to get all under one platform.

• Driver quality: Improvements in drivers with better working environment and better wages will help in a big way to improve the driver’s capability and capacity in the long run.

• Joint Procurement: Significant cost reduction can be carried out if the purchase and negotiation of transportation services is consolidated across both inbound/outbound transportation activities within a business unit, across units and even with no-competitors.

• Logistics compliance & security officer: Forming the chief logistics security officer will enable a company to cope with global logistics law and to anticipate security lapses within the logistics network.

5.8 Facility location

Facilities and their locations are major issues in an organization’s logistics system efficiency and its ability to successfully implement its competitive advantage. Facility location decisions are of major importance to a company’s ability to compete in the market. Determining appropriate locations for facilities such as plants, warehouses, retail stores, hospitals etc. represents an important strategic decision. Location decisions come under the category of long-term decisions. They involve long-term commitments. A plant location decision cannot be reviewed until after quite sometime as they involve huge investments. Location decisions also have effect on the operating costs/revenues. For example, a bad location decision may call for excessive transportation costs, shortage of skilled workforce, loss of competitive advantage, inadequate supply of raw materials etc. Organizations are involved in location decisions for a variety of reasons such as the followings

• Expansion of existing facilities

• Addition of new facilities

• Closing down the plant at one location and moving to another

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5.8.1 Plant location

Choice of location for a plant is one of the earliest problems facing management. But location, perhaps, is one of the most neglected aspects of business, although the manufacturing and distribution costs may vary by over 10 percent simply by virtue of choice of location.

There are two types of factors (or criteria) on which location decisions are based: quantitative (or objective factors and qualitative or subjective) factors. The objective factors involve cost of land, transportation costs, utilities rates etc. The subjective factors include labour availability, climate, community environment, quality of life, local politics etc. The presence of objective and subjective factors results in greater degree of complexity in the structure of the plant location problem as well as its solution. A decision made on these factors is difficult as they are consistent over all locations. For example, a plant may be located far from work but have lower utility bills related to the area closer to work. Some factors may be more dominant than others. For example, on mineral production plants, raw materials dominate the situations due to which processing is located near mines. On the other hand, output oriented activities, such as service organizations tend to be located near consumers. Table 5.1 presents a list of some of the important location factors.

Transportation Factors

Utilities Factors

Labor Factors Climate, Community, Environment

etc.

States and Local

Political Factors

• Proximity to raw material sources

• Power

• Labor supply

• Climate and living conditions

• Taxation policies

• Closeness to markets

• Water

• Labor management relations

• Education

• Tax structure

• Modes of transportation

• Fuel

• Availability of skilled labor

• Community attitude

• Transportation costs

• Waste disposal

• Labor costs • Religions factors

Table 5.1: Location Factors Determining Plant Location

Steps for Location Planning

Location planning involves the following steps:

(i) Determine the criteria to evaluate location alternatives (for example: minimize costs)

(ii) Identify relevant location factors

(iii) Develop location alternatives

(a) Identify the general region

(b) Identify a small number of community site alternatives

(iv) Evaluate the alternatives and make a selection

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5.9 Evaluation of Location Alternatives

As stated earlier, the plant location problem involves both qualitative and quantitative factors. Finding the best location alternative considering all the above factors is not an easy one. Attempts have been made to combine the qualitative and quantitative factors and score the alternatives. One of the scoring (or rating) models is outlined below (Table5.2).

The procedure starts by listing the various factors and assigning weight to each factor to represent the relative importance of various factors. The score for each alternative is found by multiplying each factor’s score by its weight and summing the results. Table 5.2 gives the details of this rating approach for two locations A and B.

Table 5.2: Rating Approach

Score (out of 100) Weighted Score Location factor Weight

A B A B

1. Labor Costs

2. Water supply

3. Climate

4. Proximity to Raw Materials

5. Transportation Costs

0.40

0.10

0.15

0.20

0.10

0.05

70

80

80

40

80

80

90

90

90

70

90

80

70 × 0.40 = 28

8

12

8

8

4

90 × 0.40 = 36

9

13.5

14

9

4

Total Score 68 85.5

From Table 5.2, we see that location B that has a higher score is preferred. However one has to be careful in the use of the rating approach because of the assessment of scores, which might have involved some amount of subjectivity. For example, if the total score for location B were 70, which is very close to that of A, one need to go for further analysis before arriving at the final decision.

5.10 Distribution problem

The distribution problem is concerned with the allocation of goods flow to minimize overall distribution cost. Most distribution systems are three-tired structures in which goods start from the plant; flow to warehouse and ultimately to outlets. Warehousing plays a crucial role in the total distribution system. Consider for example, a large chain, which manufactures many products, maintains regional distribution centers and owns a large number of retail store. The firm has control over the location of all intermediate members of the logistics system.

In the absence of any warehouse, shipments of finished goods would have to be made directly from the plant to the retail stores. If the plant is located far from the raw materials sources, inbound transportation costs would be high and delivery times would be high, thereby increasing the chances of material shortage for production. If the plant is located far from the group of retail stores, then also transportation costs (i.e. outbound transportation costs) would be high and its takes longer to deliver orders to retail stores. This may result in out-of-stock situations thereby reducing the level of customer service. Warehouses placed close to the market can provide quick and efficient delivery to retail stores, while still permitting the plants to be placed near raw material sources. Warehouses and distribution centers play an important intermediary between plants and retail stores. They allow a company to store finished goods for efficient distribution to points of use. The role of warehouses is illustrated in Figure 5.4

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Plant 1 Plant 2 Plant 1 Plant 2

Figure 5.4: Role of Warehouse

It can be seen from the above figure that the provision of intermediaries like warehouse reduces considerably the number of interactions. There are other benefits associated with the provision of warehouses since they support both manufacturing as well as retail stores. For example, warehouse facilitates consolidation of orders.

A number of decisions should be made with regard to warehousing. Among the most important warehousing decisions are the determination of number and location of warehouses. The other decisions include the following:

• Which products should be stored in each warehouse?

• Should public or private warehouses be used ?

• What type of material-handling equipment should be used ?

• Which customers should be assigned to each warehouse ?

We shall discuss the warehouse location problem in the following section.

5.11 Warehouse location

We present here method(s) for determining the location(s) for warehouse(s). It is assumed that there are a number of existing facilities in place and we wish to find the optimum location of a new facility (or new facilities). The existing facilities could be plants, retail stores. The new facilities could be warehouse. The approaches take into account the locations of plants and markets, volume of goods moved and transportation costs. All these models focus on minimizing transportation costs.

Measures of Distance

Two measures of distance for movement of items are commonly used: Rectilinear distance and Euclidean (straight line) distance.

Let the existing facility be located at the point (a, b) and let (x, y) be the location of the new facility. The rectilinear distance between (a, b) and (x, y) is byax −++− , whereas

The Euclidean distance is ( ) 22 )( byax −+−

1 2 3 4 5 1 2 3

Warehouse

4 5

Retail Stores Retail Stores

(a) Without Warehouse (a) With Warehouse

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Rectilinear distance is appropriate for many location problems such as in metropolitan areas. In many manufacturing situations, material is transported along aisles arranged in rectilinear pattern. Fortunately, rectilinear distance problem is easier to solve than Euclidean distance problem.

The problem of locating a simple new facility with respect to a number of existing problems is known as the single facility location problem whereas the problem of locating multiple new facilities is known as the multi-facility location problem.

5.12 Single Facility Location Problem

Here, we will discuss about Single Facility Rectilinear Distance Location Problem, Squared Euclidean Distance Problem (known as the Gravity Problem) and the Straight-Line Distance Problem.

5.12.1 Single Facility Rectilinear Distance Location Problem

Let there be “n” existing facilities located at points ). ,( )......, ,( ), ,( 2211 nn bababa

The objective is to locate the new facility to minimize a weighted sum of the rectilinear distance from the new facility to existing facilities. The goal is to find the values of x and y such that

minimize ( )∑=

−+−=n

iiii byaxwyxf

1 ) ,( , where

wi is the flow of material / goods between the new facility and ith existing facility. The optimum values of x and y can be determined separately.

),( )( ) ,( 21 ygxgyxf += where

i

n

ii

n

iii bywygaxwxg −=−= ∑∑

== 12

11 )( and )(

An example of the single facility location problem could be location of a new storage warehouse for a company with an existing network of production and distribution centers.

5.12.2 Euclidean Distance Problems

Although the rectilinear distance measure is applicable in many location problems, there are situations in which the appropriate measure is the Euclidean or Straight-line distance. Location of power generating facilities so as to minimize the total length of electric cable that must be laid out to connect the plant and customer is an example where the Euclidean distance measure is appropriate.

We shall discuss here the squared Euclidean Distance Problem (known as the Gravity Problem) and the Euclidean Distance Location Problem.

5.12.3 The Gravity Problem

The Gravity problem corresponds to the case where the distance measure is square of the Euclidean distance. This measure is appropriate for location of emergency facilities. The objective is to find the value of (x, y) to minimize

[ ]∑ −+−= 22 )()() ,( iii byaxwyxf

The solution to this problem is straightforward and is often used as an approximation to the more common straight-line distance problem.

To find the optimum value of x and y, the partial derivatives of the objective function with respect to x and y are found and equated to zero.

49

∑=

−=∂

∂ n

iii axw

xyxfgetWe

1)(2),( ,

∑=

−=∂

∂ n

iii ayw

yyxfgetWe

1

)(2),( ,

Setting these partial derivatives equal to zero and solving for x and y, we get

∑

∑

∑

∑

=

=+

=

=+

=

=

n

ii

n

iii

n

ii

n

iii

w

awY

w

awX

1

1

1

1

Thus X+ and Y+ are the weighted averages of x and y coordinates and hence the name Gravity problem.

5.12.4 The Straight-Line Distance Problem

The straight-line distance measure arises much more frequently than the Gravity problem. The objective is to find (x, y) to minimize

∑=

−+−=n

iiii byaxwyxf

1

22 )()( ),(

Unfortunately, it is not easy to find the optimum solution mathematically. The partial derivatives become undefined when the location of the new facility coincides with that of an existing facility. There are no known simple algebraic solutions; all existing methods require an iterative procedure. The Gravity solution is usually selected as the starting solution for this iterative process.

5.13 Multi-facility Location Problem

The problem of locating multiple new facilities with respect to existing facilities is known as the Multi-facility Location Problem. For example, a countrywide consumer goods manufacturer might be considering where to locate four new regional warehouses. There is interaction among new facilities as well as between new and existing facilities. In some special situations, multi-facility location problem can be solved a s a sequence of single facility location problems.

Linear programming can be used to solve the multi-facility rectilinear distance location problem. Assume that there are “n: existing facilities located at points (a1, b1), (a2, b2), … (an, bn). Suppose the new facilities are to be located at (x1, y1), (x2, y2), … (xm, ym).

The objective function to be minimized is written as minimize )()( 21 yfxf +

ijji

n

i

m

jkjjk

mkji

axwxxVxfWhere −+−= ∑∑∑==≤≤≤

)( 11

1

50

ijji

n

i

m

jkjjk

mkji

aywyyVyfand −+−= ∑∑∑==≤≤≤

)( 11

21

Vjk represents the interaction between new facilities j and k and wji represents the interaction between new facility j and existing facility i. The optimum x and y values can be determined independently as in the case of single facility location problem.

Multi-facility gravity problems require the solution of a system of linear equations, so that gravity problems involving large number of facilities are easily solved. Multi-facility Euclidean distance location problems are solved by using multi dimensional version of the interaction solution procedure described in the previous section.

5.14 Retail facility location

The major criterion used for retail facility location is the volume of demand and hence estimates of demand must be known for potential locations. For locating facilities that are oriented toward sales, the principal factors are market related and the important data are demographic in nature. Other intangible factors, which affect retail location, are competition, zoning laws, traffic patterns and accessibility etc. Like in plant location, scoring models may be used to rank potential sites.

***************

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PART B

ADVANCED TOPICS

CHAPTER VI Supply Chain Management and Multi-echelon Inventories

6.1 Introduction

In this section, we consider a multi facility supply chain that belongs to a single firm. The objective of the firm is to manage inventory so as to reduce system wide cost. Therefore it is important to consider the interaction of the various facilities and the impact of this interaction on the inventory policy that should be employed by each facility.

6.2 Multi-echelon inventory system

Multi-echelon models examine the entire system; they might recommend holding just a few parts at the warehouse level, and none at the retailers, system-wide savings could be enormous. With faster moving parts of course it is likely that more units would be held at the retail level. Multi-echelon methods help to determine how many to hold at the retailers and how many to hold at the warehouse level.

Fig. 6.1: A Three-Echelon System

The first echelons, retail outlets, are replenished from branch warehouses (the second echelon), which are supplied from a central warehouse (the third echelon). Finally, it is supposed that central warehouse is replenished from outside sources.

Inventory management in this system is complex because demand at the central warehouse is dependent on the demand (and stocking decisions) at the branches. And demand at the branches is dependent on the demand (and stocking decisions) at the retail outlets. More generally, we refer to this as dependent demand situation (as compared to other inventory control module where demand for different stock keeping unit was considered to be independent). Multistage manufacturing situation are conceptually very similar to multi-echelon inventory systems.

6.2.1 Deterministic demand environment

Here external demand rates are known with certainty. The model will reveal the basic interactions among replenishment quantities at the different situations. Here stocking points are serially connected

Fig. 6.2: A Typical Supply-Chain

We consider here two stages denoted by a warehouse (W) and a retailer (R).

Fig. 6.3: A Two-stage Supply Chain

Here we will discuss the optimum quantity to be ordered for a two-stage supply chain problem under deterministic environment Notation used:

D = Deterministic demand at retailer’s end (unit/unit time)

AW = Fixed (set up) cost associated with a replenishment at the warehouse

AR = Fixed (set up) cost associated with a replenishment at the retailer

VW = Unit variable cost or value of the item at the warehouse in Rs/unit

VR = Unit variable cost or value of the item at the retailer in Rs/unit

r = Inventory carrying charge in Rs/Rs/unit time

QW = Replenishment quantity at the warehouse in units

QR =Replenishment quantity at the retailer in units

Here, the two controllable variables are the replenishment sizes QW and QR. For the deterministic case warehouse QW would be an integer multiple of QR. Therefore, QW = nQR where n=1,2,3, is a positive integer.

Now, the two decision variables here will be QR and n.

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Fig. 6.4: Warehouse Inventory

Fig. 6.5: Retailer Inventory Level

From the above two figures, one can say that

Inventory at the warehouse does not follow the usual saw-tooth pattern, even though the end usage is deterministic and constant with time.

The withdrawals from the warehouse inventory are of size QR.

Determination of average inventory levels becomes complicated.

To determine the value of inventory, here a concept of echelon stock is introduced. Below, we give the definition of two types of stock:

Installation Stock: It is simply the stock at the given location without regard for the downstream stock.

Echelon Stock: To understand multi-echelon inventory, it is necessary to understand the concept of echelon inventory. As mentioned above, in the distribution system, each stage or level is i.e. the warehouse or the retailer is referred to as an echelon. The echelon inventory at any stage or level of the system is equal to the inventory on hand at the echelon, plus all downstream inventories.

For example, the echelon inventory at the warehouse is equal to the inventory at the warehouse, plus the inventory in transit to the retailer and inventory in stocks at the retailers. Similarly, the echelon inventory position at the warehouse is the echelon inventory at the warehouse, plus those items ordered by the warehouse that have not yet arrived minus all items that are backordered. When backorders are permitted the echelon stock can be negative. Each echelon stock has a saw-tooth pattern with time and therefore, it is simple to calculate the average value of an echelon stock.

57

However, we cannot simply multiply each average echelon stock by the standard holding cost term and sum to obtain total inventory carrying costs.

The reason is that the same physical units of stock can appear in more than one echelon inventory.

When the decision being made is whether to store inventory at an upstream location or at a downstream location that it supplies, the relevant holding cost is the incremental cost of moving the product to the retailer.

The warehouse echelon inventory is valued at,

ww VV =′ while the retailer echelon inventory is valued at only wRR VVV −=′ .

In a production assembly context, the echelon valuation ′V at a particular stage is given by,

∑−=′ jii VVV where the summation is all immediate predecessor J.

The total relevant cost per unit time for the two stages are given by,

( ) rVIQ

DArVIQ

DAQQTRC RR

R

RWW

W

WRw ′++′+=,

where, WI = Average value of the warehouse echelon inventories in units

RI = Average value of the retailer echelon inventories in units

Using the concept of echelon stock,

wW VV =′ and wRR VVV −=′ . Echelon stock follows saw-tooth pattern.

( ) ⎥⎦

⎤⎢⎣

⎡ ′++

′+=

22, rVQ

QDArVQ

nnQ

DAQnTRC RR

R

RWR

R

WR

( ⎥⎦

⎤⎢⎣

⎡′+′+⎟

⎠⎞

⎜⎝⎛ += RW

RWR

R

VVnrQn

AA

QD

2) (1)

Now, ( ) 022 =′+′+⎟

⎠⎞

⎜⎝⎛ +−=

∂∂

RWW

RRR

VVnrn

AA

QD

QTRC

Or, ( ) ( )rVVn

Dn

AA

nQRW

WR

R ′+′

⎟⎠⎞

⎜⎝⎛ +

=2

* (2)

Substituting the value of in eqn. (2), we get ( )nQR*

( ) ( )RWW

R VVnn

AADrnTRC ′+′⎟

⎠⎞

⎜⎝⎛ += 2* (3)

We have to determine integer value of n that minimizes ( )nTRC* . The value of n that minimizes the expression is,

58

( ) ( )RWW

R VVnn

AAnF ′+′⎟

⎠⎞

⎜⎝⎛ += (4)

For minimization,

( ) 0=∂

∂nnF

,

( ) ,02 =′⎟⎠⎞

⎜⎝⎛ ++′+′⎥⎦

⎤⎢⎣⎡−=

∂∂

⇒ WW

RRWW

R

Vn

AAVVn

nA

QTRC

,02 =′+′⎟⎠⎞

⎜⎝⎛−=

∂∂

⇒ WRRW

R

VAVnA

QTRC

WR

RW

VAVA

n′′

=⇒ * (5)

which in general, will not be an integer.

If , assume and calculate the respective value. Otherwise, ascertain two-integer values and that surround .

1* <n 1=n1n 2n *n

( ) ( )RWW

R VVnnA

AnF ′+′⎟⎟⎠

⎞⎜⎜⎝

⎛+= 1

11

and, ( ) ( )RWW

R VVnnA

AnF ′+′⎟⎟⎠

⎞⎜⎜⎝

⎛+= 2

22 .

If use ( ) ( )21 nFnF ≤ 1nn = ,

If use . ( ) ( )21 nFnF > 2nn =

Then evaluate,

( )rVVn

Dn

AA

QRW

WR

R ′+′

⎟⎠⎞

⎜⎝⎛ +

=2

, then calculate, RW nQQ = . (6)

6.2.2 Multi-echelon stocking points with time varying demand

Here, end item demand is known but varies from period to period. ( )JD in period . Carrying costs are incurred only on period ending inventories. Now we have a multi-stage assembly or distribution system and we will discuss the situation for an assembly structure.

J

One approach would be simply to use sequential decision echelon by echelon. As for example, Silver-Meal heuristic could be utilized to schedule replenishments for the retailer. This would imply a pattern of requirements for the warehouse (a primary processing stage in production), which would then be used as input to the Silver-Meal heuristic to plan the replenishments of the warehouse.

Though this approach is simple, it ignores the cost interdependency of the two echelons. Specifically, in choosing the replenishment strategy at the retailer, the method does not take account of the cost implications at the warehouse. Here, a procedure is developed that can be applied sequentially to keep implementation effort at the reasonable level.

59

It captures the essence of cost interdependencies. An examination of level demand case provides considerable insight.

( ) ( ⎥⎦

⎤⎢⎣

⎡′+′+⎟

⎠

⎞⎜⎝

⎛ += RWRW

RR

RW VVnrQ

nA

AQDQQTRC

2, ) where, RW nQQ = .

This problem is analogous to a single echelon problem (i.e. the selection of ) if the adjusted fixed replenishment cost is represented as,

RQ

nA

AA WRR +=ˆ ,

and adjusted unit variable cost of the item is, RWR VVnV ′+′=ˆ

The term n

AW reflects that there is a warehouse setup only every nth retailer setup.

We can select from, RQ ( )rVVn

Dn

AAQ

RW

WR

R ′+′

⎟⎠⎞

⎜⎝⎛ +

=2

.

Properly taking account of the cost impact at the warehouse if we have a good pre-estimate of n. Further, we can use the steps followed in the earlier problem.

Step 1

WR

RW

VAVA

n′′

=* (7)

Step 2

Ascertain the two integer values and that surround . 1n 2n *n

Step 3

Evaluate, ( ) ( )RWW

R VVnnA

AnF ′+′⎟⎟⎠

⎞⎜⎜⎝

⎛+= 1

11

and, ( ) ( )RWW

R VVnnA

AnF ′+′⎟⎟⎠

⎞⎜⎜⎝

⎛+= 2

22 .

If use , ( ) ( )21 nFnF ≤ 1nn =

If use . ( ) ( )21 nFnF > 2nn =

But, Blackburn and Miller have found that simply using WR

RW

VAVA

n′′

=* and ensuring that n is ‘at

least’ unity works well, particularly for more complex assembly structure.

Thus, ⎥⎥⎦

⎤

⎢⎢⎣

⎡′′

= 1 ,WR

RW

VAVA

n (8)

For time-varying demand, compute n and then employ n value to obtain adjusted setup and unit variable costs for the retailer according to following equation,

60

nA

AA WRR +=ˆ ,

RWR VVnV ′+′=ˆ

The Silver-Meal heuristic can then be applied to the retailer using and . The resulting replenishment again implies a replenishment pattern for the warehouse. Subsequently, the Silver-Meal or another lot-sizing procedure is used at the warehouse with and .

RA RV

wA wV

6.3 Coordinated Ordering

In the earlier inventory control problem studied in the literature, it is assumed that different items in an inventory could be controlled independently.

In this part, we now relax this assumption and consider situations where there is a need to coordinate orders for different items. When coordinating the replenishments for different items, it is common to use cyclic schedules and especially so-called ‘power of two policies’. One reason for coordinated replenishments is that, we want to trigger orders for a group of items at the same time. This can be advantageous in many situations.

• It may be possible to get a discount if the total order from the same vendor is greater than a certain break point.

• It may also be possible to reduce the transportation costs, e.g., by filling a truckload.

• Sometimes, set up costs can also be lowered substantially if a group of items are produced together in a machine.

6.3.1 Power of two ordering policies

It is very common to use power of two policies in connection with coordinated replenishments. Here cycle times are restricted to be powers of two times of a certain basic period.

Suppose we order two items with cycle times 5 weeks and 7 weeks respectively. Now suppose both the items are ordered in week 1. Then item with cycle time 5 week is ordered in week 1, 6, 11, 16, 21, 26, 31, 36 etc and the other item in weeks 1, 8, 15, 22, 29, 36 etc.

Thus week 36 is the first time after week 1 when both the items are ordered together.

We will show that a restriction of power of two policy will give a solution, which is very close to the optimal solution. If we could somehow synchronize the order interval so that order for different products often arrives on same day, we could greatly reduce our coordination costs.

Roundy (1985) devised an elegant and simple method called ‘power of two ordering policies’ to ensure that order for multiple products are well synchronized.

Roundy proved that using the power of two policy, to round the order interval to a neighboring power of 2 will increase the sum of ordering and holding cost at most 6%. The result of power of two policy is that different products will be ordered frequently at the same time. In many cases, this will greatly reduce coordination cost. In most circumstances this policy will reduce coordination cost by more than maximum possible 6% increase in total cost.

6.3.2 Proof of Roundy’s result

For an arbitrary order quantity Q, the total inventory related cost is given by,

61

( )2

hQQADQTC +=

( ) gTTAhDT

TATTC +=+=⇒

2, where,

2hDg = .

Total cost at EOQ is given as ( ) ADhQTC 2* = ,

Or, Optimal cost per unit time ( ) AgTTC 2* = .

It is natural to consider policies where the reorder interval T is restricted to values that can be easily implementable.

Now, we put power of two restrictions and T is restricted to be a power of multiple of some fixed base planning period TB. B

kBTT 2= , where, { },.....3 ,2 ,1 ,0∈k (9)

Such policy is called power of two policy. The base planning period may represent a day, week or month, etc and is usually fixed beforehand. It represents minimum possible reorder interval.

BT

Now, the basic question is that how does one find the best power of two policy that minimize the cost over all possible power of two policy?

Secondly, how much this best power of the policy deviates from the optimal policy?

gAT =* is the optimal reorder interval under unrestricted condition and let T be the optimal power

of two reorder interval.

Since total cost is a convex function, the optimal k in (9) is the smallest integer k satisfying the condition.

( ) ( )122 +≤ kB

kB TTCTTC

⎟⎟⎠

⎞⎜⎜⎝

⎛+≤⎟⎟

⎠

⎞⎜⎜⎝

⎛+⇒ +

+1

1 22

22

kBk

B

kBk

B

gTT

AgTT

A

( )122211

2−≤⎥⎦

⎤⎢⎣⎡ −⇒ k

BkB

gTT

A

( )222

kBT

gA

≤⇒

( )kBT

gA 2

21

≤⇒

TT ≤⇒ *

21

(10)

By the definition of optimal k, it must also satisfy the following condition since the cost curve is convex in nature that is,

( ) ( )122 −≤ kB

kB TTCTTC

62

⎟⎟⎠

⎞⎜⎜⎝

⎛+≤⎟⎟

⎠

⎞⎜⎜⎝

⎛+⇒ −

−1

1 22

22

kBk

B

kBk

B

gTT

AgTT

A

⎟⎠⎞

⎜⎝⎛−≤⎥⎦

⎤⎢⎣⎡ −⇒ − 2

12211

2 1k

BkB

gTT

A

( )kBk

B

gTT

A 22

2≥⇒

( )222 kBT

gA≥⇒

gAT k

B 22 ≤⇒

*2TT ≤⇒ (11)

From equation (10) and (11), ** 22

1 TTT ≤≤ (12)

Hence the optimal power of two policy for a given base planning period TB must be in the interval B

⎥⎦

⎤⎢⎣

⎡ ** 2,2

1 TT .

The maximum discrepancy between the total cost for the power of two ordering policy and the total

cost for *T will occur if power of two reorder interval equals either *2T or, *

21 T .

We have already derived that,

( )( ) ⎥

⎦

⎤⎢⎣

⎡+=⎟⎟

⎠

⎞⎜⎜⎝

⎛+=

QQ

QQ

ADhhQ

QAD

QTCQTC *

** 211

2,

Since, DQT

** = and

DQT =

( )( ) ⎥

⎦

⎤⎢⎣

⎡+=

TT

TT

TTCTTC *

** 21

Now, upper bound of *2TT = and lower bound of *

21 TT = .

Then, ( ) .06.12

1221

22

212

1 , ,2

*

*

*

*

*

**

=⎥⎦

⎤⎢⎣

⎡+=⎥

⎦

⎤⎢⎣

⎡+=

⎟⎠

⎞⎜⎝

⎛

TT

TT

TTC

TorTTC

Thus, average inventory purchasing and carrying cost of the best power of two policy is guaranteed to be within 6% of the average cost of overall minimum policy.

Let us consider the following example to understand the power of two ordering policy

Three products are with reorder intervals of 3.5 days, 5.6 days and 9.2 days respectively

63

( )Bk TT 22* = ,

Let, =1 period. BT

Starting with power of two k =0, 0225.3 ≤⇒ . It is found that condition is not satisfied. We now increase the power k to 1 and

check whether it satisfies the condition. It is found that 1225.3 ≤

88.2244.15.3 ≤≤⇒ x and therefore not satisfied the condition.

Again increasing the power k to two, it is found that

2225.3 ≤

425.3 x≤

76.5444.15.3 ≤≤⇒ x , it satisfies the condition i.e. power of =4 weeks. 22

Similarly for second case, 76.5226.5 2 ≤≤ . This will satisfy the condition, so follow order interval =4 weeks. 22

Similarly for the third case, 3244.12.9 x≤3244.12.9 x≤

844.12.9 x≤⇒ , it satisfies the condition. So, follow the order interval =8 weeks. 32

So, according to Roundy’s policy, quantity should be ordered at , , and order interval time. Thus orders for two items will be given at the end of 4 weeks whereas order for the third item should be given at the end of 8 weeks.

22 22 32

Reference

Silver,E A., Pyke. D.F, Peterson, R., and; Inventory Management and Production Planning and Scheduling; Publisher: John Wiley and Sons

**************

64

CHAPTER VII Supply Chain Contract and Coordination

7.1 Introduction Managing the flows in the supply chain network implies the presence of many decision makers within the supply chain where each one operates a part of it. These decision makers could be either distinct firms or managers of different departments within a firm. Every individual decision maker will attempt to maximize his own profit keeping in mind that others will also do the same thing. This individual competetive behavior of the members of the supply chain adversely affect the overall performance goal of the supply chain.

Thus to avoid this undesirable situation, one of the most important issue arises in the management of supply chain is how to have perfect coordination amongsts the members of a supply chain to have cost savings and increase in channel efficiency. Philosophy of supply chain contracting is to develop coordiantion policies through pair wise interaction of an upstream (supplier) and downstream (retilaer) agent at a period of time. Decision-making at different levels in the organization should be so coordinated that operating policies are optimal for the organization as a whole. Without coordination, improvement at one level may be lost due to inefficiencies at another level. As for example, reducing inventory at one level may not be beneficial if it is accumulated in any succeeding stage. To avoid this undesirable situation, academicians have studied the issue and developed various mathematical models. Porter (1985) has mentioned that cooperative relationship between buyer and supplier is not a zero sum game in which one gains only at the expense of the other, but a relationship in which both gain. Coordination is actually in the form of cooperative decisions, that is, the individual entities make decisions, which are in the best interests of the entire supply chain.

As discussed above, it is clear that in a supply chain (SC), there are multiple firms owned and operated by different parties, and each of these firms takes decisions, which are in line with their own goals and objectives. As in all decentralized systems, the actions chosen by SC participants might not always lead to the “optimal” outcome if one considers the supply chain as one entity. That is, since each player acts out of self-interest, we usually see inefficiencies in the system, i.e., the results look different than if the system was managed “optimally” by a single decision-maker who could decide on behalf of these players and enforce the type of behavior dictated by this globally (or centrally) optimal solution. In this section, we will take a look at the nature of inefficiencies that might result from the decentralized decision-making in supply chains, and if and how one can design contracts such that even though each player acts out of self interest, the decentralized solution might approach the centralized optimal solution. For excellent reviews on SC contracts and coordination, we refer the reader to Tsay, Nahmias and Agrawal (1999) and Cachon (2001).

Mechanisms of channel coordination and vertical control in production/distribution channels have received attention from several disciplines such as Economics, Marketing apart from Operations Management. The models in Economics literature generally assume a deterministic demand function. Marketing literature has focused on channel coordination to maximize joint profits. Though Marketing and Economics literature provide different motivations for coordination yet, ultimately the motivations share the common objective of maximizing system welfare.

7.2 Supply chain Contract

To better formalize the coordination ideas, we will look at a simple stylized two-stage supply chain with one supplier and one retailer, where the retailer buys goods from the supplier and sells them in the end market:

For simplicity, assume that the supplier is uncapacitated, and has unit cost of production c. The retailer faces a market where the price is inversely related to the quantity sold. For simplicity, let us assume a linear demand curve P = a - bq where P is the market price and q is the quantity sold by the retailer6. Assume that all of this information is common knowledge.

First let us consider the simple “wholesale price” contract where the supplier charges the retailer

DSC CSC

Supplier’s wholesale price (w) w = (a + c)/2 w

Retailer’s quantity (q)

q = (a - c)/4b

Q* = (a - c)/2b

Market Price (P)

P = (3a + c)/4

P* = (a + c)/2

Supplier’s profit (Πs)

ΠS = (a - c)2/8b

Π*S = (w - c)q

Retailer’s profit (ΠR)

ΠR = (a - c)2/16b Π*R = (P - w)q

Total SC profits (Π) Π = 3(a - c)2/16b Π= (a - c)2/4b

Table 1: Wholesale price contract versus CSC.

w per unit. The supplier’s and the retailer’s profits are ΠS = (w - c)q and ΠR = (a - bq - w) q, respectively. The supply chain’s profits are Π = ΠS + ΠR = (a - bq - c)q. Note that the choice of w only indirectly affects the total SC profits, since the choice of w impacts the choice of q.

Decentralized Supply Chain (DSC)

As in most real-world supply chains, suppose that the supplier and the retailer are two independently owned and managed firms, where each party is trying to maximize his/her own profits. The supplier chooses the unit wholesale price w and after observing w, the retailer chooses the order quantity q. Note that this is a dynamic game of complete information with two players, supplier and retailer, where the supplier moves first and the retailer moves second. Hence, we can solve this game using backwards induction. Given a w, first we need to find the retailer’s best response q(w). The retailer will choose q to maximize ΠR = (a - bq - w)q. This is a concave function of q, and hence from FOC we get

bwawq

wbqaq

R

2)(

02

−=⇒

=−−=∂∂π

66

Next, given the retailer’s best response q(w) = (a-w)/2b, the supplier maximizes ΠS = (w-c)q = (w - c)(a - w)/2b. This is a concave function of w and from FOC we get

20 cawcwwa

ws +

=⇒=+−−=∂∂π

The equilibrium solution for this decentralized supply chain is given in the second column of Table 1. In this contractual setting, the supplier gets two-thirds of the SC profits, the retailer gets only one-third. This is partly due to the first-mover advantage of the supplier.

Now, let us consider a centralized (integrated) supply chain (CSC) where both the retailer and the supplier are part of the same organization and managed by the same entity. Centralized Supply Chain (CSC): In this case there is a single decision-maker who is concerned with maximizing the entire chain’s profits Π= (a-bq -c)q. This is a concave function of q and from first order condition (FOC), we get

bcaqcbqa

q 202 * −

=⇒=−−=∂∂π

The solution for the CSC is given in the third column of Table 1. From Table 1, we see that the quantity sold as well as the profits are higher and the price is lower in the CSC than in the DCS. Hence, both the supply chain and the consumers are better off in the CSC. What about the retailer and the supplier? Are they both better off, or is one of them worse off in the CSC? What is the wholesale price? How does the choice of w affect the market price, quantity, and the supply chain profits? A closer look would reveal that w has no impact on these quantities. Any positive w would result in the same outcome for the CSC because the firm would be paying the wholesale price to itself ! However, the choice of w in the CSC is still very important as it determines how the profits will be allocated between the supplier and the retailer. We can interpret w as a form of transfer payment from the retailer to the supplier. What is the minimum w that is reasonable? For positive supplier profits, we need w c. If we set w = c, the supplier’s profits are zero whereas the retailer captures the entire supply chain’s profits. What is the w that splits the SC profits equally between the retailer and the supplier? If we set w = (a+3c)/4, w -c = P -w = (a-c)/4 and each party’s profits are (a-c)

≥

2 /8b. Note that this is the same as the supplier’s profits in the DSC. Hence, if the supplier and the retailer split the profits equally in the CSC, the supplier is at least as good, and the retailer is strictly better off than in the DCS. In the DSC, the outcomes are worse for all the parties involved (supplier, retailer, supply chain, and consumer) compared to the CSC, because in the DSC both the retailer and the supplier independently try to maximize their own profits, i.e., they each try to get a margin, P - w and w - c, respectively. This effect is called “double marginalization” (DM).

In a serial supply chain with multiple firms there is coordination failure because each firm charges a margin and neither firm considers the entire supply chain’s margin when making a decision.

In this stylized model, the profit loss in the DSC due to DM is 25% (also referred to as the DM loss). It is clearly in the firms’ interest to eliminate or reduce double marginalization, especially if this can be done while allocating the additional profits to the firms such that both firms benefit. This simple model suggests that vertical integration could be one possible way of eliminating double marginalization. However, for reasons we discussed at the beginning of this chapter, vertical integration is usually not desirable, or not practical. Then the question is, can we change the terms of the trade so that independently managed companies act as if they are vertically integrated? This is the concept known as “supply chain coordination.” In this stylized model, the retailer should choose q* = (a - c)/2b in any coordinating contract. One can easily think of some very simple alternative contracts to eliminate double marginalization:

67

7.2.1 Take-it-or-leave-it-contract

The supplier offers the following contract to the retailer: Buy q* at the wholesale price w = (a + c)/2, or nothing. In this case the supplier’s profit is Π, i.e., the supplier captures 100% of the CSC profits. The supplier offers the contract and grabs whole of the CSC profit as the first-mover advantage. This contract requires a very powerful supplier.

7.2.2 Marginal pricing

The supplier sets w = c. In this case, the retailer’s profit is Π*, i.e., the retailer captures 100% of the CSC profits. The supplier sets the wholesale price equal to its marginal price, so that the retailer grabs whole of the CSC profit. This contract requires a very powerful retailer.

Note that the take-it-or-leave-it contract would require a very powerful supplier whereas the marginal pricing contract would require a very powerful retailer. In practice, neither the supplier nor the retailer is so powerful in general to dictate such contract terms. Hence, we need to consider alternative contracts that coordinate the supply chain.

Above cases are the extreme cases of supply chain contracting and are not easily implementable when both supplier and retailer have similar bargaining powers. The goal of negotiations of supply chain contracting is to design “channel coordinated” contracts where the players’ Nash equilibrium coincides with the supply chain optimum1 and at the same time satisfies the conditions of individual rationality (IR) and incentive compatibility (IC) (Wu, 2004). Before getting into the types of supply chains that fall within these extreme cases we discuss a few characteristics of supply chain contracts in the following.

Profitability : Achieve profits close to optimum

Fairness and flexibility Allow the flexible division of profits

Implementability : Should be easy and low-cost to administer

7.2.3 Revenue Sharing Contract

In a revenue sharing contract the retailer shares a fraction 1<α of his revenues with the supplier.

Cost of each unit sold , where, and are the unit costs of the supplier and the retailer.

RS ccc += Sc Rc

( ) =qR retailer’s revenue as a function of quantity . q

In the CSC, the profits are given by ( ) cqqR −=Π and from FOC we have, ( ) cqqR

=∂

∂. This means

that, the marginal revenue is equal to the marginal cost at the optimum.

In the DSC, the retailer’s profits are ( ) ( )qcwqR RR +−=Π where the wholesale price is . From

FOC we have,

w( )

RcwqqR

+=∂

∂. Note that if *qq < Rccw −> , i.e., unless the supplier sells at

marginal cost, the retailer orders less than the optimal CSC quantity resulting in double marginalization.

The retailer’s profits in the revenue sharing contract are ( ) ( ) ( )qcwqRq RR +−=Π α

From FOC we have, ( ) ( )

αRcw

qqR +

=∂

∂. We had

( ) cqqR

=∂

∂ in the integrated chain. Hence, by

setting the two right hand sides equal to each other, we achieve the same quantities CSC and DSC.

68

That is ccw R α=+ , i.e., Rccw −=α , we have marginal revenue equal to marginal cost in the DSC as well, and . In this case, the retailer’s profit is *qq =

( ) ( ) ( )( ) *** Π=−=−−−=Π αααα cqqRqcqccqR RRR , i.e., the retailer captures α fraction of the optimal SC profit.

Note that in the revenue sharing contract, the retailer’s objective function becomes an affine transformation of the supply chain’s objective function, i.e., the retailer’s and the supply chain’s objectives are perfectly aligned. Since the total SC profits are higher compared to the traditional wholesale price contract, partners can choose α such that both parties benefit. The choice of α depends on several factors, including the bargaining power of the supplier and the retailer.

Drawbacks:

1. The additional cost and administrative burden it creates compared to the straightforward wholesale price contract. This takes an organizational effort to set up the deal and follow its progress. It is worthwhile to go into such contracts only if the increase in profits is relatively large compared to the additional cost and the administrative effort.

2. In case of multiple retailers, coordination is not guaranteed unless the supplier has the flexibility to offer different contracts to different retailers. Unfortunately, such discriminations may not be possible always due to legal considerations.

3. This contract looses its appeal if the revenues depend on retailer’s sales effort. For a retailer who is taking only a fraction of the revenues he generates, the incentive to improve sales goes down.

Example of revenue sharing contracts implemented in practice-

Blockbuster is a retailer, which purchases movies from the studios (suppliers) and rents them to customers. The supplier’s wholesale price impacts how many videos Blockbuster orders and hence, how many units are eventually rented by customers. Before 1998, the price of purchasing a tape from the studio was very high, around $65. Given that rental fees are in the order of $3-4, Blockbuster could purchase only a limited number of videos and this resulted in lost demand; especially during the initial release period, where the demand was high (peak demand usually lasts less than 10 weeks), 20% of customers could not find what they were looking for on the shelf. Hence, the studio’s high wholesale price impacted the quantity purchased by Blockbuster, and in turn, the revenues and the profitability of both firms. Seeing this problem, Blockbuster and the studios went into a revenue sharing agreement. According to this, Blockbuster pays only $8 per tape initially, but then gives a portion (somewhere around 30 to 45%) of the revenues generated from that tape back to the supplier. Since this agreement reduces Blockbuster’s initial investment in movies, it orders more tapes from the studio, hence, is able to meet more demand, generate more revenues, and give back a portion of those revenues back to the supplier. Blockbuster increased its overall market share from 25% to 31% and its cash flow by 61% using this agreement. This is clearly a win-win situation. The supplier might be better of even if he sells each unit below its productioncost. A similar agreement is used between studios and theaters as well. (Cnet News.com, October 18, 2000) [7]

7.2.4 Buyback Contract

One of the reasons of ordering less than the optimal quantity by the retailer is due to risk of excess inventory. Buyback contract allocate the risk between the supplier and the retailer. Retailer can return the goods to the supplier and get some money back. In this contract the supplier purchases the leftover units at the end of the selling season for b per unit, where wb < .

The retailer’s revenue = , where ( ) ( )qpSqR = p is the unit selling price and is the total amount of sales made by the retailer, where

( )qS( ) qqS < .

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In the DSC, the supplier’s profit is

=Π R revenue + returns – cost – purchase cost

= ( ) ( ) ( ) ( ) ( )qbwcpbqRqwc

pqRqbqR RR −+−⎟⎟

⎠

⎞⎜⎜⎝

⎛−=+−⎟⎟

⎠

⎞⎜⎜⎝

⎛−+ 1

In revenue sharing contracts we have, ( )( )*** cqqRR −=Π=Π αα , i.e., the retailer’s profit is an affine transformation of the centralized supply chain’s profits. Hence the retailer’s optimal quantity is the same as the centralized supply chain’s optimal quantity. For a similar situation to hold buyback contracts, the retailer’s revenue should be equal to , i.e., *Πα

( ) ( ) *1 Π=−+−⎟⎟⎠

⎞⎜⎜⎝

⎛− αqbwc

pbqR R and we need to have ( ) cbwcR α=−+ and α=⎟⎟

⎠

⎞⎜⎜⎝

⎛−

pb1 .

( )αα −=⇒=⎟⎟⎠

⎞⎜⎜⎝

⎛− 1;1 pb

pb

and, ( ) ( ) RRbR ccpcbcwcbwc −+−=−+=⇒=−+ αααα 1;

Hence, if the supplier chooses and b in this fashion, the retailer will get a fraction of the CSC’s profits.

w

In the revenue sharing contract we had, Rccw −=α . In buyback contracts, we have, ( ) Rb ccpw −+−= αα1 and, ( )α−= 1pb . That is, wbwb += . Hence, in the buyback contract

the supplier charges a little more for the wholesale price and in return guarantees to give back b for any unit that is not sold.

In revenue sharing contracts the wholesale price did not depend on the selling price. But in a buyback contract, the wholesale price and the return price depend on the selling price. Hence, in a buyback contract the supplier should set w and b as functions of the selling price p. Is the buyback contract flexible in terms of how the profits are shared between the supplier and the retailer? The answer is yes. Actually, for every revenue sharing contract, there is an equivalent buyback contract and vice versa.

7.2.4 Quantity flexibility contract

In quantity flexibility contract, the supplier provides full refund for returned (unsold) items as long as the number of return is no larger than a certain quantity. Thus this contract gives full refund for a portion of the returned items, whereas a buyback contract provides partial refund for all returned items.

7.2.5 Two-Part Tariff

In a two-part tariff, the supplier charges a fixed fee and a wholesale price per unit. F w

In the CSC, the optimality condition is ( )

SR cccqqR

+==∂

∂, i.e., marginal revenue is equal to

marginal cost.

In the DSC, under two-tariff the retailer’s profit is ( ) ( ) FqcwqR RR −+−=Π . From the FOC

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( ) ( )RR

R cwqqRcw

qqR

q+=

∂∂

⇒=−−∂

∂=

∂Π∂ 0

In order to have the CSC solution, we need scw = , i.e, supplier must sell at cost. In this case the

retailer gets ( ) ( ) ( ) ( ) FFqccqRFqcwqR RSRR −Π=−+−=−+−=Π ***** . Supplier’s profit

is . ( ) FqcwF SS =−+=Π *

Notice that determines the allocation of profits between the supplier and the retailer. This is a flexible contract as any allocation is possible.

F

7.2.6 Quantity Discount Contract

In quantity discount contract ifs, if the retailer purchases more units then the supplier lowers the unit price, i.e, the supplier charges where is a decreasing function of q . In general is a step function. But for simplicity we assume it is continuous and differentiable.

( )qw w ( )qw

The retailer’s profit in DSC is ( ) ( )( )qcqwqR RR +−=Π . We observed earlier that the DSC has the same optimal quantity as the CSC, if is an affine transformation of RΠ Π . Hence we need,

( ) ( )( ) ( )( ) ( ) ( ) ( ) ccqwqcqRqcqwqR RRR ααα +−⎟⎟⎠

⎞⎜⎜⎝

⎛−=⇒−=+−=Π ****** 1

qqR*

*

.

If the supplier sets the wholesale price as ( ) ( ) ( ) ccqqRqw R αα +−⎟⎟⎠

⎞⎜⎜⎝

⎛−= 1 , there is a tradeoff for the

retailer. If he chooses , he will pay too much per unit, increasing his marginal cost. If he chooses , then the unit price will decrease but the marginal revenue will decrease more, making the extra unit unprofitable. Hence the optimal quantity choice for the retailer is . The supplier’s profit in this case is

*qq <*qq >

*q

( ) ( ) ( ) ( ) ( ) ( ) ( )Π−=−−−=⎟⎟⎠

⎞⎜⎜⎝

⎛−+−

−=−=Π ααααα 1111 ***

*

** cqqRqccc

qqRqcw SRss

In both revenue sharing and quantity discount contract the retailer’s (and the supplier’s) revenue is proportional to the centralized supply chain’s profit. If there is demand uncertainty, in a quantity discount contract the retailer bears the risk.

7.3 Present trend in the study of supply chain coordination from Operations Management perspective

In the last couple of years, interest in the field of supply chain coordination from Operations Management perspective has grown considerably. One line of research employs quantity discount and quantity commitment as coordination mechanism (Aderohunmu et al. 1995; Lariviere, 1999). Further, authors like Ertogral et al. (2001) have put emphasis on the need to incorporate negotiation process in supply chain coordination. Negotiation process focusses on dynamic sharing of surplus between the two parties where both can take part in the decision. Here, the negotiation ends with the win-win feeling for both. This is considered superior to a pre-determined static division of surplus through side payment strategy for a decentralized supply chain.

Many authors (e.g. Kohli and Park, 1989) have assumed that both buyer and the supplier have full information about each other. But in practice, such a comfortable situation hardly exists. Realizing this fact, recently some authors (e.g. Corbett et al., 2000; Ha, 2001) have incorporated the information asymmetry factor in their models of supply chain coordination problem.

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Another area of supply chain coordination that has drawn the attention of researchers is on development of suitable mechanism to coordinate the logistic processes that are controlled by various companies. Swenseth et al. (2002) have reported that often about 50% of total annual logistics cost of a product can be attributed to transportation cost. Therefore, for the overall performance improvement of the supply chain, there is a need to develop coordination mechanism to coordinate the logistics processes between the various parties of a supply chain. Particularly, in the multi buyers case where buyers are located in different geographical regions, individual shipments to the buyers by the vendor increases the total system cost. In such a situation, coordinated shipment from the vendors to multiple buyers helps to reduce channel cost.

7.4 Supply chain coordiantion through Quantity discount

Our intention here is to cover only quantity discount supply chain coordination mechanism literature. Since quantity discount is considered to be one of the most popular mechanisms of coordination between the business entities, this section primarily investigates supply chain coordination models that have used quantity discount as coordination tool under deterministic environment. Here, the word vendor, supplier and manufacturer is used alternatively to represent the same upstream member in the supply chain who sells the item to the buyer unless specifically mentioned.

In many practical situations, it is found that manufacturer has a high production setup cost or high shipping cost. In such situations, the manufacturer prefers to produce or ship the product in large quantities. The manufacturer encourages buyers to order in large quantities by offering some incentives. Similarly, manufacturer also by ordering larger quantity to its raw material supplier may ask for some incentives from his supplier. Larger orders will lead to fewer productions setup. Larger orders may allow the vendor to take the advantage of economy of scale in transportation. Thus, it helps in improving the overall efficiency of the channel. The joint economic lot sizing literature has examined the case where the supplier wishes to induce the buyer to choose a higher lot size than she would of her own accord. Thus, in any supply chain, where buyer, manufacture and supplier are involved, there is also a scope to reduce channel cost and inefficiency. This is possible if channel partners jointly optimize order quantities/batch sizes and agree to the resultant benefits. Recently, some authors (e.g. Munson et al., 2001; Yang et al., 2001) have extended the two-stage supply chain model to a three-stage supply chain model where the raw material supplier to the manufacturer is the third partner.

Quantity discount and credit by supplier to the buying firms are used as instruments for developing coordination mechanism between the two parties. Gurnani (2001) in his recent work has mentioned that industrial supplier-buyer relations have undergone significant changes with increasing emphasis on coordination and information sharing. This is made with an attempt to reduce transaction related costs and to capture a larger market share.

Notation:

Subscript 1 and 2 represent vendor and buyer respectively,

D = the buyer’s annual demand for the product,

Si = Setup and ordering cost for the firm i, i = {1,2}

ri = Annual inventory holding cost expressed as a percentage of the value of the item for the firm i, i = {1,2}

Q = the buyer’s order quantity

M2 = the vendor/manufacturer’s gross profit on sales expressed as a percentage

dk = discount per unit offered by the manufacturer

R2 = the manufacturer’s production rate in unit per year

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P0 = the buyer’s base purchase price without quantity discount

C2 = the manufacturer’e manufacturing cost per unit excluding order processing, setup, and inventory holding costs per unit

7.4.1 Background

Study of integrated inventory models can be viewed as one of the origin of supply chain coordination study from Operations Management perspective. These models mainly examine the benefits accrued in the system due to coordination in order quantities between the two parties. Earlier, Goyal and Gupta (1989) have reviewed the literature of buyer vendor coordination models. Benton and Park (1996) and Munson and Rosenblatt(1998) have also reviewed some of the papers discussed here under different context. We have mainly considered here the literature of channel coordination/ supply chain coordination models that have operations approach. Operations approach mainly concentrates on the operating cost of the channel. Operating cost is considered as a function of retailer’s / buyer’s order quantity where a fixed retail price is assumed and this leads to a fixed final demand.

The traditional inventory model assumes that a rational buyer would prefer to purchase his optimal order quantity (EOQ) as any deviation from this quantity would increase his total cost. The buyer’s annual total cost for order quantity Q can be expressed as

( ) 0110 2PrQS

QDDPQTC ⎟

⎠⎞

⎜⎝⎛+⎟⎟

⎠

⎞⎜⎜⎝

⎛+= (1)

When quantity discount is not allowed, the buyer’s optimal order size is given as

01

1* 2Pr

DSQ = . Thus, the total annual cost is given as ( ) 0110* 2 PrDSDPQTC += (2)

Corresponding to buyer’s order quantity Q, the vendor’s/ manufacturer’s yearly net profit considering only order processing and setup costs for lot for lot policy can be written as

⎟⎟⎠

⎞⎜⎜⎝

⎛−=

QDSPDMYNP 2

022 (3)

The total channel cost is the sum of the individual cost component of buyer and vendor respectively

and can be writen as ( ) 01210 2)( PrQSS

QDDPQTCC ⎟

⎠⎞

⎜⎝⎛+++= (4)

Vendor’s/ manufacturer’s order processing and setup cost per order are considered to be larger than the buyer’s order processing cost per order. If a buyer adopts his EOQ as the order quantity for minimizing his total annual costs, the vendor/ manufacturer incurs a significant cost penalty. Therefore, vendor /manufacturer induces the buyer through quantity discounts to order larger quantity to maximize his profits. Manufacturer can maximize his profit when the lot size is as high as infinity ! When buyer purchases in larger order quantity Q > EOQ then there is an increase in profit for the vendor because of potential savings in order processing cost, manufacturing setup costs and transportation costs. By selling fewer but larger orders, the vendor generates lower sales cost. Also, vendor may save by seeking quantity discounts on raw materials he receives from his supplier. The increase size of order quantity or lot size ultimately helps in improving the channel profits. Increase in profits should be shared in some equitable fashion so that coordination in real sense is useful and parties in the channel shows interest to coordinate.

Here, we have categorised the various coordination models as follows

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(i) One can maximize the supplier’s yearly net profit as shown by equation (3) in our general model by adopting different lot size by giving incentive to the buyer. The authors who have attempted the coordination problem from this perspective are classified here as vendor’s /manufacture’s perspective coordination models.

(ii) Similarly, one can minimize the total system cost with respect to coordinated lot size or the order quantity as shown by equation (4) and thereby improves the system savings. We have classified here those models as joint buyer and seller / manufacturer perspective coordination models.

(iii) On the otherhand, some authors have studied the buyer vendor coordination through quantity discount as a non cooperative and cooperative game. In a non cooperative game, each member will try to maximize his profit or minimize his cost. Thus the objective will be here to maximize equation (3) and minimize equation (1) of the general model. However, in a cooperative game, the objective will be to maximize system profit subject to the constraint that no player looses or incurs more from their non cooperative solution. We have categorized these models as a buyer and a seller/ manufacturer coordination models under game theoretic frame work.

In this stream of literature, most of the models have assumed that seller /manufacturer knows or can estimate the buyer’s setup and holding costs. Further, EOQ assumptions are considered for the buyer. The buyer is assumed to act optimally and order the quantity leading to his lowest total cost.

7.5 (a) Manufacturer’s / Seller’s perspective coordination models

Monahan (1984) in his model suggested that a vendor could encourage his customer to increase the order quantities from EOQ by offering a price discount. With the quantity discount, the buyer will be motivated to increase the order size up to KQ* where K is a factor by which the vendor entices the buyer’s order size The amount of discount offered by the vendor compensates buyer’s increased in inventory costs. For the increased order size, total cost of the buyer is given as

( ) ( )⎟⎟⎠

⎞⎜⎜⎝

⎛ −++=

KKPrDSDPKQTC

2112

2

0111* (5)

The increase in cost resulting from larger order size is the difference between the costs at the EOQ and costs at the order size KO* as given by equation (5). The vendor offers a price discount per

unit equal to the increase in cost at buyer’s side, which is given as ( )

⎭⎬⎫

⎩⎨⎧ −

=K

KD

PrSdk 212 2

011

(6)

Supplier’s yearly net profit after giving discount amount is given as follows

( ) 2*022 SKQ

DdPMDYNP k −−= (7)

Substituting the value of dk in equation (7), maximize the supplier’s profit equation YNP2 with respect

to K. The optimal value of K is obtained as !1

2* +=SSK (8)

From the expression of K * in equation (8), one can easily say that when the value of S2 is large, the supplier can entice the buyer to order in larger quantity and the value of K* is independent of the amount of discount offered by the supplier. One important issue here is that when buyer is exactly compensated for increase in cost due to larger order size, buyer will be indifferent towards increasing

74

his order quantity. Monahan developed the model considering lot-for-lot policy, an all unit quantity discount schedule with single price break

(b) Joint buyer and seller / manufacturer coordination models

Some authors have used quantity discount as a coordination mechanism to maximize the joint profit of the buyer and the vendor. The objective function here in all likelihood is to minimize the total channel cost as shown by equation (4). The models here provide some explicit mechanism for division of surplus generated in the channel due to coordination. Like the seller’s perspective model, here also it is assumed that seller’s have full information about buyer’s cost structure.

The idea of joint optimization for buyer and vendor was initiated by Goyal (1976) and later reinforced by Banerjee (1986a). The objective of Goyal’s model was to minimize total relevant cost for both the vendor and the buyer for the order quantity Q. He assumed that manufacturer does not produce the item and in fact purchases it from another supplier. Moreover, he assumed that inventory holding costs are independent of the price of the item

Banerjee (1986a) formulated a joint economic lot size (JELS) model for a buyer and a vendor system where the vendor has a finite production rate. He determines the JELS Q* by differentiating the total system cost equation with respect to Q.

( ) ( ) ⎟⎟⎠

⎞⎜⎜⎝

⎛+++= 2

2021 2

CRDPrQSS

QDQTC (11)

( )

⎟⎟⎠

⎞⎜⎜⎝

⎛+

+=

22

0

21* 2

CRDPr

SSDQ (12)

The assumption they consider is that a production setup is incurred every time when an order is placed. He finds that without quantity discount, the buyer incurs loss, but the supplier gets benefit if JELS is adopted rather than buyer’s EOQ. He developed the two bounds of discounts that allow the joint benefit to both the parties if the buyer increases the order quantity from EOQ to the JELS quantity. When discount amount is fixed at lower bound, all the benefits go to the supplier and the buyer is indifferent where as when amount of discount is set at maximum level, all benefits shift to the buyer and the supplier is indifferent. While suggesting equal distribution of the gains from Joint Economic Ordering, Banerjee (1986a) mentioned that question of pricing and lot-sizing decisions are settled through negotiations between the buyer and the seller. Later on, we will see how some authors have incorporated in their model the bargaining power of the channel members in fixing the order quantity and amount of discounts.

Viswanathan (1998) in his paper has compared two supply policies for an integrated vendor buyer inventory model. In first policy, the vendor produces a batch and supply to the buyer in number of equal shipment size at constant interval. The second policy is to supply the production batch to the buyer in increasing shipment size. He identified problem parameters under which the equal shipment size policy and increasing shipment size policy is optimal. The author has observed that neither of the two policies dominates the other for all problem parameters. The second policy attempts to shift inventory to the buyer as quickly as possible. This type of strategy works better if the holding cost for the buyer is not much higher than that for the vendor.

Three level coordination models

Munson and Rosenblatt (2001) have extended the two level supply chain to a three level supply chain by considering a supplier (who is supplying raw materials to manufacturer), a manufacturer and a retailer and they explored the benefit of using quantity discount on both ends of the supply chain to decrease cost. Like the earlier scenario, manufacturer’s production lot size is an integer multiple of the buyer’s order quantity and the manufacturer orders an integer multiple of his production lot size to the

75

raw materials supplier. They have shown that by quantity discount mechanism; company can coordinate its purchasing and production functions. This creates an integrated plan that dictates order and production quantities throughout a three firm channel. They have considered manufacture as the dominant member in the channel who takes the lead role in coordinating the channel.

Yang and Wee (2001) in their paper have also considered integration of producer, distributors, and retailers a three-stage supply chain. They have developed an economic ordering policy under constant demand for the arborescent (i.e. a tree like) inventory model structure. They have shown that the integrated approach results in a significant cost reduction compared to that of independent decision making by each individual entity of the supply chain. The model however, has not considered how the increase in cost at retailer level is to be compensated due to implementation of the integrated policy.

Khouja (2003a) has also considered three stage supply chain of tree like inventory model structure. He has considered three coordination mechanisms between the members of the supply chain and has shown that some of the coordination mechanisms can lead to significant reduction in total cost. The author however, has not considered the distribution of savings between the different members of the supply chain.

Khouja (2003b) also studied coordination of the entire supply chain from raw materials to customer considering single and multiple components. They consider components scheduling decisions at each stage in which manufacturing occurs and its impact on the holding cost. They have shown that complete synchronization in the chain leads to loss of some members of the supply chain. They provide an algorithm for optimal synchronization of supply chain and incentive alignment along the supply chain.

(c ) A buyer and a seller/ manufacturer coordination models under game theoretic framework

Some authors have viewed the buyer vendor coordination problem through quantity discount mechanism as a two-person game. They can be formulated as non-zero sum game having elements of both conflict and cooperation. In a non-cooperative game playing independently, the intention of the players (vendor and the buyer) is to maximize their individual gain. The objective function for this game from the general model can be written as

Minimize 011

0 2PrQ

QDSDPTC ++=

Maximize Q

DSPDMYNP 2022 −=

Generally, the solution to the non-cooperative game can be obtained by using established equilibrium concept. Different types of game models have different solution concept. In the Stackelberg game, the player who holds more powerful position is called the leader and enforces his strategy on the other and the other player who reacts to the leader decision is called the follower. The solution obtained to this game is the Stackelberg equilibrium solution.

On the other hand in a cooperative game both buyer and seller would consider maximizing system profit subject to buyer’s total annual cost at cooperation should be less than or at most equal to those at non-cooperation. Similarly, seller’s total annual profit at cooperation should be greater than or at least equal to those at non-cooperation. The objective function for this game from the general model can be written as

Max ( )TCYNP λλ −− 12

Subject to TC ≤ TC*

YNP2 YNP≥ 2*

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Where TC* and YNP2* represents the cost and profit of buyer and seller before cooperation.

Depending upon the bargaining power of the seller and the buyer, the value of λ varies between 0 and 1. In the cooperative game a group of strategies is called a pareto efficient point when at least one player will be better off and no player will be worse off from the initial condition. In the decentralized supply chain where the members belong to two different firms, the method of bargaining and negotiated solution which is dynamic in nature may result better coordination in the supply chain as compared to static coordinated solution in a centralized supply chain.

7.6 Future directions of research

From the study of the above models, it is seen that this stream of literature describes the supply chain in a highly aggregated level and often considers only two decision makers. The important insight provided by the above literature is that there is an increase in profit for the manufacturer when the buyer purchases more than his EOQ. It is based primarily on the facts that (i) Manufacturer’s setup cost is much higher compared to the buyer’s ordering cost, and (ii) Manufacturer may use a production cycle which is an integer multiple of ordering cycle of the buyer.

• Excepting a few, majority of the models are developed considering deterministic demand, zero lead-time, no stockouts. Holding cost of buyer is considered to be independent of purchase price.

• With a few exception, rest of the models are developed where supplier offers all unit quantity discount with a single price break point. Further, the manufacturer is assumed to have two ways of acquiring the item either by outside purchasing or manufacturing the item subject to specific production capacity.

• Most of the models simplify the purchasing / production system to one product and one machine.

• Many of the models fail to specify how the incremental savings to the manufacturer can be passed onto the buyer. Some authors have mentioned about equal splitting of the surplus, whereas some have suggested splitting the surplus according to their investment. Most of the models are silent about conflict resolution between the supply chain partners e.g. division of surplus between buyers and suppliers. Such new problem may call for the use of game theoretic negotiation model.

• Most of the models assume that a supply chain partner has complete information (including cost, demand, lead time etc.) about the other partner. This is considered to be major limitations of these models. In a decentralized supply chain, hardly will be the situation where complete information will be available with the parties. Coordination under limited information sharing is an important issue of concern to be studied for the decentralized supply chain.

• Single vendor multiple buyers’ literature is still in its infancy state. Particularly, how a supplier should develop a quantity discount schedule when dealing with many buyers with different demand and cost structure is not known. Thus, mechanism for additional profit sharing between vendor and multiple heterogeneous buyers is an important issue that needs investigation.

• In single vendor multiple buyers’ literature very little work is available considering vendor as a manufacturer producing the items to supply multiple heterogeneous buyers. Under such situation, how to tackle the discrete vendor inventory depletion into the model is an area that requires further study.

In conclusion, in this section mainly we have tried to give an exposure on the various research issues on supply chain coordination in future.

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References

[1] Bannerjee, A., (Summer) 1986a, A joint economic lot size model for purchaser and vendor, Decision Science, Vol. 17, No. 3, pp. 292-311.

[2] Cachon, P.G. and Lariviere, A. M., 2001, Contracting to assure supply: How to share demand forecasts in a supply chain, Management Science, Vol. 47, No. 5, pp. 629-646.

[3] Corbett, C. J., and Tang, C. S., 1999, Designing supply contracts: contract type and information asymmetry, In: S. Tayur, M. Magazine, R. Ganeshan, (Eds.), Quantitative models for supply chain management, Published by Kluwer Academic Publishers, 1999, pp. 269-297.

[4] Goyal, S. K., 1976, An integrated inventory model for a single supplier single customer problem, International Journal of Production Research, Vol. 15, No. 1, pp. 107-111.

[5] Ha, A., 2001, Supplier buyer contracting: Asymmetric cost information and cut off level policy for buyer participation, Naval Research Logistics, Vol. 48, No. 1, pp. 41-64.

[6] Khouja, Moutaz., 2003, Optimizing inventory decisions in a multistage multi customer supply chain, Transportation Research part E, Vol. 39, No. 3, pp. 193-208.

[7] Munson, L.C. and Rosenblatt, J.M., 2001, Coordinating a three level supply chain with quantity discounts, IIE Transactions, Vol. 33, No. 4, pp. 371-384.

[8] Sarmah, S P., Acharya, D., Goyal, S.K., 2006. Buyer vendor cooridnation models in supply chain management: An invited review, Europoen Journal of Operational Research, Vol.175, pp. 1-15

[9] TSay, A., Nahmias,S., & Agarwal,N., 1999, Modeling supply chain contracts: A review, In: S. Tayur, M. Magazine, R. Ganeshan, (Eds.), Quantitative models for supply chain management, Published by Kluwer academic publishers, 1999, pp. 301-336

[10] Viswanathan, S., 2001, Coordinating supply chain inventories through common replenishment epoch, European Journal of Operational Research, Vol. 129, No. 2, pp. 277-286.

[11] Viswanathan, S., 1998, Optimal strategy for the integrated vendor buyer inventory model, European Journal of Operational Research, Vol. 105, No. 1, pp. 38-42.

[12] Yang, C.P. and Wee, M.H., 2001, An arborescent inventory model in a supply chain system, Production Planning and Control, Vol. 12, No. 8, pp. 728-735.

*********

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CHAPTER VIII A Method for Supply Base Rationalization Considering Supply Risk*

8.1 Introduction

Collaborative sourcing (alternatively named as “partnership sourcing”) has been widely proposed in the literature [1,2,3] to foster a long-term collaboration between a buyer and its suppliers based on trust and cooperation, with the buyer relying on a single or a small number of preferred suppliers for sourcing a product. It has merits over adversarial competition because of its lower operational costs [1] arising out of fewer dedicated suppliers and because risks and rewards are shared between them. A prerequisite for developing a strong buyer-supplier relationship is to have a small and rational supplier base. However, it is a very tedious task for a practicing manager to take its supplier base to a rational level. The word ‘rationalization’ is most commonly associated with the task. A rational supplier base leads to: (1) reduced supplier development costs, (2) close and workable supplier relationships, and (3) business rewards to its suppliers [4]. There has been much confusion with regard to the concepts underlying the supplier base reduction and supplier base rationalization. Cousins [5] and Dubois [6] have used the term ‘supplier rationalization’ to principally mean supplier base reduction. Supplier base reduction presupposes the existence of a large supplier base and is concerned with retaining only the top performers so as to limit the downsized supplier base to a predetermined size. Supplier base rationalization, on the other hand, consists of two phases: (1) Determination of the optimum size of the supplier base and (2) Identification of the suppliers who should constitute this base.

Supplier base rationalization may result in an expanded or contracted supplier base depending on the number of existing suppliers vis-à-vis the optimal size of the supplier base. Industrial firms in many developing countries still follow the traditional purchase management practices and have large supplier bases, especially for MRO items. For these organizations, the problem of identification of the constituents of the supplier base reduces to a problem of reducing the supplier base to a rational level. Supplier base rationalization may be viewed as a one-time selection of a small group of suppliers so as to reduce transactional costs and purchasing complexity and build long-term buyer-supplier relationships. However, no distinguishing approach for supplier base rationalization has been forwarded in the purchasing literature. The process of supplier base rationalization is strategic in nature, with suppliers being evaluated based on factors that represent their short- and long-term characteristics. Many factors, generally considered in the literature for supplier evaluation, are qualitative in nature. These factors can be measured, at best, subjectively, and are therefore tend to be imprecise. Thus, the process of supplier base rationalization can be thought of as a process that consists of the following three steps:

i. Determination of optimal size of supplier base,

ii. Selection of a method to be used for evaluation of suppliers, and

iii. Identification of the constituents of the supplier base.

Whereas there are large volumes of literature with regard to supplier evaluation methods, the literature on the first and the last issues are rare. Therefore, in this paper we have addressed the first and third issues only and they have been presented one after the other in the following sections. * Contributed by Ashutosh Sarkar

Lecturer, Department of Mechanical Engineering

Institute of Technology, Banaras Hindu University, Varanasi 221005

8.2. Determination of optimal size of supplier base

Majority of literature evaluates the effect of the size of the supplier base on the inventory and the replenishment lead time [7, 8, 9]. Further, these studies take the number of suppliers as input to study its effect and do not advance any method for finding the optimal supplier base. Studies that specifically deal with the problem of determining the optimal size of the supplier base are due to Agrawal and Nahmias [10], Weber et al. [11], Berger et al. [12], and Kauffman and Leszczyc [13]. Agrawal and Nahmias [10] formulated a profit maximization problem to determine the optimal lot size and optimal number of suppliers. The model assumes that having more number of suppliers reduces the yield uncertainty but increases the fixed cost associated with operating multiple suppliers. The model trades-off the cost of yield with the fixed cost to determine the optimal size of supplier base. Kauffman and Leszczyc [13] used the concept of buyer utility and decision-related costs to derive the optimal choice set size for one-time- and repeat-purchase situations. They have also used the data on actual bid prices and cost data from the industrial steel pipe market to empirically arrive at the optimal size of the choice set. Weber et al. [11] used both multi-objective programming and data envelopment analysis techniques to solve the problem. Considering that the biggest motivation for having multiple suppliers is to prevent complete disruption of supplies due to an unforeseen natural disaster (like earthquake, cyclone, tsunami, and flood) and/or man-made disaster (like power grid failure, strike, and communal violence), the above two modeling approaches are inadequate to determine the optimal size of the supplier base.

Berger et al. [12] argued that supply disruption or interruption of the inbound supply network can obstruct the functionality of the whole chain. In order to determine the optimal size of the supplier base, they considered supply risks, the risks posed by the occurrence of catastrophic events that lead to complete disruption of inbound supply network. They classified these events as (1) ‘super-events’ that can affect all suppliers simultaneously and disrupt supplies from all the suppliers, exhibiting total effect, (2) ‘semi-super-events’ that affect only a subset of suppliers, exhibiting regional effect, and (3) ‘unique-events’ that affect a particular supplier uniquely, exhibiting local effect. The purchasing environment determines the classification of an event as a super-, semi-super-, or unique-event. For example, a cyclone in a coastal region may be termed as a super-event if all suppliers of an item are located in this region and supplies from these suppliers fail. It may be labeled as a semi-super-event if a few but not all the suppliers fail. Berger et al. [12] considered the probabilities of occurrence of super- and unique-events and used the decision tree approach to find the financial loss and operating cost of working with multiple suppliers. Although Berger et al. [12] defined three types of catastrophic events, they considered only two types of events while developing their model. In this chapter, we have considered all three types of events to determine the optimal size of suppliers.

8.2.1 Supply Risk

The incidences, like 2001 World Trade Tower event, sudden deluge in Mumbai, tsunami in the Indian Ocean, and many more, have forced ‘risks to be factored into all business functions and processes’ [14], meaning that risks should be considered while modeling business processes. As inbound supply affects the supply chain performance, any study on suppliers must consider the various types of risks associated with it. The occurrence of unforeseen events, mentioned above, may disrupt the inbound supply of a supply chain and these are required to be considered for any study on inbound supply. Zsidisin et al. [15] recognized two distinct concepts - probability and impact - with the definition of inbound supply risk. The first relates to the ‘measure of how often a detrimental event that results in a loss occurs’ and the second relates to the ‘significance of that loss to the organization’. Any model, meant for determination of optimal size of supplier base, has to consider supply risks and its impact on the profitability of the purchasing firm. Considered from this point of view, Kraljic’s [16] purchasing portfolio approach is of great relevance. He classified all products, which are procured, into four portfolios, based on supply risk and profit impact. The classification gives sufficient ‘insights relevant to supply costs and risks’ [14]. In this chapter, we define supply risk as ‘the

80

probability that supplies of an item will be affected because of problems at the supplier’s end’ and the cost of supply disruption as its impact.

We assume a semi-super-event to be location specific. Occurrence of such an event disrupts all suppliers in a geographical location while it does not affect suppliers in other locations. We also assume that all suppliers in a location will have more or less similar risks due to the occurrence of a unique-event, and, so, individual variation of supply risks for the suppliers can be neglected for that location. Furthermore, a decision based on small variations in the probability of occurrence of a unique-event for each individual supplier is inappropriate, considering that precise information on all potential suppliers (both the existing suppliers as well as those who are not) may not be available and the existing information base needs to be updated over time for use during the actual process of supply base rationalization. Individual variations are needed be considered during the actual reduction of the supplier base. Thus, for our case, the supply risk due to a unique-event represents more the character of the supply market rather than the individual supplier, and it is a function of the number of suppliers engaged.

8.2.2 Relevant costs

In spite of the fact that multiple sourcing increases the fixed cost, the biggest motivation for having such a strategy is to avoid any emergency situation that may lead to sudden, complete disruption of supplies. Mohebbi and Posner [9] argued that multiple sourcing reduces the shortage costs but increases the ordering costs. An increase in the number of suppliers increases the expenditures for inviting quotations and loses the advantage of availing price discounts (for bulk buying). Further, the increase makes the decision more complex and so the time spent by the managers for taking a decision also goes up. Reducing the number of suppliers, on the other hand, increases the supply risk, causing shortages. Shortage of an item may affect the production in terms of stoppages, delay, inefficiency, and quality problems. Thus, two opposing types of cost influence the decision in optimal size determination of the supplier base.

Kraljic [16] has shown that both supply risk and corresponding impact on profit of the organization vary with the nature of item being purchased. With the same level of supply risks, the costs or impacts on the organization will vary from item to item. We recognize that it is the criticality of an item that defines the resulting impact of a supply disruption on the profitability of the business. The word ‘criticality’ that we refer here has a different meaning from that used by Kraljic. Kraljic used it to refer to those groups of items whose risks and profit impacts are high. However, we use the word ‘criticality’ in a more general sense to mean a characteristic of the item. The less critical an item, the less is its profit impact in the case of supply disruption. We refer to the cost associated with the criticality of the item as cost of criticality and denote it with . SC

8.2.3 The model

We assume that the item is sourced from suppliers at different locations. The objective is to determine the number of suppliers at each location, and therefore the total number of suppliers, who should constitute the supplier base, so that the monetary loss owing to supply disruption due to occurrence of unforeseen events is minimized.

L

Let, *P = Probability of occurrence of a super-event causing all suppliers to fail. **

lP = Probability of a localized semi-super-event causing all suppliers at location ,l

( )Ll ...,,1= , to fail.

jlρ = Probability of a unique-event causing supplier ,j ( )lJj ...,,1= at location l to fail.

81

J l = Number of suppliers available at location l .

i = Number of suppliers chosen from location l . l

= Total number of suppliers engaged by a company. y

Fig. 1 is a decision tree-like representation named here as the probability tree of the alternative ways in which supply disruptions can take place. Chance nodes (Ο ) indicate the occurrence of disjoint and collectively exhaustive events. A branch, emanating from such a chance node, indicates the occurrence of an event and is labeled by its probability of occurrence. The node, denoted as a square ( ), indicates the alternative branches to location-specific and supplier-specific events. The node, denoted with symbol , indicates that the events are multiplicative in nature. The symbol is used in two places in the diagram to show (1) the joint probability of suppliers at all locations failing due to the simultaneous occurrence of semi-super-events and (2) the joint probability of all suppliers failing due to the simultaneous occurrence of unique-events. From Fig. 1,

⊗

Suppliers

No super

Location

Location l

Location L

Semi-super-

No Semi-super event All suppliers fail because

of unique events

All suppliers fail because

All suppliers fail because of Semi-super-events

*P

*1 P−

**lP

**1 lP−. . . . . . . . . . .

Unique event

No supplier fails

∏=

kJ

jjl

1

ρ

*P

( ) ∏=

−L

llPP

1

***1

jlρ

.

.

.

.

Fig. 1 Probability tree for suppliers

∏=

K

klP

1

**

⊗

⊗

.

.

.

.

.

.

.

.

Supplier

Supplier lJ

j

Super event

No Unique event

82

83

various probabilities can be easily derived. We refer to represent the probability of occurrence of an event.

).(P

P (All suppliers at location l fail because of a semi-super-event)

( ) ***1 lPP−=

P (All suppliers at location l fail because of either a super-event or a semi-super-event)

[ ] ( )[ ]**** 1 lPPP −+=

P (All suppliers at all locations fail because of either a super-event or a semi-super-event)

[ ] ( )[ ]****2

**1

** ...1 lPPPPP −+=

Similarly we get, P (All suppliers at all locations fail because of either a super-event or a semi-super-event or a unique event)

[ ] ( ) ( ) ( )⎥⎥

⎦

⎤

⎢⎢

⎣

⎡

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

−−+⎥⎥⎦

⎤

⎢⎢⎣

⎡−+= ∏ ∏∏

= ==

L

l

J

jjll

L

ll

l

PPPPP1 1

***

1

**** 111 ρ Assuming, , we can

rewrite the equation (1) as

llJll lρρρρ ==== .......21 (1)

(2) [ ] ( ) ( ) ( ){ }⎥⎥⎦

⎤

⎢⎢⎣

⎡−−+

⎥⎥⎦

⎤

⎢⎢⎣

⎡−+= ∏∏

==

L

l

Jll

L

ll

lPPPPP1

***

1

**** 111 ρ

The number of units short during the period of supplier failure can be estimated from the information on demand and the duration of time required for recovering ( )TT −′ from a catastrophic event failing the suppliers to supply the products. Number of units short during the period ( )TT −′ is ( )TTD −′

(3) Total shortage cost during the period of supply failure, ( ) ( )TTDTTCC ST −′−′=

We refer to as the cost of supply disruption. Thus, the total cost of engaging suppliers, TC y ( )yf , is the sum of cost of operating suppliers and costs due to shortages when all suppliers fail to make their supplies.

y

( ) ( ) ( ) ( ) ( ){ }

⎥⎥⎦

⎤

⎢⎢⎣

⎡−−+−++= ∏ ∏

= =

L

l

L

l

illlT

lPPPPPCyCyf1 1

******* 111 ρ (4)

Where, is the cost of operating suppliers and . The problem, thus, is to find the

optimum number of suppliers that minimizes the sum of the cost of operating the suppliers and the cost of shortage due to supply disruption. Stated more succinctly, the problem is to

( )yC y ∑=

=L

lliy

1

y

“Find y that minimizes (4).”

84

This problem is hereafter called Supplier Size Problem where is the decision variable and . If the choice of suppliers has to be economically justified compared to the choice of

y 0>yy 1−y

suppliers, the following condition must be satisfied:

( ) ( ) ( )( ) ( )ly

llT

PPC

CyC ρρ −−−<− − 1111 1***

We assume that the operating cost, , has two components: (1) a fixed component, a and (2) a variable component, b , that varies with the size of the supplier base, . Therefore,

and inequality (5) can be rewritten as

( )yCy

)()( ybayC +=

(5)

(6) ( )( ) ( )ly

llT

PPCb ρρ −−−< − 111 1***

From inequality (6) it can be concluded that the existing supplier base could be reduced as long as the following condition holds:

( )( )( )[ ]{ } ( )ρρ ln111ln1 ***llT PPCby −−−+> (7)

(8)

Inequality (6) can also be extended to multiple locations and can be rewritten as for the case when choosing suppliers is superior to choosing suppliers 1+y y

( ) ( ) ( )∏=

−−−<u

lu

yll

T

lPPCb

1

*** 111 ρρ

where, represent the number of suppliers chosen from location l , ly ⎟⎟⎠

⎞⎜⎜⎝

⎛=∑

=

yyu

ll

1

( )ul ...,,2,1= and the )1( +y th supplier is chosen from location u .

8.2.4 Solution approach to the supplier size problem

The decision tree approach, when used in the formulation of the problem with semi-super event, results in unmanageable number of trees. For example, Fig. 2 shows the decision tree for a problem of three locations with only two suppliers in each location. The decision maker has three choices for each location, namely (1) no supplier chosen from a location (0S), (2) only one supplier is chosen (1S), and (3) two suppliers are chosen from that location (2S). Considering that at least one supplier from a location is to be selected, the total number of decision alternatives that are to be evaluated is 26 (=33-1). The number of decision alternatives grows geometrically as the number of suppliers in a location increases and as the number of locations increases. We propose an alternative method to solve the supplier size problem – a method that is both elegant and simple. The proposed solution method for the supplier size problem avoids evaluation of a large number of non-optimal solutions. Thus the proposed method is a partially enumerative one. The avoidance of alternatives from evaluation is justified using the theorems presented in the next section.

85

Location 2

Location 2

Location 3

Location 3

Location 3

Location 3

Location 3

Location 3

Location 3

Location 3

Location 3

0S

0S

0S

0S

0S

0S

0S

0S

0S

0S

0S

0S

1S

2S

2S

2S

2S

2S

2S

2S

2S

2S

2S

2S

2S

2S

1S

1S

1S

1S

1S

1S

1S

1S

1S

1S

1S

1S

Fig. 2 Decision tree

0 suppliers (not permitted)

1 supplier

2 suppliers 1 supplier

2 suppliers

3 suppliers 2 suppliers

3 suppliers

4 suppliers 1 supplier

2 suppliers

3 suppliers 2 suppliers

3 suppliers

4 suppliers 3 suppliers

4 suppliers

5 suppliers

2 suppliers

3 suppliers

4 suppliers 3 suppliers

4 suppliers

5 suppliers 4 suppliers

5 suppliers

6 suppliers

8.2.4.1 Theorems to Reduce the Feasible Solution Space

It can be observed in Fig. 2 that the supplier size problem is combinatorial in nature and finding an optimal solution to the problem is computationally cumbersome. The proposed solution method is based on the following theorems which help to reduce the solution space considerably and make the solution computationally very simple.

Theorem 1: If selecting suppliers from a set of locations is advantageous compared to selecting suppliers from the same set of locations then the former will be more economic compared to

selecting suppliers chosen from the same locations.

y u1+y

2+y

86

Proof: If the selection of suppliers from a set of u locations is economically more advantageous compared to choosing

y1+y suppliers then from inequality (8), we get

( ) ( ) ( )∏=

−−−>u

lu

yll

T

lPPCb

1

*** 111 ρρ (9)

assuming that 1+y th supplier is chosen from location u .

Similarly, assuming that both 1+y th and 2+y th supplier are chosen from location u , the selection of suppliers is economically more advantageous compared to selecting suppliers from the same set of locations if the following condition holds:

2+y 1+y

(10) ( ) ( ) ( )∏=

−−−<u

luu

yll

T

lPPCb

1

*** 111 ρρρ

Since 1<uρ , inequality (10) contradicts (9) and thus

( ) ( ) ( )∏=

−−−</u

luu

yll

T

lPPCb

1

*** 111 ρρρ

Thus, if for same the set of locations, selecting suppliers is more economical than selecting y 1+y suppliers, then it will be also more economical than selecting 2+y suppliers from the same set of locations.

Theorem 2: If suppliers are to be chosen from a set of locations, with at least one from each location, then it is always economically advantageous to choose as many suppliers as possible from the location that has the minimum of unique-event probabilities.

y L

Proof: Let us consider that suppliers are to be chosen from u locations. Then the total cost

function is given as y

( )yf

( ) }]....)1)...(1)(1(....[{)( 11

****1

*****1

* uyu

yuuT PPPPPPCyCyf ρρ−−−+++=

where, , , …, represent the number of suppliers chosen from the location , respectively.

1y 2y uy u,...,2,1

Instead of selecting a supplier from location 1, if we select a supplier from location u , then the total cost

( )yf ′ is given as

( ) ( ) ( )( ) ( ){ }[ ]111

****1

*****1

* ....1...11.... 1 +−−−−+++=′ uyu

yuuT PPPPPPCycyf ρρ

If the latter alternative has to be more advantageous than the former, then the following condition has to be satisfied.

( ) ( ) 0>′− yfyf

87

or, ( )( ) ( ) 01....1...111

1****

1* 1 >

⎭⎬⎫

⎩⎨⎧−−−−ρρ

ρρ uyu

yu

uPPP

or, 11

<ρρu

The above condition will be violated only if 1ρρ >u .

While searching for solutions to the supplier size problem two questions has to be answered. First, what should be the total number of suppliers to be engaged and second, how these chosen suppliers will be distributed among the various locations. The above two theorem helps in searching the solution to the supplier size problem efficiently. Theorem 1 restricts the engagement of more number of suppliers from a set of locations once the total cost starts increasing, thus, limits evaluations for higher values of . The number of ways a given number of suppliers may be chosen from a set a locations is large. Theorem 2 allows us to evaluate only the combination that selects as much suppliers as possible from the location for which

)( yfy y

lρ is least. Thus theorem 1 and 2 helps in reducing the solution space for the supplier size problem considerably.

8.2.4.2 A Tabular Method for Solving the Supplier Size Problem

We follow a tabular method of finding an optimal solution to the above-mentioned problem. The method is much like the tabular method often used when a dynamic programming solution approach is adopted. The problem of selecting suppliers from locations is combinatorial: y L

• All suppliers may be chosen from a single location. The number of ways the location can be selected is . 1CL

• All suppliers may be chosen from any two locations. The number of ways two locations can be selected is . 2CL

And so on.

Thus, if we have to choose suppliers from locations, then we have y L q′ different combinations of locations from where these suppliers can be chosen where, y

LLLL CCCq +++=′ .......21

Thus, the number of combinations of v′ suppliers chosen from locations is . When L vLC v′ is

fixed, there are a number of ways in which suppliers can be selected from these locations. The proposed tabular method evaluates each alternative for each combination of locations separately and helps find the optimal number of suppliers to be chosen from each location that minimizes the total cost, . For example, given five locations if we have to evaluate for various alternative values of

, there will be five separate tables. The first table evaluates the various alternative values of for each location, the second table for all possible combinations of two locations, the third for three locations, and so on. The minimum possible value of in a table will be equal to , the number of locations considered in that table, i.e., at least one supplier will be chosen from each location for a combination. For a given value of , all possible alternatives for the number of suppliers that can be chosen from each location are considered for evaluation. Table 1 shows an example of such tables when . The first column in the table represents the various alternative values of , the

v′

( )yfy y

y v′

y

4=′v y

88

variables , , , represent the number of suppliers chosen from the first, second, third and the

fourth location, respectively. Location 1:2:3:5 signifies that suppliers are to be chosen from location 1, 2, 3 and 5 only for that column. Entries in these columns are to be the total cost.

y1 y2 y3 y4

Table 8.1: Decision alternatives for a problem 45C

y y1 y2

y3 y4

Location 1:2:3:4

Location 1:2:3:5

Location 1:2:4:5

Location 1:3:4:5

Location 2:3:4:5

4 1 1 1 1

5 2 1 1 1

5 1 2 1 1

5 1 1 2 1

5 1 1 1 2

6 3 1 1 1

6 1 3 1 1

6 1 1 3 1

6 1 1 1 3

6 2 2 1 1

6 2 1 2 1

6 2 1 1 2

6 1 2 2 1

6 1 2 1 2

6 1 1 2 2

Based on Theorem 1 it can be argued that total costs for a column in the table has to be calculated to a value of such that y ( ) ( )1−> yfyf for that column. Thus, for a column, there is no need for calculating the total cost beyond a value of for which the condition y ( ) ( 1−> yfyf ) is satisfied. For a given value of , the number of ways in which can be distributed among the locations is large. However, if we arrange the locations sequentially in increasing order of their unique-event probabilities, then Theorem 2 allows us to retain only those rows that choose maximum possible number of suppliers from the first location while discarding all other alternatives for the given value of . Thus, if we assume that the unique-event probabilities are related as , then the table in Table. 1 can be reduced. Table 2 is the reduced table with various non-optimal rows deleted. It can be observed that the reduced table has twelve rows less than the table shown in Table 1. This leads to significant saving in computational time. The optimum solution is the minimum of all best solutions for the individual table.

y y

y 521 ... ρρρ <<<

89

Table 8.2: Reduced table for decision alternatives for a problem 45C

y y1 y2

y3 y4

Location 1:2:3:4

Location 1:2:3:5

Location 1:2:4:5

Location 1:3:4:5

Location 2:3:4:5

4 1 1 1 1

5 2 1 1 1

6 3 1 1 1

8.2.5 Illustrative example

We take the following values of various parameters for illustrating the application of the method:

D = 2 stock keeping units per day T = 7 days T ′ = 30 days

(Rupees) 50=a 20=b (Rupees/supplier)

Super-event probability: 0.01 =P*

Semi-super-event probabilities: = 0.02 PPPP **4

**3

**2

**1 ===

Unique-event probabilities: ρρρρ 13121111

.......i

==== = 0.031

ρρρρ 23222122

.......i

==== = 0.030

ρρρρ 33323133

.......i

==== = 0.033

ρρρρ 43424144

.......i

==== = 0.032

Number of locations: = 4 L

Cost of criticality, 33.00 (Rupees/unit time/unit of the item) =Cs

Therefore,

Cost of supply disruption (vide Eq. 2.6), 33.00× 23 × 2 × 23 = 35000 (Rupees) =CT

In the example, there are four locations ( 4=L ). We arrange these locations in order of their unique-event probabilities and label them as location A, B, C and D respectively. Table 3 gives the assumed occurrence probability values for the semi-super and the unique-events. Later, these probability values have been changed to study their effects.

Table 8.3 Location labels and assumed probabilities

Location A Location B Location C Location D

Semi Super Event 0.02 0.02 0.02 0.02

Unique Event 0.03 0.031 0.032 0.033

As there are four locations, the first table will have 4 ( ) columns and all suppliers are to be chosen from a single location only; the second table will have 6 ( ) columns with all suppliers to be chosen from any two locations; the third table will have 4 ( ) columns with all suppliers to be

chosen from any three locations; and the fourth table will have one ( ) column with all suppliers to be chosen from any four locations. We have also assumed a maximum of three suppliers can be chosen from each location. The variables , , , and represent the number of suppliers chosen from the first, second, third and the fourth location, respectively. When all suppliers are selected from one location (Table 4), the maximum value of can be 3, because we have assumed every location to have three suppliers. When two locations are selected (Table 5), a maximum of six suppliers can be chosen. It is not necessary to compute total costs for each case, however. A good approach for achieving computational efficiency is to compute the total cost for each location starting from the lowest value of . We can stop whenever a higher value of the total cost is obtained. Thereafter we proceed to the next location and adopt the same procedure. Table 6 and 7 are the reduced tables for the number of locations considered for selecting suppliers and 2, respectively. The entries in each cell of the tables are the total cost of engaging suppliers selected from the corresponding set of locations. The minimum costs for individual Tables are highlighted. Although there is no reduction in the size of Table 6, the number of rows in Table 7, have reduced to 3 from 15 in Table 5. It may be noted that Table 7 could have been directly obtained without considering the last 12 rows of Table 5. Table 8 and Table 9 show the total costs of various alternatives for and , respectively. The minimum of all the best values from each table is the solution for the supplier size problem. The minimum occurs in Table 8. The optimal solution for the above example, therefore, is obtained as

14C=

24C=

34C=

44C=

1y 2y 3y 4y

y

y

1=′vy

3=′v 4=′v

3=y and with one supplier chosen from each of the A, B, and C.

Table 8.4: Complete tabular evaluation of all alternatives for 1=′vy Location A Location B Location C Location D

1 2131.710000 2165.667000 2199.624000 2233.581000 2 1163.561300 1165.632677 1167.771968 1169.979173

3 1153.916839 1154.011613 1154.112703 1154.220313

Table 8.5: Complete tabular evaluation of all alternatives for 2=′v

90

y

y1 y2

Location A:B

Location A:C

Location A:D

Location B:C

Location B:D

Location C:D

2 1 1 484.8084098

485.8067456

486.8050814

486.8716371

487.9032508

489.00142023 2 1 474.788452

3474.818402

4474.848352

4474.883360

8474.915340

8474.984525

43 1 2 474.8194007

474.8822959

474.9471877

474.9163724

474.9834273

475.01966694 3 1 493.887853

6493.888752

1493.889650

6493.891724

2493.892715

6493.895984

84 1 3 493.8897414

493.8927135

493.8958772

493.8938039

493.8970731

493.898269 4 2 2 493.888782

0493.890668

9493.892615

6493.892747

5493.894826

2493.897109

35 4 1 513.8608356

513.8608626

513.8608895

513.8609834

513.8610142

513.86115155 1 4 513.860922

0513.861046

8513.861183

9513.861081

7513.861223

4513.861262

95 3 2 513.8608635

513.8609201

513.8609785

513.8610152

513.8610796

513.86118755 2 3 513.860892

2513.860981

4513.861076

3513.861047

9513.861149

3513.861224

66 5 1 533.8600251

533.8600259

533.8600267

533.8600305

533.8600314

533.8600368

91

6 1 5 533.8600286

533.8600335

533.8600391

533.8600346

533.8600404

533.86004176 4 2 533.860025

9533.860027

6533.860029

4533.860031

5533.860033

5533.860038

6 2 4 533.8600277

533.8600314

533.8600355

533.8600335

533.8600379

533.86004046 3 3 533.860026

8533.860029

4533.860032

3533.860032

5533.860035

6533.860039

2

Table 8.6: Reduced tabular evaluation of all alternatives for 1=′vy Location A Location B Location C Location D 1 2131.710000 2165.667000 2199.624000 2233.581000 2 1163.561300 1165.632677 1167.771968 1169.979173 3 1153.916839 1154.011613 1154.112703 1154.220313

Table 8.7: Reduced tabular evaluation of all alternatives for 2=′vy

y1

y2

Location A:B

Location A:C

Location A:D

Location B:C

Location B:D Location C:D

2 1 1 484.8084098 485.8067456 486.8050814 486.8716371 487.9032508 489.00142023 2 1 474.7884523 474.8184024 474.8483524 474.8833608 474.9153408 474.98452544 3 1 493.8878536 493.888752 493.8896506 493.8917242 493.8927156 493.8959848

Table 8.8: Reduced tabular evaluation of all alternatives for 3=′vy y1

y2 y3

Location A:B:C

Location A:B:D

Location A:C:D

Location B:C:D

3 1 1 1 461.2477421 461.2780716 461.3103578 461.34479634 2 1 1 480.3063163 480.3072261 480.3081947 480.3102955

Table 8.9: Complete tabular evaluation of all alternatives for 4=′vy y1

y2 y3

y 4 Location

A:B:C:D 4 1 1 1 1 480.03693135 2 1 1 1 500.0064856

8.3. The supply base rationalization problem

8.3.1 Nature of purchase and supplier relationship

Effective and efficient supplier relationship management greatly contributes to the building up of competitive advantage of an organization. Identification of key buyer-supplier relationships helps an organization to allocate its resources for building and developing such relationships [17]. Raw materials and MRO items are usually sourced from a large number of suppliers. Portfolio analysis has been continuously used to get useful insights into the management of the supplier relationships and development of possible action plans. Nellore and Soderquist [18] observed that purchasing portfolio models have three steps in common and they are: (1) Analysis of the products and classification, (2) Analysis of the supplier relationships required, and (3) Action plan to match requirements. Based on profit impact and supply risk Kraljic [16] classified purchases into routine, bottleneck, leverage and strategic purchase. Extending this work, Olsen and Ellram [19] suggested a classification method based on (1) the strategic importance of the purchase and (2) the difficulty in managing the purchase. Strategic importance of the purchase can be measured on the basis of internal factors such as its

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contribution to develop core competencies and boost the buyer’s image among customers and suppliers. Difficulty of managing a purchase situation can be assessed on the basis of external factors such as the nature of the product, the characteristics of the supplier market, and the risk and uncertainty associated with a supply. The nature of the purchase influences many purchasing decisions like the size of supplier base, the extent of resources to commit to supplier development and other long-term involvement with suppliers. Fig. 3 lists various categories of purchases and their features and possible action plans. The buyer-supplier relationships for each category of purchase will be different from the others. As a strategy, long-term relationships are preferred for sourcing of bottleneck items, and a medium-to-long-term relationship with one or a smaller group of suppliers is prescribed for strategic items. For other types of items, the relationship is of very short duration for which procurements can be done in a more traditional manner. For bottleneck items, there are very few suppliers available in the market, and so a supplier reduction strategy may not be desirable in this case. However, a common approach can be adopted for identifying the constituents of the choice set. Supplier base rationalization process should be done through a careful evaluation of suppliers considering both their short-term performances and long-term capabilities.

Purchase Category Features

Routine Items More number of suppliers available

Very short term supplier relationship

Supplier Monitoring

Simplification and automation of purchasing procedure

Delegation of decision making power to lower level of management

Bottleneck Items Monopolistic supplier market

Long term supplier relationship

Security of inventories

Internally develop alternatives

Contingency planning

Delegation of decision making power to higher level of management

Leverage Items More number of suppliers available

Short term supplier relationship

Exploitation of full purchasing power

Delegation of decision making power to medium level of management

Strategic Items Few suppliers are available

Medium/ long term supplier relationship

Detailed evaluation of suppliers

Supplier development efforts

Delegation of decision making power to top level of management

Fig. 3 Characteristics of purchases

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8.3.2 Performance versus capability of a supplier

Sourcing decisions should be normally based on the consideration of a large number of factors [20]. However, a majority of practitioners focus only on such factors as cost, quality and service, while neglecting other important factors like technological and financial capabilities, quality systems, etc. This has not helped in distinguishing suppliers with strong long-term capabilities from those who excel when measured against short-term criteria. In their study on evaluation and rationalization of suppliers, Narasimhan et al. [21] have used organization’s capability factors as input resources and performance factors as output variable in their DEA study. We suggest that the supplier evaluation criteria can be classified based on their influence on the short-term and the long-term goals of the supply chain. We define two important dimensions of a supplier’s abilities: performance and capability. Performance is defined as the demonstrated ability of a supplier to meet a buyer’s short-term requirements in terms of cost, quality, service, and other short-term criteria. Capability is defined as the supplier’s potential that can be leveraged to the buyer’s advantages in the long term. Various criteria identified for supplier selection by Katsikaes et al. [22], Choi and Hartley [23], Swift [24], and Weber et al. [20] are classified as performance and capability factors and are presented in Table 10.

Table 8.10: Performance and capability factors

Capability Factors Reference Performance Factors Reference

Quality Systems in operation at the Supplier’s place/ Quality Philosophy

23 Price 20,22,23,24

Financial Capability of the supplier 20,23,24

Quality/Reliability

of the product

20, 23,24

Technological Capability/ R&D Capability 20, 22, 23

Ability to meet delivery promise/ Delivery lead time/ Consistent Delivery

20, 22, 23, 24

Reputation for Integrity/

Believability and Honesty/ Vendor’s Image

20, 22, 23, 24

Management sensitivity to buyer’s requirements/Attitude

20

Existence of IT standards/ Communication System

20, 22

After-Sales Support/ Technical support available

22, 23, 24

Performance Awards/ Performance History 20, 23 Positive attitudes towards complaints

22

Bidding Procedural Compliance 20

Profitability of Suppliers 23

Breadth of product line/Ability of a supplier to supply a number of items

24

Supplier’s Proximity/ Geographic Location 20, 24

Management and Organization 20

Contribution to Productivity 24

Conflict Resolution 23

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Production Facilities and Capacity 20

Communication Openness 23

Labour problems at the supplier’s place 20

Business volume/Amount of Past business 20

It can be seen in Table 10 that while most of the performance factors are quantitative and can be measured relatively easily, most of the capability factors are qualitative and present measurement problems.

8.3.3 Supplier evaluation methods

A consensus approach to supplier evaluation is generally followed in practice in view of the multidimensional and subjective nature of the problem of supplier evaluation. Based on an exhaustive search of the literature, De Boer et al. [25] have divided the various supplier selection methods into five categories: (1) Linear Weighting models [26, 27, 28, 29]), (2) Total Cost of Ownership models [26, 30, 31]), (3) Mathematical Programming models [32, 33, 34, 35, 36, 37], (4) Statistical models [38, 39], and (5) Artificial Intelligence (AI) based models [40]. The problem of supplier base rationalization belongs to the pre-qualification phase, rather than to the choice phase. De Boer et al. have identified a number of models that are particularly suitable for pre-qualification of suppliers, notable among them being case-based reasoning [41, 42], cluster analysis [43, 44], and data envelopment analysis [21]. Sarkar and Mohapatra [45] highlighted the limitations of the above methods and recommended the use of fuzzy set theoretic analysis for evaluation of suppliers.

8.3.4 The proposed method

A methodology for supplier base rationalization has been proposed (Fig. 4) following the framework of supplier selection proposed by De Boer et al. [25]. The process starts with an analysis of the nature of purchase to identify the type of relationship that is desired. An analysis of the supplier market is also necessary to perceive the risk and set a target size of the supplier base. The type of relationship that is desired also determines the supplier characteristics that are to be considered for evaluation in order to find a best-fit supplier. As has been discussed extensively in Section 3.2, the supplier base rationalization process should group these supplier characteristics (factors) into long- and short term factors. Vokurka et al. [40] and Hong et al. [44] argued that the supplier selection problem should also include suppliers that are not in the existing supplier base. Identifying potential suppliers requires a supplier market survey to be conducted. Whereas the organization may have rich experience and strong information database on known, existing suppliers, it has to obtain information about unknown suppliers from the Internet [41] and by peer feedback and onsite visit to the suppliers’ facilities [46].

Sometimes the potential suppliers list may be very long. In such cases, screening of the suppliers is done to reduce the list of potential suppliers. The supplier’s willingness to associate is for sustaining a relationship and it can be assessed from the amount of incentives the buyer provides to the supplier. Suppliers will be more committed when the business volume is more. However, for routine items, the supply risk and the profit impact are very low and the buyer’s interest is only to have a working relationship. So, we define supplier incentive to do business with the buyer as the total value spent by the buyer on purchase of items that are/(can be) supplied by the supplier. An initial screening of suppliers can be done based on the supplier’s incentives to do business with the buyer.

Fig. 5 depicts a matrix where supplier incentive is put against the length of relationship desired by the buyer. The ‘tick’ and ‘cross’ marks are used to show whether, the incentive will be sufficient to attract supplier’s willingness. The matrix gives an easy guideline for initial screening of the suppliers.

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As many of the criteria used for supplier evaluation are qualitative in nature, we propose the use of experts’ opinion-based methods for ranking the suppliers. Experts may be drawn from the users of the product and from the purchase personnel. Senior management can also be involved if the purchase has strategic implications. The experts give their subjective assessment of the relative importance of the factors and evaluate the suppliers against each factor in terms of scores in a predefined scale. Factor ratings with capability rankings along the horizontal direction and Performance Ratings in the vertical direction are plotted in a matrix (Fig. 6). We call this the ‘capability-performance matrix’. The position of a supplier in the matrix denotes the rank the supplier has considering the two factor categories. It can be used to arrive at an ordered list of suppliers with decreasing preference. Finally, this order of preference is used to retain the desired numbers of suppliers.

Scores secured by a supplier against the capability and performance ratings define the position of the supplier in Fig. 6. A diagonal line drawn from the top-left corner to the right-bottom corner divides the suppliers into three classes: (1) Balanced suppliers, (2) Motivated suppliers, and (3) De-motivated suppliers. All suppliers on the diagonal line are balanced suppliers (supplier H). They are deemed to have a performance level which is commensurate with their level of capability. The suppliers lying below the diagonal line (suppliers E, D, and B) fail to match their performance with their capability. They are the de-motivated suppliers. They are not sufficiently motivated to leverage on their

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capability. Either such a supplier is unable to use his capability efficiently or he does not know how to operate within the framework of the relationship. Suppliers who are above the diagonal line (suppliers A, C, F, and G) have performed better than their capability. They are the motivated suppliers. They either have high stake in doing business with the buyer or are committed and are able to efficiently capitalize on their capability. However, a cross-evaluation of the reasons for over-performance for this category of suppliers is very necessary. An evaluation of the sustainability of this performance in the long run should also be carried out. The cost of capability enhancement for them for consistent performance over a long period should be traded-off against the cost of motivating and improving the performance of a de-motivated supplier in case of a tie.

In case two suppliers have the same location on the diagonal and the same length of the perpendicular, ranking is made arbitrarily. In this case the buyer will have to evaluate the cost of supplier motivational efforts against the cost of capability enhancement. The first option is relatively easier to implement whereas the latter will demand a lot of resources for both the suppliers and the buyer. Supplier location on the matrix diagonal as a reference to determine the order of preference is logical when we give equal importance to both performance and capability ranking. For a supplier the sum of its rank in the performance factors and the capability factors can also be used in place of using the intersecting point. In such a case the supplier with a greater sum will be placed ahead of the others in the order of preference.

8.3.5 Illustrative example

We take a hypothetical case for illustrating the proposed method of supplier base rationalization. There are ten suppliers and the objective is to reduce the present number of suppliers to two. We consider four performance factors and ten capability factors given in Table 11 for the evaluation purpose.

Table 8.11: Performance and capability factors considered for the illustrative example

Performance Factors Capability Factors

Price

Quality

Delivery lead time

Attitude

Quality systems in operation at the supplier’s place

Financial capability of the supplier

Production facilities and capacity

Management and organization

Technological capability

Breadth of product line

Supplier’s proximity

Existence of IT standards

Labour problems at the supplier’s place

Reputation

The ranks of the suppliers are given in Table 12.

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Table 8.12: Rankings of suppliers

Supplier Rank

Based on Performance Factors

Based on Capability Factors

01. A2 A10

02. A7 A4

03. A4 A6

04. A10 A9

05. A6 A3

06. A3 A1

07. A8 A7

08. A9 A2

09. A1 A8

10. A5 A5

The individual ranks of suppliers for capability and performance factors are plotted in the capability-performance matrix and are shown in Fig. 8. The capability-performance matrix shows that supplier A5 lies on the diagonal and is a balanced supplier; however, it performs the least both on the capability and the performance factors. Suppliers A7, A2, and A8 are located above the diagonal and suppliers A1, A3, A4, A6, A9 and A10 are located below the diagonal. The suppliers A7, A2, and A8 are the motivated suppliers and the suppliers A1, A3, A4, A6, A9, and A10 are the de-motivated suppliers. The locations of suppliers A4 and A10 on the diagonal are same, as those of A2 and A7. On the basis of the length of the perpendiculars, A4 is ranked ahead of A10 and A7 is ranked ahead of A2.

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In order to rank the suppliers based on the preferences for developing long-term relationships we move from the top-left corner of the capability-performance matrix and sequence the supplier based on their locations on the diagonal. The order of preference is shown in Table 13.

Table 8.13: Final order of preference for suppliers

Order of preference 01 02 03 04 05 06 07 08 09 10

Supplier A4 A10 A6 A7 A2 A3 A9 A1 A8 A5

As we have to retain only two suppliers for the case, we retain suppliers A4 and A10 and all other suppliers are removed from the registered supplier list.

8. 4. Conclusions

Supplier base reduction is a prerequisite for developing long-term relationships with suppliers. In this chapter, we propose a structured method of rationalizing the supplier base. A major contribution of our work is that the problem has been addressed considering, in addition to the above two types of supply risks, the possibility of occurrence of a semi-super-events. The decision tree approach, when all the three types of supply risks are considered, results in an unmanageable number of decision alternatives. In order to avoid it, we develop a simple, but elegant, method to determine the optimal size of supplier base. The method uses tables to evaluate all possible decision alternatives. From the practitioners point of view this tool is very simple and spreadsheets can be very easily used to find the optimal solution.

One of the important points of the proposed method is the use of the capability-performance matrix, which increases the visibility about each supplier’s strengths and weaknesses and facilitates a more rational judgment. The increased visibility through the use of capability-performance matrix helps classifying the suppliers into ‘motivated’ and ‘de-motivated’ ones. Tracing the causes for a supplier being ‘motivated’ or ‘de-motivated’ can reveal important information with respect to ‘consistency’ in the supplier performance. The ‘capability-performance matrix’ also helps easy ranking of the suppliers with whom a sustainable long-term relationship can be established.

An issue that needs further development is how to develop a mechanism for continuously evaluating supplier performance and maintaining of a knowledge base of suppliers. While the knowledge about a new supplier is usually continually updated over time, the issue is how to include such new information on suppliers in the method. After the supplier base is rationalized, issues related to the subsequent management of the supplier base have to be addressed. How to develop and build a sustainable relationship with this reduced supplier base is also an area that needs further development. Development of such long-term relationship requires efforts and resources on the part of the supplier as well as the buyer. A future area of research can be to include and adapt the dynamics of supplier-development potential as well as related costs into the proposed method.

References

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[42] Luu, D.T., Ng, S.T., Chen, S.E. & Jefferies, M., (2006). A strategy for evaluating a fuzzy case-based construction procurement selection system. Advances in Engineering Software, 37, 159-171.

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[46] Avery, S., (1999) MRO Report: Supplier Alliances Help Power Wisconsin Electric. Purchasing, June 3, 62-64.

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CHAPTER IX E-procurement

9.1. Introduction

Online procurement (e-procurement) is a technology solution to facilitate corporate buying using the Internet and other Information and Communication Technologies (ICT). It has been identified as the most important element of e-business [1]. As specified in the literature and experienced by top corporate houses the benefits of e-procurement are manifold. These advantages include reducing administrative costs, shortening the order fulfillment cycle time, lowering inventory levels and the price paid for goods, and preparing organizations for increased technological collaboration and planning with business partners[1].

The potential is so great that e-procurement has turned the formerly looked-down-upon traditional purchasing function into a competitive weapon. One such example is General Electric’s (GE’s) Trading Process Network (TPN) [2]. Here the buyer posts a request for proposal on the Internet for access by pre-qualified suppliers. The suppliers download the request and submit bids electronically. The buyer evaluates the bids, negotiates on line, and places the order with the lowest bidder. The system also facilitates transaction processing by, for example, automatically reconciling purchase orders with invoices as part of the payment process. A solution like the TPN impacts both the supplier selection and contract agreement components of the purchasing process.

Some of the benefits that GE has realized from its e-procurement operations in the initial days are listed below [2]

• Reductions in labor costs in the purchasing process are one of the reasons that transaction costs fall so precipitously with e-procurement. For example, in traditional labor-intensive, paper-based purchasing process in GE the transaction cost. was ranging from $70 to $300 per purchase order. GE saw those costs drop 30%.

• Material cost reductions in the range of 5% to 20% were realized because GE’s e-procurement solution helped the firm reach a wider supplier base and identify heretofore unidentified and qualified sources of supply.

• The system also allows the Company’s purchasing departments around the world to share information about their best suppliers. GE’s purchasing departments gained 6 to 8 days per month to work on more strategic initiatives.

• e-procurement systems enable firms to more efficiently and accurately capture and aggregate how much they are spending corporate-wide in various purchased product areas, allowing the firm to bring what may be significant buying power leverage to market. This benefit contributes to the 5% to 20% material cost reductions that GE has experienced.

Adoption of technology solution requires reengineering of the traditional purchasing process. We describe below the traditional purchasing process and compare with a typical reengineered process.

9.2. Traditional purchasing process

Fig. 1 provides an overview of a typical purchasing process. It begins with the need to define buying requirements based on the demands of the firm’s final customer. At this stage, specifications are developed. The step involves early purchasing involvement (EPI) and early supplier involvement (ESI), as well as inputs of a cross-functional buying team that may include, in addition to supply and engineering, representatives from operations and marketing. Once the specifications have been developed, a buying team led by the supply manager will pre-qualify suppliers, generate requests for

proposals, evaluate the proposals, and select a supplier based on established selection criteria. Contract negotiations result in the terms and conditions of a formal contract. Ordering routines and transaction-processing guidelines are established for all purchases that take place under the umbrella of the negotiated contract. Closing the loop is a supplier evaluation system that assesses supplier performance that provides information to be used as the basis for rating the supplier (e.g., excellent, good, fair, unacceptable).

.

Define Requirement Select

Supplier Supplier Evaluation

Contract Agreement

• Pre-qualification • RFP • Select Supplier

• Negotiate • Formalize Contract • Establish Ordering

Routines/ Transaction Processing

• Assessing supplier performance

• Supplier Rating

• Specification Development

• EPI/ESI • Cross-

Functional Teams

Fig. 1 The traditional purchasing process.

(Adapted from A.J. Van Weele, Purchasing Management 1994)

9.3. Purchasing process reengineered using ICT

The basic activities in the purchasing process are not altered in an organization using ICT tools and techniques. Rather they are reengineered for the convenience of automation leading to value creation. Figure 2 (adopted from [3]) provides an overview of the main activities in sourcing and procurement in a typical e-procurement setup.

Spend Analysis Sourcing Settlement Procurement

• Catalog buying / Contract negotiation

• RFx, • Supplier selection

Vendor Management

• Requisitioning • Approval workflow • Supplier enablement • Catalog Management

• Purchase ordering • Invoicing • Payables • Receivables

• Data aggregation

• Sourcing strategy

Fig. 2. Overview of a Purchasing Department Activities Reengineered using ICT

i. Spend Analysis: The focus of this activity is to develop an aggregate view of the procurement spend across the organization using the transaction data. The aggregate spend by commodity, supplier, plant etc provides a basis for identifying cost saving strategies. A typical example is to find commodity classes or plants where reducing suppliers and increasing volume to a small number of (preferred) suppliers might allow for better price negotiations. Another piece of analysis is to track the performance of each supplier based on past behavior. This is a strategic activity.

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ii. Sourcing: One of the fundamental aspects of sourcing is supplier selection (for a commodity class identified by spend analysis) using one of many negotiation techniques (such as RFx, auctions etc). Once the suppliers are selected the relationship with the selected suppliers is then managed through the negotiated contracts. This step operationalizes the strategy developed by spend analysis.

iii. Procurement: This is a tactical activity where purchasing is (ideally) performed within the umbrella of existing contracts. Typical purchasing within an enterprise starts with a requisition that is approved and purchased from within catalogs of selected suppliers. An additional activity that is supported at this level is the enablement of (new) supplier catalogs and the management of these catalogs.

iv. Settlement: This is the follow through activity where the purchase is ordered, invoiced etc. This is the routine of bookkeeping of each purchase and will not be elaborately discussed.

Comparison of Fig 1 and Fig. 2 reveals the following facts:

• The basic activities for developing product specification is an unstructured decision making activity involving human intelligence, awareness of the recent trends and technologies and other external factors. Thus, they are not the candidates for automation. Therefore, absence of these activities in Fig. 2 is not surprising.

• Integration of the information systems across geographically dispersed units of an organization, developments in data warehousing and mining technologies has enabled the managements to take strategic decisions on spend management. Therefore, these activities do not appear in Fig. 1.

• Spending in purchasing is directly affected by the suppliers’ performance. Therefore, this strategic activity is integrated with spend management (Fig. 2).

• Other strategic decision making activities which are automated are clubbed together under the umbrella of sourcing in Fig 2. These activities were otherwise spread under supplier selection and contract negotiation steps of the traditional process.

• All the activities involving ICT in tactical decision making, such as organization-wide workflow, connecting to suppliers’ catalog systems are under the umbrella of procurement (Fig 2.). This step is not present in the traditional purchasing process.

• The transaction activities which do not involve any decision making tools are clubbed together as settlement activity (Fig 2). In Fig. 1, these activities were considered as a part of contract negotiation.

We describe below the ICT tools and techniques used for different steps of the reengineered purchasing process [3].

9.4. Spend Analysis

This term is a general umbrella term used to capture various strategic activities that are important for designing a sourcing strategy for the corporation. Following steps are involved in spend analysis.

9.4.1 Data Warehouse for Spend Analysis

As evident from GE’s case e-procurement systems enable firms to more efficiently and accurately capture and aggregate how much they are spending corporate-wide in various purchased product areas, allowing the firm to bring what may be significant buying power leverage to market. This requires the creation of a homogeneous data warehouse from disparate (heterogeneous) databases (from various departments, locations). Some of the subtasks to creating a data warehouse are:

a) Supplier Normalization: It is likely that the same supplier (e.g. HP) might have been referred differently in different systems (e.g. HP India, H.P., etc.). These aliases should be mapped to a unique supplier name before creation of the data warehouse. Moreover, the parent child relationships often need to be resolved – this is particularly difficult since mergers and acquisitions often lead to parent-child relations within companies that are completely different. This entails the creation of a list of distinct suppliers so that transactions to the same supplier can be grouped together.

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b) Commodity Mapping: This requires each transaction to be mapped to an appropriate commodity code, such as the UNSPSC code or a company proprietary code. This is due to the fact that the transaction records at the invoice level often provide only a part level description of the commodity and maybe associated supplier codes.

c) Data Visualization: Once the data is scrubbed and cleansed, a set of visualization and rendering tools are required to view the different cross sections of the data so as to get an enterprise-wide view of procurement spend.

9.4.2 Sourcing Strategy

Once a data warehouse is available for analyzing the procurement spend, the next step is to evaluate the different sourcing options for each commodity class or other dimension and identify the potential cost savings. This then provides a basis for a list of actionable sourcing initiatives. The subtasks to creating such a strategy report as follows:

a) Demand Aggregation: The data warehouse provides a means to examine spend by each category, supplier, plant etc. An important first step is to establish the number of suppliers being used for each commodity class across all plants. Often such an exercise might reveal that the number of suppliers that are being used for each commodity is very large and it presents an opportunity to allocate the demand to a few suppliers and leverage the aggregate demand volume to negotiate better prices.

b) Supplier Scorecarding: While consolidating the supplier set for any commodity class it is important to analyze the supplier performance against a set of company’s strategic metrics. The score carding function helps identify the top suppliers to whom future allocation awards would likely go (despite potentially having higher prices) as well as the bottom suppliers who would need to be more aggressively managed as part of the “supplier relationship” activities.

c) E-Procurement model Selection: Different kinds of product require different e-procurement models. Mostly four types of models are discussed in the literature: Use of E-Procurement Software, Internet market exchange, B-to-B auctions, and Internet purchasing consortia (Table 1). These models can be broadly classified under two technology options (Table 1): Catalog Buying and Contract Negotiation. These topics will be elaborated in the next section.

d) Report Generation: Finally a report needs to be generated that outlines the sourcing strategy based on the spend analysis.

Table 1 E-Procurement Models and Technology Options[1]E-Procurement model Description

E-Procurement software Technology Option • Catalog Buying

Any Internet-based software application that enables employees to purchase goods from approved electronic catalogues in accordance with company buying rules, while capturing necessary purchasing data in the process.

Internet market exchanges Technology Option • Catalog Buying • Contract Negotiation

Web sites that bring multiple buyers and sellers together in one central virtual market space and enable them to buy and sell from each other at a dynamic price that is determined in accordance with the rules of the exchanges.

Internet B2B auctions Technology Option • Contract Negotiation

Internet B2B auctions are events in which multiple buyers place bids to acquire goods or services at an Internet site. There are a variety of e-auction formats. Auctions enable organizational buyers to identify the best offer from an expanded base of potential suppliers from around the world. Sellers benefit by obtaining access to bid for business on a level playing field rather than attempting to obtain business based on networks of personal relationships.

Internet purchasing consortia Technology Option • Catalog Buying • Contract Negotiation

Internet service that gathers the purchasing power of many buyers to negotiate more aggressive discounts. Some organizations aggregate buying power for manufacturing inputs, while others perform similar functions for indirect goods.

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9.5 Sourcing

Activities carried out under this step depend on the type of the e-procurement model and the corresponding technology selected at the strategic level.

9.5.1 Catalog buying

Catalog buying is carried out using Internet-based software application that enables employees to purchase goods from approved electronic catalogues in accordance with company buying rules, while capturing necessary purchasing data in the process. The employee’s selection of a good for purchase from a supplier catalogue is automatically routed through the necessary approval processes and protocols. E-Procurement software investment may take several forms, including purchase of a software package from a third party technology provider (e.g., Ariba, CommerceOne), use of an e-procurement system embedded in an Internet market exchange, subscription to e-procurement software hosted and supported by an application service provider (ASP), or development of a proprietary in-house system.

9.5.2 Contract Pricing

Another aspect of a sourcing strategy is to decide on a negotiation technique for contract pricing. The ability to provide good estimates depends on how well the cost types of the suppliers can be characterized. In addition, it is important to model and analyze the risks associated with the uncertainty in the demand and choose contracted volumes optimally.

9.5.3 Core Ingredients of Contract Pricing

1. RFx: The RFI (Request for Information), RFQ (Request for Quotation), and RFP (Request for Proposal) – collectively referred to as RFx – each represents a document and a means for a buyer to specify the requirements of a purchase along multiple dimensions from multiple suppliers.

2. Protocol for price discovery: It can be done in a single round or in multiple rounds. The single round systems are equivalent to sealed bids in traditional purchasing environment. Multiple round auctions are like traditional English auctions and called reverse auctions for obvious reason.

3. Contracts: The negotiations lead to a contract which is then executed with one or more suppliers.

9.6 RFx

In B2B settings, the specification of purchases can get quite complex and require sophisticated capabilities that allow the specification of complex items or services. Complex RFQs also need to allow for a variety of bid structures that exploit complementarities and economies of scale in cost structures of suppliers. An RFx is a document with an associated process initiated by a buyer in order to solicit information, competitive quotes, or proposals from multiple suppliers. A sourcing platform should make the RFx process as easy and straightforward as possible for all of the parties involved. It should also be versatile enough to be used for both goods and services, and for both direct and indirect spend categories. It should also support a wide range of RFx types and sizes, from simple RFIs to complex RFPs.

Most RFx applications support a common set of capabilities such as the creation and editing of an RFx document that mimics its paper-based counterpart. For example, this includes being able to add any number of questions with response fields of the attribute types expected by the buying organization (e.g., numeric, date, text, units of measurement, etc.) for each line item. Table 2 summarizes some of the major requirements in terms of bids (from the seller side) that are supported in RFx systems.

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Table 2. Description of Complex Bid Types

Bid Types Description

Simple multi-line bids A bid includes multiple items, and specifies the unit price for each item.

Multi-attribute bids

A bid includes multiple items, and specifies various relevant attributes for each item, including unit price.

Bundled bids

A bid includes multiple items, specifies the quantity of each item, and provides a total bid price for all the items.

Volume discount bids A bid includes multiple items, and specifies the price curve of each item.

Configurable bids A bid includes multiple items, and specifies various relevant sets of values for each attribute for each item. This provides a compact representation for a large number of configurations (e.g. PCs) and needs to support mark-up based pricing.

9.7 Protocols

9.7.1. Single round price discovery

RFQs are often used in a single round process that is similar to a one shot sealed bid auction where the winners are selected (based on the recommendations of the bid evaluation engine) once all the bids are in. After receiving such bids the buyer needs to identify the set of bids that minimizes total procurement cost subject to business rules such as:

• The number of winning suppliers should be greater than a certain number (to avoid depending too heavily on just a few suppliers), but smaller than a certain number (to avoid too much administrative overhead);

• The maximum amount purchased from each supplier is bounded to a certain limit;

• At least one supplier(s) from a target group (e.g., minority) needs to be chosen; and

• If there are multiple winning bid sets, then one needs to pick the set that arrived first.

Decision support capabilities are essential to facilitate the creation and evaluation of such complex RFQs and bids. Identifying the cost minimizing bid set subject to these business rules is a hard optimization problem and difficult to do by hand (as is a common practice today). In addition, the buyer is required to specify a scoring function that specifies the tradeoff of the non-price attributes against price. This is difficult to do in a consistent manner without a rational process to elicit the tradeoffs.

However, in a price negotiation context, it is often desirable to have a multiround process where after each round the suppliers are allowed to reformulate their bids based on information about the winning bids (more like based on feedback from the auctioneer).

9.7.2. Multiple round price discovery

A typical flow for negotiation is to get a bid response to the RFQ from the suppliers and choose the appropriate bid/bids that satisfy the requirements of the purchase at minimum cost. With the advent of the Internet, online reverse auctions represent a new tool in the purchasing department’s toolbox to

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potentially increase competition through open, real-time, competitive bidding, which requires an iterative bidding protocol.

Fig. 3 Process Flow for Iterative Auctions

Such a multi-round process is illustrated in Fig 3. The bid evaluation engine provides the decision support for all the three functions required for multi-round negotiations and iterative auctions. Winner determination identifies the winning bids from a given set of bids to minimize the total procurement cost, the pricing module prescribes the payment to be made by each winner (this could be in general different from the bid price to promote efficiency in the market), and signaling provides a “market clearing” price for bid reformulation. This iterative process continues until there are no new bids or closing time.

Most reverse auction formats allow for live, real-time, open, competitive bidding where bidders must outbid the current winning bid in order to win the business. There are a variety of auction formats and settings. The most basic reverse auction is a price-only auction for a single item. Most auction providers (and there are many) provide a wide range of formats and settings including multiple quantities of a line item, multiple line items, time extensions, start and reserve prices, partial quantity bids and award allocations, and bundled bids – to name only a few. However, there are three advanced auction formats/settings of note:

• Combinatorial Auction – allows suppliers to mix bundled bids along with un-bundled bids

• Volume Discount Auction – allows suppliers to establish price discounts at certain quantities

• Multi-attribute Reverse Auction – the winner(s) is determined by a score (rather than just price) calculated using the buyer’s weights and preferences for price, quantity, and any number of other attributes.

9.8 Contract Management

One of the main goals of a sourcing project is to execute one or more purchasing contracts with one or more suppliers. The prices and terms of the line items covered by a contract were previously negotiated in RFx and auction rounds. The contracts themselves, however, also go through a different form of negotiation at a more legally precise level. Once executed, these contracts are meant to be used to procure the contracted line items (perhaps via a procurement system) using the negotiated

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prices and terms. A sourcing platform should provide a means to generate a contract based on its preceding RFx and auction negotiations, support contract negotiations, and monitor compliance to the contracts’ business commitments over time.

Contract monitoring capabilities incorporated into contract management software include:

• An alert notification is sent when a contract is soon to expire.

• The buyer’s purchase volume commitments can be monitored with alert notifications sent if there is danger of buying under the minimum quantity within the designated time period.

• Notifications can be sent alerting the buyer and/or supplier of a supplier’s violation of a delivery commitment.

In all of the contract management solutions today, there is a specific and important shortcoming. Namely, there are two key parts enabling automated contract monitoring which currently must be performed manually.

• First, the contract commitments to be monitored must be manually selected out of the contract’s negotiated legalese into a structure easier to analysis.

• Second, the business process data and raw transaction data needed to assess whether commitments are being fulfilled or violated is also captured mannually.

Business process integration and management (BPIM) and business activity monitoring (BAM) systems are beginning to address the challenges associated with contract monitoring systems.

9.9 Risks Associated with e-Procurement Technologies

In a survey conducted by Davila et al. [1], respondents perceive certain risks linked to the adoption of e-procurement technologies that need to be addressed before these technologies are widely accepted. These risks include:

Internal business risks: companies are uncertain about whether they have the appropriate resources to successfully implement an e-procurement solution. The experimentation of the companies following a ‘wait and see’ strategy may help to develop the required absorptive capacities. Implementing an e-procurement solution requires not only that the system itself successfully performs the purchasing process, but most important, that it integrates with the existing information infrastructure. This internal information infrastructure includes systems such as accounting, human resources, asset management, inventory management, accounts payable, production planning, and cash management systems. Most organizations adopting or looking to adopt e-procurement software already have significant investments in these other systems; integrating these new technologies with existing platforms should happen as smoothly as possible. Failure to integrate creates duplicative work steps and jeopardizes the reliability of organizational information.

External business risks: e-procurement solutions need not only ‘talk’ with internal information systems, but also need to cooperate with external constituencies — mainly customers and suppliers. External constituencies need to develop internal systems that facilitate the communication through electronic means — an issue that demands technology investments as well as incentives for these constituencies. For e-procurement technologies to succeed, suppliers must be accessible via the Internet and must provide sufficient catalogue choices to satisfy the requirements of their customers. Ideally, suppliers will provide e-catalogues in the formats required by customers, reflecting custom pricing and/or special contractual agreements, and will send updates on a regular basis. However, suppliers, especially in low margin industries, may be hesitant or even unable to meet such demands without guarantees of future revenue streams. Lack of a critical mass of suppliers accessible through the organization’s e-procurement system would limit the network effects that underlie these technologies, further hindering the acceptance and adoption of the technology. Cooperation with external parties also requires new suppliers and customers to meet the business criteria that organizations have set to accept them in their networks. Since some of the business models associated with e-procurement technologies (e.g. auctions, consortia, and exchanges) clearly envision the use of suppliers with whom the buyer has not previously transacted business, companies need to develop

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mechanisms that provide the buyer with assurances that the supplier meets or exceeds recognizable and industry enforced standards relating to supplier quality, service, and delivery capabilities.

Technology risks: companies fear the lack of a widely accepted standard and a clear understanding of which e-procurement technologies best suit the needs of each company. The lack of a widely accepted solution blocks the integration of different e-procurement software across the supply chain. The significance of this risk factor seems to suggest the need for clear and open standards that would facilitate inter-organization e-procurement technologies. Without widely accepted standards for coding, technical, and process specifications, e-procurement technology adoption will be slow and will fail to deliver many of the benefits expected.

E-procurement process risks: another set of risks has to do with the security and control of the e-procurement process itself. Organizations must be confident, for example, that unauthorized actions will not disrupt production or other supply chain activities when committing to e-procurement technologies. Thus, the challenge for the e- procurement technology adoption is to provide evidence to non-users that these technologies (1) do not undermine control, security, or privacy requirements; (2) they are not so technically complex that organizations without a sufficient technology skill set cannot use them, and (3) the new business model provides the right incentives to supply chain constituencies to effectively use these technologies.

Table 3 identifies the changes in the buyer – supplier relationship as a major barrier to e-procurement technology use. While technology is perceived as a barrier, reflected in the ‘lack of common standards’ concerns for e-procurement software, most barriers point to the need for redesigning these relationships. If, for example, the use of e-procurement undermines amicable trading relationships, buyers are concerned about how they will obtain needed goods when supplies get tight. Buyers are also concerned that these technologies will push prices down to the point where suppliers cannot invest in new technology or product development, upgrade facilities, or add additional productive capacity. Additional price pressures can even push suppliers with a poor understanding of their cost structure out of business. Finally, integration with existing mechanisms is seen as another barrier.

Table 3: Three Most Frequently Identified Barriers to E-Procurement Technologies Utilization E Procurement Software

• Problems integrating with existing system • Lack of common standard for e-commerce software development • Lack of suppliers’ accessibility to the organization’s e-procurement system and/or lack of

supplier investment in catalog development Internet Exchanges

• Not enough suppliers to create a liquid marketplace • Suppliers reluctance to participate in selling environments where preeminent focus is on price • Supplier’s reluctance to participate because control is lost over the presentation of brand name

and product features. E-Auctions

• Organizational discomfort with auctions, as opposed to honoring commitment to supplier partnering and consolidation.

• Downward price pressure on vendors resulting in diminished customer service or quality. • Inability to identify potential items for auction.

Purchasing consortia • Pricing is not significantly better than available without consortia. • Getting sufficient number of vendors into the process. • Ensuring conformance to state laws and regulations

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Reference

[1] Antonio Davila, Mahendra Gupta and Richard Palmer Moving Procurement Systems to the Internet:: the Adoption and Use of E-Procurement Technology Models European Management Journal, Volume 21, Issue 1, February 2003, Pages 11-23

[2] William D. Presutti Supply management and e-procurement: creating value added in the supply chain Industrial Marketing Management, Volume 32, Issue 3, April 2003, Pages 219-226

[3] Robert Guttman, Jayant Kalagnanam, Rakesh Mohan and Moninder Singh, Strategic Sourcing and Procurement, In Supply Chain Management on Demand Strategies, Technologies, Application, Chae An and Hansjörg Fromm Eds., Springer Berlin Heidelberg, 2005, pages 117 – 142

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CHAPTER X Economic Theory of Auctions

10.1. Introduction

Auctions have been widely adopted as tool for buying and selling goods and services. Auctions can be used to sell (allocate) almost all kinds of goods. The governments use them to sell public resources such as radio spectrum licenses and oil drilling rights; the firms and individuals use them to sell houses, flowers, antiques, etc. They also find applications in fields of computer science, such as allocating bandwidth in the communication networks. The online auction business model is the one in which participants bid for products and services over the Internet. The functionality of buying and selling in an auction format is made possible through auction software which regulates the various processes involved. eBay, the world's largest online auction site, is one of the better known examples of buying auctions. Similarly, a reverse auction is a tool used in industrial business-to-business procurement. It is a type of auction in which the role of the buyer and seller are reversed, with the primary objective to drive purchase prices downward. One example of a reverse auction site could be Metal Junction – a consortium of Tata Steel and SAIL. A less know online auction business is adopted by Dell and GM. They use auction to sell second hand products (used) which they buyback from their clients. A well designed auction mechanism can add to company’s profitability. Similarly, a well designed bidding strategy can save a bidder falling into a trap like that of winner’s curse. Studying auction theory can help understanding basic design issues. Auction theory itself is an important part of economic theory and it helps to understand properties of the markets, such as the price formulation and information structures.

10.2. Basic Auction Types

The exposition of auction theory typically starts with the introduction of four basic auction formats: two open auctions and two based on sealed bids:

Open bids

• Ascending bid auction (also called English Auction) – In this auction the price is successively raised until one bidder remains. This bidder wins the object at the final price. The auction can run by the auctioneer calling price, the bidder submitting prices, or electronic bids with the highest bid posted continuously. Once somebody quits the process they are not allowed back in. This auction format is common in art, livestock, and some Internet-based procurement auctions.

• Descending bid auction (also called Dutch auction) – In this auction the price starts from a high level and called down. The first bidder who accepts the current price wins. This is how the Dutch flower auctions are managed, but there are not many other examples of Dutch auction.

Sealed Bid Auctions

• First price sealed bid auction (1SB) – Each bidder submits a single bid (independently) and the item is sold to the highest bidder who pays the winning bid. This is the most common form in procurement auctions and in many government contract auctions.

• Second price sealed bid auction (2SB) – Each bidders submit a single bid (independently) and the item is sold to the highest bidder. Unlike the first price sealed bid auction, however, the

price that the winner pays is the second highest price (“second price”). This type of auction was first suggested by Vickrey. Although it is rarely practiced, its value is the insight it offers to other auction mechanisms.

10.3. A framework for defining and categorizing auction

Kalagnanam and Parkes[1] have developed a framework for classifying auctions based on the requirements that need to be considered to set up an auction. These core components are:

Resources The first step is to identify the set of resources over which the negotiation is to be conducted. The resource could be a single item or multiple items, with a single or multiple units of each item. An additional consideration common in real settings is the type of the item, i.e. is this standard commodity or multi-attribute commodity. In the case of multi-attribute items, the agents might need to specify the non-price attributes and some utility/scoring function to tradeoff across these attributes.

Market Structure An auction provides a mechanism for negotiation between buyers and sellers. In forward auctions a single seller is selling resources to multiple buyers. Alternately, in reverse auctions, a single buyer is sourcing resources from multiple suppliers, as is common in procurement. Auctions with multiple buyers and sellers are called double auctions or exchanges, and these are commonly used for trading securities and financial instruments and increasingly within the supply chain.

Preference Structure The preference structure of agents in an auction is important and impacts some of the other factors. The preferences define an agent's utility for different outcomes. For example, when negotiating over multiple units agents might indicate a decreasing marginal utility for additional units. An agent's preference structure is important when negotiation over attributes for an item, for designing scoring rules used to signal information.

Bid Structure The structure of the bids allowed within the auction defines the flexibility with which agents can express their resource requirements. For a simple single unit, single item commodity, the bids required, are simple statements of willingness to pay/accept. However, for multiunit identical items setting bids need to specify price and quantity. Already this introduces the possibility for allowing volume discounts, where a bid defines the price as a function of the quantity. With multiple items, bids may specify all-or-nothing bids with a price on a basket of items. In addition, agents might wish to provide several alternative bids but restrict the choice of bids.

Matching Supply to Demand A key aspect of auctions is matching supply to demand also referred to as market clearing, or winner determination. The main choice here is whether to use single-sourcing, in which pairs of buyers and sellers are matched, or multi-sourcing, in which multiple suppliers can be matched with a single buyer, or vice-versa. The form of matching influences the complexity of winner determination, and problems range the entire spectrum from simple sorting problems to NP-hard optimization problems.

Information Feedback Another important aspect of an auction is whether the protocol is a direct mechanism or an indirect mechanism. In a direct mechanism, such as the first price sealed bid auction, agents submit bids without receiving feedback, such as price signals, from the auction. In an indirect mechanism, such as an ascending-price auction, agents can adjust bids in response to information feedback from the auction. Feedback about the state of the auction is usually characterized by a price signal and a provisional allocation, and provides sufficient information about the bids of other agents to enable an agent to refine its bids. In complex settings, such as multi-item auctions with bundled bids, a direct mechanism can require an exponential number of bids to specify an agent's preference structure. In comparison, indirect mechanisms allow incremental revelation of preference information, on a “as required basis”. The focus in the design of indirect mechanisms is to identify how much preference information is sufficient to achieve desired economic properties and how to implement informationally-efficient mechanisms. A related strand of research is to provide compact bidding languages for direct mechanisms.

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Each of the six dimensions provides a vector of choices that are available to set up the auction. Putting all of these together generates a matrix of auction types. The choices made for each of these dimensions will have a major impact on the complexity of the analysis required to characterize the market structure that emerges, on the complexity on agents and the intermediary to implement the mechanism, and ultimately on our ability to design mechanisms that satisfy desirable economic and computational properties.

10. 4. Evaluating Auctions

When deciding between various auction mechanisms, the auctioneer has a very large number of auction designs to choose from. The most important criteria in the choice of auction format are the following [2]:

• Revenue – auctioneers are looking for the auction that will yield the maximum revenue for the item sold. While this is an important tenet of auction theory and will be assumed to be the case in the bulk of this section, other considerations are also important not only to governments but to many corporations.

• Efficiency – an auction is successful if the bidder that values the item most ex post - actually gets it. In some contexts, such as government auctions this is important, especially when the government is selling public assets. When the sale involves future delivery of services, as in many procurement auctions, efficiency means that the contract is more likely to be carried out and the service provided at a high level.

• Time and Effort – many B2B auctions involves the trading of many (sometimes tens of thousands) of items while soliciting bids from many (sometimes hundreds) of suppliers. Furthermore such auctions are conducted periodically, every year (for many MRO services) or every product model (for direct material). Auctioning organizations have to devote a great deal of time and effort to such auctions and thus mechanisms that minimize the time and effort involved are the ones that will be used.

• Simplicity – One of the objectives of auctioneers in most auctions is to get as many participants as possible. Keeping the rules simple, especially knowing that many suppliers have to respond to hundreds of auctions every month, helps participation in many situations.

While the literature on auction theory focuses on the first two criteria, the last two are very important in procurement auctions.

10.5. Basic Approaches for Auction Design:

The basic economic methodology used in the design of electronic intermediaries first models the preferences, behavior, and information available to agents, and then designs a mechanism in which agent strategies result in outcomes with desirable properties. Kalagnanam and Parkes[1] consider two approaches to modeling agent behavior:

Game-theoretic/mechanism design The first model of agent behavior is game theoretic and relates to mechanism design theory. In this model the equilibrium state is defined by the condition that agents play a best-response strategy to each other and cannot benefit from a unilateral deviation to an alternative strategy.

Price-taking/competitive equilibrium The second model of agent behavior is price-taking, or myopic best-response, and relates to competitive equilibrium theory. In this model the equilibrium state is defined by the condition that an agent plays a best-response to the current price and allocation in the market, without modeling either the strategies of other agents or the effect of its own actions on the future state of the market.

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10.5.1 Game-theoretic/mechanism design approach for auction design

An auction is a game with partial information where a player's valuation of an object is hidden from other players. It is served as a popular way in resources (goods) allocation by specifying a set of rules to determine the winner(s) and the related payments. A typical setting of the auction is that a seller attempts to sell one or more items to a set of bidders. The involved players (seller and bidders) do not have complete information about the value of the items on sale in the sense that they do not know other’s value but know their own value, which may or may not be affected by others. All players are assumed to be selfish and payoff-maximizing. The auction theory studies the behavior of the players in this non-cooperative environment.

10.5.1.1 Basic Auction Models

In any auction process the seller does not know the value that bidders place on the item auctioned off and bidders do not know with certainty how other bidders value the item. The information available to bidders and the corresponding type of auctions can be classified as follows:

• Private value (PV) auctions – where each bidder knows only the value of the item to himself. Even if he would know what other bidders are willing to pay this would not affect his own valuation. Such a model is appropriate when the value of an item to a bidder is derived from its consumption alone and not from later resale.

• Interdependent value auctions – where the value of the items sold is not known to the bidders. Each bidder has only an estimate (signal) regarding the value (this may be an expert opinion or a test result). If a given bidder would have known the signals of other bidders, his own estimate of the true value may change.

• Common value (CV) auctions – a special case of interdependent values in which the value of the item ex post is the same for all bidders. For example, when oil leases are auctioned off, bidders have only their own test results regarding the actual amount of oil in the tract being leased. After the auction, however, the winner will find out exactly the amount of oil in the ground and this oil has a certain market value.

The simplest and most thoroughly investigated auction model is the symmetric independent private values (SIPV) model with risk-neutral bidders, in which (1) a single indivisible object is for sale (single-object auction); (2) each bidder knows his own valuation about the object but no one else dose (private value). The unknown valuations are independent and identically distributed (independence, symmetry); (3) all bidders are ex ante identical (symmetry); (4) Bidders are risk-neutral. This model serves as a prototype in the research of auction theory. All results in the literature either can be derived directly from this model or come from relaxing some assumptions or with some other features and information in different situations.

10.5.1.2 Mechanism Design Problem

The mechanism design approach to solving distributed allocation problems with self-interested agents formulates the design problem as an optimization problem. Mechanism design addresses the problem of implementing solutions to distributed problems despite the fact that agents have private information about the quality of different solutions and that agents are self-interested and happy to misreport their private information if that can improve the solution in their favor. A mechanism takes information from agents and makes a decision about the outcome and payments that are implemented. It is useful to imagine the role of a mechanism designer as that of a game designer, able to determine the rules of the game but not the strategies that agents will follow.

A mechanism defines a set of feasible strategies, which restrict the kinds of messages that agents can send to the mechanism, and makes a commitment to use a particular allocation rule and a particular payment rule to select an outcome and determine agent payments, as a function of their strategies.

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Game theoretic methods are used to analyze the properties of a mechanism, under the assumption that agents are rational and will follow expected-utility maximizing strategies in equilibrium.

10.5.1.3 Direct Revelation Mechanism

The space of possible mechanisms is huge, allowing for example for multiple rounds of interaction between agents and the mechanism, and for arbitrarily complex allocation and payment rules. Given this, the problem of determining the best mechanism from the space of all possible mechanisms can appear impossibly difficult. The revelation principle allows an important simplification. The revelation principle states that it is sufficient to restrict attention to incentive compatible direct-revelation mechanisms. In a direct-revelation mechanism (DRM) each agent is simultaneously asked to report its type. In an incentive-compatible (IC) mechanism each agent finds it in their own best interest to report its type truthfully. The mechanism design problem reduces to defining functions that map types to outcomes, subject to constraints that ensure that the mechanism is incentive-compatible.

The IC constraints require that when other agents truthfully report their types an agent's best response is to truthfully report its own type, for all possible types. In technical terms, this ensures that truth-revelation is a Bayesian-Nash equilibrium, and we say that the mechanism is Bayesian-Nash incentive compatible. In a Bayesian-Nash equilibrium every agent is plays a strategy that is an expected utility maximizing response to its beliefs over the distribution over the strategies of other agents. An agent need not play a best-response to the actual strategy of another agent, given its actual type. This equilibrium is strengthened in a dominant strategy equilibrium, in which truth-revelation is the best-response for an agent whatever the strategies and preferences of other agents.

10.5.1.4 Efficiency Vis-à-vis Optimality in Auction

There are two natural design goals in the application of mechanism design to auctions and markets. One goal is allocative efficiency, in which the mechanism implements a solution that maximizes the total valuation across all agents. This is the efficient mechanism design problem. Another goal is payoff maximization, in which the mechanism implements a solution that maximizes the payoff to a particular agent.

Optimal auctions are designed to maximize the expected revenue of the seller by a set of tools including posing a reserve price or charging an entry fee, whereas the objective of efficient auctions is to maximize the social welfare, the sum of the players' surplus. Roughly speaking, the efficient design aims to maximize the system welfare, whereas the optimal design aims to maximize the seller's individual revenue. Since optimality and efficiency usually can not be achieved simultaneously, the auction designers have to make the choice before starting address the rules. A financial self-interested agent may prefer the optimal auctions, while a public agent like the government may prefer the efficient auctions to gain more social welfare. Nevertheless, all agents need to balance optimality and sufficiency to make the auctions practical.

10.5.1.5 Efficient Auction Mechanism

The efficiency problem in the single-object auction where buyers have private values is theoretically solved in Vickrey's pioneering work. The winner is the buyer whose valuation of the good is highest and will pay the second highest value. Truth-telling is a weakly dominant strategy for any buyer. The mechanism is known as Vickrey's auction, which also applies in the case of multiple identical objects. This format is significantly extended to a mechanism called Vickrey-Clarke-Groves (VCG) mechanism4. The VCG mechanism works for homogeneous goods as well as heterogeneous ones in private value environment.

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10.5.1.5.1 Vickrey’s Mechanism: an incentive compatible direct revelation mechanism

In the sealed second price auction the dominant strategy is to bid the actual valuation, regardless of the other players’ strategy. To illustrate the validity of this strategy, we demonstrate using a hypothetical situation

Consider bidding ( , if the highest bid other than this is , then the following cases arise: )

)

Δ−v maxb

a. If v < , the bid is not won (same as bidding v ) maxb

b. If ( )> , the bid is won and payment of (same as bidding ) Δ−v maxb maxb v

c. If v > >maxb ( Δ−v , bidding ( )Δ−v means the auction is lost. However bidding v would

have helped win the auction with a surplus of ( )Δ−v

Similarly, now consider bidding . If the highest bid other than this is , then the following cases may occur:

( Δ+v )

)

maxb

a. If ( )< , the bid is not won (same as bidding ) Δ+v maxb v

b. If v > , the bid is won and payment of (same as bidding v ) maxb maxb

c. If v < <maxb ( Δ+v , bidding ( )Δ+v means the auction is won. However, while bidding would have meant losing the auction the final payment is greater than the valuation by an

amount of with a surplus of . v

maxb

Therefore, as demonstrated, bidding more than or less than the actual valuation never helps and may cause losses. Bidding the true value is the dominant strategy.

VA is concerned with auctioning off a single good. Clarke and Groves mechanism extends the concept of strategyproofness to the combinatorial auction setting.

10.5.1.5.2 Optimal Auctions

The problem of designing an auction that maximizes the seller's revenue (optimal auction) is usually more challenging, especially in the case of multiple heterogeneous objects. However, Myerson's revelation principle allows one, without loss of generality, to restrict attention to a direct-revelation mechanism that is used to prove a surprising result called “revenue equivalence theorem". His paper provides the framework that has become the paradigm for the research of optimal mechanism design for selling one or more homogeneous objects.

In this section, we will review the results on the revenue equivalence theorem under different circumstances. In the case that this theorem does not hold, we compare different auction formats based on the revenue-generating ability. Since the reserve price is one of the main tools to maximize the seller's revenue, we also present the main results in this aspect. We will discuss the tradeoff between optimality and efficiency in the auctions design in the next section.

10.5.1.5.3 Strategic Equivalence of Auctions

Open auctions require the bidders to be all present (physically or digitally) at the same time and place, offering bidders the opportunity to observe the behavior of other participants. Sealed bids, on the other hand, are mailed in or submitted electronically with no opportunity for information feed-back. Interestingly, some of these differences do not matter to rational decision makers.

Note first that submitting the winning bid in a 1SB auction is equivalent to buying the item at that price through Dutch auction, provided the item is still available. This is true despite the fact that a

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(first price or otherwise) sealed bid auction offers no opportunity to observe other bidders behavior while the Dutch auction is “open.” Naturally, the reason is that in a Dutch auction the first time that somebody enters, he wins and the auction terminates. Thus, in 1SB and Dutch auction bidders have to decide a-priori how much to bid and the auctioneer will get what the highest bidder submitted. In economic terms, 1SB auctions are strategically equivalent to Dutch auctions in the sense that for every realization of bidders’ estimates the two auctions induce identical equilibrium outcomes (i.e., winners and prices).

In English auctions it makes sense for a bidder to stay in the auction until his value has been reached (in fact, it is his dominant strategy, as discussed below). He should not drop beforehand and certainly should not stay longer. Thus the winning bidder will stay until the next-to-last bidder drops out, meaning that he will pay the price in which the last bidder dropped out, or the second highest bid. Thus English auctions are equivalent to 2SB auctions. This equivalence is not as strong as the one between 1SB and Dutch auctions since with English auctions bidders do get information throughout the process when they see the prices in which other bidders drop out and can therefore adjust their estimate of the value of the item being sold and their strategy. This is not a consideration with private value auctions but important in common and interdependent value auctions. These equivalences are depicted in the following Figure

The most common types of procurement auctions (where the winner is the participant who offered the lowest bid) are the 1SB, where the winner gets to supply the item to the buyer at the lowest bid price and English auction, where the winner supplies the item or service at the second lowest price. English auctions became popular with procurement managers only during the 1990-s with the advent of Internet-based auctions.

10.5.1.5.4 Revenue Equivalence

In this section we obtain the revenue’s generated from the First Price and Second Price auction models.

Assume that there are n bidders and their values { }NVVV ,...,, 21 are drawn from independent identical distributions, F (v) with density function f (v). Also the order statistics are { }and the density function of the kth lowest value is given by

NVVV ′′′ ,...,, 21

Dutch 1st Price CV

PV

English 2nd Price PV

Equivalence of Open and Sealed Bid Auctions

119

( ) ( ) ( ) ( )[ ] ( )[ ] knkk vFvFvf

knknvf −− −⋅⋅⋅

−⋅−=⎟

⎠⎞⎜

⎝⎛ ′ 1

!!11

Considering that the valuations are drawn from a uniform distribution U [0, 1], the distribution of the kth order statistics is given by:

( ) ( ) [ ] knk vvnvf −− −⋅⋅=⎟⎞⎜⎛ ′ 11 k knk −⋅−⎠⎝ !!1

This is a beta distribution with parameters k and n-k+1, the mean of the distribution is

1+=⎥⎦⎢⎣ n

vE k ⎤⎡ ′ k

In the First Price Auctions, the bidders shave their bids by a certain amount. The winner is the bidder with the highest value, in other words the highest order statistic, which from the calculations above is

First Price Revenue

[ ]1+

=n

nvE n

Further, from the previous section, the best bidding strategy is

[ ]nvEn

n −∗ 1b ⋅=

Therefore the expected winning bid is

11−

=∗ n+n

r’s payoff.

In the Second Price Auction system the winner pays the second highest bid. Therefore, continuing the earlier illustrations, the expected payoff for the auctioneer will be the expected value of the second highest bid in the auction. From Section 4, this is

b

This is also the auctionee

Second Price Revenue

[ ]11

1 +−

=− nnvE n

Therefore, we observe that the First Price and the Second Price Auction formats lead to the same expected payoff for the auctioneer. This is the illustration of one of the most fundamental results in

ue Equivalence Theorem. This theorem basically states that under auction mechanism lead to the same amount of payoff for the

nce Theorem

The Reof the th

ism in which (i) the object always goes to the buyer with the highest signal,

auction theory, namely the Revencertain conditions, any type of auctioneer.

Revenue Equivale

venue Equivalence Theorem is the most fundamental result of Auction Theory. The statement eorem is:

Assume each of a given number of risk neutral potential buyers of an object has a privately known signal independently drawn from a common, strictly increasing distribution. Then any auction mechan

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and (ii) any bidder with the lowest feasible signal expects zero surplus, yields the same

odel; owever the signals have to independent. Thus all the common auction formats, ascending bid,

rice sealed bid and even some non – standard auction ue for the auctioneer under the stated conditions.

’ m e for a single unit. ame

expected revenue and results in each bidder making the same expected payment as a function of their signal.

This result applies to both private value models as well as the more general common value mhdescending bid, first price sealed bid, second pformats always lead to the same expected reven

Proof of the Revenue Equivalence Theorem

We consider the ‘independent private values odel, where n bidders competBidder i, values the unit at iv drawn from the s continuous distribution ( )vF on [ ]vv, (so

that ( ) ( ) 1== vvF ) with density function,0 F ( )vf . All bidders are risk neutral.

( )vSiFor any given mechanism, for a given bidder i, let be the expected surplus, as a function of the type. Let be the probability of receiving the object. The following equation is the key ( )vPi

( ) ( ) ( ) ( )vPvvvSvS ii~~~ ⋅−+≥

The right hand side is the surplus that player i would obtain if she had type but deviated from equilibrium behavior and followed the strategy of type

vv~ . In equilibrium, must not prefer to deviate

om it, so the left hand side must (weakly) exceed the right hand side. Therefore we have,

+

And since must not want to imitate type we have

Combining the previous two equations,

v fr

( ) ( ) ( ) ( )dvvPdvSvS iii +⋅−+≥ dvv

dvv + v

( ) ( ) ( ) ( )vPdvvSdvvS iii ⋅+≥+

( ) ( ) ( ) (vPdvvP iii

i ≥ )dv

vSdvvS −+

Taking the li it a , we obtain

≥+

s 0→dvm

( )vPdv i

i =dS

Integrating,

121

( ) ( ) ( )∫=

+=v

vxiii dxxPvSvS

At any type v the slope of the surplus function is ( )vPi ˆ , so if we know ( )vSi , we have the complete picture. Now, considering any two mechanisms with the same ( )vSi and the same ( )vPi functions for all v and for every player i. They have the same ( )vSi function. So any given t , of player i,

akes the sa expected payment in each of the two mechanisms. Therefore the expected payment the same. Since this is true for all bidders, these er.

in the form of a signal, which is a random variable. The

nal regarding the value.

s intend to r private consumption (such as a house or a painting), they may keep an eye

orm of positive correlation. Such treatment makes sense because in most cases the

yield higher revenue for the ce auction. The reason is that the transmission of information allows bidders

y not be the bidder with the highest value.26 Efficiency e signals are such that the ex post values of all bidders can be ordered in the same

ype, vm me averaged across the different possible types is alsomechanisms yield the same revenue for the auctione

10.5.1.5.5 Auctions with Interdependent values

In auctions with interdependent values it is assumed that bidders have only partial information about the value of the item being auctioned off, extreme case of interdependent value auctions are common value auctions in which all bidders have the same ex post value for the item being auctioned off. Before the auction, however, each bidder has only a random sig

Naturally, each bidder’s estimate of the true underlying value of the item will improve if he gets information about the other bidders’ signals – for example, when they drop from the process during an English auction.

Interdependent value auctions are important to consider since it is difficult to imagine a situation in which there is not at least some portion of the value which is common. Even if bidderpurchase an item only fotowards selling it in the future, meaning that they would like also to consider the “future market value” of the item purchased, which is common, or at least depends on others’ valuations.

Revenue Comparison

When analyzing interdependent and common value auctions, many economists also drop the assumption that the signals are independent and assume that bidders’ signals are affiliated. Affiliation is a strong ftechnology used to get the signals is similar. Since the revenue equivalence does not hold any more with affiliated signals, it is natural to question which auction format yields more revenue to theauctioneer.

With affiliated signals and common values the English auction may

auctioneer than a first prito continuously upgrade the quality of their estimate of the true value of the item being sold, and thus they need to hedge less, bidding higher to the benefit of the auctioneer.

Efficiency of Auctions

In pure common value auctions, there is no issue of efficiency since ex post all bidders value the item identically. In auction with interdependent values, however, the item sold will be awarded to the bidder with the highest signal, who may or mais assured only if thway that their signals are ordered. Under this condition, all the standard auctions will be efficient, provided only that the signals are symmetric.

Winner’s Curse

An interesting phenomenon which takes place when bidders fail to account for it and in the presence of interdependent values is the winner’s curse. The winner’s curse takes place when winners pay too much, due to their failure to anticipate and correct, in their bidding strategy, a bias in their estimate of the value of the item being auctioned off. The phenomenon arises since the winner of an auction with

122

interdependent values is the bidder who submitted the highest bid, which is the bidder who had the ws one thing for sure – his signal was higher tion. This means that the winner is the bidder

to in order

cially if the number of bidders in the fray is low.

n supporting the case for setting a reservation pric o

Assof r o all r, tvaluations is U [0, possible:

b. ing

is 2F(r) [1=F(r)].

t a reservation price.

ptima eservation price we use th

he auct neer considers raising it by a small

If the highest bidder bids above (r+ δ), then there is a gain of δ and the probability of the occurrence is a bad move if the highest bidder bids between r

and (r+ δ) r and the probability of this happening is )

highest signal. When notified of the win, the bidder knothan the signal of all other (n -1) participants in the aucwho most overestimated the value of the item sold.

10.5.1.5.6 Basic Design Considerations for Auctions

There are several considerations to be kept in mind while designing auction mechanisms. Even in the case of established auction mechanisms, there are several aspects that have to be cateredfor the auctioneer to gain the most benefit. This section illustrates some such aspects.

Number of Bidders: It is observed that the auctioneer increases the returns from the auction process with an increase in the number of bidders. Therefore the auctioneer should invest in getting more participants in the bidding process, espe

Reservation Price: Auctioneers should set a reservation price, i.e. a minimum asking price below which they are not willing to sell the item. The setting of this reservation price should be such so as to maximize the returns to the auctioneer. As an illustratio

e, c nsider the following scenario:

ume that the auctioneer’s value for the item is w = 0 and he sets a positive (low) reservation price . F r sm he probability that a bid is less than r is low, that is F(r) << 1. The range of

1], the following cases are

a. If both bids are above r, setting r did not hurt the auctioneer

If both bids are below r, then there is no sale. However the probability of this event occurris ( )[ ]2rF and the maximum loss is r

c. If one bid is below it and one bid is above then the winner pays r instead of the secondhighest price and the auctioneer gains. The probability of this occurring

Thus for small r, the probability of gaining is proportional to F(r) and the probability of losing is proportional to ( )[ ]2rF . Hence it is clearly advantageous to se

For determining the o l r e following argument:

Assume that the auctioneers value is 0, there are n bidders and the reserve price is set to r (where r>0) and t io amount δ.

of this event is ( )[ ] ( )[ ]δ+−⋅⋅ − rFrFn n 11 . This. The auctioneer loses

( )[ ] ( ) ([ ]rFrFrFn n −+⋅⋅ − δ1

Therefore the net gain per increment of δ in r is:

( ) ( ) ( )[ ] ( ) ( ) ( ) rrFrFrFnrFrFnG nn

⋅−+

⋅⋅−⋅+−⋅⋅

= −−

δδδ

δδδ 1

1 1

crement of the reservation price goes to zero: Simplifying and taking the limit as the in

( ) ( ) ( )[ ] ( ){ }rrfrFrFnG n ⋅−−⋅⋅= −

→

11

0lim δδ

Setting G (δ) = 0 for optimum, we have

( )( )

1 F rr

r′

f′−

′ =

123

The optimal reservation price r ′ is the solution of the above equation; this is independent of the

and a set of prices and implies that the allocation is optimal and therefore efficient.

yopi

competitive equilibriumdefine the combina at

uce variables,

has value for bundle . Assume for the purpose of exposition that we have knowledge of nteger programming problem.

max ( )

s.t.( ) 1,

( )

ii ix S S G i I

i

i

x S v

x S i I

number of bidders.

10.5.2 Price-taking/competitive equilibrium approach for auction design

Competitive equilibrium theory is built around a model of agent price-taking behavior. At its heart is nothing more than linear-programming duality theory. One formulates a primal problem to represent an efficient allocation problem, and a dual problem to represent a pricing problem. A competitive equilibrium condition precisely characterizes complementary-slackness conditions between an allocation Competitive equilibrium conditions are useful because they can be evaluated based on myopic best-response bid information from agents, and without requiring complete information about agent valuations. This is the sense in which prices can decentralize decision-making in resource allocation problems.

The modeling assumption of price-taking behavior states that agents will take prices as given and demand items that maximize payoff given their valuations and the current prices. This is commonly described as price-taking or myopic best-response behavior. In the language of mechanism design, this can be considered a form of m c, or bounded, incentive-compatibility.

To illustrate (CE) prices we will impose some structure on choice set K and torial alloc ion problem (CAP). Let G define a set of items, and S K∈ a subset,

or bundle, of items. A choice k K∈ defines a feasible allocation of bundles to agents. Introd( ) {0,1}ix S ∉ , to indicate that agent i receives bundle S in a particular allocation. Agent

i ( )iv S Sagent valuations. The CAP can be formulated as the following i

( )

S

S j i( ) 1,

{0,1}

iI

x S j G∋ ∈

x S

⊆ ∈

≤ ∀ ∉

≤ ∀ ∉

∑

∈

∑

∑

where, indicates that bundle contains item < j. To apply linear-programming duality theory we must relax this IP formulation, and construct an integral LP formulation. Consider [LP1] in which the above equation is relaxed to . Then, the dual is simply written as:

iip j i j

i i

p j

p j v S i I S G

i j

ππ

π

+

+ ≥ ∀ ∈ ∀ ⊆

∀

∑ ∑

∑

∑∑

S j∋ S

( ) 0ix S ≥

, ( )min ( )

s.t.( ) ( ), ,

, ( ) 0, ,j S

i p jπ∈

≥

124

< The dual introduces variables for items ( ) 0p j ≥ which we can interpret as prices on items. Given prices, ( )p j , the optimal dual solution sets max { ( )i S iv Sπ = − ( ),0}

j Sp j

∈∑ . This is the maximal

payoff to agent i, given the prices. The dual problem computes prices on items to minimize the sum of the payoffs across all agents. These ar a set of CE prices when the primal solution is integral. The complementary-slackness (CS) conditions on a feasible primal,

e precisely( )ix S , and feasible

dual ( )p j , solution define conditions for competitive equilibrium:

0 ( ) 1

( ) 0 ( ) 1,

i ix S i

p j x S jS

S j i

( ) 0 ( ) ( ) ,

iI

i i ij S

x S p j v S i Sπ∋ ∉

∈

> ⇒ + ≥ ∀ ∀∑

These conditions have a natural economic interpretation. First and third conditions state that the allocation must maximize the payoff for every agent at t

π > ⇒ = ∀

> ⇒ = ∀

∑

∑∑

he prices. The second condition states that the eller must sell every item with a positive price, and maximize the payoff to the seller at the prices.

seller can announce an efficient allocation maximizes its own payoff at the prices. In

le designs (such as the English, Vickrey etc.) where only a single ization problem can be solved in a straightforward

s combinatorial auction problem. Combinatorial auctions are also proposed oblems in m

s formu

ear asi

sThe prices are said to support the efficient allocation. A and CE prices, and let every agent verify that the allocationpractice we will need an auction to provide incentives for agents to reveal the information about their valuations, and to converge towards a set of CE prices.

10.6. Formulating the Winner Determination Problem

In the context of auctions, the problem of computing an allocation is often referred to as the winner-determination problem. In simpwinner is permitted in the allocation, the optimfashion. However, in settings where the allocation rule permits multiple winners, the optimization problem that needs to be solved can become quite computationally complex depending on the market and bid structures. In this section we outline the different settings and the associated complexity of the winner determination problem.

Example: A Combinatorial Reverse Auction

Auctions can be hosted for multiple heterogeneous items, where multiple items can be auctioned together. This is known afor procurement pr arkets with one buyer and multiple sellers. The reverse combinatorial auction i lated as a set covering problem rather than a set packing problem. An interesting (and complicating) issue that arises in this setting is that there are various business rules that are used to constrain the choice of winners. These business rules app side constraints in the winner determ nation problem.

Formulation: Let (1,.., )G N= denote the set of items for sale. The bidders are allowed to specify undles ce on the entire bundle and submit bids for multiple bundles. We

formulate this problem troducing a decision variable S G⊆ with a single pri

by in ( )ix Sb

for each bundle offered by

bidder . Each bidder provides a bid set Let

Si 2G

iB ⊆ ( )ip S denote the price offered by agent for bundle . The winner determination problem with no side constraints can be formulated as follows:

iS

125

( )

S j

m in ( ) ( )

s .t.( ) 1,

( ) 1,

( ) {0 ,1} , ,

ii

i

i

i ix S S B i

iS B

iS B

i

x S p S

x S i

x S j

x S i

∈

∈

∈ ∋

≤ ∀

≥ ∀

∈ ∀

∑ ∑

∑

∑ ∑S

This is posed as a cost minimization problem with a demand covering constraint. In this formulation the problem is to procure a single unit of each good, but this can be generalized by increasing the RHS of the first set of constraints.

Introducing side constraints: In a real world setting there are several considerations beside cost minimization. These considerations often arise from business practice and/or operational considerations and are specified as a set of constraints that need to be satisfied general, the specific form of these side constraints depends on the market structure. Some of these constraints are:

Number of Winning Suppliers: An important consideration while choosing winning bids is to make sure that the entire supply is not sourced from too few suppliers, since this creates a high exposure if some of them are not able to deliver on their promise. On the other hand, having too many suppliers creates a high overhead cost in terms of managing a large number of supplier relationships. These considerations introduce constraints on the minimum, , and maximum, , number of winning suppliers in the solution to the winner determination problem.

SL SU

( ) , i

i i iS B

S i Si

y x S Ky i

L y U∈

≤ ≤ ∀

≤ ≤

N∈∑

∑

Budget Limits on Trades: A common constraint that is often placed is an upper limit on the total volume of the transaction with a particular supplier. These limits could either be on the total spend or on the total quantity that is sourced to a supplier. These types of constraints are largely motivated (in a procurement setting) by considerations that the dependency on any particular supplier is managed. Similarly, often constraints are placed on the minimum amount or minimum spend on any transaction, i.e. if a supplier is picked for sourcing then the transaction should be of a minimum size. Such constraints reduce the overhead of managing a large number of very small contracts.

Marketshare Constraints: Another common consideration, especially in situations where the relationships are longterm, is to restrict the market share that any supplier is awarded. The motivations are similar to the previous case.

Reservation Prices: A reservation price allows the buyer to place an additional constraint on the most she will pay for some items. This can arise, for example, due to a fall-back option such as an external commodity market.

References

[1] Jayant Kalagnanam and David C. Parkes, Auctions, Bidding and Exchange Design, In Handbook of Quantitative Supply Chain Analysis: Modeling in the E-Business Era, David Simchi-Levi, S. David Wu, and Max Shen (eds.), Chapter 5, Kluwer, 2004. http://www.eecs.harvard.edu/econcs/pubs/ehandbook.pdf

[2] MIT Open Course Ware: Auction Theory, http://ocw.mit.edu/NR/rdonlyres/ Engineering-Systems-Division/ESD-260JFall2003/2CECCCEB-0165-42A3-B86A-B4BBA5A6930B/0/l18ch22auctheory.pdf

[3] Roger L. Zhan and Zuo-Jun Max Shen, Optimality and Efficiency in Auctions Design: A Survey (With Z. Shen, to appear in Pareto Optimality, Game Theory and Equilibria, A. Migdalas, P.M. Pardalos, and L. Pitsoulis, eds, Springer (2006), http://plaza.ufl.edu/zhan/paper/zhanShenV2.pdf

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CHAPTER XI Technologies for Supply-Chain Integration

11.1 Introduction

Information and communication technology (ICT) has emerged as the key enabler of supply chain integration. Specifically, the businesses can use the Internet to gain global visibility across their extended network of trading partners and help them respond quickly to a range of variables, from customer demand to resource shortages. This chapter shall discuss the existing and emerging technologies for supply chain integration.

11.2. B2B integration

B2B integration or B2Bi provides a technology framework for supply chain integration. It promises to dramatically transform the way business is conducted between partners, suppliers and customers. All companies (i.e., large, medium, small and new) can experience increased growth and success through tightly integrated partnerships. Companies from across a variety of industries from consumer packaged goods, high technology products, logistics, pharmaceuticals to Chemical, manufacturing and financial services, are embracing B2Bi.

They are realizing the enormous competitive advantage of B2Bi, through faster time to market, reduced cycle times and increased customer service. Through integration of business and technical processes, companies are able to strengthen relationships with service partners and customers, achieve seamless integration inside and outside the enterprise, gain real-time views of customer accounts, increase operational efficiencies and reduce costs.

B2B integration is a challenging task because of the following factors:

• Information formats are becoming more diverse.

• The information space is large and dynamic.

• Semantic integration of data is far from resolved.

• Most systems are autonomous (i.e. their architecture is not centralized).

• The integration needs to be simple, fast, secure and adaptable

Business-to-business integration and enterprise application integration (EAI) are the top priorities of companies these days. B2Bi requires exchange of data and sharing of business processes across multiple trading partners, such as buyers, suppliers and distributors. EAI, on the other hand, requires internal applications, such as CRM, ERP and legacy systems, to interact with each other seamlessly.B2Bi and EAI are accomplished by data, application and business process integration. The integration challenges in both B2Bi and EAI have a lot in common and can be overcome through a single, integrated solution.

Fig 1: B2B Integration at a glance

Fig 2. B2Bi and EAI integrate all internal and external applications

11.3. Middleware technologies for B2B integration

Integration can be conducted at the various layers of an e-commerce system, using various Middleware technologies. Middleware is defined as a set of common business-unaware services that enable applications processes (i.e. components) and end users to interact and interoperate with each other across a network. In essence, middleware is the software that resides above the network and below the business-aware application software. The ideal middleware should mask out the differences in network protocols, operating systems, platforms and programming languages in addition to the

128

quality attributes such as performance, reliability, safety, security and real-time issues. In other terms, the perfect middleware would make the process and the content connectivity among dispersed autonomous systems as if they are components of the same systems.

Fig 3: B2B Interoperability layers

The B2B Integration Framework provides integration at the following levels:

i. Communication Layer (Transport) is concerned with the exchange of messages among partners. Multiple protocols and frameworks have been developed that varies from network level of communication to the distributed objects frameworks. This layer’s interoperability objective is to provide independence from such protocols and frameworks by translating and converting messages between heterogeneous protocols

ii. Content Layer (Data) resolves semantic and structural heterogeneity issues. For example, it determines if a document represents a purchase order or a request for a quote or a product description etc. Structural differences arise from the use of diverse information formats. Semantic differences come from different interpretations of the same concept. For example, a data item called “price” can mean a price that includes or excludes tax. Therefore, this layer’s interoperability objective is to provide independence from data models, formats and languages. Solutions are based on information translation and integration, Examples include wrappers and mediators.

iii. Business Process Layer (Process Flows) deals with the semantics of interactions that correspond to joint business processes. For example, the following steps constitute a joint business process: send order, process order, deliver product, and make payment. This layer resolves issues such as what is the meaning of a message, what actions are allowed, what responses are expected, etc. Therefore, the layer’s interoperability objective is to allow transparent peer-to-peer interactions with any partners. This is a very difficult problem. Amongst potential solutions are: Application Programming Interface (API), Document-based solutions and Workflow-based solutions.

Each of these three layers is discussed in detail below:

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11. 3.1 Communication (transport) level middleware

The term low-level middleware refers to the integration process that is based on the lowest levels of interconnectivity. Two concepts are included at this level: the Network protocols that are used to transfer raw data and the remote procedure call (RPC) as first form of distributed computing framework.

11.3.1.1 Network-protocol level

Early distributed systems were built using few available options to achieve interconnectivity. Those options are using relatively low-level programming and more related to the underlying network protocols. Distributed systems that are built directly over the TCP/IP protocol-suite, have considered these protocols as an underlying middleware. TCP/IP enables the communication of different systems across many operating systems, platforms and programming languages as it is defined for a diverse set of operating systems and platforms. APIs are also defined for many programming languages. Yet this approach needs tremendous programming skills and knowledge of low-level issues of the network. Therefore, it is time consuming, cumbersome and can be error-prone as the programmer has to reinvent the wheel in every program code.

11.3.1.2 Object and component-based middleware

Object Oriented and component-based middleware have become the leading methodologies in developing distributed systems. The different types of middleware presented in this section are best suited for the centralized architecture for B2B distributed systems because they support tightly coupled connectivity. The nature of tightly coupled connectivity of this middleware mandates that the interconnected components be aware of each other object/component interface, therefore, it is not a good choice for inter-enterprise connectivity.

Object Oriented (OO) development approach has emerged steadily as a paradigm that focuses on granularity, productivity and low maintenance. Unlike the procedural paradigm, which is still endorsed by some developers, the OO has proven itself to be an effective development method. This is because it represents real-time entities as objects, therefore, reducing the gap between real-world and software concepts. Nevertheless, OO is not the silver bullet for software development because not everything can be modeled as an object. There should be a more granularity level that allows a number of objects to cooperate to perform an atomic task and to be used as a plug-and- play component in software. The ambition of the component development paradigm is to make the construction and software as simple as possible.

A component is an independent delivery piece of functionality presented as a black-box that provides access to its services through a defined interface. Objects and components have been in use in the distributed computing in such a way that an object or a component can invoke another remote object or component functionality through their interfaces. This requires a middleware environment that implements the communication issues of objects/components interaction. Two variations of such middleware are: Java-based (RMI and EJB) and OMG CORBA.

The platform-independent Java language has now become the dominant language for developing networked systems. The early maturity and stability of Java has led to the development of Java-based frameworks for facilitating the B2B distributed systems development. There are two major Java frameworks: RMI which is developed on top of the TCP/IP and the EJB which is developed on top of the RMI. EJB provides a framework for “plugging in” server components into a larger server application, thereby extending that application’s functionality.

It is intended to hide the low-level system details of managing server resources such as transaction processing, threads management and databases access. It also helps in clearly identifying the

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responsibilities of the client, the server and all other individual components. EJB deals with issues such as scalability, replication, distributed processing, deployment, security, or transactions.

CORBA: One of the most important standards that have emerged from the Object Management Group (OMG) is the Common Object Request Broker Architecture (CORBA). The CORBA’s infrastructure provides mechanisms to deal with platform heterogeneity, transparent location and implementation of objects, interoperability and communication between software components of a distributed object environment. CORBA uses a language-independent Interface Definition Language (IDL), and is therefore more interoperable than Java RMI. However, Java RMI and CORBA distributed objects can be integrated through the Java RMI versions that run on top of the Internet Inter-ORB Protocol (IIOP). CORBA can also be integrated with the EJB because EJB specification does not dictate the protocol used to communicate with an EJB.

11.4. Content level middleware

A data transfer format defines how the data should be formatted in order for different components, not only within the same distributed business system, but also across different systems resides in different companies and technologies, to exchange messages, data, and even documents that they can interpret and understand. Both sender and receiver should agree initially on the shared format. The sender component usually has software (i.e. embedded component) that encodes the contents of the message into a well-defined format that is sent using the middleware protocols to remote object/component. This receiving component interprets the message to retrieve the original application data.

Typical distributed systems components agree on specific data format that each component uses to encode and interpret sent and received data. Having non- standard data formats (incompatible data format) would not allow different components in different system to interact unless extra adapter software is provided to convert between the two different data formats. This difficulty becomes very clear when considering distributed systems integrated with the web. In most situations, the web browser would act as a client that should be able to use the same data format of the system it is interacting with. Having a standard data format would allow new business systems not to bother about how their web clients would interpret exchanged data.

Two major content level middleware are: Electronic Data Interchange (EDI) and a number of XML-based frameworks.

11.4.1 Electronic Data Interchange (EDI) based integration frameworks

Electronic Data Interchange (EDI) is a set of standards for structuring information to be electronically exchanged between and within businesses, organizations, government entities and other groups. The standards describe structures that emulate documents, for example purchase orders to automate purchasing. The term EDI is also used to refer to the implementation and operation of systems and processes for creating, transmitting, and receiving EDI documents.

EDI offers the prospect of easy and cheap communication of structured information throughout the corporate community, and is capable of facilitating much closer integration among hitherto remote organizations. A more careful definition of EDI is 'the exchange of documents in standardized electronic form, between organizations, in an automated manner, directly from a computer application in one organization to an application in another. Despite being relatively unheralded, in this era of technologies such as XML services, the Internet and the World Wide Web, EDI is still the data format used by the vast majority of electronic commerce transactions in the world.

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11.4.1.1 Architecture for EDI

The essential elements of EDI are:

• The use of an electronic transmission medium (originally a value-added network, but increasingly the open, public Internet) rather than the dispatch of physical storage media such as magnetic tapes and disks;

• The use of structured, formatted messages based on agreed standards (such that messages can be translated, interpreted and checked for compliance with an explicit set of rules);

• Relatively fast delivery of electronic documents from sender to receiver (generally implying receipt within hours, or even minutes); and

• Direct communication between applications (rather than merely between computers).

EDI depends on a moderately sophisticated information technology infrastructure. This must include data processing, data management and networking capabilities, to enable the efficient capture of data into electronic form, the processing and retention of data, controlled access to it, and efficient and reliable data transmission between remote sites.

A common connection point is needed for all participants, together with a set of electronic mailboxes (so that the organizations' computers are not interrupted by one another), and security and communications management features. It is entirely feasible for organizations to implement EDI directly with one another, but it generally proves advantageous to use a third-party network services provider.

11.4.1.2 Benefits of EDI

EDI's saves unnecessary re-capture and re-keying of data. This leads to faster transfer of data, far fewer errors, less time wasted on exception-handling, and hence a more stream-lined business process. EDI also eliminates other paper-handling tasks.

Benefits can be achieved in such areas as inventory management, transport and distribution, administration and cash management. EDI offers the prospect of easy and cheap communication of structured information throughout the government community, and between government agencies and their suppliers and clients.

EDI can be used to automate existing processes. In addition, the opportunity can be taken to rationalize procedures, and thereby reduce costs, and improve the speed and quality of services. Because EDI necessarily involves business partners, it can be used as a catalyst for gaining efficiencies across organizational boundaries by reducing business cycles. This strategic potential inherent in EDI is expected to be, in the medium term, even more significant that the short-term cost, speed and quality benefits.

EDI can positively impact customer service factors such as incidence of errors and timeliness. Other advantages accrue to organizations that utilize EDI as an opportunity to rationalize operations via business process.

11.4.1.3 Limitations of EDI

EDI is an established technique for B2B integration. When assessing its role in the various interoperability layers, EDI is more focused on communication interoperability where VANs are used to handle message delivery and routing. EDI standards also provide a single homogeneous solution for content interoperability, but the set of supported document types is limited. This means that EDI is limited to enable a rich set of possible B2B interactions. In addition, EDI standards, as currently defined, do not support interoperability at the business process level

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11.4.1.4 Translating the benefits of EDI into a positive ROI

The benefits of implementing EDI can be both strategic and operational. As an example of the strategic benefits of EDI, the Gartner Group reports that gross sales for a supplier firm to a major retailer increased 18% after the company implemented an EDI program (Gartner Group, 1996).

• Administrative cost reduction: Studies suggest that it costs an average of Rs.30 to manually process a customer order, whereas implementing an EDI-enabled order entry program would reduce that cost to Rs.10 per customer order.

• Personnel Reduction: Studies have shown as much as a 50% reduction in required staff can result from a fully functional EDI implementation (Price Waterhouse, 1995)

• Cycle time reduction: In a case study involving the Pfaltzgraff Company (a supplier) and Best Products (a retailer), a 50% reduction in order cycle time was achieved after implementing an EDI program. (Gartner Group, 1995).

• Inventory Reduction The Gartner Group (1995) reports an average inventory reduction of 10% at EDI-enabled manufacturing firms.

• Cash flow improvement: Decreased operating expenses and improved accuracy in procurement areas are directly beneficial to a firm's financial cash flow. Although this area is difficult to quantify, most accounting departments can attest to the numerous benefits of improved corporate cash flow

9.4.2 XML-based frameworks

The Extensible Markup Language (XML) is an emergent set of open standards in the production and consumption of content managed by the World Wide Web Consortium. XML is a data oriented technology, based on a lightweight subset of the Standard Generalization Markup Language (SGML), suitable for the definition, storage and retrieval of structured data.

XML is inherently language independent. XML brings with it a huge promise in the same way Java did for portability of code; XML claims to do for portability of data. Sun has even been touting the slogan: “Java + XML = Portable Code + Portable Data”. XML is a compromise between the strong rigid structure of databases and the weak semantics and comparative freedom of the HTML. It has been devised with the objective to facilitate sharing of data among applications, both across platforms within the same enterprise and sharing data between enterprises.

11.4.2.1 The Role of Extensible Markup Language (XML) in B2Bi

XML has become the lingua franca of the B2B e-business revolution. It has created a mechanism to publish, share, and exchange data using open standards over the Internet. There is no doubt that in the future XML will be used in each and every B2B application.

XML is not, however, an integration solution in itself - it is just a data definition language. Without global XML standards there can be no seamless business among companies spread out all over the world. These standards are a common set of industry-specific definitions representing business processes. For XML messages to be interpreted by all companies participating in B2Bi they need to agree on a common XML-based B2B standard, which will define the document formats, allowable information, and process descriptions.

The need for industry-wide B2B e-commerce standards in vertical industries is becoming increasingly critical and obvious. Several organizations have been working to define these market-segment-specific definitions. Standards and groups such as RosettaNet, CIDX, and OASIS are making it possible for companies to share information with one another without having to completely reengineer their internal applications. These standards will automate the flow of information across all companies within a given industry, independent of the underlying software or hardware infrastructure supporting the activities related to these transactions

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Inspired by the promising future of being the standard format for data transfer and object communication in the distributed systems, many special purpose frameworks are built around XML making use of its extensibility features.

The Electronic Commerce(eCO) framework aims to address the problem of interoperability by providing standard facilities for businesses to discover each other, determine possible trading relationships, determine capabilities of trading partner systems, and establish trading relationships regardless of the e-commerce standards and protocols.

Commerce XML (cXML) targets non-strategic transactions which are Maintenance, Repair and Operations (MRO) of materials, office supplies, laboratory supplies, etc. It is a simplified, XML and Internet-based version of EDI.

BizTalk’s approach to applications interoperation is based on leveraging several existing standards and technologies including the SOAP, XML and Multipurpose Internet Mail extensions (MIME).

RosettaNet provides a set of XML-based standard interfaces for supply chain management in information technology and electronic component industry.

The Electronic-Business-XML (ebXML) vision is to create a single global electronic marketplace where enterprises of any size and in any geographical location can meet and conduct business with each other through the exchange of XML based messages.

11. 4.2.2 Advantages of XML

• It is a simultaneously human and machine-readable format;

• It is platform independent

• It supports Unicode, allowing almost any information in any written human language to be communicated;

• It can represent the most general computer science data structures: records, lists and trees;

• Its self-documenting format describes structure and field names as well as specific values;

• The strict syntax and parsing requirements make the necessary parsing algorithms extremely simple, efficient, and consistent.

• XML is heavily used as a format for document storage and processing, both online and offline,

• It is based on international standards;

• It allows validation using schema languages such as XSD and Schematron, which makes effective unit-testing, firewalls, acceptance testing, contractual specification and software construction easier;

• The hierarchical structure is suitable for most (but not all) types of documents;

• It manifests as plain text files, which are less restrictive than other proprietary document formats;

• Its predecessor, SGML, has been in use since 1986, so there is extensive experience and software available.

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11.4.2.3 Disadvantages of XML

• XML syntax is redundant or large relative to binary representations of similar data. The redundancy may affect application efficiency through higher storage, transmission and processing costs.

• XML syntax is too verbose relative to other alternative 'text-based' data transmission formats.

• No intrinsic data type support:XML provides no specific notion of "integer", "string", "boolean", "date", and so on.

• The hierarchical model for representation is limited in comparison to the relational model or an object oriented graph.

• Expressing overlapping (non-hierarchical) node relationships requires extra effort.

• XML namespaces are problematic to use and namespace support can be difficult to correctly implement in an XML parser.

11.5. Business process middleware

Business processes are descriptions of the activities required by an organization to fulfill its mission, such as, a business contract or satisfying a specific customer request. Gaining control of these processes allows an organization to reengineer and improve each process or adapt them to changing requirements. A business process is defined as a set of one or more linked procedures or activities which collectively realize a business objective or policy goal, normally within the context of an organizational structure defining functional roles and relationships. A few approaches have emerged for the purpose of facilitating inter-business process interoperability. Among these approaches are:

• Application Programming Interface (API): consists of working out business processes offline, determining the overall connection and coordination of operations and then defining universally-agreed abstract interfaces which provide remote operation invocations and connectors for back-end systems. Middleware and database technologies are then used for mapping these abstract interfaces to physical implementations. Example of such approaches is CORBA-based solutions.

• Document-based solutions: a set of documents is exchanged according to a protocol. There is no prior agreement as partners publish their documents independently. Each document is self-describing and contains enough information about the business process involved. Examples include EDI, BizTalk, eCO and RosettaNet.

• Traditional workflow systems are based on the promise that the success of an enterprise requires the management of business processes in their entirety. Indeed, an increasing number of organizations have already automated their internal process management using workflows and enjoyed substantial benefits in doing so. Current business processes within an organization are integrated and managed either using ERP systems such as SAP/R3, Baan, People-Soft or various workflow systems like IBM’s MQ Series Workflow or integrated manually on demand- basis.

• The Web Services model is emerging steadily as a loosely- coupled, document-based integration framework for Internet-based applications. Unlike traditional workflows, Web services support inter-enterprise workflows where business processes across enterprises can interact in a loosely coupled fashion by exchanging XML document-based messages. The Web Services model idea is to break down web accessible applications into smaller services.

11.5.1 Web Services

"Web Services provide a simplified mechanism to connect applications regardless of the technology or devices they use, or their location. They are based on industry standard protocols with universal vendor support that can leverage the internet for low cost communications, as well as other transport

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mechanisms. The loosely coupled messaging approach supports multiple connectivity and information sharing scenarios via services that are self describing and can be automatically discovered."

These services are accessible through electronic means, namely the Internet. They are self-describing and provide semantically well-defined functionality that allows users to access and perform the offered tasks. Such services can be distributed and deployed over a number of Internet-connected machines.

Fig 4: Web services standards

A service provider is able to describe a service, publish the service and allow invocation of the service by parties wishing to do so. A service requester may request a service location through a service broker that also support service search. Web services are loosely coupled allowing external applications to bind to them. Web services are also reusable allowing many different parties to use and reuse a service provided.

As shown above there are three emerging standards for web services:

1. Simple Object Access Protocol (SOAP) which is an XML-based protocol for exchanging document-based messages across the Internet.An XML-based, extensible message envelope format, with "bindings" to underlying protocols. The primary protocols are HTTP and HTTPS, although bindings for others, including SMTP and XMPP, have been written.

2. Web Services Description Language (WSDL), which is a general purpose XML-based language for describing the interface, protocol bindings and the deployment details of Web services, Typically used to generate server and client code, and for configuration.

3. Universal Description, Discovery and Integration (UDDI) which refers to a set of specifications related to efficiently publishing and discovering information about Web services, to enable applications to find Web services, either at design time or runtime.

In conclusion, Web Services model is trying to use XML as basis to re-invent or improve the integration model by using SOAP at the communication layer and inter-enterprise workflow at the business process layer.

11.5.2 Essential Features of a B2B Integration Solution

1) Firstly, the integration solution should be able to enable any transaction, any time - end-to-end and partner-to-partner. It should be able to fully automate real-time exchange of data between disparate applications.

2) Secondly, the solution should be able to conduct all transactions securely, maintain audit logs, etc.

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3) Thirdly, the solution should support diverse sets of file formats, protocols, and security standards.

4) Fourthly, the solution should be based on open standards that allow a company and its partners to send transactions using any combination of applications and file formats, telecommunication pathways, communication protocols and B2B protocols, and XML standards such as RosettaNet, ebXML, OAG, Biztalk, OBI, etc. The solution should also provide support for Web Services.

5) Lastly, the solution should be scalable, that is, companies should be able to scale it horizontally and vertically. Further, it should offer robust load balancing features, critical to the success of large applications.

A few leading B2Bi solutions include: IBM MQSeries Integrator; Extricity; BEA eLink; webMethods B2B Enterprise; NEON eBusiness Integration Servers; Vitria BusinessWare; and Microsoft BizTalk Server.

11.5.3 Web Services as the essential traits of B2B applications

11.5.3.1 Distributed Transaction Management:

It is very tough to maintain distributed transaction control even within disparate systems and applications within an enterprise. B2B transactions may be spread over disparate systems and applications across different enterprises, making them several times more difficult to maintain and control. In their current state, Web Services are not transactional in nature and provide basic "request/response" functionality.

11.5.3.2 Security:

B2Bi requires two levels of security. Firstly, B2Bi necessitates opening up corporate firewalls to enable cross boundary communication between enterprises. Thus, whatever mode of integration is used, companies have to secure their internal network against malicious attacks through these open ports.

Secondly, the data transmitted over dedicated leased lines, such as EDI, Internet, or any other mode, has to be secured. The data may contain classified information, such as corporate information and business transaction information, and thus cannot be left unguarded. In their current state, Web Services lack broad support and facilities for security. Thus, Web Services based B2Bi architecture may potentially have big security loopholes.

11.5.3.3 Dynamic

For companies to participate in true dynamic business with other companies, integration between the systems of the two companies has to happen in real-time. Further, this integration is only possible if B2Bi is done using open standards over the Internet.

Web Services do provide a dynamic approach to integration by offering dynamic interfaces. Web Services are based on open standards such as UDDI, SOAP, and HTTP, and this is probably the single most important factor that would lead to the wide adoption of Web Services for B2Bi.

11.5.3.4 Integration Mode

The integration mode or pattern is the most important element of B2B integration. Is the B2Bi data-, business process-, application-, function-, or portal-oriented? The answer to this question determines a lot of answers involved in the modalities and technology used for B2Bi. Typically in B2B integration, companies involved take a joint decision based on the technology available in-house, budgets, and level of synchronization needed to support business functionalities.

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In this generation of Web Services, it is possible to achieve only function level integration between applications The next generation of Web Services, however, will be functionally and technologically advanced, offering user interface encapsulation and security. They will be able to package an application and embed it into another application.

11.5.4 Example of Web Services for B2Bi

The following diagram (Fig 5) shows an example of using Web Services in a B2Bi scenario. In this example, the corporate procurement application running within an application server requests quotes from multiple vendors. The procurement application of the buyer gets information about Web Services offered by suppliers using a private UDDI registry and invokes these services over the Internet to get quotes for a specific item.

Fig 5: B2B Use of Web services for procurement

The sequence of steps is as follows:

1. The Buyer's procurement application, running within an application server, has to generate a purchase order for a specific item.

2. The procurement application gets information about Web Services of different suppliers for that specific item by doing a look up in the private UDDI registry.

3. The location of and WSDL binding information for the Web Services is sent to the procurement application.

4. The application invokes the Web Services published by the suppliers to get quotes for that item. The communication is based on SOAP over the Internet.

5. The application receives quotes from different suppliers. The communication is based on SOAP over the Internet.

6. The information is then analyzed, leading to the creation of the purchase order.

11.5.5 EAI and Web Services

As companies move in the direction of collaborative business-to-business e-commerce, they will first have to look inward to their own internal systems, applications and processes. Several business processes span across multiple internal applications. These applications must be able to communicate dynamically in real-time before a company can effectively e-communicate with the outside world.

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B2Bi requires exchange of data and sharing of business processes across multiple trading partners, such as buyers, suppliers and distributors. EAI, on the other hand, requires internal applications, such as CRM, ERP and legacy systems, to interact with each other seamlessly.

Most companies have an environment of disparate legacy systems, applications, processes, and data sources, which typically interact by a maze of interconnections that are poorly documented and expensive to maintain. Web Services are not EAI in and of themselves. Rather, Web Services are just another technology that enables EAI, and it can significantly change the traditional point-to-point approach.

Using Web Services that loosely integrate applications, a company achieves just a subsection of EAI. EAI, on the other hand, takes a complete holistic approach of tightly integrating and connecting all applications and systems that support a company's business. EAI takes years of continued commitment and effort from different business and technical units within the company, high investment, and substantial resources.

The current EAI solutions that predominately focus on integrating applications will have to be changed significantly, as packaged applications in the future will expose their functions as services using technologies such as XML, SOAP, and UDDI. Thus, the EAI solutions will have to provide a broad support for service integration rather than application integration.

11.6. Conclusion

B2B integration is the pervasive enabler of most current business strategies such as collaborative e-commerce, collaborative networks, supply chain management (SCM) and customer relationship management (CRM) across multiple channels of delivery including wireless devices and the Internet.

B2Bi strategy should be laid out and executed in such a way so as to: have an integrated, real-time application-to-application, system-to- system interaction with all the existing and new trading partners; eliminate all manual steps in business processes; conduct secure and real-time commerce transactions over the Internet; have the flexibility to accommodate the different mode of interactions of each partner; and, finally, have the ability to adapt to change quickly and easily in this dynamic age of B2B collaborative e-commerce. This is what B2Bi is all about — the end-to-end automation and integration of cross-organization business processes, data, applications and systems.

Web Services certainly have the potential of redefining the whole paradigm of B2B integration by making it truly dynamic, easily implemented in a modular fashion, and in the longer run being cheaper. The application of Web Services for B2Bi, however, will be limited if services for authentication, encryption, access control, and data integrity are not available. Web Services intermediaries that provide services such as UDDI repository hosting, security services, quality assurance of Web Services, performance checks, etc., will have a big role to play in the B2Bi space.

References

[1] Feras T. Dabous, Fethi A. Rabhi, Pradeep K. Ray and Boualem Benatallah.Middleware Technologies for B2B Integration. The International Engineering Consortium (IEC) Annual Reviews of Communications, IEC Press, USA, Vol 56,July, 2003

[2] Christoph Bussler, The Role of B2B Engines in B2B Integration Architectures, , SIGMOID Record, Vol 31,No. 1, March 2002

[3] Boualem Benatallah, Olivier Perrin, Fethi Rabhi, Claude Godart: Web Service Computing: Overview and Directions. Book chapter. In Handbook of Innovative Computing. Editor: Albert Y. Zomaya. Springer, 2005.

[4] Bussler C, B2B Integration technology architecture,.; Fourth IEEE International Workshop on Advanced Issues of E-Commerce and Web-Based Information Systems, 2002. (WECWIS 2002). Proceedings.

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[5] Jones R., B2B Integration, IET Manufacturing Engineer Vol 80,Issue 4,Aug 2001, Pages 165-168

[6] http://en.wikipedia.org/

[7] http://www.webservicesarchitect.com/content/articles/samtani02.asp

[8] http://www.worldscibooks.com/business/etextbook/p263/p263_chap1.pdf

[9] http://www.webservicesarchitect.com/content/articles/samtani01.asp

[10] http://www.msc-inc.net/Documents/EDI_roi.htm

[11] http://www.webservicesarchitect.com/content/articles/samtani07.asp

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CHAPTER 12 Security and Payment Issues in Integrated Supply Chain

12.1. Introduction

In today’s Internet world, it is relatively easy to create, alter and transmit information. The advancement in computing capacity and interconnectivity has presented a situation where small efforts can cause potentially large losses. Both accidental and intentional breaches are easier and more likely. This is a major challenge to businesses that want to take advantage of the current information technology. Concern for information security is fairly widespread. Those in banking, health care, finance, and telecommunications rate information security as the highest business priority, with retailers a little less concerned. In every sector, security is regarded as a key business driver.

12.2 Need for e-Business security

There is almost an uncountable number of ways that an e-business setup could be attacked by hackers, crackers and disgruntled insiders. Common threats include hacking, cracking, masquerading, eavesdropping, spoofing, sniffing, Trojan horses, viruses, bombs, wiretaps, etc. While the list of actual manifestation is long, conceptually, they break down to a few categories. These are spoofing, unauthorized disclosure, unauthorized action, and data alteration. From a business perspective Denial of Service (DoS) attacks appear to be the most serious threat. DoS attacks consist of malicious acts that prevent access to resources that would otherwise be available. Even though data may not be lost, the financial losses that could be incurred from not being able to supply a service to customers could be of much higher value.

In conducting e-business, every organization ought to be able to:

• Positively identify or confirm the identity of the party they are dealing with on the other end of the transaction;

• Determine that the activities being engaged in by an individual or machine is commensurate with the level of authorization assigned to the individual or machine;

• Confirm the action taken by the individual or machine and be able to prove to a third party that the entity (person or machine) did in fact perform the action;

• Protect information from being altered either in storage or in transit;

• Be certain that only authorized entities have access to information;

• Ensure that every component of the e-business infrastructure is available when needed;

• Be capable of generating an audit trail for verification of transactions.

12.3 e-Business Security Categories

Effective information security policy must have the following six objectives: confidentiality; integrity; availability; legitimate use (identification, authentication, and authorization); auditing or traceability; and non-repudiation. If these objectives could be achieved, it would alleviate most of the information security concerns. Each information security objective is discussed below with emphasis on the specific challenges it poses to Internet mediated businesses.

12.3.1 Confidentiality: Confidentiality deals with protecting the content of messages or data transmitted over the Internet from the unauthorized people. For example, it is essential for you to

protect your credit card number from the hackers. Besides other e-business setups, concern for confidentiality is also major concern in healthcare, insurance, and banking industry. To maintain the confidentiality of Web users’ information, organizations have to find ways to keep the information from unauthorized view. From an operational point of view, that means information that is stored has to be secured in a way that it can only by accessed by authorized parties. Similarly, information in transit has to be kept from the view of unauthorized parties and that it is retrieved only by a legitimate entity.

12.3.2 Integrity: Integrity is related to preventing data from being modified by an attacker. Transmitting information over the Internet (or any other network) is similar to sending a package by mail. The package may travel across numerous trusted and untrusted networks before reaching its final destination. It is possible for the data to be intercepted and modified while in transit. This modification could be the work of a hacker, network administrator, disgruntled employee, government agents or corporate business intelligence gatherer; it could also be unintentional.

12.3.3 Availability: Availability means that systems, data, and other resources are usable when needed despite subsystem outages and environmental disruptions. Lack of availability is essentially loss of use. The most commonly known cause of availability problems is Denial of Service (DoS) attacks even though there are other common causes such as outages, network issues, or host problems. The goal is to ensure that system components provide continuous service by preventing failures that could result from accidents or attacks. From a security point of view, availability is enhanced through measures to prevent malicious denials of service. Closely related to availability and very important to e-businesses are reliability and responsiveness. Reliability implies that a system performs functionally as expected. Responsiveness is a measure of how quickly service could be restored after a system failure. In other words, it is a measure of system survivability.

12.3.4 Legitimate use: Legitimate use has three components: identification, authentication and authorization. Identification involves a process of a user positively identifying itself (human or machine) to the host (server) that it wishes to conduct a transaction with. The most common method for establishing identity is by means of username and password. The response to identification is authentication. Without authentication, it is possible for the system to be accessed by an impersonator. Authentication needs to work both ways: for users to authenticate the server they are contacting, and for servers to identify their clients. Authentication usually requires the entity that presents its identity to confirm it either with something the client knows (e.g. password or PIN), something the client has (e.g. a smart card, identity card) or something the client is (biometrics: finger print or retinal scan). Biometric authentication has been proven to be the most precise way of authenticating a user's identity. However, biometric processes such as scanning retina or matching fingerprints to one stored in a database are often considered intrusive, and there always exists some measure of fear that this information will be misused.

The approach to authentication that is gaining acceptance in the e-business world is by the use of digital certificates. A digital certificate contains unique information about the user including encryption key values. These public/private encryption key pairs can be used to create hash codes and digitally sign data. The authenticity of the digital certificate is attested to by a trusted third party known as a "Certificate Authority." The entire process constitutes Public Key Infrastructure.

Once an entity is certified as uniquely identified, the next step in establishing legitimate use is to ensure that the entity’s activities within the system are limited to what it has the right to do. This may include access to files, manipulation of data, changing system settings, etc. A secured system will establish very well defined authorization policy together with a means of detecting unauthorized activity.

12.3.5 Auditing or Traceability: From an accounting perspective, auditing is the process of officially examining accounts. Similarly, in an e-business security context, auditing is the process of examining transactions. Trust is enhanced if users can be assured that transactions can be traced from origin to completion. If there is a discrepancy or dispute, it will be possible to work back through each step in the process to determine where the problem occurred and, probably, who is responsible. Order confirmation, receipts, sales slips, etc. are examples of documents that enable traceability. In a well-

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secured system, it should be possible to trace and recreate transactions, including every subcomponent, after they are done. An effective auditing system should be able to produce records of users, activities, applications used, system settings that have been varied, etc., together with time stamps so that complete transactions can be reconstructed.

12.3.6 Non-repudiation: It is an attribute of secure system that prevents the sender of a message from denying having sent it.Non-repudiation is the ability of an originator or recipient of a transaction to prove to a third party that their counterpart did in fact take the action in question. Thus the sender of a message should be able to prove to a third party that the intended recipient got the message and the recipient should be able to prove to a third party that the originator did actually send the message. This requirement proves useful to verify claims by the parties concerned and to apportion responsibility is cases of liability. Obviously, this is a crucial requirement in any business transaction when orders are placed and both buyers and sellers need to be confident that not only are they dealing with the appropriate parties but also that they have proof to support the claims of any action taken in the process. Non-repudiation protocol is also useful in forensic computing where the goal is to collect, analyze and present data to a court of law.

12.4 Introduction to Security Technology 12.4.1 Cryptography Cryptography is a technique by which data, called plaintext, is scrambled or encrypted in such a way that it becomes extremely difficult, expensive and time consuming for an unauthorized person to unscramble or decrypt it. The encrypted text is called the ciphertext. The steps in Cryptography

Encryption: Sender of a message Msg uses an encryption algorithm Encrypt and an encryption key keye to generate the encrypted version of the message EncryptedMsg EncryptedMsg = Encrypt ( Msg, keye)

Decryption: The receiver of EncryptedMsg uses an decryption key keyd to regenerate the message Msg. Msg = Decrypt (EncryptedMsg, keyd)

Cryptographic algorithms can be classified into two broad classes: symmetric key cryptography or asymmetric key cryptography. If keye= keyd in the above encryption and decryption algorithm then the corresponding the algorithm is called a Symmetric Key Algorithm. Example of such algorithms are DES (Data Encryption Standard), TDES, IDEA, RC2, RC4, and RC5. These algorithms can be implemented either in software form or in hardware form. The hardware implementation is typically 100 times faster than the software implementation. The major problem associated with the symmetric key algorithm is key distribution. The keys are to be securely distributed before starting the actual secure communication. Symmetric key algorithm cannot be used for authentication or non-repudiation of the communication process. This is another disadvantage. If keye is not equal to keyd then the corresponding algorithm is called an asymmetric key cryptography algorithm. The entities wishing to use the algorithm must posses a pair of keys: a public and a private key. The public key is known to all the outside entities. RSA (named after its inventors: Ron Rivest, Adi Shamir and Leonard Adleman) is an example of an asymmetric cryptography algorithm. Asymmetric key algorithms are much slower than symmetric key cryptographic algorithms. For example, RSA is 100 times slower than DES. Private Key operation time grows with k3 where as the private key operation time grows with k2, where k is the length of the key in bits. In real-life situations RSA is never used for bulk data transfer. Rather it is used for bulk encryption key (symmetric key) exchange.

12.4.2 Hash Function A hash function is a reproducible method of turning some kind of data into a (relatively) small number that may serve as a digital "fingerprint" of the data. The algorithm substitutes or transposes

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the data to create such fingerprints. The fingerprints are called hash sums, hash values, hash codes or simply hashes. In cryptography, a cryptographic hash function is a hash function with certain additional security properties to make it suitable for use as a primitive in various information security applications, such as authentication and message integrity. A hash function takes a long string (or 'message') of any length as input and produces a fixed length string as output, sometimes termed a message digest or a digital fingerprint. In various standards and applications, the two most-commonly used hash functions are MD5 and SHA-1. Properties of the Hash function are:

• It should be easy to compute h(Msg), where, Msg is the message to be sent. • It should be hard to obtain Msg given h(Msg) • It should be very hard to find another message Msg/ such that h(Msg) = h(Msg/)

12.4.3 Digital signature In cryptography, a digital signature or digital signature scheme is a type of asymmetric cryptography used to simulate the security properties of a signature in digital, rather than written, form. Digital signature schemes normally give two algorithms, one for signing which involves the user's secret or private key, and one for verifying signatures which involves the user's public key. The output of the signature process is called the “digital signature”. Digital signatures, like written signatures, are used to provide authentication of the associated input, usually called a "message." Messages may be anything, from electronic mail to a contract, or even a message sent in a more complicated cryptographic protocol. The process of digital signature generation and verification is shown below.

Msg

Hashfunction

MD

Msg

Hashfunction

MD

Encryption Decryption MD

=?

Msg

MD

Msg

MDInternet

A’s Private Key A’s Public Key

Site A Site BMessage Sent to BMessage Received

from A

MD4MD5SHASHA-1

Digital Signature Generation Digital Signature Verification

Figure 1: digital signature generation and verification process Digital signatures are used to create public key infrastructure (PKI) schemes in which a user's public key (whether for public-key encryption, digital signatures, or any other purpose) is tied to a user by a digital identity certificate issued by a certificate authority. PKI schemes attempt to unbreakably bind user information (name, address, phone number, etc.) to a public key, so that public keys can be used as a form of identification. 12.4.4 TLS / SSL Protocol Transport Layer Security (TLS) and its predecessor, Secure Sockets Layer (SSL), are cryptographic protocols which provide secure communications on the Internet for such things as web browsing, e-mail, Internet faxing, instant messaging and other data transfers. There are slight differences between SSL 3.0 and TLS 1.0, but the protocol remains substantially the same. The TLS protocol(s) allow

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applications to communicate across a network in a way designed to prevent eavesdropping, tampering, and message forgery. TLS provides endpoint authentication and communications privacy over the Internet using cryptography. Typically, only the server is authenticated (i.e., its identity is ensured) while the client remains unauthenticated; this means that the end user (whether an individual or an application, such as a Web browser) can be sure with whom they are communicating. The next level of security — in which both ends of the "conversation" are sure with whom they are communicating — is known as mutual authentication. Mutual authentication requires public key infrastructure (PKI) deployment to clients. As explained in the figure below, TLS involves two basic protocols with following functions: TLS Handshake protocol

• Peer negotiation for algorithm support • Public key encryption -based key exchange and certificate-based authentication

TLS record protocol • Symmetric cipher -based traffic encryption

HTTP

TLS Handshake Protocol

TLS Record Protocol

TCP

TLS

-Negotiation of cryptographic and com pression algorithm s-Exchange of secrets through PK-Generation of secrete key

-Encryption/decryption-M essage authentication-Com pression/Decom pression

Figure 2: TLS protocol in context

12.5 Public Key Infrastructure -Solution to the e-Business security

Public Key cryptography supports security mechanisms such as confidentiality, integrity, authentication, and non-repudiation. To successfully implement these security mechanisms, an infrastructure to manage them should be planned. A public key infrastructure(PKI) is a foundation on which other applications, system, and network security components are built. In this chapter first we will discuss about PKI and then some secure protocols which are used in e-Business transactions.

12.5.1 PKI Framework

The framework of a PKI consists of security and operational policies, security services, and interoperability protocols supporting the use of public-key cryptography for the management of keys and certificates. The generation, distribution, and management of public keys and associated certificates normally occur through the use of Certification Authorities (CAs), Registration Authorities (RAs), and directory services, which can be used to establish a hierarchy or chain of trust. CA, RA, and directory services allow for the implementation of digital certificates that can be used to identify different entities. The purpose of a PKI framework is to enable and support the secured exchange of data, credentials, and value (such as monetary instruments) in various environments that are typically insecure, such as the Internet.

A major benefit of a PKI is the establishment of a trust hierarchy because this scales well in heterogeneous network environments. Entities that are unknown to one another, each individually establish a trust relationship with a CA. The CA performs some level of entity authentication, according to its established rules as noted in its Certificate Practices Statement or CPS, and then

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issues each individual a digital certificate. That certificate is signed by the CA and thus vouches for the identity of the individuals. Unknown individuals can now use their certificates to establish trust between them because they trust the CA to have performed an appropriate entity authentication, and the CA's signing of the certificates attests to this fact.

A hierarchical trust model represents the most typical implementation of a PKI. In its most simple instantiation, this trust model allows end entities’ certificates to be signed by a single CA. In this trust model, the hierarchy consists of a series of CAs that are arranged based on a predetermined set of rules and conventions For example, in the financial services world, rather than have a single authority sign all end entities’ certificates, there may be one CA at a national level that signs the certificates of particular financial institutions. Then each institution would itself be a CA that signs the certificates of their individual account holders. Within a hierarchical trust model there is a trust point for each certificate issued. In this case, the trust point for the financial institution's certificate is the national or root CA. The trust point for an individual account holder is their institution's CA. This approach allows for an extensible, efficient, and scalable PKI.

12.5.2 Security Services

The principal business objectives and risk management controls that can be implemented by a PKI are presented in this section:

Confidentiality: Confidentiality means ensuring that the secrecy and privacy of data is provided with cryptographic encryption mechanisms. Encryption of data is possible by using either public (asymmetric), or secret (symmetric) cryptography. Since public key cryptography is not as efficient as secret key cryptography for data encipherment, it is normally used to encipher relatively small data objects such as secret keys used by symmetric based encryption systems. Symmetric cryptographic systems are often incorporated into PKIs for bulk data encryption; thus, they are normally the actual mechanism used to provide confidentiality.

Integrity: Integrity means ensuring that data cannot be corrupted or modified and transactions cannot be altered. Integrity can be provided within a PKI by the use of either public (asymmetric), or secret (symmetric) cryptography. An example of secret key cryptography used for integrity is DES in Cipher Block Chaining mode where a Message Authentication Code (MAC) is generated. In the PKI environment, using symmetric cryptographic systems for implementing integrity does not scale particularly well. Public key cryptography is typically used in conjunction with a hashing algorithm such as SHA-1 or MD5 to provide integrity.

Authentication: Authentication means verifying that the identity of entities is provided by the use of public key certificates and digital signature envelopes. Authentication in the e-commerce environment is performed very well by public key cryptographic systems incorporated into PKIs. The primary goal of authentication in a PKI is to support the remote and unambiguous authentication between entities unknown to each other, using public key certificates and CA trust hierarchies. Authentication in a PKI environment relies on the mathematical relationship between the public and private keys. Messages signed by one entity can be tested by any relying entity. The relying entity can be confident that only the owner of the private key originated the message, because only the owner has access to the private key.

Non-Repudiation: Non-repudiation means ensuring that data cannot be renounced or a transaction denied. This is provided through public key cryptography by digital signing. Non-repudiation is a by-product of using public key cryptography. When data is cryptographically signed using the private key of a key pair, anyone who has access to the public key of that pair can determine that only the owner of the key pair itself could have signed the data in question. For this reason, it is paramount that end entities secure and protect their private keys used for digitally signing data.

12.5.3 PKI Logical Components

Different logical components comprise a PKI. The following outlines the typical logical components in a PKI:

• End entities or subscribers

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• Certificate authorities

• Certificate policies

• Certificate practices statement

• Hardware security modules

• Public key certificates

• Certificate extensions

• Registration authorities

• Certificate depositories

End Entities or Subscribers: An end entity or subscriber is any user or thing, including inanimate objects, such as computers that have a need for a digital certificate to identify them for some reason. The end entity normally must have the capacity to generate a public/private key pair and some means of securely storing and using the private key. By definition, an end entity is not a CA.

Certificate Authorities: A Certificate Authority plays a critical role in a PKI. According to the IETF, a CA is “an authority trusted by one or more users to create and assign public key certificates.” [Internet X.509 Public Key Infrastructure PKIX Roadmap, March 10, 2000]. CA functions as a trusted third party and provides various key management services A CA’s public keys must be distributed to all entities that trust the CA’s certificates. If a CA is a Root CA, that is, at the top of the trust hierarchy and has no superior CA to vouch for it, then the CA must distribute its public keys as self-signed certificates with an acceptable key certificate format and distribution protocol. The CA must also make its clear text public keys available, so that relying entities can resolve the self-signed certificates.

Certificate Policy: A primary tenet of e-commerce security is the CP statement. The CP statement provides the overall guiding principles that an organization endorses regarding who may do what and how to systems and data. A CP also specifies how controls are managed. In addition, a CP names a set of rules that indicates the applicability of a public key certificate to a particular community or class of applications with common security requirements.

Certificate Practice Statement: The details of a policy statement should be published in a Certificate Practices Statement or CPS. The CPS is a statement of the practices that a CA employs in issuing public key certificates. The CPS document enumerates the procedural and operational practices of a PKI. The CPS should detail all processes within the life cycle of a public key certificate including its generation, issuance, management, storage, deployment, and revocation. The CPS should also specify the original entity authentication process that an end entity must be validated through before participating in a PKI. The objective of the CPS is to instill trust in the PKI such that the user community at large will have sufficient confidence to participate in it.

Hardware Security Modules: Hardware Security Modules (HSMs) are another primary component of a CA. A CA must instill trust in not only its client base but also in those who rely upon the certificates issued to subscribers. Since that trust is predicated upon the security and integrity of the CA's private keys used to sign the public key certificates of subscribers, it is necessary that those private keys be secured as best as possible. For this reason, CAs should only store and use their private keys in specialized computer equipment known as HSMs. HSMs are also known as Tamper Resistant. Various standards are used to categorize HSMs, for example, FIPS-140-1.

Public Key Certificates: A CA’s primary purpose is to support the generation, management, storage, deployment, and revocation of public key certificates. A public key certificate demonstrates or attests to the binding of an end entity’s identity and its public key. The basic constructs of a certificate should include the name of an entity, identifying information about the entity, expiration period for the certificate, and the entity’s public key. Other additional and useful information may be included in a certificate: serial numbers, the CA's name, the CA's public key certificate itself, the type of algorithms used to generate and verify the keys and certificate, and any other information that the CA generating

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the certificate considers useful. The most widely used format for digital certificates are those based on the IETF X.509 standards

Certificate Extensions: Certificate extensions provide additional information within a certificate and allow them to be tailored for the particular needs of an organization. Certificate extensions can affect the interoperability of certificates if a relying party does not recognize the structure or content of the certificate extensions. The types of information that may be found in a certificate extension include: policy, usage, revocation, and naming data, which provide particular details unique to an organization’s PKI.

Registration Authorities: A Registration Authority (RA) is an optional but common component of a PKI. An RA is used to perform some of the administrative tasks that a CA would normally undertake. Most importantly, an RA is delegated, with the CA’s explicit permission, the authority to perform tasks on behalf of the CA. The primary purpose of an RA is to verify an end entity’s identity and determine if an end entity is entitled to have a public key certificate issued. The RA must enforce all policies and procedures defined in the CA’s CP and CPS.

Certificate Depositories: A certificate depository, sometimes referred to as a certificate directory, is also an optional but common component of a PKI. A certificate depository may be an efficient solution for closed systems (e.g., intranet) or those in isolated processing environments (e.g., chipcard-based applications) where the Root CA public key is distributed locally or revocation lists are stored locally.

Certificate distribution can be accomplished by simply publishing certificates in a directory controlled by a CA or RA. When the directory is controlled by the CA or RA, the certificate distribution process is greatly simplified. Rather than trying to distribute every certificate to a unique point, the CA simply updates the directory. A critical factor is that only the CA must have the authority to update or modify the directory, but the directory must be publicly readable. LDAP is a good example of a simple and efficient standards based directory format and protocol that can be used for certificate distribution.

12.5.4 PKI Functions

This basic processes which are common to all PKIs are:

• Public key cryptography – Includes the generation, distribution, administration, and control of cryptographic keys.

• Certificate issuance – Binds a public-key to an individual, organization, or other entity, or to some other data—for example, an email or purchase order.

• Certificate validation – Verifies that a trust relationship or binding exists and that a certificate is still valid for specific operations.

• Certificate revocation – Cancels a previously issued certificate and either publishes the cancellation to a Certificate Revocation List or enables an Online Certificate Status Protocol process.

12.5.5 Public Key Certificate Binding

To participate in a PKI, an end entity must enroll or register in a PKI. The result of this process is the generation of a public key certificate. The binding is declared when a trusted CA digitally signs the public key certificate with its private key. To generate a certificate, the CA performs the following steps.

1. Acquire a public key from the end entity.

2. Verify the identity of the end entity.

3. Determine the attributes needed for this certificate, if any.

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4. Format the certificate.

5. Digitally sign the certificate data.

Acquiring the Public Key: Depending on the value proposition and business risks associated with the issuance of a public key certificate, the CA may take basic measures when obtaining the required identification and certificate information. For example, the CA may allow it to be sent electronically over the Internet, or the CA could use more sophisticated means such as mandating out of band manual methods (e.g., using bonded couriers). The type and integrity of the credentials requested by the CA during the enrollment process also depends on the intentions of the CA.

Verify the Identity of the End Entity: Depending on the business model in place and the amount of reliance on the public key certificates, the CA may take simple measures to authenticate the end entity. In any case, the most important factor in the binding process is to ensure that an entity’s identity is verified unambiguously.

Formatting the Certificate: Before the certificate is signed by the CA, all data to be placed into the certificate is collected and formatted. The specific data content and format of a public key certificate can vary depending on the needs of the PKI.

Signing the Certificate: Finally, the certificate is digitally signed under the private key of the CA used for signing certificates. Once signed it can be distributed and/or published using different vehicles.

Certificate Validation: Any digital certificate issued by a CA will be valid only for a specified amount of time mentioned in the certificate. After this period the use of certificate is no longer valid. So before using any certificate its expiry date must be verified. Along with the expiry date validation the certificate must also be validated whether the certificate has been signed by the appropriate certifying authority.

Certificate Revocation: Although public key certificates are issued for a fixed period of time before they become void, situations can arise where they are no longer trustworthy and thus must be prematurely expired. This is known as certificate revocation. Certificate revocation must be initiated by the CA or their delegate such as an RA, which originated the end entity's certificate. The predominant vehicle for certificate revocation is known as a Certificate Revocation List or CRL. A CRL is a list generated by the CA that contains unique information about the revoked certificates which enables relying entities to determine if a certificate is valid or not. A CRL must be published in a publicly available repository or directory.

12.6. Electronic Payment Systems

A Payment System is a mechanism that facilitates transfer of value between a payer and a beneficiary by which the payer discharges the payment obligations to the beneficiary. Payment system enables two-way flow of payments in exchange of goods and services in the economy. An electronic payment system is needed for compensation for information, goods and services provided through the Internet - such as access to copyrighted materials, database searches or consumption of system resources - or as a convenient form of payment for external goods and services - such as merchandise and services provided outside the Internet. It helps to automate sales activities, extends the potential number of customers and may reduce the amount of paperwork.

12.6.1 Business to Business Payment Systems

Changes in the marketplace for business-to-business (B2B) payments increasingly demand executives' attention. Growing numbers of e-payments, decreasing paper check volumes, and new legislation are the main motivators. Businesses are beginning to realize they must add new payment options to a process still dominated by paper checks, wire transfers, and automated clearinghouse (ACH) transactions.

B2B payments involve up to five distinct components:

The buyer - purchases goods and services;

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The buyer's financial institution - provides banking services to the buyer;

The supplier - provides goods and services;

The supplier's financial institution - provides banking services to the supplier; and

The Federal Reserve or other check-clearing institution - facilitates the check-sorting process.

Figure 3 outlines the traditional B2B check-clearing process in which a buyer writes a check that subsequently flows through the supplier, the supplier's bank, the Federal Reserve (or other check-clearing institution), the buyer's bank, and finally back to the buyer. This process is supported by human intervention throughout the supply chain, including manual entry of check amounts and associated transaction data.

Figure 3: B-to-B Payment traditional Vis-à-vis electronic

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Shifting payment processes to an electronic format requires an IT infrastructure to route payments and related data through an integrated network. Using such end-to-end infrastructure poses a significant adoption challenge because users' technological sophistication with regard to the payment supply chain is so varied. Creating uniformity in the e-payment infrastructure is further complicated by the fact that the standards of the Federal Reserve infrastructure apply to Fed products but not to products outside the Fed's domain. Therefore, building the infrastructure necessary to securely and reliably transmit e-payments and related transaction information among a large group of diverse users is a significant obstacle and expense. However, the result of an improved payment network, as in Figure 3, is a streamlined process supporting the continuous exchange of information and funds.

12.6.1.1 B-to-B E-Payment Options

Three notable e-payment technologies promise to simultaneously address key adoption challenges, accelerate B2B e-payment adoption, and deliver across multiple value dimensions: Automated clearing house (ACH) -based bank proprietary e-payment platforms; Enhanced purchasing card technology; and Open network systems.

ACH-Based Bank Platforms: ACH-based transactions (such as for direct deposit of employee salaries) have existed in one form or another since the early 1970s. Bank proprietary systems are e-payment solutions built on the ACH network. In the past decade, banks have invested heavily in developing software packages that have improved the functionality of B2B ACH transactions. They now combine financial processing and electronic data warehousing capabilities into one comprehensive service. Buyers and suppliers send complete trade information (including contracts, pricing, orders, receipts, and invoices) to bank systems; the information is then stored in a document available to both parties. These platforms seek to provide comprehensive electronic bill payment and presentment from the beginning to the end of the payment cycle.

From the standpoint of value attribute comparisons, ACH-based payment systems meet many of the requirements identified by B2B transaction participants. The importance of these attributes is the foundation of historical ACH popularity in facilitating relatively small recurring financial transactions between businesses and consumers. One particularly troublesome issue is the relatively high implementation costs of ACH-based platforms, particularly for small- and medium-size businesses. This issue further complicates systems-integration problems. Another significant issue plaguing ACH-based solutions is that they rely on bilateral closed networks. This means that businesses must share sensitive financial information, in turn increasing the risk of fraud. Global coverage is another concern with any form of ACH-based payment, as ACH systems are typically built for domestic use. Due to the foregoing, along with the lack of other desired characteristics, ACH-based e-payment systems have not gained significant traction in the B2B e-payment market. Despite this, bank proprietary solutions are well suited for large corporations and government agencies that execute relatively low- to medium-value recurring payments within the country.

Enhanced Purchasing Cards: Enhanced purchasing cards, or p-cards, (such as MasterCard's e-P3) are e-payment solutions that offer management services for B2B purchasing, presentment, and payment. This e-payment strategy is the result of collaboration between outside software vendors and existing p-card issuers to provide businesses with customized end-to-end e-commerce. P-card solutions allow buyers and sellers to collaborate within a common environment, streamline payment processes, and use p-cards for payment. Businesses transmit orders electronically, view the status of orders and invoices online, control the initiation of payments, and integrate data into existing financial systems.

Enhanced p-cards share many of the positive characteristics of desirable e-payment networks. They are typically Web-based and relatively easy to integrate with legacy software and hardware environments. This ease of integration greatly reduces the cost and complexity of implementation. Another p-card benefit is reduced potential for fraud and credit loss, due to enhanced settlement processes. P-card transactions settle through an online "credit gateway" associated with the p-card issuer that allows institutions to exchange encrypted information for authorization purposes. These systems provide a secure environment for payments because sensitive account information is not exchanged directly, and transactions are easily traceable and challenged when disputes arise.

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One significant disadvantage of enhanced p-cards is that interchange fees typically still apply. This means that suppliers may be averse to accepting the enhanced p-card for medium- to large-value transactions, as fees increase as the dollar value of the transaction increases. Given these benefits and limitations, the enhanced p-card represents a good way for current users of p-cards and prospective e-payment-orientated businesses to conduct low-value nonrecurring transactions.

Open Network Systems: Open network systems, like Visa's "Visa Commerce" platform, are rules-based e-payment systems that utilize open, secure, global-settlement networks to process buyer-initiated payments. Open networks interface directly with front-end procurement and accounts-payable systems to provide buyers and suppliers alike with a seamless, integrated corporate payment solution. Open networks also enable buyers to initiate and settle payments based on preestablished terms with suppliers.

Unlike enhanced p-card and ACH-based platforms, open networks employ a flexible fee schedule, allowing for the minimization of fees for relatively high-dollar-amount transactions. In addition, open-network solutions may be easier to integrate with legacy environments. For example, Visa Commerce was designed to integrate with existing procurement applications, regardless of their sophistication. This integration strategy means that financial institutions, buyers, and suppliers that want to use an open network can do so with minimal up-front investment. Finally, open networks typically offer global coverage, a major consideration for moderate-to-large-size firms. Open network payments appear to be best suited for relatively large-value, multiple-invoice directed payments, as they circumvent interchange fees and support robust transaction data.

12.6.1.2 Challenges with B-to-B E-Payment Systems

Four main challenges confront businesses interested in adopting e-payment processes: systems integration; the absence of remittance standards; security; and uncertainty in the return on investment; in addition, candidate technologies must be considered in light of their ability to deliver value in multiple dimensions.

Systems integration: The most daunting barrier to the adoption of e-payments is a lack of integration across the many systems needed to support the process.

Remittance standards: Complicating these challenges is a lack of even minimum standards pertaining to e-payment data. Early adopters of e-payment technologies still complain that inconsistencies in remittance standards have resulted, in some cases, in the corruption of important remittance data, including invoice numbers, and other information, including payment amounts and payer identification.

Security: Since B2B transactions involve multiple parties, they flow across diverse technology architectures. Each party in an electronic transaction is subject to the security procedures of other members in its financial supply chain. Arguably, paper-based payments are subject to similar security challenges, though they are well understood and have long been accounted for.

Value proposition uncertainty: E-payment networks (like other networks) are subject to "network externalities," so the value of participation is contingent on the size of the network itself; that is, the greater the number of participants, the more valuable the network is to each participant. Given that the technical aspects of e-payment networks are still evolving, it has been difficult for potential participants to estimate the value of joining and assess the appropriate level and speed of the related investment. Investment decisions in this context are influenced by businesses' perceptions of the likelihood of agreement on the issues related to standards and integration. The value proposition of e-payment networks is further clouded by the realization that connecting accounts for only a portion of the total cost.

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12.6.2 Business to Consumer Payment Systems

Business-to-consumer is a form of electronic commerce in which products or services are directly sold from a firm or company to a consumer without any intermediation. Internet has been proved to be an effective medium for direct selling. B 2 C on-line payment occurs when the enterprise (the seller) and the individual (the buyer) settle their transaction on the E-business web site on the Internet and the bank provide them with on-line fund settlement service. Traditional B-to-C payment services can be cash based, cheque based or credit card based payment. Out of these mechanisms, credit card based payment is the most popular.

Credit Card Based Payment: Ideally Internet based credit card payment must works in a similar way like that of the traditional credit card based system. However, security becomes the primary concern in an Internet based system. The credit card details once submitted to the merchant’s Web site can be reused by the merchant himself. The protocol like SET (Secure Electronic Transaction) helps preventing this kind of situation. SET is pragmatic approach that paves the way for easy, fast and secure transaction over the internet. That transaction is initiated with a handshake, with the merchant authenticating itself to the payer and fixing all payment data. The payer then uses a sophisticated encryption scheme to generate payment slip. The goal of the encryption scheme is to protect the sensitive payment information such as credit card number. Next the payment slip is signed by the payer and is sent to the merchant. The merchant sends the slip to its acquirer gateway, to authorize and capture payment. The acquirer checks all signatures and the slip, verifies the credibility of the payer and sends either a positive or a negative signed acknowledgement back to merchant and the payer. SET however is not widely accepted as it is not computationally efficient and requires heavy investment. Another alternative is to use the Web sites of the Trusted Third Parties to submit credit card details (Ex. Paypal).

Cheque-like System: Electronic cheques work in a similar way like that of paper cheques. The customer need a wallet software to store information on the browser about the checking account. During cheque payment the customer sends encrypted customer’s payment information with banks public key and other information for the merchant encrypted with the customer’s private key to the merchant. The merchant sends the payment information to the appropriate bank and get the fund transferred to its account. CyberCash is one example of such a chequing system.

Digital Cash: In a digital cash system, user can withdraw e-cash coins from a bank and use them to pay other users. Digital cash is kept as computer files containing huge random numbers digitally stamped by the bank, i.e. encrypted with banks private key followed by customer’s pubic key. They are maintained by the customer’s wallet software. Digital cash can be classified as identifiable or anonymous depending on whether it requires the identity of the spender. Depending on the direct participation of the bank it can be classified as online or offline. One major problem associated the money when it is both anonymous and Offline is the problem of double spending. In case of identifiable Electronic Money a serial number is provided by the bank for each of the computer file. Example of this type of money is CyberCash. In case of anonymous electronic money the serial number is created and blinded by the customer. It is subsequently signed by the bank. DigiCash is one such example. Online Electronic Money is validated online with active participation of the bank (CyberCash) whereas offline money is not validated online during the transaction. This scheme is appropriate for peer to peer transaction where bank need not be an active participant (DigiCash).

12.7. Conclusion

E-business depends on providing customers, partners, and employees with access to information, in a way that is controlled and secure. Managing e-business security is a multifaceted challenge and requires the coordination of business policy and practice with appropriate technology. In addition to deploying standards bases, flexible and interoperable systems, the technology must provide assurance of the security provided in the products. As technology matures and secure e-business systems are deployed, companies will be better positioned to manage the risks associated with disintermediation of data access. Through this process businesses will enhance their competitive edge while also

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working to protect critical business infrastructures from malefactors like hackers, disgruntled employees, criminals and corporate spies.

The volume of e-payment activity has risen steadily since 1979 and shows no sign of slowing down. Motivating this trend has been consumer willingness to submit payments electronically. Increasingly, businesses have sought to improve their understanding of the value of e-payment systems. Businesses that identify and adopt effective e-payment strategies are more likely to realize streamlined business processes and significant bottom-line savings compared to their peer organizations that don't. Those seeking to implement e-payment technologies must consider not only their emerging technology options but evolving payment needs as well.

References

[1] Eben Otuteye, A Systematic Approach to E-business Security, http://ausweb.scu.edu.au/aw03/papers/otuteye/paper.html

[2] An Oracle white paper, Managing e-Business Security Challenges, http://www.oracle.com/technology/deploy/security/oracle9ir2/pdf/9iR2hisec.PDF

[3] E-business Resource Group Security Guidelines, http://www.bc.pitt.edu/ebusiness/arEBSecurityGuide.pdf

[4] Muhammad M. Satti, Brain J. Garner, Mahmood M. Nagril, Information Security Standards for e-Business, http://www.macquarietelecom.com/whitepapers/Info%20Security%20Standards%20e-biz.pdf

[5] Canadian banker association, Minding your e-Business, Security and Privacy Matter, http://www.cba.ca/en/content/general/MYEBUS_Final_Report.pdf

[6] Public Key Infrastructure Overview, By Joel Weise – SUN PS Global Security Practice, Sun Blueprints Online, August 2001, http://www.sun.com/blueprints/0801/publickey.pdf

[7] Cutting Checks: Challenges and Choices for the Adoption of B2B Electronic Payments, Mark J. Cotteleer, Christopher A. Cotteleer, Andrew W. Prochnow, Communications of the ACM, Volume 50, Number 6 (2007), Pages 56-61

[8] The State of the Art in Electronic Payment Systems, N. Asokan, Phillipe A. Janson, Michael Steiner, Michael Waidner, IEEE Computer, Vol 30 , Issue 9 ( 1997) Pages: 28 - 35

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CHAPTER XIII Automatic Data Capture using RFID and its implications

13.1 Introduction

Optimizing inventory level across the coordinating organizations is an important aspect of supply chain management. Such efforts require real-time inventory data to be shared among the coordinating organizations. Collection and dissemination of real-time inventory data requires integration of automatic data capture and transfer technologies. Automated Identification and Data Capture (AIDC) includes technology to identify objects, and automatically collects data about them and updates the data into software systems without human intervention. Some examples of AIDC technologies include bar codes, RFID, smart cards, voice and facial recognition, and so forth.

Modern AIDC heavily relies upon barcodes, for automated data capture. A barcode basically is a machine-readable visual representation of information printed on the surface of the objects. The encoded data on the barcodes is read by barcode readers, which update the backend ERP, SCM, or WMS systems. However there are some inherent issues with using a barcode as shown in Table 1. To overcome these issues the industry is now looking at the possibility of using new generation AIDC technology like the RFID. For example, objects tagged with RFID can be sensed in a wide area, and there is no need to individually scan all the objects in front of an optical scanner. It can also offer item-level tagging - that is, each item within a product range can be uniquely identified. The data collected using RFID technology helps associate production events with each inventory item, which leads to a tighter inventory control approach that relies on such real time data.

Table 1: Bar code vis-à-vis RFID solution for automated data capture

Bar Code Deficiency RFID improved solution

Line of Sight Technology Able to scan and read from different angles and through certain materials

Unable to withstand harsh conditions (dust, corrosive), must be clean and not deformed

Able to function in much harsher condition

No potential for further technology advancement

Technology advancement is possible due to new chip and packaging technique

Can only identify the items generally and not as unique objects

EPC code will enable to identify up to 296 items uniquely

Poor tracking Technology, labor intensive and slow

Potential to track the items in real time as they move through the supply chain.

13.2 Technology overview

An RFID system is composed of three main elements: an RFID tag (inlay), which contains data that uniquely identifies an object; an RFID reader, which writes this unique data on the tags and, when requested, can read this unique identifier; and an RFID middleware, which processes the data acquired from the reader and then updates it to the backend database or ERP systems.

A typical RFID system is shown in Figure 1. When the RFID tag comes in the range of the RFID reader, the reader activates the tag to transmit its unique information. This unique information is propagated to the RFID middleware, which appropriately processes the gathered information and then updates the backend database.

Figure 1: A Typical RFID Setup

There are several versions of RFID that operate at different radio frequencies. The choice of frequency is dependent on the business requirements and read environment – it is not a technology where ‘one size fits all’ applications. Three primary frequency bands are being used for RFID:

• Low Frequency (125/134KHz) – Most commonly used for access control, animal tracking and asset tracking. Tags in this range are not affected by metallic surroundings and hence are ideal for identifying metal objects like vehicles, tools, containers, and metallic equipments. The reading range varies from a few centimeters to a meter depending upon the size of the antennae and the reader used. These tags can also penetrate through water and body tissues, and hence often used for animal identification. Most LF-based systems can only read one tag at a time—that is, they do not support reading multiple tags simultaneously

• High -Frequency (13.56 MHz) – Used where medium data rate and read ranges up to about 1.5 meters are acceptable. HF tags can penetrate through most materials including water and body tissues; however they are affected by metal surroundings. HF tags are comparatively cheaper than the LF tags. The data transfer rate is higher compared to LF tags, for example, 20ms for read operation. This is because at high frequency the communication is faster. The reader can read multiple tags simultaneously.

• Ultra High-Frequency (850 MHz to 950 MHz) – offer the longest read ranges of up to approximately 3 -6 meters and high reading speeds. UHF tags are normally less expensive than HF. Such tags are commonly used on objects that are moving at a very high speed, and a large number of tags are scanned per second in the business contexts such as supply chain, warehouse, and logistics. UHF tags do not work well in liquid and in metal surroundings. Larger read range limits their use to banking and access control applications, because the access card may be scanned from a longer distance and some unauthorized person might gain entry in restricted premises.

13.2.1 RFID Tags

An RFID /tag is a microchip attached with an antenna to a product that needs to be tracked. The tag picks up signals from the reader and reflects back the information to the reader. The tag usually contains a unique serial number, which may represent information, such as a customer’s name, address, and so forth.

RFID tags can be classified using two schemes. First, the tags can be classified based on their ability to perform radio communication -active, semi-active (semi-passive), and passive tags. Second, the tags can be classified based upon their memory -read-only, read-write or write-once, and read-many.

Active vs. Semi-Active(or Semi-Passive) vs. Passive RFID tags

• Active Tags have a battery that provides necessary energy to the microchip for transmitting a radio signal to the reader. These tags generate the RF energy and apply it to the antennae and transmit to the reader instead of reflecting back a signal from the reader. These batteries need to be recharged or replaced once they are discharged. Some tags have to be disposed off when the batteries run out of power. These tags have a read range of several 100 meters and are very expensive and hence are

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used for tracking expensive items; for example, the U.S. military uses these tags to track supplies at ports

• Semi-Active Tags (or semi-passive or battery-assisted) also contain a battery, which is used to run the circuitry on the microchip, however it still relies on the reader’s magnetic field to transmit the radio signal (i.e., information). These tags have a larger range because all the energy supplied by the reader can be reflected back to the reader, which means it can work at low-power signal levels as well. These tags have a read range up to 100 meters and may cost a dollar or more. Some of these tags often are dormant - that is, they are activated by the presence of a reader’s magnetic field. Once activated, the battery runs the circuitry and responds back to the reader. This is a mechanism to save the battery power.

• Passive Tags completely rely on the energy provided by the reader’s magnetic field to transmit the radio signal to and from the reader. It does not have a battery. As a result, the read range varies depending upon the reader used. A maximum distance of 15 meters (or 50 feet) can be achieved with a strong reader antennae and RF-friendly environment.

Read-Write vs. Read-Only RFID Tags

• Read-only tags: The reader can only read data stored on such tags. The data cannot be modified in any manner. The tag manufacturer programs the data on the tag. Such tags are comparatively very cheap.

• Write-once read-many (WORM): The owner of the tag can program the data by writing the content on the tag. Data stored on this tag can be written only once; however it can be read many times.

• Read-write tags: Data stored on such tags can be easily edited when the tag is within the range of the reader. Such tags are more expensive and are not often used for commodity tracking. These tags are reusable; hence they can be reused within an organization.

13.2.2 RFID Readers

RFID readers send radio waves to the RFID tags to enquire about their data contents. The tags then respond by sending back the requested data. The readers may have some processing and storage capabilities. The reader is linked via the RFID middleware with the backend database to do any other computationally intensive data processing. RFID readers can be classified using two different schemes. First, the readers can be classified based on their location as handheld readers and fixed readers. Second, the tags can be classified based upon the frequency in which they operate -single frequency and multi-frequency.

Fixed Readers vs. Handheld Readers

• Fixed RFID Readers are fixed at one location (e.g., choke point). In a supply chain and warehouse scenario, the preferred location of a reader can be along the conveyor belt, dock door antennae or portals, depalletization stations, or any other mobile location.

• Portable or Handheld RFID Readers are designed for Mobile Mount Applications, for example, vehicles in a warehouse or to be carried by inventory personnel, and so forth.

Single Frequency vs. Multi-Frequency

• Single-frequency operation readers operate in one frequency zone, either in LF, HF, or UHF. Such readers become inconvenient if tags in a warehouse are operating in different frequencies.

• Multi-frequency operation readers can operate in multiple frequencies. Such readers can conveniently read tags, which operate in different frequencies (i.e., LF, HF, or UHF). Hence these are more useful from a practical perspective, however such readers come at a premium price.

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13.2.3 RFID Middleware

In a general, the RFID middleware manages the readers and extracts Electronic Product Code (EPC – To be explained latter) data from the readers; performs tag data filtering, aggregating, and counting; and sends the data to the enterprise WMSs (warehouse management systems), backend database, and information exchange broker. Figure 1 shows the relationship between tag, reader, RFID middleware, and backend database. An RFID middleware works within the organization, moving information (i.e., EPC data) from the RFID tag to the integration point of high-level supply-chain management systems through a series of data-related services.

From the architectural perspective, RFID middleware has four layers of functionality: reader API, data management, security, and integration management. The reader API provides the upper layer of the interface interacting with the reader. Meanwhile, it supports flexible interaction patterns (e.g., asynchronous subscription) and an active “context-ware” strategy to sense the reader. The data management layer mainly deals with filtering redundant data, aggregating duplicate data, and routing data to appropriate destination based on the content.

The integration layer provides data connectivity to legacy data source and supporting systems at different integration levels and thus can be further divided into three sub-layers: application integra-tion, partner integration, and process integration. The application integration provides varieties of reliable connection mechanisms (e.g., messaging, adaptor, or the driver) that connect the RFID data with existing enterprise systems such as ERP or WMS. The partner integration enables the RFID middleware to share the RFID data with other RFID systems via other system communication components (e.g., the Data Exchange Broker in Figure 2). The process integration provides capability to orchestrate the RFID-enabled business process. The security layer obtains input data from the data management layer, and detects data tampering which might occur either in the tag by a wicked RFID reader during the transportation or in the backend internal database by malicious attacks. The overall architecture of RFID middleware and its related information systems in an organization are depicted in Figure 2.

Figure 2. RFID middleware architecture

The backend DB component stores the complete record of RFID items. It maintains the detailed item information as well as tag data, which has to be coherent with those read from the RFID. It is worth noting that the backend database is one of the data-tampering sources where malicious attacks might occur to change the nature of RFID item data by circumventing the protection of an organization’s

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firewall. The WMS integrates mechanical and human activities with an information system to effectively manage warehouse business processes and direct warehouse activities. The WMS automates receiving, put-away, picking, and shipping in warehouses, and prompts workers to do inventory cycle counts.

The RFID middleware employs the integration layer to allow real-time data transfer towards the WMS. The data exchange broker is employed in this architecture to share, query, and update the public data structure and schema of RFID tag data by exchanging XML documents. Any update of the data structure will be reflected and propagate to all involved RFID data items stored in the backend database. From the standardization view, it enables users to exchange RFID-related data with trading partners through the Internet. From the implementation angle, it might be a virtual Web services consumer and provider running as peers in the distributed logistics network.

13.3 Applications of RFID

Applications fall into two principal categories: firstly, short range applications where the reader and tag must be in close proximity (such as in access control) and secondly, medium to long application, where the distance may be greater (such as reading across a distribution centre dock door). A sample of applications is shown below:

Access control for people: there are many areas where RFID tags are carried by people to allow them to gain access to facilities or services:

• Secure access to work place

• Safety access to dangerous/secure equipment

• Access to a computer or vehicle

• Access to travel on trains/buses

• Access to leisure facilities

Access control for vehicles:

• Secure access on site

• Road tolling

• Instant payment for fuel

Manufacturing automation:

• Control of flexible manufacturing processes by recognizing items being built on a production line (mass customization enabler)

• Labeling key components for later recycling

Logistics and distribution:

• Tracking parcels from shipment to end customer

• Tracking goods from manufacture through to retail

Retail:

• Supply chain management

• Stock taking

• Reducing loss through shrinkage

• Reverse logistics

• Product availability

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Maintenance:

• Plant & Equipment

• Fixed assets

• Patients

Product security:

• Tamper evidence

• Product authentication

• Anti-counterfeiting

13.4 RFID technology in the supply chain

The efficiency of the supply chain has a direct impact on the profitability of a company. It is no surprise therefore to find that many large corporate companies have made it a key part of their strategy, and invested heavily in software systems (ERP, WMS.) and IT infrastructure designed to control inventory, track products and manage associated finance. RFID will bring a new dimension to supply chain management by providing a more efficient way of being able to identify and track items at the various stages throughout the supply chain. It will allow product data to be captured automatically, and therefore be more quickly available for use by other processes such as stock management and real time billing.

Even though RFID applications are still at the early stages of deployment, many companies running pilot systems have been able to demonstrate some of the significant benefits that RFID promises. There is no doubt that more will be discovered as the industry adopts the technology on a wider scale. The following are examples of what has been identified so far by the different studies and tests/pilots recently completed within the supply chain.

Advanced Shipping Notices (ASN): RFID is able to automatically detect when either a pallet or shipment has left the warehouse or Distribution Center. This will allow to not only generate an electronic ASN and notify the recipient, but also to bill clients in real time instead of waiting until the end of the week or month, and doing a batch operation.

Shrinkage: One of the major problems in the supply chain is product loss or shrinkage, which can account for anything from 2 to 5 % of stock. The causes may vary from misplaced orders, employee and customer theft or inefficient stock management. RFID with its superior tracking and identification capability will be able to localize where losses are occurring.

Returned Goods: Full visibility and automation can be potentially achieved on returned goods- thereby reducing fraud.

Anti –counterfeit: Illegal duplication and manufacture of high value products, is one of the industries most well known problems. By integrating a tag into items, for example the body of an expensive ladies handbag, RFID has the potential to authenticate a product, and combat the sale of false goods on the black markets.

Supply Chain efficiency: RFID will enable the traceability and reduction in the number of discrepancies between what a supplier invoiced, and what a customer actually received.

Improved stock management: Managing stock is the key priority for many retailers. Studies have shown that on average, products are not on the store shelves 7% of the time due to inefficiencies in stock management, which means of course a potential purchase loss. Implementing RFID at the item level and on shelves will give an automatic way of knowing and managing stock levels. However in order to achieve this on a large scale, it is recognized that tags will have to come down in price to around 5 cents or less, and readers to around 100 USD.

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Reduction in labor costs: At DC ‘s (Distribution Centers) labor accounts for nearly 70% of costs. It is estimated that RFID could reduce this by nearly 30% by removing the need for manual intervention and use of barcodes when loading cases or stocking pallets.

13.5 Enabling RFID Adoption Move in the Supply Chain – The Electronic Product Code

At the heart of the current RFID based technology drive to improve supply chain efficiency and reduce operating costs, is the EPC (Electronic Product Code).

13.5.1 EPC Origins

In October 1999 the Auto-ID center was created in the Department of Mechanical Engineering by a number of leading figures at MIT. The potential benefits of RFID tags had been identified long before, what was stopping the adoption of the technology in the supply chain was the cost of the tags. The AutoID recognized that in order to solve this problem, tags needed to be as simple as possible, and act instead as pointers to information held on servers in the same way as information is stored on the internet. This lead to the idea of the EPC (Electronic Product Code) which would provide fast and detailed information of products anywhere in the supply chain. The goal however, was not to replace bar codes, but rather to create a migration path for companies to move from bar code to RFID. The Auto-ID Center officially closed on October 26th, 2003. The final board meeting was held in Tokyo, Japan. The Center had completed its work and transferred its technology to EPCglobal (www.epcglobalinc.org), which will administer and develop EPC standards going forward.

13.5.2 EPC layout

The code is similar to the UPC (Universal Product Code) used in bar codes, and ranges from 64 bits to 256 bits with 4 distinct fields described below in fig 3. .What sets the EPC apart from bar codes is its serial number which allows to distinguish the uniqueness of an item, and track it through the supply chain.

Fig 3. Layout of an EPC which is 96 bits in length

• Header (0- 7) bits: The Header is 8 bits, and defines the length of the code in this case O1 indicates an EPC type 1 number which is 96 bits in length. The EPC length ranges from 64 to 256 bits.

• EPC manager (8- 35) bits: Will typically contain the manufacturer of the Product the EPC tag is attached to

• Object Class (36-59) bits: Refers to the exact type of product in the same way a an SKU (Stock Keeping Unit)

• Serial Number (60 – 96) bits: Provides a unique identifier for up to 296 products

13.5.3 EPC infrastructure

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Fig. 4 The basic steps of EPC infrastructure

EPC infrastructure will allow immediate access to information, which will not only optimize existing services such ASN, but also have the potential to create new services, for example; a retailer could automatically lower prices as the expiry date approaches, or a manufacturer could recall a specific batch of products due to health concerns, and if needed pinpoint source of the problem down to a unique product.

Middleware or Savant Software The sheer potential volume of data created by billions of EPC tags would very quickly grind most existing companies’ enterprise software and IT infrastructure to a standstill within a matter of minutes. The answer to this problem is middleware or Savants. RFID savants serve as a software buffer which sits almost invisible between the RFID readers, and the servers storing the product information. It allows companies to process relatively unstructured tag data taken from many RFID readers, and direct it to the appropriate information systems. Savants are able to perform many different operations, such as monitor the RFID reader devices, manage false reads, cache data and finally query an Object Naming Service (ONS).

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Object Name Service (ONS) ONS matches the EPC code to information about the product via a querying mechanism similar to the DNS (Domain Naming system) used in the internet, which is already proven technology capable of handling the volumes data expected in an EPC RFID system. The ONS server provides the IP address of a PML Server that stores information relevant to the EPC.

Physical Markup Language (PML) Whilst the EPC is able to identify the individual product, the real useful information is written in a new standard software language called Physical Markup Language. PML itself is based on the widely used and accepted extensible markup language (XML), designed as a document format to exchange data across the internet. It is not surprising therefore, with so much of the infrastructure for EPC being borrowed from the internet (DNS,XML..), that it is often referred to as ¨ the internet of things ¨.

PML is designed to store any relevant information about a product; for example,

(1) Location information e.g., tag X was detected by reader Y, which is located at loading dock Z;

(2) Telemetry information [Physical properties of an object e.g., its mass; Physical properties of the environment, in which a group of objects is located, e.g., ambient temperature];

(3) Composition information e.g., the composition of an individual logistical unit made up of a pallet, cases and items. The information model will also include the history of the various information elements listed above e.g., a collection of the various single location readings will result in a location trace.

(4) Manufacturing and expiry dates

Figure 5 explains how EPC will automate the supply chain.

13.6 Adoption strategy

An adoption strategy provides a roadmap to implement the technology in a way that is consistent with an organization’s strategic vision and goal. A road map comprises four essential fundamental principles for RFID to be thoroughly adopted in the organization.

Shared Understanding: In an organization, each decision maker or potential stakeholder of the RFID adoption may have a considerably different understanding from his or her own perspective towards RFID. The likelihood of successful adoption of RFID in effect hinges on the capability of the organization to enforce a broader shared understanding.

Goal Setting: The organization has to set a very clear unambiguous goal for RFID adoption. This goal is to be aligned with the organization’s existing business goal. With the goal, important stakeholders can thus be explicitly (rather than implicitly or potentially) identified.

Justification: A cost-benefit analysis (e.g., ROI estimation) to suffice the financial concern will significantly increase the possibility of RFID being accepted by those hesitant stakeholders. Last, potential challenges and issues during and after deploying RFID have to be clearly identified at the early stage. Each challenge should be addressed by possible solutions submitted to all involved stakeholders.

Planning and Approaching: A good project plan includes evaluation of technology option, standard-based deployment approach, measurements and optimization techniques, and a continuous improvement roadmap. During the implementation, an incremental approach shall be taken. Thus pilot tests and milestones can be used for checking the progress of the implementation and ensuring the desired outcome is achieved through the delta part implemented in the current adoption iteration.

Strategic step based on these aforementioned principles are explored below.

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Fig 5. Using EPC

Steps in RFID Adoption:

Core Competency Reaffirmation: The core competency (CC) is defined as one thing that an organization can do better than its competitors, and is crucial to its success. Before the organization

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adopts any candidate technologies, it has to assess them against the core competency. For example, when retailer giant Wal-Mart realizes that the core competence lies in its dominant distribution channels, which can greatly benefit from RFID technology, it demands all of its top 100 suppliers to attach RFID tags onto their goods. On the contrary, if a company XYZ’s CC is manufacturing high-quality automobile engine assemblies, it should be very cautious in adopting RFID unless its primary retailers at the downstream urge it to do so, because the distribution is not XYZ’s core competencies and doing so will only distract itself from doing what they are good at. Therefore, in general, an organization should be wary of taking aggressive steps in implementing RFID solutions before the core competence has been thoroughly reaffirmed and evaluated.

Feasibility Analysis: If the RFID technology is being considered to be aligned with the CC, it is time for the organization to perform the feasibility analysis, which deals with four major aspects:

• It is very important to estimate the capability of existing information systems. Since RFID will dramatically increase the amount of the data captured from instance level tags, the information systems have to capture, process, and analyze such a huge amount of data efficiently. Any insufficiency of information systems will definitely hinder the RFID technology to realize its full potential.

• RFID solutions could be quite costly; hence continuous and dedicated RFID funding support is essential.

• Personnel preparation is another feasibility issue needing consideration. Once implemented, the RFID might change the business process as well as fundamental information systems operations, which incur substantial IT training and adaptation study. The learning capability of involved working staff also determines the success of RFID adoption.

• Lastly, the organization has to choose the appropriate time to deploy RFID. Early adoption of RFID has advantages as well as risks. The feasibility of time should study whether the company is ready for RFID at that particular time, and moreover can bear the risk associated, even if the prerequisite conditions are all met.

Candidate Scenarios

The next step is to identify candidate scenarios that would benefit from RFID and to measure the potential benefit in a self-defined scale. For example, for manufacturing units, RFID can be used to support quality control by querying components and subassemblies as they enter the facility. This is a typical candidate scenario that can be identified. It is recommended for the organization to enumerate all the potential RFID-involved candidate scenarios that can impact an organization’s core competences. A typical tabular description of a sample candidate scenario (based on RosettaNet, 2001) is presented in Table 1.

Scenario Prioritization

In this step, the company need to prioritize this list against a set of criteria such as estimated impacts to CC, the ROI, the cost, and so forth. Such a prioritizing process also needs to consider the preference from different RFID stakeholders. This is achieved by:

1. organizing the criteria set into a hierarchical structure

2. performing the pair-wise comparison between any two candidate scenarios against one specific criterion

3. providing the pair-wise comparison between any two criteria for each RFID stakeholder

4. computing the stakeholders-aggregated preferences for each criteria

5. calculating the overall weight for each scenario allowing for all criteria with different preferences ranking the scenario list against the weight value generated in #5, with the biggest weight value

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being positioned in the first rank. This step eventually generates a Prioritized Scenario List for further justification review.

Justification Review:

In this step, for each candidate scenario in the Prioritized Scenario List, the organization conducts the justification review, a static analysis of RFID deployment only related to that particular candidate scenario. For example, traditional ROI (return on investment) methods can be utilized to examine whether RFID technology should be deployed in this candidate scenario. For each candidate scenario, if the justification review produces negative results, it is removed from the prioritized scenario list, and the next scenario will be considered to perform the justification review. Otherwise, this scenario will be subsequently chosen for the following pilot test.

Adoption Issues

Before the pilot test, a clear consensus of issues and their solutions is necessary among all the stakeholders. Hence in this step, the stakeholders need to unanimously identify adoption challenges specific to this scenario. The organization itemizes all the possible problems that it might encounter during the pilot test. More importantly, the solutions that address these challenges should be proposed and planned in this step. This ensures the smooth progress of the pilot test. Moreover, the solutions can also be validated and modified during the pilot test.

Pilot Test

Once the justification review is confirmed positively, the pilot implementation can be carried out in an experimental environment. It can be an RFID prototype in a smaller scope or scale of this candidate scenario. For example, a pilot deployment in one or two locations allows evaluation of RFID vendors, equipment, and software, and provides the opportunity for different stakeholders to gain experience with RFID. Furthermore, such a pilot is to produce the impact analysis for this candidate scenario. As many impacts can be associated with the RFID deployment, some of them are beneficial to some shareholders, while some are negative to other shareholders. Hence the pilot implementation can estimate such impacts brought by RFID deployment. If most of the results tend to be negative, the likelihood that the RFID to be implemented in this scenario appears to be very low. The organization might need to consider the next candidate scenario along the Prioritized Scenario List. In contrast, positive pilot test results with the pilot feedback suggest the start of the RFID implementation in this candidate scenario.

Implementation

Once the pilot test is passed, the formal implementation can be carried out in an incremental manner through a set of iterations with a couple of milestones, which ensure that the implementation cost and risk can be controlled and mitigated to the minimal level. Each milestone has different focuses. Table 3 lists possible sequential tasks within one milestone. Particular attention is given for fostering the transition between milestones.

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2

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3

13.7 RFID Adoption Challenges

Several key adoption issues will be discussed in this section. The main issues that we address in this section are cost associated with the deployment of RFID system, security and privacy concerns, and finally more technical issues in deployment of an RFID system.

Cost

A cost-estimation model for a full-fledged deployment of an RFID system in a supply chain environ-ment should consider the following factors.

RFID Tags: When deploying an RFID system, one should consider the cost of buying RFID tags. It is a good idea to consider renewable tags (if possible) as a means to reduce cost. Normally the cost of active tags is more than the passive tags. Apart from the cost of the tags, companies should also consider the cost of testing the passive tags. Finally there is cost associated with replacing the defective tags.

RFID Printers: An RFID label has a similar functionality as an RFID tag. However it can be stuck on like a label. The RFID label can be printed using RFID printers. Hence if you are planning to use RFID labels, the cost of the RFID printer should be considered. In some applications RFID labels are much more preferred because of the environment and the products. For example applications like express parcel delivery, library book/video checkout, sensitive document tracking, ticketing (sports, concerts, ski lifts, etc.), and pharmaceuticals prefer RFID labels (Zebra, 2006).

RFID Readers: When deploying an RFID system, one should consider the cost of buying RFID readers. The fixed readers are normally cheaper than the portable readers. Dumb readers are usually cheaper, as they do not have any computing capability. On the other hand intelligent readers offer computing capability to filter data, store information, and execute commands. Agile readers can communicate with tags using a variety of protocols, while multi-frequency readers can read tags using different frequencies. All these features contribute to the cost of readers, and the organization should select a proper reader based on its application requirements.

RFID Antennas: Almost all the readers are equipped with one or more antennas. However in some cases the need for additional high-power antennas cannot be ruled out and hence this additional cost should be considered before deciding to deploy an RFID system.

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RFID Middleware: RFID middleware contributes a major portion of RFID investment. Many vendors supply RFID middleware, and the cost can vary depending upon the capabilities of the middleware. Usually factors that contribute to cost include complexity of the application and the number of places the middleware would be installed. Apart from the middleware, the companies should also consider the cost of edge servers, which are normally deployed in the warehouse, distribution center, or production facility. The edge servers are simple servers, which are connected to the RFID reader using a Universal Serial Bus (USB) port.

Training Existing Staff: The organization will need to train its employees, particularly engineering staff who will manage readers in manufacturing and warehouse facilities, and IT staff who will work on the systems that manage RFID data.

Hiring Technology Expertise: Most of the companies, as of now, would not have the expertise to deploy a complete RFID system. This is partly attributed to the fact that RFID is a relatively new technology. Hence an organization would need to outsource this task to a third party who knows how to install the readers, decide the appropriate location for fixing the tag on the products, ascertain that the data gathered by the reader is properly propagated to the middleware in the right format, and so on. This is quite important because RFID systems can be sometimes difficult to install, as there are several factors that can affect the optimum performance of such a system. Hence a major portion of RFID investment has to be targeted to this area.

Other Miscellaneous Costs: The miscellaneous costs might include regular maintenance of the RFID readers or replacement of damaged tags or antennas.

Cost Estimation for RFID Deployment in Supply Chain Tracking

When deploying an RFID system for inventory management and control in a supply chain, all the above-mentioned costs should be considered. According to Forrester Research, the estimated cost of middleware is around $183,000 for a $12 billion manufacturer looking to meet the RFID tagging requirements of a major retailer. In the same manner the estimated price of $128,000 could be spent for consulting and integration, $315,000 for the time of the internal project team, and $80,000 for tag and reader testing. A simple estimate is provided in Table 3.

On the lower end an estimated cost of around $3 million should be invested in an RFID project for inventory tracking and management if a total of 10 million items are tagged. The number of readers, printers, and edge servers would vary depending upon the number of distribution centers and warehouses in use. Here we assumed 100 readers and printers, and 50 edge servers. These numbers are used just as an example; it can vary depending upon each company.

4

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Read-Rate Accuracy

Achieving 100% read-rate accuracy is a major adoption challenge with RFID deployment. Supply chains and warehouse management solutions based on RFID are highly vulnerable to read-rate inaccuracy because of the number of RFID-tagged items that need to be scanned every second. Consider the scenario when a palette containing 1,000 RFID-tagged items is scanned at the warehouse exit. There is a high probability that the reader would not scan a few tags. It is difficult to list the main reasons that result in inaccurate readings because there are too many “ifs” and “buts”. Accuracy is dependent on so many unrelated variables that it is difficult to list the main factors behind the cause. However we attempt to outline some basic parameters, which results in inaccurate readings. Some of the main reasons for inaccurate readings include the environment in which RFID system works, material of the item being tracked, reader configuration, reader and tag placements, tag orientation, and so forth. To successfully deploy an RFID system, some key parameters should be considered to achieve accurate readings.

Tagged Material

Maintain some consistency when tracking materials. It is not a good idea to standardize the reader configuration to track cartons, trolleys, pallets, glass materials, documents, or metal or plastic bins. This is because different materials behave differently to RF energy; some materials are RF friendly, while others are RF absorbent or RF opaque. A reader configured to read tags from RF-friendly material would definitely fail to give 100% read-rate accuracy if used to track RF-absorbent or RF-opaque items.

Preplanned Object Movement

To assure good read rates, it is advised to move the tagged objects on a predefined route (or pattern). You cannot expect good rates if the cartons are moved through a forklift, people, metals trolleys, plastic trolleys, and so forth. There should be just one or two modes of transport well tested for 100% read-rate accuracy.

Tags from Different Vendors

Read rate is also affected if tags are used from different vendors because the performance of such tags varies significantly. Another reason is the use of different standard-compliant tags (like EPC Gen1 & EPC Gen2). It is also difficult to configure the reader power level at an optimum level where it supports all different tags with 100% read rate. Ideally one should try to use a single standard and single vendor tag in one ecosystem. Nevertheless this may change as the standards improve.

Tag Orientation

Orientation is one of the big factors for providing good read rates. Even though dual dipole tags perform much better in all orientations, it is still advised to follow a policy on tag placement and tag orientation (TPTO). A standard policy on TPTO across an organization would definitely improve the read-rate accuracy.

13.8 Security in RFID Systems

RFID tags used in the supply chain will contain data ranging from simple ID numbers (EPC), to more important information about a product. For example in the health industry, it could be the blood type of a sample. The main goal of any security system designed to protect data stored in mediums such as tags, computer disk drives, or smart cards is basically to prevent any unauthorized person from being able to either;

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a) Obtain access and learn the data contents

b) Obtain access and modify/corrupt/erase the data contents

c) Copy the data contents to a similar storage device (duplicate)

In a complete system, security of data as defined above not only involves the storage medium, but also how data is created and transferred from a host to the medium (or vice versa). For example, when an engineer broke the security of a French bank credit card a few years ago, he did it not by compromising the chip security, but by hacking the reader terminal. The following are scenarios that could happen in the supply chain.

1) Industrial Sabotage – somebody with a grievance against a company decides to start corrupting data in tags by using a hand held device, and erasing or modifying the contents.

2) Industrial Espionage – A rather unlawful competitor would like to know how many, and what type of products are being manufactured, and shipped by your company. He could possibly achieve this in the following ways

i. Eavesdropping – listening in on longer range communication systems like UHF which broadcast signals (albeit very weak) up to 100 meters – some protocols have a basic security which ensures that the ID N° is never transmitted completely in one stream.

ii. Placing bogus well concealed readers linked to a PC somewhere in the proximity of the tags moving through the production line

iii. Using hand held devices

3) Counterfeiting – Being able to read or intercept data being written into a tag which uniquely identifies or certifies a product. Once the data is known, similar read/write tags could be purchased and updated with the authentic data, thus creating the real possibility of counterfeiting products which are supposed to be protected by a tag. All the above scenarios are potential risks if no security is implemented in the tag and reader. The importance attached to protecting data in the supply chain will depend on the application, and the company’s strategy towards security. In some cases legislation will impose it. Of course bar codes which are used today, can be easily read, decrypted, and even destroyed, but not on the wide-spread and automatic scale possible with RFID. Even the simplest security costs silicon area, and therefore will impact on the final tag price. This goes against the current trend of trying to produce the smallest and cheapest tag possible. Every company is therefore faced with this tradeoff between cheaper unsecured tags, and the potential security risks they entail.

13.9 Privacy

If RFID is deployed in a full scale, it may result in many privacy concerns because RFID can be used to track consumer behavior, which can further be used to analyze consumer habits. It can even be used for hidden surveillance, for example, deploying secret RFIDs for tracking. With the size of RFIDs reducing day by day, it has now become possible to hide them within products without the owners’ consent. For example, RFID tags have already been hidden in packaging. A scenario of hidden RFID testing was discovered in a Wal-Mart store in Broken Arrow, Oklahoma, where secret RFID readers tracked customer action. Criminals with RFID readers can look for people carrying valuable items and can launch selective attacks. However most of these issues can be tackled by privacy enforcement laws, which can be incorporated into the nation’s legal framework. All these privacy issues have created a lot of fear in the consumer community. In order to address these issues, several approaches are proposed in the literature.

13.10 RFID Implementation Examples

In an academic study performed at Wal-Mart, RFID reduced Out of Stocks by 30 percent for products selling between 0.1 and 15 units a day. Second, the RFID technology can prevent or reduce the

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sources of errors. Benefits of using RFID include the reduction of labour costs, the simplification of business processes and the reduction of inventory inaccuracies.

Wal-Mart and the United States Department of Defense have published requirements that their vendors place RFID tags on all shipments to improve supply chain management. Due to the size of these two organizations, their RFID mandates impact thousands of companies worldwide. The deadlines have been extended several times because many vendors face significant difficulties implementing RFID systems. In practice, the successful read rates currently run only 80%, due to radio wave attenuation caused by the products and packaging. In time it is expected that even small companies will be able to place RFID tags on their outbound shipments.

Since January, 2005, Wal-Mart has required its top 100 suppliers to apply RFID labels to all shipments. To meet this requirement, vendors use RFID printer/encoders to label cases and pallets that require EPC tags for Wal-Mart. These smart labels are produced by embedding RFID inlays inside the label material, and then printing bar code and other visible information on the surface of the label.

The Table below lists few more initiatives and success stories.

13.10 Conclusions

The need to organize and make decisions based on the data provided by the RFID tags is prominent. To date, research that conjoins RFID technology and item- level inventory management on the shop floor is at a preliminary stage, only inferring benefits upon application. The challenge is to collect RFID data in a timely manner, to process such voluminous data, and to make timely decisions that are tied into manufacturing execution systems. If the challenge is overcome, then the benefits such as waste elimination, inventory reduction, automatic replenishment, stock-out reduction and overall cost savings can be easily realized. Therefore, there is a need for RFID data-based effective decision making algorithms that can lead to such benefits. In this study, a forecasting integrated inventory management model that relies on real-time RFID data is presented. The goal of this research is threefold: (1) to model and analyze the decisions made to manage inventory levels of time sensitive materials in a shop floor manufacturing environment; (2) to investigate new decision making algorithms that substantiate the use of RFID for real- time visibility; and (3) to explore the impact of fundamental control parameters on performance measures.

References:

[1] V. Potdar, C. Wu, E. Chang, E-Supply Chain Technologies and Management, chapter ‘Automated Data Capture Technologies – RFID,’ in IDEA Group Reference, Hershey, PA, USA, March 2007

[2] Radio Frequency Identification news and commentary, http://www.rfidgazette.org/walmart/

[3] A BASIC INTRODUCTION TO RFID TECHNOLOGY AND ITS USE IN THE SUPPLY CHAIN, Steve Lewis, White Paper from Ship2save ship2save.com/page_images/wp_printronix_rfid_supplychain.pdf

[4] Want, R., An introduction to RFID technology, Intel Res., Santa Clara, CA, USA; IEEE Pervasive Computing, Jan.-March 2006

[5] Introduction to RFID Technology, http://www.rfidc.com/docs/introductiontorfid_technology.htm

[6] Radio frequency identification, http://en.wikipedia.org/wiki/RFID

[7] Tony Gale, Divakar Rajamani, Chelliah Sriskandarajah, The Impact of RFID on Supply Chain Performance, Working Paper, School of Management, University of Texas at Dallas http://som.utdallas.edu/c4isn/documents/c4isn-Impact-RFID-SC-Perform.pdf

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CHAPTER XIV Dynamic Vehicle Routing with GPS and GIS

14.1 Introduction

Logistics especially distribution management is a vital component of supply chain management. Physical distribution includes a set of activities executed to obtain the delivery of a product from the production location to the end customer. Efficient distribution of goods entails, among other things, a determination of routes and schedules for the fleet of vehicles so that total distribution costs are minimized, while various requirements (constraints) are met. The constraints concern various facets of the operation, such as vehicle capacities, time windows on pick up and/or delivery, time availability of vehicles, etc.

The most central model in distribution management is the vehicle routing problem (VRP). In the standard capacitated vehicle routing problem (CVRP) a homogeneous fleet of vehicles serves a set of customers from a single distribution centre such that:

• the fixed capacity of a vehicle cannot be exceeded;

• each customer has known demand that must be satisfied;

• the demand of each customer is satisfied by exactly one visit of a single vehicle;

• each vehicle must leave and return to the distribution centre.

The objective is to generate a sequence of deliveries for each vehicle so that all customers are serviced and the total distance traveled by the fleet is minimized. However, there exist several important problems that must be solved in real-time. In what follows, we review the main applications that motivate the research in the field of the real-time VRPs.

(i) Dynamic fleet management: Several large-scale trucking operations require real-time dispatching of vehicles for the purpose of collecting or delivering shipments. Important savings can be achieved by optimizing these operations.

(ii) Vendor-managed distribution systems: In vendor-managed systems, distribution companies estimate customer inventory level in such a way to replenish them before they run out of stock. Hence, demands are known beforehand in principle and all customers are static. However, because demand is uncertain, some customers (usually a small percentage) may run out of stock and have to be serviced urgently.

(iii) Couriers: Long-distance courier need to collect locally outbound parcels before sending them to a remote terminal to consolidate loads. Also, loads coming from remote terminals have to be distributed locally. Most pick-up requests are dynamic and have to be serviced the same day if possible.

(iv) Rescue and repair service companies: There are several companies providing rescue or repair services (broken car rescue, appliance repair, etc.).

(v) Dial-a-ride systems: Dial-a-ride systems provide transportation services to people between given origin–destination pairs. Customers can book a trip one day in advance (static customers) or make a request at short notice (dynamic customers).

(vi) Emergency services: Emergency services comprise police, fire fighting and ambulance services. By definition, all customers are dynamic. Moreover, the demand rate is usually low so that vehicles become idle from time to time. In this context, relocating idle vehicles in order to anticipate future demands or to escape from downtown rush hour traffic jam is a major issue.

(vii) Taxi cab services: In taxi cab services, almost every customer is dynamic. As in emergency services, relocating temporary idle vehicles is an issue.

Due to recent advances in information and communication technologies, vehicle fleets can now be managed in real-time. When jointly used, devices like geographic information systems (GIS), global positioning systems (GPS), traffic flow sensors and cellular telephones are able to provide relevant real-time data, such as current vehicle locations, new customer requests and periodic estimates of road travel times. If suitably processed, this large amount of data can be in principle be used to reduce cost and improve service level. To this end, revised routes have to be timely generated as soon as new events occur. In recent years, algorithms relevant in a real-time context have been developed to handle vehicle routing problem. These algorithms are celled real-time vehicle routing algorithm or the dynamic vehicle algorithm.

14.2 Technical Requirements

In this section we provide a brief introduction to some of the most essential technologies when dealing with real-life applications of vehicle routing problems within a dynamic environment.

Communication and Positioning Equipment

The communication between the drivers of the vehicles and the dispatching center is essential in order to feed the most up-to-date information into the routing system. The equipment for determining the current position of the vehicles and the communication equipment for passing information on between the dispatching center and the drivers in the vehicles will be introduced below.

• Naturally, positioning equipment like the GPS (Global Positioning System) is essential to a dynamic vehicle routing system. The GPS is a constellation of 24 satellites orbiting Earth that constantly send out signals giving their positions and time. Signals from three or four different satellites at any given time can provide receivers on the ground with enough information to calculate their precise location within a few meters depending on which version of the GPS system is used.

• The communication equipment between the vehicle and the dispatching center is essential for the structure of the routing system. Mobile telephone communication systems are one example of a technology capable of providing this information. Another technology is a dedicated radio based communications system. The main difference in these technologies is the differences in initial and operating costs. A mobile telephone communications system is relatively costly to operate, but has low initial costs because the basic technology is provided by the telephone companies and the GSM system today offers almost full coverage in most western industrialized countries. The initial costs in implementing a radio based communications system are on the other hand very high, because transmission masts will have to be put up and relatively expensive radio equipment must be installed in every vehicle. In all, a radio based communication system has very high initial costs, while the operating costs are almost negligible. Furthermore, the radio based system does not offer the same flexibility compared to the mobile telephone communications system.

In Figure 1.2 the basic information flows between the vehicle and the dispatching center are shown. Ideally, the dispatching center will know in which state the vehicle and the driver are at any given point in time. However, as the above description indicates, this may prove to be infeasible for some applications due to the operating costs of this method. However, within a real-life setting the positioning information is transmitted at fixed intervals and an interpolation scheme is employed in order to estimate the positions of the vehicles. Alternatively, the driver sends a message about his current status and position to the dispatch center, each time he finishes the service at a customer. Obviously, this approach does not offer the same level of information for the dispatcher to support her decision as to which vehicle to dispatch to the next customer to be served. If the new information provided by the now idle driver/vehicle makes the dispatcher change her mind on the current planned route, she will have to call the other drivers manually to inform them about the changes in the current routes. However, the conclusion of this discussion must be that a careful analysis will have to show which approach to choose when designing the system. Of course, history shows that the prices of communication decrease rapidly over the years. This could be the motivation to go for telecommunications based system. We below describe the principles behind GPS – crucial component of the communication system.

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Figure 1. Sketch of the information flow in a GPS based vehicle routing system.

Global positioning system (GPS)

Global Positioning System (GPS) is a technology that uses the position of satellites to determine locations on earth anytime, in any weather, anywhere. GPS is a constellation of U.S. Government satellites providing the most advanced and accurate positioning and navigation service. Twenty-four GPS satellites orbit 12,000 miles above the earth, constantly transmitting the precise time and their position in space. The GPS receivers listen to these satellite signals and use the information to determine the location of the receiver, as well as how fast and in what direction it is moving. GPS satellites circle the earth twice a day in a very precise orbit and transmit signal information to earth. GPS receivers take this information and use the principle of triangulation to calculate the user's exact location. Satellites are equipped with very precise clocks that keep accurate time to within three nanoseconds - that’s 0.000000003, or three billionths, of a second. This precision timing is important because the receiver must determine exactly how long it takes for signals to travel from each GPS satellite.

GPS has 3 parts: the space segment, the user segment, and the control segment. The space segment consists of 24 satellites, each in its own orbit 11,000 nautical miles above the Earth. The GPS satellites each take 12 hours to orbit the Earth. The user segment consists of receivers, which we can hold in your hand or mount in the vehicle. These receivers detect, decode, and process GPS satellite signals. The control segment consists of ground stations (five of them, located around the world) that make sure the satellites are working properly.

Geographic Information Systems (GIS)

Transportation data is usually associated with spatial data, like traffic counts from particular sites, the traffic volumes along particular roads or links, etc. Geographical Information System (GIS) can be used as a database for storing transportation data. The primary advantage of using GIS as a database for transportation data is the fact that GIS can integrate the spatial data and display the attribute data in a user-chosen format. The chief sources of spatial data are the existing digitized files (e.g.: Topologically Integrated Geographic Encoding and Referencing (TIGER) files in the US). The Global Positioning System (GPS) is widely being used as a tool for collecting the spatial data. Systems which

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chiefly use GPS as a spatial data source for a GIS are called as GPS-GIS integrated systems. Most western industrialized countries now have almost fully detailed road network databases.

14.3 The Vehicle Routing Problem Vehicle routing refers to a broad group of problems that could be expressed as following: a finite set of customers at fixed locations with defined demand, must be supplied with goods by a number of vehicles having a finite capacity and predefined starting points and terminals. The vehicle routing and scheduling problem consists of two sub-problems: the customer grouping to routes (clustering) and the definition of the optimum tour for every route (cluster). Therefore, route is the total number of deliveries made by a single vehicle and tour is their sequence. The solution of these sub-problems results to the routes and tours that minimize the total transportation cost.

The VRP is a combinatorial problem whose ground set is the edges of a graph G(V,E). The notation used for this problem is as follows:

• is a vertex set, where: 0 1{ , ,..., }nV v v v=

o Consider a depot to be located at . 0v

o Let be used as the set of n cities. 0\ { }V V v′ =

• {( , ) | , , }i j i j iA v v v v V i j= ∈ ≠ is an arc set.

• C is a matrix of non-negative costs or distance cij between customers vi and vj.

• d is a vector of the customer demands.

• Ri is the route for vehicle i.

• m is the number or vehicles (all identical). One route is assigned to each vehicle.

When cij=cji for all the problem is said to be symmetric and it is then common to replace

A with the edge set

( , )i jv v A∈

{( , ) | , , }i j i j iE v v v v V i j= ∈ < .

With each vertex vi in V ′ is associated a quantity qi of some goods to be delivered by a vehicle. The VRP thus consists of determining a set of m vehicle routes of minimal total cost, starting and ending at a depot, such that every vertex in V is visited exactly once by one vehicle. ′

We will consider a service time iδ (time needed to unload all goods), required by a vehicle to unload the quantity qi at vi. It is required that the total duration of any vehicle route (travel plus service times) may not surpass a given bound D, so, in this context the cost cij is taken to be the travel time between the cities. The VRP defined above is NP-Hard.

A feasible solution is composed of:

• a partition R1,…, Rm of V;

• a permutation iσ of specifying the order of the customers on route i.. 0iR ∪

The cost of a given route ( ), where 0 1 1{ , ,..., }i mR v v v += for 1..iv V i m∈ = and (0

denotes the depot), is given by:

0 1 0mv v += =

0 0

( )m m

i iji i

C R c ijδ= =

= +∑ ∑ .

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A route Ri is feasible if the vehicle stop exactly once in each customer and the total duration of the route does not exceed a prespecified bound D: ( )iC R D≤ .

Finally, the cost of the problem solution S is: 1

( ) ( )m

VRP ii

F S C=

= ∑ R .

The characteristics of the attributes of the information forming the input for the vehicle routing problem are as follows.

• Evolution of information: In static settings the information does not change, nor is the information updated. In dynamic settings the information will generally be revealed as time goes on.

• Quality of information: Inputs could either; 1) be known with certainty (deterministic), 2) be known with uncertainty (forecasts) or 3) follow prescribed probability distributions (probabilistic). Usually, the quality of the information in a dynamic setting is good for near-term events and poorer for distant events.

• Availability of information: Information could either be local or global. One example of local information is when the driver learns of the precise amount of oil the current customer needs, while a globally based information system would be able to inform the dispatcher of the current status of all the customers' oil tanks. The rapid advances within information technologies increase the availability of information. This fast growth in the amount of information available raises the issue of when to reveal/make use of the information. For instance, the dispatcher may choose to reveal only the information that is needed by the drivers although she might have access to all information.

• Processing of information: In a centralized system all information is collected and processed by a central unit. In a decentralized system some of the information could for instance be processed by the driver of each truck.

A VRP is said to be static if its input data (travel times, demands,. . .) do not depend explicitly on time, otherwise it is dynamic. Moreover, a VRP is deterministic if all input data are known when designing vehicle routes, otherwise it is stochastic.

The Static Vehicle Routing Problem can be defined as the one in which:

1. All information relevant to the planning of the routes is assumed to be known by the planner before the routing process begins.

2. Information relevant to the routing does not change after the routes have been constructed.

A static problem can be either deterministic or stochastic. In deterministic and static VRPs all data are known in advance and time is not taken into account explicitly. In stochastic and static VRPs vehicle routes are designed at the beginning of the planning horizon, before uncertain data become known. Uncertainty may affect which service requests are present, user demands, user service times or travel times. If input data are uncertain, it is usually impossible to satisfy the constraints for all realizations of the random variables. If uncertainty affects the constraints but the objective function is deterministic, it can be required that constraints be satisfied with a given probability (chance constrained programming, CCP). In a more general approach, a first phase solution is constructed before uncertain data are available and corrective (or recourse) actions are taken at a second stage once all the realizations of the random variables become known. The objective to be minimized is the first stage cost plus the expected recourse cost (stochastic programming with recourse, SPR).

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14.4 The dynamic vehicle routing problem

It is argued that a relatively high number of the routing problems being modeled as static problems do in fact include dynamic elements in a real-life situation. Examples of this phenomenon is the fact that on site service times as well as travel times despite extensive empirical data are often filled with noise. This implies that the preplanned routes collapse because of the new temporal conditions of the system. That is what seemed to be an optimal solution (or at least a good quality solution) might turn out to be a sub-optimal solution.

In a dynamic Vehicle Routing Problem setting

1. Not all information relevant to the planning of the routes is known by the planner when the routing process begins.

2. Information can change after the initial routes have been constructed.

The dynamic vehicle routing problem calls for online algorithms that work in real-time since the immediate requests should be served, if possible. As conventional static vehicle routing problems are NP−hard, it is not always possible to find optimal solutions to problems of realistic sizes in a reasonable amount of computation time. This implies that the dynamic vehicle routing problem also belongs to the class of NP−hard problems, since a static VRP should be solved each time a new immediate request is received.

Figure 2: A dynamic vehicle routing scenario with 8 advance and 2 immediate request customers.

In Figure 2 a simple example of a dynamic vehicle routing situation is shown. In the example, two un-capacitated vehicles must service both advance and immediate request customers without time windows. The advance request customers are represented by black nodes, while those that are immediate requests are depicted by white nodes. The solid lines represent the two routes the dispatcher has planned prior to the vehicles leaving the depot. The two thick arcs indicate the vehicle positions at the time the dynamic requests are received. Ideally, the new customers should be inserted into the already planned routes without the order of the non-visited customers being changed and with minimal delay. This is the case depicted on the right hand side route. However, in practice, the insertion of new customers will usually be a much more complicated task and will imply a re-planning of the non-visited part of the route system. This is illustrated by the left hand side route where servicing the new customer creates a large detour.

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Generally, the more restricted and complex the routing problem is, the more complicated the insertion of new dynamic customers will be. For instance, the insertion of new customers in a time window constrained routing problem will usually be much more difficult than in a non-time constrained problem. Note that in an online routing system customers may even be denied service, if it is not possible to find a feasible spot to insert them. Often this policy of rejecting customers includes an offer to serve the customers the following day of operation. However, in some systems – as for instance the pick-up of long-distance courier mail - the service provider (distributor) will have to forward the customer to a competitor when they are not able to serve them.

A dynamic problem can also be deterministic or stochastic. In deterministic and dynamic problems, all data are known in advance and some elements of information depend on time. For instance, the VRP with time windows belongs to this class of problems. Similarly, the traveling salesman problem (TSP) with time-dependent travel times is deterministic and dynamic. In this problem, a traveling salesperson has to find the shortest closed tour among several cities passing through all cities exactly once, and travel times may vary throughout the day. Finally, in stochastic and dynamic problems (also known as real-time routing and dispatching problems) uncertain data are represented by stochastic processes. For instance, user requests can behave as a Poisson process. Since uncertain data are gradually revealed during the operational interval, routes are not constructed beforehand. Instead, user requests are dispatched to vehicles in an on-going fashion as new data arrive.

The events that lead to a plan modification can be: (i) the arrival of new user requests, (ii) the arrival of a vehicle at a destination, (iii) the update of travel times. Every event must be processed according to the policies set by the vehicle fleet operator. As a rule, when a new request is received, one must decide whether it can be serviced on the same day, or whether it must be delayed or rejected. If the request is accepted, it is temporarily assigned to a position in a vehicle route. The request is effectively serviced as planned if no other event occurs in the meantime. Otherwise, it can be assigned to a different position in the same vehicle route, or even dispatched to a different vehicle. It is worth noting that at any time each driver just needs to know his next stop. Hence, when a vehicle reaches a destination it has to be assigned a new destination. Because of the difficulty of estimating the current position of a moving vehicle, reassignments could not easily made until quite recently. However, due to advances in communication technologies, route diversions and reassignments are now a feasible option and should take place if this results in a cost saving or in an improved service level. Finally, if an improved estimation of vehicle travel times is available, it may be useful to modify the current routes or even the decision of accepting a request or not. For example, if an unexpected traffic jam occurs, some user services can be deferred. It is worth noting that when the demand rate is low, it is useful to relocate idle vehicles in order to anticipate future demands or to escape a forecasted traffic congestion.

Particular features

A dynamic VRPs possess a number of peculiar features, some of which have just been described. In the following, the remaining characteristics are outlined.

(i) Quick response: Real-time routing and dispatching algorithms must provide a quick response so that route modifications can be transmitted timely to the fleet. To this end, two approaches can be used: simple policies (like the first-come first served (FCFS) policy), or more involved algorithms running on parallel hardware (like the tabu search (TS) heuristics). As will be explained, the choice between them depends mainly on the objective, the degree of dynamism and the demand rate.

(ii) Denied or deferred service: In some applications it is valid to deny service to some users, or to forward them to a competitor, in order to avoid excessive delays or unacceptable costs. For instance, the requests that cannot be serviced with a given time windows are rejected. When no time windows are imposed, some user requests can be postponed indefinitely because of their unfavorable location. This phenomenon can be avoided by imposing dummy time windows, or by adding a nonlinear delay penalty to the objective function.

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(iii) Congestion: If the demand rate exceeds a given threshold, the system becomes saturated, i.e., the expected waiting time of a request grows to infinity.

The degree of dynamism of a problem

Designing a real-time routing algorithm depends to a large extent on how much the problem is dynamic. Degree of dynamism of a problem quantifies this concept. Without loss of generality, we assume that the planning horizon is a given interval [0 , possibly divided into a finite number of smaller intervals. Let n

, ]Ts and nd be the number of static and dynamic requests, respectively. Moreover,

let be the occurrence time of service request i. Static requests are such that while

dynamic ones have Lund et al. [32] define the degree of dynamism d as

[0, ]it ∈ T 0it =

[0, )it T∈ d

s d

nn n

δ =+

which may vary between 0 and 1. Its meaning is straightforward. For instance, if δ is equal to 0.3, then 3 customers out of 10 are dynamic. In his recent doctoral thesis, Larsen [10] generalizes the definition proposed by Lund et al. in order to take into account both dynamic request occurrence times and possible time windows. He observes that, for a given δ value, a problem is more dynamic if immediate requests occur at the end of the operational interval[0 . As a result he introduces a new measure of dynamism:

, ]T

' 1/s dn n

ii

s d

t Tn n

δ+

==+

∑

It is worth noting that 'δ ranges between 0 and 1. It is equal to 0 if all user requests are known in advance while it is equal to 1 if all user requests occur at time T . Finally, Larsen extends the definition of 'δ to take into account possible time windows on user service time. Let ai and bi be the ready time and deadline of client i , respectively. Then, i it a b≤ ≤ i

'' 1[ ( )] /s dn n

i ii

s d

T b t Tn n

δ+

=− −

=+

∑

It can be shown that i it a bi≤ ≤ also varies between 0 and 1. Moreover, if no time windows are

imposed (i.e., ), then ' and i i ia t b T= = ''δ δ= . As a rule, vendor based distribution systems (such as those distributing heating oil) are weakly dynamic. Problems faced by long-distance couriers and appliance repair service companies are moderately dynamic. Finally, emergency services and taxi cab services exhibit a strong dynamic behavior.

14.5 An Example of DVRP- The Traveling Repairman

Consider the situation that arises when for instance a bank teller machine breaks down and must be repaired by a service technician. The route to be followed by the technician may be determined using a distance based objective or it might take the urgency of the call (is the teller-machine located in a high intensity area or is it located in a remote area?) into consideration. This problem is often referred to as the Dynamic Traveling Repairman Problem" (DTRP) and is one of the most well-studied dynamic vehicle routing problems. A similar example is the repairman from the electric power company traveling from house to house to repair sudden break-downs in the electric power supply.

The Dynamic Traveling Repairman Problem (DTRP) was introduced by Bertsimas and Van Ryzin in the paper entitled \A Stochastic and Dynamic Vehicle Routing Problem in the Euclidean Plane". Bertsimas's and Van Ryzin's work on the DTRP is by far the most extensive and mathematically concise work on dynamic vehicle routing problems. Bertsimas and Van Ryzin mention that in real

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distribution systems orders/demands arrive randomly in time and the dispatching of vehicles is a continuous process of collecting demands, forming tours and dispatching vehicles. In dynamic settings the waiting time is often more important than the travel cost. Examples of applications where the waiting time is the important factor include the replenishment of stocks in a manufacturing context, the management of taxi cabs, the dispatch of emergency services, geographically dispersed failures to be serviced by a mobile repairman.

Bertsimas and Van Ryzin define the DTRP as follows:

• A repairman (or a vehicle/server) travels at unit velocity in a bounded convex region A of area A.

• All demands are dynamic and arrive in time according to a Poisson process with the intensity parameter λ . The locations of the demands are independently and uniformly distributed in A.

• Each demand requires an independently and identically distributed amount of on-site service time with mean duration s and second moment 2s . The fraction of time that the server spends on on-site servicing the demands is denoted by ρ . For stable systems sρ λ= .

• The system time, , of demand i is defined as the elapsed time between the arrival of demand i and the time the server completes the service of the demand. The steady-state system time denoted by T is defined by .

iT

lim [ ]iiT E

→∞= T

i

• The waiting time, , of demand i is defined as the time elapsed from the demand arrived until the service starts. Thus, . The steady-state waiting time, W, is defined as

iW

i iT W s= +W T s= − .

The problem is to design a routing policy that minimizes T . The optimal value of is denoted . Bertsimas and Van Ryzin stress that although the DTRP resembles a traditional queuing system, queuing theory does not apply due to the fact that the system time T includes the travel times which cannot not be regarded as independent variables. The approach of the authors is to derive lower bounds for all policies for the average system time T . After that Bertsimas and Van Ryzin analyze several policies and compare their performance to the lower bounds. To obtain these results the authors use techniques from combinatorial optimization, queueing theory, geometrical probability and simulation.

*T

Bertsimas and Van Ryzin analyze a wide range of routing policies for the DTRP. A brief description of these policies is given below.

• First Come First Served (FCFS): The demands are served in the order in which they are received by the dispatcher.

• Stochastic Queue Median (FCFS-SQM): The FCFS-SQM policy is a modification of the FCFS policy. According to the FCFS-SQM policy the server travels directly from the median of the service region to the location of the demand. After the service has been completed, the server returns to the median and waits for the next demand.

• Nearest Neighbor (NN): After completing service at one location the server travels to the nearest neighboring demand.

• Traveling Salesman Problem (TSP): The demands are batched into sets of size n. Each time a new set of demands has been collected, a Traveling Salesman Problem is solved. The demands are served according to the optimal TSP tour. If more than one set exists at the same time, the sets are served in an FCFS manner.

• Space Filling Curve (SFC): The demands are served, as they are encountered during repeated clockwise sweeps of a circle C that covers the service region.

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14.6 The integration of information technology and OR algorithms

Heuristics for the vehicle routing problem

Many authors have suggested a number of solution methods for the vehicle routing problem. Heuristics have been developed and applied to many routing and scheduling case studies and several researchers have compared and evaluated these methods. The advantage of the heuristics is their ability to handle efficiently a large number of constraints and parameters of the routing problem. They perform a relatively limited exploration of the search space and generally produce good quality solutions within modest computing time. Therefore, classical heuristics are still widely used in commercial software packages. Heuristics for the vehicle routing problem can be classified into two main classes: classical heuristics, developed between 1960 and 1990 and metaheuristics whose growth has occurred in the last decade . The most well known classical heuristics are the Savings and Sweep algorithms. The most successful metaheuristic approach is the tabu search heuristics. In terms of the solution procedure, classical heuristics can be classified in sequential and parallel. Sequential heuristic algorithms solve the vehicle routing sub-problems (clustering and finding best tour) separately and consecutively. Parallel heuristics produce routes and tours concurrently and can be classified in construction and improvement methods. In metaheuristics, the emphasis is on performing a deep exploration of the most promising regions of the solution space. These methods typically combine sophisticated neighbourhood search rules, memory structures and recombination of solutions. The quality of solutions produced by these methods is usually much higher than that obtained by classical heuristics but the computing time is increased.

Advanced planning and scheduling systems: The OR practice using IT

Modern information technology supports the definition and analysis of the factors influencing the routing process, a problem difficult to solve using empirical methods. Nowadays, there are software applications that manage supply chain processes at all three decisional levels of management, including features of transportation planning and execution:

• High level (strategic)––these applications offer transportation planning and fleet composition modules;

• Middle level (tactical)––these applications allocate resources to the general transportation plan derived from the previous level;

• Low level (operational)––at this level the applications are oriented to detailed scheduling of routes and tours on a daily basis.

The mathematical technique most commonly applied to strategic transportation planning is linear programming and related algorithms like network optimization and mixed integer programming. Linear programming based approaches model the current transportation business including revenues and costs. The models are extended to include decisions on the location of new facilities, acquisition of transport resources and implementation of transport strategies. Transportation planning applications at the tactical level include linear programming based models similar to the ones mentioned above. Their functionality is found in most ERP systems. The operational level applications utilize mostly heuristic algorithms. These algorithms can find a near-optimum solution for complex and multi-parameter problems in short time. Today, the advanced computer programming languages and the powerful hardware supports the use of these algorithms in advanced planning and scheduling (APS) software systems. The ability to implement APS systems as a decision support tool directed in transportation, has been greatly enhanced by improvements in telecommunications, better supply chain management systems, more comprehensive ERP implementations, and more powerful forecasting tools. The power of OR models and optimization methods can be enhanced incorporating them within a decision support system, which takes advantage of modern information technology.

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14.7 A Generic Architecture for Dynamic Real-Time VRS

Dynamic Real-Time Vehicle Management Model

Most solution approaches to the VRP are in practice implemented in a centralized computer resource (normally at headquarters), producing a daily plan to be provided to the vehicles before the beginning of the distribution execution. Some of these approaches have been implemented in commercial systems that are successfully used by numerous transportation, logistics, and manufacturing companies over the last twenty years. These systems have not, however, been designed to address the case in which the execution of delivery cannot follow the prescribed plan due to some unforeseen event. When there is a need for real-time intervention, it may be necessary to re-compute the plan using new input data. If a typical VRP approach is used for re-planning (i.e. re-planning the whole schedule from scratch), many vehicle schedules may be affected, thus causing significant performance inefficiencies (high overhead, nervousness, errors and high costs).

Thus, re-planning based on classical VRP solution methods may not be a realistic option. In the absence of algorithms capable of ‘isolating’ the part of the VRP affected by the unexpected event in order to minimize the disturbance to the overall schedule, interventions are typically performed manually (for example, through voice communication between drivers and the logistics manager), and the quality of decisions taken is naturally affected. The need to enhance existing methods, or develop novel approaches, becomes clearer in view of recent advancements in mobile and positioning technologies. Using such technologies, information about unforeseen events may be transmitted when they occur directly from the affected truck(s) through a mobile network to headquarters and/or other parts of the fleet. Given an efficient re-planning algorithm, appropriate and implementable plan modifications may be transmitted back to the fleet in a timely fashion to respond effectively to the new system state.

A real-time vehicle management model is schematically depicted in Figure 3, using control system formalism. The model includes the following:

a) Control & Detection of the system’s state: This concerns the selection of the parameters to be monitored, such as truck position, truck speed, truck inventory, and so on. These parameters need to be regularly monitored, as they will trigger intervention if needed. It is noted that interventions may lead to system “nervousness”; thus, the cost of intervention should be balanced against expected benefits.

b) Projection: This concerns the revised distribution plan that will be generated by the system, when it is observed that a vehicle is out of schedule. Typical parameters that cause significant disturbances to the original routing schedule are traffic jams, road works and negative environmental conditions.

c) Decision-making and execution: The selection of the problem objectives has significant effects on the decision-making and execution mechanisms employed. Objectives to be considered may include: minimize the deviation from the original plan, minimize the cost of non-conformance, minimize risk, and others.

Fig. 3: Systemic representation of a real-time vehicle management system

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Important decision-making issues include modeling of the real-time rerouting problem, and development of appropriate solution methods. In this case, problem complexity and computational time play a significant role. The reduction of complexity appears to be a necessary condition in providing timely, implementable solutions. A classic way to reduce complexity is by using a hierarchical approach, whereby, a complex monolithic problem is decomposed or disaggregated to multiple, simpler problems that can be solved independently. The solutions of these lower-level problems are combined to yield the solution of the global, higher-level, problem. By doing so, one needs to consider the trade-off between optimality and computational efficiency.

System Implementation Issues

The model presented in Figure 3 can be realized through the use of mobile technologies, real-time decision-making algorithms (along the lines presented in the previous section) and back-office automated processing. In addition to providing the appropriate directions to the drivers of the fleet, the customer base may be kept informed in regard to changes in the initial schedule, therefore improving the company’s service quality and customer relations.

The proposed system architecture is shown in Figure 4. It comprises three major sub-systems. The back-end system consists of a decision making module to facilitate automated decision making and ERP connectivity. The Wireless Communication sub-system allows a two-way communication between the back-end and front-end systems. The Front-end system enables a) a robust user interface, and b) interaction between the software platform that is installed in the on-board truck computer and the company’s back-end system.

Back-end sub-system

The back-end system is a decision support system that incorporates algorithms needed for real-time routing, scheduling and monitoring of the current state of the fleet, as well as a robust database containing both static (customers, geographical information of the road network, and so on) and dynamic (orders, quantities, time window information, and so on) data. The back-end system also provides ERP connectivity, which is especially useful in ex-van sales to provide information, such as customer sales history, customer credit, and other decision-critical data.

Wireless Communication sub-system

The wireless communication sub-system consists of two parts: a) The mobile access terrestrial network, which is responsible for the wireless interconnection of the back-end system with the front-end on-board devices, and b) the positioning system, which is responsible for vehicle tracking.

The Mobile Access Terrestrial Network can be based on any of a number of existing or emerging mobile technologies. In examining the options available to support an integrated distribution system, bandwidth is perhaps the most important issue. The bandwidth requirements depend on the computational model chosen. If vehicle onboard devices support much of the computations, then the demand for bandwidth is different than in the case in which much of the computation is performed at the headquarters. In either case, however, the demand for bandwidth is greater when compared to existing applications, such as fleet tracking, graphical representation of real-time information in digital maps, and voice communication.

GPRS, TETRA, and UMTS can provide always-on, packet-switched connectivity and high-speed data rates. GSM is a mature technology. However, it cannot support high-data transmission effectively. GPRS combines high data rates, always-on connectivity, mature technology, and has also been used in fleet management systems. As far as TETRA is concerned, it is worth mentioning that it provides much better security than GPRS, as well as supporting point-to-multipoint voice broadcasting. UMTS is an emerging standard, and its use cannot be assessed prior to thorough validation testing.

As far as the Positioning System is concerned, positional accuracy of less than 100m is deemed acceptable for urban distribution (accuracy requirements can, of course, be relaxed in non-urban settings). An analysis of the technologies that can be used for location identification goes beyond the scope of this paper.

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Fig. 4: Generic architecture for dynamic real-time vehicle management

Front-end system (Access media)

The front–end system generally consists of a mobile device, to which all necessary information is sent from the headquarters. The selection of the front-end device is important both from a user interface and from a computational performance perspective. Typical mobile devices that can be used on-board include mobile phones, personal digital assistants (PDAs), and Tablet PCs. In their present state, mobile phones do not appear capable of coping with the requirements of the applications under consideration. By contrast, PDAs and Tablet PCs are already used for specific distribution applications, such as back-end ERP connectivity. These devices include high-resolution displays, wireless networking capabilities, and integrated support for peripherals.

14.8 Conclusions

Real-time vehicle management is important in supporting supply chain execution systems, and minimizing the related logistics risks. It has been demonstrated that a good, near-optimal, distribution plan is necessary but not sufficient for high performance distribution. This needs to be complemented by the ability to make and implement sophisticated decisions in real –time, in order to respond effectively to unforeseen events. The emergence of technologies and information systems allowing for seamless mobile and wireless connectivity between delivery vehicles and distribution facilities is paving the way for innovative approaches in addressing this requirement.

In order to develop robust, practical approaches to the real-time vehicle management problem, research efforts should focus on three fronts: systems design, decision support methods, and system implementation. In the first area, significant issues to be tackled include the definition of the system’s objectives (minimize cost, risk and/or deviation from the original plan), the observability of the

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system’s state, balance of intervention costs vs. expected benefits, the extent of interventions (local vs. global), and other parameters. System designs cannot be generalized beyond the extent achieved in this chapter due to their heavy dependence on the characteristics of the problem addressed, and the algorithmic approach chosen for intervention. Therefore, future research can assess alternative design specifications against real-life case studies of real-time vehicle routing problems.

In the second area, a review of the vast existing literature on the vehicle routing problem has indicated that some research is relevant and can be used as a basis for the development of appropriate enhancements and/or novel decision support approaches in real-time vehicle re-planning. In this case, problem complexity and computational time play a significant role in system effectiveness.

In the implementation area, it appears that there exist mature technologies to sufficiently address the requirements of the real-time vehicle management system. In terms of the communication subsystem, GPRS and TETRA are appropriate mobile access networks, while GPS technologies meet all the related positioning requirements. For the front-end system, PDAs and Tablet PCs have significant potential, since both their interface capability and computational power support efficient user interaction and the local computational system requirements, respectively. All three fronts discussed above present interesting challenges with significant implications for both VRP-related research and the technology that will support effective logistics execution.

References

[1] Dimitris Bertsimas and Garrett Van Ryzin. A Stochastic and Dynamic Vehicle Routing Problem in the Euclidean Plane. Operations Research, 39:601-615, 1991.

[2] Allan Larsen, The Dynamic Vehicle Routing Problem, PhD Thesis, Technical University of Denmark, 1999.

[3] Vasileios Zeimpekis and George M. Giaglis, A Dynamic Real-Time Vehicle Routing System for Distribution Operations in Georgios J. Doukidis and Adam P. Vrechopoulos eds., Consumer Driven Electronic Transformation- applying New Technologies to Enthuse Consumers and Transform the Supply Chain part-1, Springer Berlin Heidelberg, 2005, pp 23-37

[4] Gianpaolo Ghiani, Francesca Guerriero, Gilbert Laporte and Roberto Musmanno, Real-time vehicle routing: Solution concepts, algorithms and parallel computing strategies, European Journal of Operational Research, Volume 151, Issue 1, 16 November 2003, Pages 1-11

[5] Sotiris P. Gayialis and Ilias P. Tatsiopoulos, Design of an IT-driven decision support system for vehicle routing and scheduling, European Journal of Operational Research, Volume 152, Issue 2, 16 January 2004, Pages 382-398

[6] D. Tarantilis, D. Diakoulaki and C. T. Kiranoudis Combination of geographical information system and efficient routing algorithms for real life distribution operations European Journal of Operational Research, Volume 152, Issue 2, 16 January 2004, Pages 437-453

[7] Vehicle Routing Problem (VRP), by Wolfgang Garn http://osiris.tuwien.ac.at/~wgarn/VehicleRouting/vehicle_routing.html

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Chapter 15 Optimization of the Supply Chain Network: Simulation, Taguchi, and

Psychoclonal Algorithm Embedded approach*

In today’s market, increased level of competitiveness and uneven fall of the final demands are pushing the enterprises to make an effort for optimization of their process management. It involves collaboration in multiple dimensions like information sharing, capacity planning, and reliability among players. One of the most important dimensions of the supply chain network is to determine its optimal operating conditions incurring minimum total costs. However, this is even a tough job due to the complexities involved in the dynamic interaction among multiple facilities and locations. In order to resolve these complexities and to identify the optimal operating condition we have proposed a hybrid approach incorporating simulation, Taguchi method, robust multiple nonlinear regression analysis and the Psychoclonal algorithm. The Psychoclonal algorithm is an evolutionary algorithm that inherits its traits from Maslow need hierarchy theory and the artificial immune system. The results obtained using the proposed hybrid approach is compared with those found out by replacing Psychoclonal algorithm with the AIS and Response Surface Methodology (RSM) respectively. This research makes it possible for the firms to understand the intricacies of the dynamics and interdependency among the various factors involved in the supply chain. It provides guidelines to the manufacturers for the selection of appropriate plant capacity and also proposes a justified strategy for delayed differentiation.

Key word: supply chain, simulation, Taguchi orthogonal array, Regression analysis, Psychoclonal algorithm

1 Introduction

In today’s competitive and global market, the success of an industry is reliant upon the management of its supply chains. Supply chain is the interlinked network of suppliers, manufacturers, distributors and customers interconnected by transportation, information sharing, and financial infrastructure (Chopra and Meindle, 2001). The supply chain aims to provide quality products and services to the end consumer in most efficient and economical mode (Sahin and Robinson 2002).

Plethora of researches addressing various issues related to different aspects of the supply chains is available in the literature. Gavirneni et al. (1999) have focused on management of material flow. While, Strader et al. (1999), and Hewitt (1999) emphasized on the role of information technology in the supply chain network. Shunk et al. (2006) applied integrated enterprise modeling methodology – FIDO – to supply chain integration 1modeling. Spekman (1988) and Tompkins (1998) studied the interdependencies among various supply chain members like retailers, manufacturers and suppliers. Kwak et al. (2006) proposed supplier buyer model for the bargaining process, while an agent based approach for e-manufacturing and supply-chain was given by Zhang et al. (2006). Moreover, Altiparmak et al. (2006) considered supply chain network as a multiobjective optimization problem and solved it with the aid of genetic algorithm. In the past, researchers have adopted different methodologies to cope with the supply chain problems some of them have focused on the deterministic while others on non deterministic approaches. Cohen and Lee (1988), Arntzen et al.

1 Contributed by Dr. M. K. Tiwari, Department of Industrial Engineering and management, Indian Institute of Technology Kharagpur, 721302, India. This work is a part of research article which has been accepted for publication in Computers and Industrial Engineering. The other co-authors of this work are Sanjay Shukla and Dr. Ravi Shankar.

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(1995), and Hariharan et al. (1995) have deployed mathematical programming, a deterministic approach, to optimize the supply chain network. While Zheng and Zipkin (1990), and Van Houtum et al. (1996) have concentrated on stochastic process modeling to deal with the supply chain issues.

In order to attain the competitive edge a supply chain should be flexible, quick enough, dependable, and cost-efficient. These objectives are accomplished by high speed information and material flow with low overhead costs. Coordination and collaboration in different activities like sale information, inventory information, promotions, and shipment are essential to the success of the supply chain .One of the important dimension in the supply chain network is to the minimize its costs by identifying the optimal operating conditions. However, this is even a tough job due to the complexities involved in the dynamic interaction among multiple facilities and locations. To resolve these complexities and to maximize the overall supply chain performance members of the chain have to team up and define clearly their responsibilities along with the co-operation mechanism.

Many researchers have addressed the problem of a single operating condition such as at what capacity level will a supply chain achieves its best performance. But, an analytical approach addressing the issue of depicting an optimal supply chain conditions by considering two or more factors, at a time, is lacking.

In this research the proposed analysis will clarify the responsibility of each member in implementing the optimal supply chain strategy. In other words, this research attempts to minimize total supply chain costs and identify an optimal condition by considering various supply chain parameters simultaneously viz. delayed differentiation, information sharing, capacity, reorder policy, lead time, and supplier’s reliability. In order to meet this objective, we are proposing a hybrid approach encapsulating simulation, Taguchi approach (1986), non-linear regression analysis (Montgomery 2001), and the Psychoclonal algorithm (Tiwari et al. 2004). First, Simulation is used to model a comprehensive supply chain network, which deals with a range of operational components and management level. Although useful, simulation only evaluates the effectiveness of pre-specified conditions and do not provide any means for optimizing the system performance. Due to the aforementioned shortcomings authors have intended to couple the simulation model with The Taguchi orthogonal array, robust non-linear regression analysis, and the Psychoclonal algorithm.

In order to respond effectively to the unanticipated events such as changes in demand, order quantity, or delivery time and to maintain system stability one needs to develop a robust supply chain. Taguchi approach is the best way to attain such robustness as it is capable of increasing system robustness, reducing experimental costs, and improving the overall quality by considering factors at a discrete level. We have explored Taguchi’s orthogonal array to identify the best parameter settings for the robust supply chain. Montgomery (2001) pointed out that the Taguchi’s orthogonal arrays are not efficient in exploring the search space completely when process parameters vary on continuous scale. Therefore, this method is only suitable for optimizing qualitative variables but not for the quantitative. This limitation has motivated the authors to incorporate regression analysis and the Psychoclonal algorithm with simulation and Taguchi approach. Regression analysis has been employed to obtain functional relationship between quantitative process variables and response of the supply chain network (supply chain costs). Further, Psychoclonal algorithm is utilized as an optimization tool for depicting optimum setting of quantitative process variables. The Psychoclonal algorithm is an evolutionary algorithm. It inherits its properties from Maslow’s need hierarchy theory and artificial immune system. The results achieved by using the proposed hybrid approach are compared with those obtained by replacing Psychoclonal algorithm with AIS and Response Surface Methodology (RSM) respectively. These comparisons revealed that Psychoclonal algorithm dominates both the AIS and the RSM in terms of minimizing the supply chain costs.

The remainder of paper is organized as follows. Section 2 deals with the simulation model of the supply chain network and parameters affecting its performance. In section 3, the way of conducting the experiments (using Taguchi method) are explored to obtain costs of supply chain network for different parameters settings. Then in section 4 mathematical modeling is carried out to obtain functional relationship between network’s process parameters and supply chain coats. To solve the

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mathematical model developed by regression analysis a solution methodology is presented in section 5. Results and discussions are provided in section 6 while section 7 concludes the paper.

2. Simulation Modeling of a Supply Chain Network

Simulation is a convenient source for predicting different interdependencies among constituents of a supply chain network. It is used to model a comprehensive supply chain network, which deals with the range of operational components and management level. In past, researchers like Bhaskaran (1998), Beamon and Chen (2001) have used simulation model to study different dimensions of a supply chain like performance effects of operational factors, and demand amplification effects. Here, we are using simulation to model a comprehensive supply chain network with the objective of identifying optimal factor setting having minimum costs.

We have considered three echelon supply chain that consist of four retailers one manufacturer, and three suppliers as shown in figure 1.

Supplier 1

Supplier 2

Supplier 3

Manufacturer

Retailer 1

Retailer 2

Retailer 3

Retailer 4

Figure1 A supply chain network

From the figure, it is clear that each player of the supply chain is interdependent and hence output at one stage acts as input for next stage. We have chosen ARENA (Rockwell software, 2003) as our

working simulation environment. Arena is general purpose simulation software that has full visualization of model structure and parameters, run control, and animation facilities. The supply

chain under consideration manufactures and markets televisions having short life cycle. The retailers rely on Bass model (refer to appendix A) for forecasting the demand of the product. They follow the

following strategy to meet their objective:

1. After estimation of the future demand the inventory decision is made depending to its current level.

2. According to inventory level order is placed to the manufacturer

3. Inbound inventories are received from the manufacturer then inventory cost and services are assessed.

For simplicity we assume that studied problem has two product families based on the size of television frame. The demand for product is assumed to resemble an S- shaped curve with an average of 1800 units per month. Since televisions are short life cycle products hence, length of the simulation run is considered to be small (say, 18 months). The supply chain network variables considered in present simulation are listed in table 1.

However, each variable may have wide range of possible levels but we are focusing only on three for each to represent most likely variability corresponding to the first stage of simulation. A brief description of the factors influencing the supply chain performance (supply chain costs) is described below:

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Table 1. Levels of controllable and uncontrollable factors in simulation

Controllable factors Level

A Delayed differentiation No Partial Complete B Information sharing No Partial Complete C Capacity (00s) 50 100 150 D Retailer’s (S, s) Policy, in (00s) 25 50 75 E Replenishment lead time (in

days) 6 10 14

F Supplier reliability 50% 70% 90%

1. Delayed differentiation (postponement): Alderson (1950) was the first who coined the term Delayed differentiation. This is also termed as postponement. This is the most general method that can be applied for enhancing the efficiency of a marketing system. It exploits the component commonality and redesigns the production process so that expensive operation can be delayed. This is an effective means of minimizing the increased costs, with maintaining the good customer services, arisen due to product multiplicity (Swaminathan and Tayur 1998).

2. Information sharing : Information sharing plays an important role in supply chain network. Each player makes an ordering decision based on information received from the downstream player. Lack of information sharing among player results in high amplification of demand (sterman 1989). This effect is highly undesirable as it exacerbate the supply chain costs.

)i(

3. Capacity : Capacity indicates the ability to satisfy future demand or it may be defined as the amount of the products that can be produced per time period. Sharing the capacity information by each entity in a supply chain is necessary for integrated planning (Gaonkar and Viswanathan 2001). The manufacturer makes a better production decision, in terms of costs saving, when he knows the capacity of each supplier. A higher capacity level leads to greater benefit of information sharing (Gavirneni 1997).

)c(

4. Reorder policy : Each retailer fallows a)R( ( )s,S policy. When inventory level becomes equal to or falls below the reorder point, s , then retailer puts an order up to level S. This is also known as min-max policy.

5. Lead-time : Lead-time is one of the most important factors influencing the supply chain performance. It may be defined as the time interval between placing and receiving an order. Longer lead-time results in smaller benefit of information sharing (Towill et al., 1992). Large lead time imparts major contribution in enhancing bull Whip effect (Lee et al. 1994).

)(l

6. Reliability of the suppliers : Reliability of the supplier affects the performance of the supply chain in terms of availability of materials and quality components at the time of production. Unreliable supply decreases the quality of finished product; and increases demand variability.

)(r

The simulation procedure consists of three main phases that are discussed in next three subsections.

2.1 Generation of Demand and Inventory Policy

Each retailer estimates the demand by combining Bass model and demand variance. Forecasted demand is assumed to be much larger than the error arising in forecasting thus, possibility of generating negative demand is negligible (Zhao and Xie 2002). Once the demands for all retailers are generated for 18 month life-cycle, the capacity is designed to match the demand. Let the manufacturer’s monthly capacity is represented by C. Retailers follow the ( )s,S policy to control the inventory level. Under min-max policy, every weak, if inventory drops below s then the retailers put

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an order to sustain a stock at an explicit level . This explicit level is determined by calculating the quantity needed between the time the order is placed and is received along with a quantity of safety stock to allow variation on demand. Mathematically can be given as:

S

S

LTT)LTT(S +++= σΘμ …(1)

Where,

μ is the periodic expected demand which can be estimated by employing Bass model.

T is the time interval between orders which is one week for the present model.

LT is the lead time that includes production time needed when no information sharing and no product differentiation are considered.

Θ is the standard deviation associated with desired service level, for present simulation model service level is set to be 97.5%.

σ is the standard deviation of demand per time period.

Due to the -shaped demand pattern S ( )s,S is adjusted according to the bass model. Using the difference of specified in factor D of table 1, we derive the reorder point,( sS − ) s , for each order cycle. The parameters of Bass model are derived by previous sales of similar product as shown in table 2.

Table 2. Estimation of Kandυω , in Bass model

Month T

television demand dt in (00s)

Cumulative demand D (t) in (00s)

Predicted F (t)

Squared error dt - Ft

0 0 0 0 1 69 69 63.59 29.26 2 71 140 73.65 7.04 3 82 222 82.97 0.94 4 87 309 92.41 29.31 5 96 405 100.90 23.98 6 110 515 108.42 2.50 7 116 631 114.67 1.78 8 132 763 118.51 181.95 9 115 878 119.46 19.90 10 123 1001 117.32 32.31 11 104 1105 111.96 63.35 12 100 1205 104.96 24.59 13 97 1302 96.09 0.82 14 72 1394 85.49 42.33 15 74 1468 73.62 0.14 16 57 1525 62.79 33.49 17 49 1574 53.66 21.71 18 45 1619 45.27 0.07 19 42 1661 37.12 23.47 20 31 1692 29.14 3.47. Total 1692 1692.00 542.73 Estimation ω υ K 0.035 0.188 1800.32

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These parameters ( K,,υω ) are determined by minimizing the sums of the squared error using excel solver. Note that for a new product, without history, demand forecast is often estimated based on a comparable older generation product.

2.2 The Production and Delivery Decision

The manufacturer applies lot sizing method (Chung and Lin 1988) in planning of its production activities. In order to produce the right demand and to lower its WIP the manufacturer must reduce the bullwhip effect (Lee et al. 1994). Clearly, the retailers need to share more information, and the manufacturer needs to make production-planning decisions based on additional information. Under no information sharing the suppliers only know the orders placed by manufacturer and manufacturer only receives the order information from retailers. The manufacturer plans his production based on forecasts obtained from the Bass model. Under partial information sharing the manufacturer plans his production polices based on forecasts provided by Bass-model but adjusts the production based on the retailers’ projected future net requirements, which is derived from retailer’s current inventory and future forecast. When complete information is available retailers provide real-time updated inventory status. Whenever a demand occurs and manufacturer adjusts the production quantity accordingly. Therefore it is evident that Bass model is only used for purpose of planning, and that the manufacturer basically implements a pull production system.

The manufacturer makes shipping decisions from on-hand inventory after finishing the current period’s production. He will fill each retailer’s order plus back order. If on-hand inventory is insufficient each retailer will receive a share proportional to its order plus backorder. Shipment reaches the retailer after the lead time.

2.3 Simulation Run and Performance Measure

The manufactured televisions have short life cycle so we have to study the system behavior over the product’s entire life cycle. The simulation required to study this sort of system is termed as terminating simulation. This type of simulation starts at a prescribed initial state and terminates when system reaches a prescribed terminal state or time. In our case it starts at t = 0, and terminates at t=18 months. In addition to this we have made 1200 replications of the system to attain a good point estimator.

The objective of the supply chain, considered in this research, is to minimize its costs by identifying the optimal level of supply chain parameters. The total costs of supply chain consist of transportation cost, carrying cost, ordering cost, setup cost, and backorder cost. The relation between the explicit operating situations and supply chain costs are shown in figure 2.1 – 2.6.

3. Parameter Design –Taguchi Method

The aim of the parameter design is to select the optimal level of controllable system parameters. This leads the high level of performance under diverse range of conditions and makes the system robust against the noises that cause inconsistency. Studying the design parameters one at a time or by trail is a widespread approach to optimize the required response. However, this needs either to a very long time for completing the design or to sudden termination of the design process due to cost of conducting large number of experiments. By varying the design parameters one at a time; the study of 6-design parameter at 3 levels would necessitate 36 possible experimental evaluations. But the time and cost required in carrying out such a comprehensive study is not desirable. The Taguchi approach (1986) provides the convenient and effective means to determine optimal or near optimal design parameters by using orthogonal array and linear graphs. We choose the L27 (313) design for controllable factors. By using L27 array, 13 parameters can be studied at 3 levels by running 27 experiments instead of (36). To map our model completely, a linear graph is presented in figure 3.

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figure 2.1Total Cost Under Different Level of

Delayed Differentiation

105115125135145155

No Partial Complete

Delayed Differentiation

Tota

l Cos

t

DelayedDifferentiation

Figure 2.2 Total Cost Under Different Level of Information Sharing

105115125135145155

No Partial Complete

Information Sharing

Tota

l cos

t

InformationSharing

Figure 2.3 Total cost under different level of capacity

105115125135145155

50 100 150

Capacity

Tota

l cos

t

Capacity

Figure 2.4 Total Cost Under Different Level of Retailer's S-s Values

105115125135145155

25 50 75

retailer's S-s Value

Tota

l Cos

tRetailer's S-svalue

Figure 2. Total cost verses controllable variables

Figure 2.6 Total cost under Different level of Supplier Reliability

105

115

125

135

145

155

0.5 0.7 0.9

Supplier's Reliability

Tota

l Cos

t

Figure 2.5 Total Cost Under Different Level of Lead Time (days)

105115125135145155

6 10 14

Lead Time (in days)

Tota

l Cos

t

Lead Time (days)SupplierReliability

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9 10 11 12 13

D E F

B*C 8, 11

A*C 6, 7

A*B 3, 4

A 1

C 5

Figure 3 Linear graph for L27 (313) orthogonal array

In the linear graph, for L27 (313) each circle represents a column of orthogonal array. Column 1, 2 and 5 represents factor A, Band C respectively. Column 3 or 4, 6 or 7 and 8 or 11 can represent interactions between A and B, A and C , and B and C respectively column 9, 10 and 12 can be allotted randomly for remaining factors D, E, and F.

3.1 Signal-to-Noise (S/N) ratio

Signal-to-Noise ratio measures the robustness of the response obtained from the Taguchi model. Signal measures the typical value of the response whereas; noise is a measure of variability and represents the undesired components. S/N ratios are defined in such a manner that larger is numerical value, better the performance (Montgomery 2001). In this research the response has the smaller the better characteristics. Hence S/N ratio for the costs of supply chain can be given as:

S/N ratio = -10log10 So … (2)

Where ⎟⎟⎠

⎞⎜⎜⎝

⎛∑=

=n

1i2iy

n1

0S

sticcharecteri eperformancthiiy =

replicates ofnumber n =

We can draw plots between S/N ratio, obtained from Taguchi orthogonal array, and factors level as shown in figure 4.

figure 4.1 S/N Ratio for Delayed Differentiation

25

30

35

No Partial Complete

Delayed Differentiation

S/N S/N ratio for

d

Figure 4.2 S/N Ratio for Information sharing

25.526

26.527

27.528

28.529

No Partial Complete

Information sharing

S/N

S/N ratiofor factor i

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Figure 4.3 S/N ratio for manufacturing capacity

24

26

28

50 100 150Capacity

S/N S/N ratio for

capicity

Figure 4.4 S/N ratio for Retailers (s,S) policy

2526272829303132

25 50 75

Retailer's S-s Value

S/N S/N ratio for

factor R

Figure 4.6 S/N ratio for Supplier Reliability

23

24

25

26

27

28

0.5 0.7 0.9Supplier Reliability

S/N S/N ratio for

factor r

Figure 4.5 S/N ratio for Lead T ime

2525.5

2626.5

2727.5

2828.5

29

6 10 14

Lead T ime

S/N S/N ratio

for factor l

Figure 4 Plots of S/N ratio from Taguchi method results

From the plots, we find the following optimal level for each factor.

Factor A (Delayed differentiation) = Complete

Factor B (Information sharing) =Complete

Factor C (Monthly capacity) =100

Factor D (Reorder quantity) = 25

Factor E (Lead time) = 6 days

Factor F (Supplier reliability) =90%

In optimization problems use of the Taguchi orthogonal array is limited as it assumes factors at discreet level. Hence optimal level of factors, varying on continuous scale can not determined by it. Therefore, in our case only two qualitative factors (delayed differentiation, and information sharing) can be optimized by using Taguchi approach.

4. Mathematical modeling of a supply chain network using regression analysis

One of our goals is to design a network that is robust when exposed to disturbances. To attain this we have used the factors level identified by the Taguchi method as the initial point therefore, we start with:

Capacity=100

Reorder quantity=25

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Lead time=6

Suppliers reliability=90%

The Experiments are carried out with suitable step sizes of parameters as listed in table 3.

Table 3. total costs of supply chain corresponding to different settings of factors

Factors Capacity (x1) Reordered

quantity (x2) Lead-time Suppliers

reliability

S.No.

100* 25& 6$ 90#

Supply chain costs

1. 98.3 25.85 6.43 89.4 203 2. 96.6 26.7 6.86 88.8 176 3. 94.9 27.55 7.29 88.2 162 4. 93.2 28.4 7.72 87.6 148 5. 91.5 29.25 8.15 87 143 6. 89.8 30.1 8.58 86.4 138 7. 88.8 30.95 9.01 85.8 127 8. 86.4 31.8 9.44 85.2 125 9. 84.7 32.65 9.87 84.6 129 10. 83 33.5 10.3 84 130 11. 81.3 34.35 10.73 83.4 134 12. 79.6 35.2 11.16 82.8 136 13. 77.9 36.05 11.59 82.2 143 14. 76.2 36.9 12.02 81.6 150 15. 74.5 37.75 12.45 81 157 16. 72.8 38.6 12.88 80.4 163 17. 71.1 39.45 13.31 79.8 167 18. 69.4 40.3 13.74 79.2 175 19. 67.1 41.15 14.17 78.6 198 20. 66 42 14.6 78 205

* Initial point of capacity

& Initial point of reordered quantity

$ Initial point of lead time

# Initial point of supplier reliability

These step sizes have been selected based on the knowledge of the process or other practical concerns. For the instance, if the original monthly capacity is 100 units, to understand the supply chain performance when capacity is changed within range of (65,100) units, a step size of 1.7 units would be sensible. In terms of lead-time if range of experiment is (6, 15) days, step size of 0.43 days time would be reasonable. We choose step size for capacity, reorder quantity, lead time, and supplier reliability 1.7, 0.86, 0.43, and 0.6 respectively.

For simplicity, independent variables are coded in an interval of (-1, 1) by using the fallowing coding schema:

Where, ( ) ( )

( ) ( )2

ii2

iboundLoweriboundi

ifactorthi

boundLower bound

of value

−

+

−

γ

χ

…(3)

i

Upperi

ix

Upper

Actual

=

=

=

γ

χ

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The upper bound and lower bound of parameters are as follows:

Capacity = [65,100]

Reordered quantity = [25, 43]

Lead time = [6, 15]

Supplier reliability = [77, 99]

By using equation 3, network variables can be coded in following manner:

… (4)

⎟⎠⎞

⎟⎠⎞

⎟⎠⎞

⎟⎠⎞−

10

5

6

5.98Capacity

88-yreliabilitSupplier

5.3-time

32.6-quantity Reordered8

⎜⎝⎛=

⎜⎝⎛=

⎜⎝⎛=

⎜⎝⎛=

4x

3x

2x

1x

Lead

Variables in coded form are listed in table 4

Table 4. coded levels of factor

Factors S.NO. X1 X2 X3 X4

1. 0.90 -0.45 -0.90 0.91 2. 0.80 -0.41 -0.81 0.75 3. 0.68 -0.36 -0.71 0.72 4. 0.61 -0.31 -0.61 0.63 5. 0.51 -0.26 -0.52 0.54 6. 0.42 -0.21 -0.42 0.44 7. 0.36 -0.17 -0.33 0.35 8. 0.22 -0.12 -0.23 0.26 9. 0.12 -0.07 -0.14 0.17 10. 0.02 -0.027 -0.04 0.08 11. -0.07 0.02 0.05 -0.01 12. -0.16 0.06 0.15 -0.11 13. -0.26 0.11 0.24 -0.20 14. -0.36 0.16 0.33 -0.29 15. -0.46 0.21 0.43 -0.38 16. -0.55 0.25 0.53 -0.48 17. -0.65 0.30 0.62 -0.57 18. -0.75 0.35 0.72 -0.66 19. -0.88 0.39 0.81 -0.75 20. -0.94 0.44 0.91 -0.85

Regression analysis provides the functional relationship between independent variables and response. Regression equation is given as (Montgomery 2001):

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… (5) ∑ ∑ ∑∑+++== = <

k

1i

k

1i jijiij

2iiiii0 xxˆxˆxˆˆy ββββ

Where is the parameter, is the regression coefficient and is the number of process parameters. Least square estimate method is used to interpret estimated regression coefficient which is given as:

ix thi iβ k

y*

1X*'X

kkˆ::k

ˆ::i

ˆ0

ˆ

ˆ −⎟⎠⎞⎜

⎝⎛=

⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜

⎝

⎛

=

β

β

β

β

β… (6)

Where,

⎟⎟⎟⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜⎜⎜⎜

⎝

⎛

=

nnx....2nx1nx1::::::::::::

2kx....22x21x11kx....21x11x1

X … (7)

y is the response (costs of supply chain), which can be expressed as:

… (8)

⎟⎟⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜⎜⎜

⎝

⎛

=

ny::2y1y

y

By using equations 5, 6, 7, and 8, we get following regression equation (objective function).

43423241

312124

23

22

214321

001.0727.5495.4078.0274.3328.0316.1552.1109.1

689.0897.0111.20037.1717.03.120

xxxxxxxxxxxxxxx

xxxxxY

−++−+−+++

+++−+=

…(9)

Subject to 1ix ≤

Nonlinear regression equation 9 foresees interdependencies among various supply chain parameters. By solving this equation with suitable optimization tool, optimum level of these factors can be predicted.

5. Solution methodology

In past, Shang et al. (2004) have solved this type of model by using Response Surface Methodology (RSM). Measure drawback allied with RSM is to consider only one parameter as a variable during partial differentiation and rest as a constant. While in actual practice in dynamic supply chain network all players are interrelated. Therefore it is not reasonable to assume any variable as a constant at any

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time. Ridge effect also limits the use of RSM in resolving the regression equations having four or more variables. To overcome these drawbacks and for exploring search space completely, we are adopting Psychoclonal algorithm for the optimization of objective function depicted in equation 9.

5.1 Psychoclonal Algorithm: an Overview

The Psychoclonal algorithm (Tiwari et. al.2004) inherits its attributes from Maslow’s need hierarchy theory and Artificial Immune System (AIS). The silent features of this algorithm are as follows.

5.1.1 Maslow Need Hierarchy Theory

Abraham Maslow was the investigator who devised an interesting theory about human nature by amalgamating a large body of research allied to human development (Maier, N.R.F. 1965). Based on Maslow’s research, hierarchy of human needs can be viewed as a personal evolution. This hierarchy has a Pyramidal structure as shown in figure 5.

Self-actualization needs

Growth needs

Social needs

Safety needs

Physiological needs

Figure 5 Maslow’s needs Pyramid

In the hierarchy of human need, deficiency is detected from the Bottom (the starting point) to the top (the end point). Each detected deficiency is dealt with and removed before progressing to the next level. Analogous to human needs an evolutionary algorithm can also be considered to have deficiencies. For efficient evolution of an algorithm all deficiencies should be detected and then dealt with them. The analogy between Maslow need hierarchy theory and an algorithmic evolution is shown as follows.

1. Physiological needs: The physiological need lies at the bottom of Maslow Pyramid and refers to the hunger, thirst, and bodily comfort of people. In optimization, this refers to generation of feasible solutions based upon the problem environment.

2. Safety needs: This is the second set of needs. It has to do physical and physiological safety from external threats to our well-being. External threats in engineering milieu are constraints imposed on the problem. For continued existence, solutions are subjected to these threats or constraints and proper evaluation.

3. Social needs: Next, social needs become dominant; a person will strive for meaningful relations with others. In the optimization, this refers to the selection of the candidate solution through interaction between candidates.

4. Growth needs: Every entity desires to produce entities of its kind through reproduction. Here, candidate solution diversifies to extend its search space. This is the basic mechanism of every evolutionary process.

5. Self actualization needs: Self-actualization is the need to maximize one’s potential and to the fulfillment allied with the realizations of one’s capabilities. In term of Maslow “this need might be phrased as the desire to become more and more what one is, to become everything that one is capable

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of becoming”. This is very true whether it is human or solution to an optimization problem. As per above description, it has been concluded that more the self-actualization needs are fulfilled stronger the individual become. This is the reason for carrying out a number of iterations to decide the near-optimal/optimal solution.

5.1.2 A brief description of Artificial Immune System (AIS)

In last few decades the researchers, involved in designing and optimization of engineering systems, has paid considerable attention to the evolutionary processes like Artificial Immune System (AIS), Genetic Algorithms (GA), and Artificial Neural Networks (ANN). AIS provides a way to deal with the complex computational problems like pattern recognition, elimination, machine learning and optimization.

The vertebrate immune system is a complex system having large number of functional components. The main task of the immune system is to survey the organism in search of malfunctioning cells from their own body and foreign substances that are recognized by system called antigens. The constituents of the immune system that recognize antigens are called antibodies. There are two major categories of immune cells: B cells and T cells. B cell recognizes antigens free in solution (in blood stream) on the other hand T cells require antigens to be presented by accessory cells. After generation of T-cells they migrate in to thymus where they mature. During maturation, all T-cells that recognize self-antigen are excluded from population of T-cells this process is termed as negative selection (Nossal 1994). If a B-cell interacts with a non-self antigen with affinity threshold; it proliferates and differentiates in the memory and effecter cells, this process is termed as clonal selection (Ada and Nossal 1987). In contrast, if a B-cell recognizes a self-antigen, it might results in suppression as recommended by the immune network theory (Jerne 1974).

When a non-self antigen is recognized by a B-cell receptor with threshold affinity, it is selected to proliferate and produce high volume of antibodies as shown in figure 6.

Selection

None-self antigen

Proliferation and Maturation

Cells with maturedReceptor

Figure 6. Schematic representation of proliferation and maturation

During reproduction the B-cell progenies (clones) having strong selective pressure participates in hypermutation process. The whole process of mutation and selection is known as maturation of the immune response.

Depending upon affinity, a selection is made from matured pool of antibodies. This results a high quality solution that exhibit prudence. From engineering point of view this is the most alluring characteristic of the immune system because, the candidate solutions with higher affinity must

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somehow be preserved as high quality candidate solutions and will only be replaced by matured clones. In a T cell dependent immune response, the repertoire of antigen-activated B cells is diversified by two mechanisms: hypermutation and receptor editing as shown in figure 7.

Antigen-binding site

Aff

init

Figure 7 Antigen-binding sites Random changes are introduced in to the genes responsible for the Ag-Ab interaction, and occasionally one such change will lead to an increase in affinity of the antibody. The hypermutation operator works in a similar fashion to mutation. The difference lies in the rate of modification, which depends upon antigenic affinity. Antibodies with higher affinity are hypermutated at low rate, while antibodies with a lower affinity are hypermutated at high rate. This phenomenon is called receptor editing and governs the hypermutation that guides the solutions toward local optima, while receptor editing helps to avoid local optima.

Based on the Clonal principal and Maslow needs the proposed psycho-clonal methodology is conceived and it is described in the coming section.

5.2 Proposed Algorithm

On considering the evolution of AIS on the track similar to that of Maslow need hierarchy theory we come across with the Psychoclonal algorithm. Need level 5 make the real difference between AIS and the Psychoclonal algorithm. In Psychoclonal algorithm the novel feasible antibodies are constantly introduced to make a boarder exploration of the search space and prevent the saturation of the population with similar antibodies. This feature inherits a unique ability of maintaining the diversity of population. Evolution of the Psychoclonal algorithm is shown in figure 8.

Need level 1. Psychological needs: Defining a problem-specific objective function is pre-requisite. A randomly generated initial population of antibodies depending upon the problem-environment is also required.

Need level 2. Safety needs: Here, The initial population is exposed to the threats posed by the antigens.

2.1 From population Agm randomly choose an antigen Agj and expose it to all antibodies Ab.

2.2 According to the objective function, determine affinity vector f k that contains the affinity of Ag j to the entire N, antibodies (Ab’s) in the set Ab.

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Self- actualization

needs

Safety needs

Growth needs

Social needs

Physiological needs

Editing(Abd)

Affinity (f)

Hypermutation (c*

k)

Clone (ck)

Generate (Ab)

Figure 8. Flowchart illustrating Psycho-Clonal Algorithm

Memory set(Abd)

Affinity (f*)

Hypermutation (c*)

Clone (ck)

Generate (Ab)

Input: Ab, gen, n, ρ d, fk,, fk*,

If gen < max_gen

N

Y Output: Ab, fk

The figure clearly depicts the sequence in which Maslow’s needs are fulfilled. Acronyms used in algorithm are explained in appendix B.

Need level 3. Social needs: Here, interaction is carried out between antibodies with a view as to identify the relation with each other.

3.1 From randomly generated set Ab select n highest affinity antibodies and compose a new set Ab k, n of high affinity antibodies in the relation to Agj.

3.2 Now the new composed set of antibodies is cloned proportional to their antigenic affinities, generating a repertoire C k of clones. Higher the antigenic affinity, the higher the number of clones generated for each n selected Ab’s

Need level 4. Growth needs: Set Ck is submitted for hypermutation, inversely proportional to the antigenic affinity, generating a population C*

k of matured clones (the higher the affinity, the smaller the mutation rate).

After satisfaction of need level 4 it is necessary to check the need level 2 once again for these entities, as they are new denizens of society. Therefore, they must be exposed to the threats and properly evaluated as per objective function.

Need level 2. Safety needs: Determine the affinity of the matured clones*kf

*kc with relation to an

antigen. Now the whole of n, Ab’s are selected to compose the memory set.

Need level 5. Self-actualization needs: finally replace the d lowest affinity Ab’s from Abd and choose the best among them to fulfill the self-actualization level. As mentioned above, this level becomes stronger and stronger after a number of generations. Thus, the process is continuing until N = Ngen . (maximum number of generation).

5.3. Implementation of the Psychoclonal Algorithm for Optimization of Supply Chain Network

5.3.1 Need level 1

Physiological needs: Defining search space and then selecting antibodies from search space randomly satisfy physiological needs of problem. The antibodies consist of string of candidate solution, taken from search space (-1, 1), shown in figure 9.

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0.19 -0.59 0.90 0.85

x1 x2 x3 x4

Supply chain’s cost (affinity) = $121.564 million

Figure 9. Representation of initial antibody

Figure 9 provides randomly chosen initial level of all four factors . )4x,3x2x,1x(

5.3.2. Need level 2

Safety needs: By taking solution from the infeasible region to feasible region satisfies safety needs. Once the solution moves into feasible region its affinity vectors is calculated using objective function given in equation (9). This equation guides the solution towards local optima and helps in satisfying initial safety need of the antibodies.

5.3.3. Need level 3

Social needs: antibodies satisfy their social needs by interacting with other antibodies in the solution pool.

5.3.3.1 Select n highest affinity antibodies from the set of antibodies filtered from need level 2.

5.3.3.2 Selected antibodies are then cloned independently and proportionally using equation (10).

⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜

⎝

⎛

=

∑=

ηφη .f

fn

1ii

ii,c … (10)

=η total number of antibodies

=i,cη number of clones generated for the ith antibody

=if antigenic affinity of ith antibody

=n size of population

( ) =.φ function for generating closest integer

5.3.4. Need level 4

Growth need: At this level repertoires of the clones created at need level 4 are subjected to hypermutation. Hypermutation rate is calculated using equation (11) and is inversely proportional to the antigenic affinity.

( )ifexp ×−= ρΠ … (11)

=Π hypermutation rate

=ρ decay factor

=if antigenic affinity of ith antibody

After satisfying the need level 4, the antibodies formed are again attacked by the antigens, affinity vector of matured clones are calculated using objective function given in equation (9). The weak clones are edited at this level using receptor editing

5.3.5 Need level 5

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Actualization need: Finally, the best solution is retained in need level 5 as a memory set. The weak solutions are also edited at this level. Thus, with number of generation, the quality of solution improves in the memory set.

Process repeats till gen = max_gen.

6. Results and discussions

In this research six factors have been considered for the simulation modeling of a supply chain network. Among these two factors are qualitative and rests are quantitative, therefore they vary on continuous and discrete scales respectively. Clearly, optimal level of the former can be identified using Taguchi orthogonal array while latter can not be dealt with this method. In the past Shang et al. (2004) have made an attempt to identify optimal setting of quantitative factors by deploying response surface methodology (RSM) on simulation based regression analysis model. Nevertheless, because of some draw backs coupled with RSM, we have motivated to use a new evolutionary algorithm named Psychoclonal algorithm for identifying the optimal setting of qualitative factors incurring minimum supply chain costs. After successful implementation of the Psychoclonal algorithm on the objective function given in equation (9), we have obtained the optimal level of the quantitative factors in coded form (in the range [-1, 1]) as listed in table 5.

Table 5. Optimal level (in coded form ) of factors obtained from Psychoclonal algorithm

S. No. Factors Optimal level

1. X1 -0.48

2. X2 0.26

3. X3 0.96

4. X4 -0.12

By using equation (3) optimal level of these factors in their natural form is enlisted in table 6.

Table 6. Optimal level (in natural form) of factors obtained from Psychoclonal algorithm

S.No. Factors Optimal level

1. Capacity 94.66 Unit

2. Reorder quantity 34.16 Unit

3. Lead time 10.10 Days

4. Supplier reliability

88.80%

In contrast to the above results, Taguchi method provides one value as an optimal out of the pre-determined level of the factors. For example with Taguchi three level orthogonal array, supplier capacity can only take up one of the three values: 100, 50, and 150 as an optimal, while Psychoclonal algorithm explores the search space completely in the range [50, 150] and finds the optimum level of the supplier’s capacity. The optimal level of supplier’s capacity using Psychoclonal Algorithm is found out to be 94.66 units. Similarly, by using Psychoclonal algorithm the optimal level obtained for the reorder quantity and supplier reliability is 34.16 units and 88.8% respectively. Authors have also solved the model presented in equation (9) by employing Artificial Immune System (AIS) and

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Response Surface Methodology (refer to appendix C). The results obtained by employing AIS are listed in table 7 and 8. While, results found out using RSM is provided in table 9 and 10.

Table 7. Optimal level (in coded form) of factors

obtained from AIS

S. No. Factors Optimal level 1. X1 -0.44 2. X2 0.17 3. X3 -0.32 4. X4 -0.85

Table 8. Optimal level (in natural form) of factors

obtained from AIS S.No. Factors Optimal Level 1 Capacity 94.98 Unit 2. Reorder quantity 33.62Unit 3. Lead time 3.7Days 4. Supplier

reliability 79.5%

Table 9. Optimal level (coded) of factors obtained from

RSM

S. No. Factors Optimal level 1. X1 0.4272 2. X2 -0.4839 3. X3 -0.4299 4. X4 0.7246

Table 10. Optimal level (in natural forms) of factors

obtained from RSM S.No. Factors Optimal level 1 Capacity 89.976 Unit 2. Reorder quantity 29.64Unit 3. Lead time 8.065Days 4. Supplier

reliability 88.209%

The programming of the Psychoclonal algorithm and AIS has been done in C++ language on IBM PC with Pentium IV CPU at 1.9GHz. The minimum total supply chain costs corresponding to the optimal setting of the factors obtained by Psychoclonal algorithm is $117.01 million. In contrast to this, at stationary point the minimum total costs obtained by AIS and RSM are $ 118.96 million and $ 120.26 million respectively. This shows the advantage of Psychoclonal algorithm over AIS and RSM. In the Psychoclonal algorithm convergence of result starts on very few generations, thus the computational burden has been considerably reduced. This can be attributed to the various levels of needs and

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hypermutation, which accelerates the multiple local searches simultaneously. The convergence trend followed by Psychoclonal algorithm and AIS is shown in figure 10.

0 25 50 75 100 125 150 175 200 225 250 275 300110

112

114

116

118

120

122

124

126

Figure 10. Convergence trend of solution

Iteration

Sup

ply

Cha

in C

ost

psychoclonalAIS

Figure 10 Convergence trend of solution

This figure shows that the Psychoclonal algorithm converges at the 147th generation to achieve the minimum possible supply chain costs of $117.01 million, whereas AIS converges at the 166th generations with the total supply chain costs of $ 118.96 million. Evidently, the Psychoclonal algorithm converges much earlier and with a lower supply chain costs. The certainty of convergence of the Psychoclonal is due to its ability to maintain the diversity of population. This is because, the feasible antibodies are constantly introduced to make a broader exploration of search space and prevent saturation of the population with similar antibodies. In nutshell, the superior performance of the Psychoclonal algorithm may be ascribed due to its ability to produce a large volume of antibodies depending upon the needs level. It is found that the probability of finding optimum solution is enhanced when the pool considered is large. Further, receptor editing helps in escaping local-optima and the self-actualization needs level makes it stronger and stronger with each generation.

Parameter tuning is a matter of major concern for any optimization problem as it affects the performance of the algorithm. Fine tuning of the parameters and scrutinizing their correlation with the problem under consideration needs random experiments. Psycho-Clonal algorithm is explored mainly by three parameters; namely: n (number of antibodies to be selected for cloning), Nc (number of clones generated), and d (the amount of low-affinity antibodies to be replaced). These mainly influence the convergence speed, and the computational complexity.

7. Conclusion

Implementation of an efficient operational strategy for a supply chain is a tough job due to difficulties in inferring the outcomes of interactions among various factors. To deal with this difficulty, we have proposed a hybrid approach encapsulating simulation, Taguchi method, nonlinear robust regression analysis and the Psychoclonal algorithm. First, simulation is carried out to model a comprehensive supply chain network. Further Taguchi method is deployed to quickly determine a robust area and to optimize qualitative factors. Subsequently, regression analysis and Psychoclonal algorithm refines the result obtained by Taguchi orthogonal array. In addition, authors have also done the same by replacing Psychoclonal algorithm with AIS and RSM. The comparisons of results revealed that the Psychoclonal algorithm explores the search space better then AIS and RSM. By adopting the

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proposed approach, supply chain players can coordinate their operations to achieve the highest efficiency for the entire chain. This study makes it possible for the firms to understand the dynamic relations among various factors, and provide guidelines for management to minimize the impact of demand uncertainty on the supply chain performance. The results obtained help the manufacturer to determine the proper plant capacity and right level of delayed differentiation strategy for its products.

Further research may include strategic costs relating to design changes of the supply chain. The strategic costs may comprise product and process redesign costs for postponement, costs for establishing information sharing through advanced information technology, and costs for reducing the lead time.

Appendix A:

2.1 Bass model

Bass model is helpful in evaluation of demand of short life cycle products (Bass 1969). In present research, authors focus their attention on short life cycle product and use Bass model to estimate the demand. In past, this model has been used by number of researchers (Lawrence and Lawton 1981 and Thomas 1985).

)1tDK)(1tD)K/((dt

tdDtd

t)(e)/(1

t)(e1KtD

−−−+==

+−+

+−−=

υω

υωυω

υω

… (12)

Where,

the demand at period t =td

the cumulative demand up to period t ( ) =tD

the potential number of customers in the market =K

=ω the coefficient of innovation or external influence

=υ the coefficient of information at internal influence

Appendix B:

Ab: available set of antibodies

Abd: set of new antibodies that will replace the amount d of the lower affinity

antibodies from Ab.

Abk,n : antibodies from Ab with highest affinity

Agm : population of m antigens.

Ck : population of Nc clones generated from Abk, n

C*k : population after hypermutation.

fk : vector containing values of objective function Y (.) as the affinity of all

antibodies in the relation to antigen

f*k : vector containing the values of antigenic affinity for matured clones

N : total number of antibodies

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Nc : total number of clones generated for each of the antigen.

Ngen : number of generations

Appendix C:

Response surface methodology (RSM)

Response surface methodology (RSM) uses a group of mathematical and statistical techniques to find the optimal condition and response (s).

The second order model (given in equation 6) can be expressed in matrix format as:

BXXbXˆY ''0 ++= β … (13)

Where

,

⎟⎟⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜⎜⎜

⎝

⎛

=

kˆ::

ˆ

ˆ

b

β

β

β

2

1

⎟⎟⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜⎜⎜

⎝

⎛

=

2/kkˆ......2/1i

ˆ::::::::::

2/k2ˆ....22

ˆ2/21ˆ

2/k1ˆ....2/12

ˆ11

ˆ

ββ

βββ

βββ

Β

The set of that optimizes the response surface is called the stationary point, where partial

derivative is Zero, i.e.

iX

0BX2bXY

=+=∂∂ … (15)

… (14)

⇒ Optimal input level is . … (16) bB5.0X 10

−−=

Optimal total costs at the stationary point thus becomes

bX5.0ˆY '000 += β … (17)

By using equation 10, 12, 14 and table 4 we obtain stationary point as:

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡−

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

−−−

−−−

−=

897.0111.2

0037.1717.0

316.1005.0863.2039.00005.0552.1247.2637.1863.2247.2109.1164.0039.0637.1164.0689.0

*5.00X

=

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

−−

7246.04299.04839.0

4227.0

Minimum total costs

[ ]⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡−

−−+=

897.0111.20037.1717.0

7246.04299.04839.04557.05.023.1200Y

= $120.26 million

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