College of Business AdministrationUniversity of Rhode Island 2013

2013 No. 6

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Office of the DeanCollege of Business AdministrationBallentine Hall7 Lippitt RoadKingston, RI 02881401-874-2337www.cba.uri.edu

Yang Yu, Dara Schniederjans, and Qing Cao

A Multi-Agent Simulation Model Cloud Computing and Its Impact on Fill Rate:

Cloud Computing and Its Impact on Fill Rate: A Multi-Agent Simulation Model

Yang Yua1

Dara Schniederjansb

Qing Caoa

aInformation Systems and Quantitative Sciences Department Rawls College of Business Adminstration

Texas Tech University Lubbock, TX 79409

United States Yang.yu@ttu.edu Qing.cao@ttu.edu

bDepartment of Marketing and Supply Chain Management

College of Business Administration University of Rhode Island

Kingston, RI 02881 United States

Schniederjans@mail.uri.edu

Abstract

Supply chain integration and collaboration are becoming a growing trend gaining acceptance in the supply chain management research realm and among organizations due to the ever-increasing complexity of global supply chains. Consequently, understanding emerging information technology and how this may impact facets of supply chain performance is vital to today’s supply chain environment. This paper seeks to shed light on a disruptive information technology trend, cloud computing technology, which is rapidly being adopted by various organizations, and has vast potential in cost effectively improving service level in organizations via business intelligence collaboration. To further analyze cloud computing’s potential, we draw on social network theory and develop a comprehensive and unique conceptualization of how cloud computing impacts collaboration and ultimately service level. Moreover, we provide insight into the three techniques of collaboration and specifically how cloud computing can impact business intelligence collaboration. This study shows that cloud computing can improve collaboration by creating an environment conducive for higher levels. Further, our multi-agent simulation results suggest that with the highest level of collaboration, fill rate significantly increases over lower levels of collaboration.

Keywords: Cloud computing, Collaboration, Fill rate, Service level, Simulation

1 Corresponding Author

1. Introduction

Technology savvy business and market consumers are now requiring supply chain partners to provide business intelligence in a cost effective and timely manner (Hosanagar et al, 2005; Oh et al., 2012). Furthermore, multi-channel supply chains are gradually growing in complexity making it more difficult to provide adequate service to their partners. One such way that organizations can reduce this complexity is through the use of information technology to facilitate business intelligence (BI) collaboration among supply chain entities. Firms are gaining significant advantages by deploying information technology to collaborate with their supply chain partners (Saeed et al., 2011). However, a major disadvantage with previous literature on this topic is the focus on older information technologies including electronic data interchange (EDI) that may not provide the level of flexibility and cost efficiency that current disruptive information technology can2

Primarily used for reducing IT costs and maintenance of external platforms (Ma, 2012), cloud computing provides foundations for greater disbursements of business intelligence among supply chain entities via on demand and massively scalable services through the internet (Rochwerger et al., 2009; Vouk, 2008). Several of the main underlying benefits of cloud computing in facilitating collaboration lies in a supply chain partner’s ability to choose a particular service, and retrieve that service at any point in time using an on demand pay-as-you-go service (Buyya et al., 2009). This capability not only reduces cost by not forcing companies to pay for and maintain a separate facility for infrastructure platforms, but also impacts the speed and delivery of communication by having different types of information including demand and forecasting available on any accessible medium per a supply chain partners’ request (Benlian and Hess, 2011; Iyer and Henderson, 2010; Marston et al., 2011).

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Despite the benefits cloud computing provides supply chains in communication, very little research has been done on cloud computing and its potential impact to both information sharing and collaboration in the supply chain. Further, collaboration is often misread as being similar or interchangeable with information sharing (Mishra and Shah, 2009). This we believe is a current problem with supply chain literature because collaboration entails both alignment of incentives as well as a deeper relationship (i.e. trust) (Hendricks and Singhal, 2003) that is not necessarily required for information sharing. While research exists on the impact of collaboration on fill rate, collaboration occurs at varied levels. As of now, there is limited simulation research that provides insight into the varied levels of collaboration and its ultimate impact on fill rate. In attempting to fill these current voids in supply chain literature and provide insights for supply chain professionals in improving overall performance we attempt to address two specific research questions: (1) what are the direct benefits and risks of cloud computing on collaboration and how do they differ from EDI? And through the use of multi-agent simulation we address (2) how does the four levels of collaboration impact fill rate?

Multi-agent simulation not only provides possible answers to these questions but also mirrors a realistic supply chain environment through the use of artificial intelligence. This will in turn allow us to model not only the main levels of collaboration including information centralization and vendor managed inventory and continuous replenishment programs but also focus on BI collaboration among supply chain

2 Disruptive information technology here refers to a computing paradigm that requires changes in the organizations architecture and in this case work processes including collaboration (Sherif and Zmud, 2006). The level of collaboration cloud computing offers is significantly higher than EDI as we will explain in this paper.

entities including collaborative planning, forecasting and replenishment. We provide evidence that higher levels of collaboration, fostered by the use of cloud computing technology, can positively impact fill rate among supply chain members. More specifically BI collaboration in comparison to one-way information sharing will increase the percentage of orders that are filled with on-hand inventory thereby optimizing this facet of service level.

In order to analyze the intricate relationships between cloud computing, collaboration and service level, the remainder of this paper is organized as follows: First we provide a conceptual model validated by social network theoretical background. Second we provide a review of past literature on cloud computing, collaboration and our service level metric fill rate. Third we present a description of our multi-agent simulation model followed by an analysis of our results. Finally we end with discussion and concluding remarks on the impact of three collaboration levels on fill rate as well as implications for researchers and professionals interested in improving service level.

2. Model and Theory

Our paper analyzes the relationships between four constructs: (1) cloud computing, (2) collaboration, (3) service level as defined by fill rate. The relationships between these constructs are depicted in figure 1 below.

In this study we use social network theory to explain the relationships between our constructs. Social network theory posits a set of members in a social network that are connected by links which determine the strength of the relationship (i.e. weak, strong, direct and indirect) between these entities (Brass 1995; Roberson and Colquitt, 2005). Previous literature in social network theory suggests that cohesiveness between organizations can reduce uncertainty (Burt and Knez, 1995; Granovetter, 1973; Gulati, 1995; Koufteros et al., 2007; Podolny, 1994) and be used as a mechanism to promote information flow fostering the exchange of detailed information between organizations (Freeman, 1991; Granovetter, 1982; Koufteros, 2007; Kraatz, 1998; Uzzi, 1996). Our study provides background into how cloud computing not only promotes information sharing (i.e. one way and two way communication), but also fosters a greater relationship through alignment of incentives and ease of sharing sensitive information between partners. Using the cloud to promote greater information flow, following social network theory, improves the strength of the relationship promoting higher levels of collaboration over time.

As depicted in tables 1 and 2 cloud computing promotes two way communication by allowing users to not only choose the services that they need but scale the cloud service to accommodate partners needs as well. The ties between the supply chain partners grow in strength ultimately leading to greater collaboration since partners generally develop preferences for initiating future opportunities with previous associates (Koufteros et al., 2007). Further a higher level of collaboration which requires a sense of security is prompted by cloud computing’s flexibility in service and payment options that can perpetuate greater relationships which improves demand sharing among each level in the supply chain improving overall fill rate all while saving cost. This is further explained in the next section.

3. Literature Review

3.1. Service Level

Service level is a multifaceted concept that has been primarily discussed in the supply chain field as comprising of two different types: (1) Type I service level which refers to “the proportion of periods in which the demand of a product (or the aggregated demand of all products) is met” (Akcay and Xu, 2004, p. 100) and (2) Type II service level refers to “the proportion of demand of a product (or aggregate demand) that is satisfied” (Akc̹ay and Xu, 2004, p. 100). Recent supply chain literature focuses on Type II service level as opposed to Type I because it often disregards batch size effect whereas Type II service level usually provides a more realistic picture of customer level service (Akc̹ay and Xu, 2004; Axsäter, 2000). Type II service level is usually measured by fill rate and stock out probability (Benjaafar et al., 2004). In this study we focus on how collaboration impacts the fill rate as opposed to stock out probability. We use fill rate as the primary measurement for a couple of reasons. First, fill rate is one of the most commonly used metrics for service level (Chen and Krass, 2001). Second, fill rate and stock out probability have been shown to be indirectly correlated via the way they are calculated (Chen and Krass, 2001; De Kok and Visschers, 1999; Hariharan and Zipkin, 1995; Kutanoglu, 2008). Kutanoglu (2008) suggests the high correlation between the two variables by calculating the fill rate as 1- stockout probability. The use of correlated dependent variables poses various concerns including statistical redundancy and misleading results especially presented if the researcher is unable to analyze the complex interactions among them (Peter et al., 1975). It is suggested that more understanding can be made by considering these variables separately (Peter et al., 1975).

Fill rate is a percentage of demand that is satisfied with stock or inventory on hand (Chen and Krass, 2001; De Kok and Visschers, 1999; Gerchak et al., 1988; Graves, 1996; Sinha and Matta, 1991; Sridharan and LaForge, 1989; Van Landeghen and Vanmaele, 2002). There are two levels of fill rate: unit and order. Unit fill rate is a disaggregate measure based on single units whereas order fill rate is an aggregate measure based on the availability of multiple units (Closs et al., 2010). Usually, higher unit and order fill rates result in higher overall service level (Closs et al., 2010). While some studies focus on consumer fill rate at one level in the supply chain (i.e. Deshpande et al., 2003; Dong and Chen, 2005), this study focuses on analyzing the fill rate of all four tiers in a supply chain. We do this in order to accurately assess the dynamics of collaboration among supply chain entities. Collaboration on one level in the supply chain can ultimately impact the fill rate on a downstream level. By analyzing the fill rates on all four tiers in the supply chain we can accurately simulate the resulting performance measure along each node.

3.2. Collaboration

While previous literature has provided interesting and well thought out insights on the benefits provided by collaboration, the current conceptualization of collaboration provides a one-dimensional view that is often confused with information sharing.

There are three primary levels associated with collaboration: (1) information centralization, (2) vendor management inventory (VMI) and continuous replenishment program and (3) BI collaborative planning, forecasting and replenishment (Chaib-draa and Müller, 2006). Information centralization is the most basic and involves the retailer presenting information to other members of the supply chain regarding market consumption (Chaib-draa and Müller, 2006). This can also be referred to as one-way information sharing. Types of information shared can be available production capacity or inventory level (Chaib-draa and Müller, 2006). Vendor management inventory and continuous replenishment programs

are the second level which involves the interaction of all supply chain partners. It permits wholesalers to use information centralization to decide when to replenish retailers so that retailers don’t have to place orders (Chaib-draa and Müller, 2006). This level can be referred to as two-way information sharing and can result in the mitigation of demand uncertainty throughout the supply chain (Chaib-draa and Müller, 2006). Collaborative planning, forecasting and replenishment (CPFR) which we refer to as BI collaboration enhances VMI and CRP by incorporating joint forecasting (Chaib-draa and Müller, 2006). With this level companies can electronically exchange data which include previous sales trends, and forecast data.

Following social network theory, collaboration is more than information sharing as it is the ability to simultaneously establish links with other partners in the supply chain through joint conflict resolution and sharing of information in order to forge effective partnerships and reach win-win outcomes (Ellram, 1990; Heide and John, 1990; Jassawalla and Sashittal, 1999; Mishra and Shah, 2009; Parmigiani et al., 2011; Sahin and Robinson, 2005; Vickery et al., 2003). When there are stronger ties between entities in the supply chain and success is contingent upon a partners’ success both parties have an incentive to maintain accuracy and speed in information processing and flow. Previous literature shows the various problems associated with a lack of adequate information being exchanged with supply chain partners including excess or depleted inventory, inefficient production, inability to make demand and reduced utilization of distribution (Akkermans and Vos, 2003; Lee et al., 1997a, 1997b). A lack of adequate inventory to fill a downstream members demand as well as reduced production and utilization of distribution reduces the likelihood of a supply chain being able to fill a customer’s order with all parts included. This leads to a reduction of service level among members in the supply chain and can greatly reduce supply chain performance. While one way to reduce demand distortion is through one way or joint information sharing, long term and sustainable reduction requires a higher level of collaboration including joint forecasting to remove potential distortion before it occurs. In order to accurately simulate a collaborative environment and its impact on fill rate we will use artificial intelligence to distinguish joint sharing of information as opposed to one way or two way information sharing. Further, the use of artificial intelligence will provide this model with a more realistic conceptualization of real time two way collaboration and its impact on fill rate at varying degrees.

3.3. Cloud Computing

Previous studies have examined the use of EDI on information sharing or information centralization in the supply chain (e.g. Hill and Scudder, 2002). Yet currently there is limited research regarding effective means of collaborative forecasting (Smaros, 2007). Cloud computing technology offers various opportunities for all three collaboration levels. Tables 1 and 2 provide fundamental benefits and risks associated with both cloud computing and electronic data interchange. They also provide an overview of the differences between these two information technologies

Table 1. Cloud Computing Benefits Literature Flexibility/Convenience Ability to choose between owned infrastructure of rented from third party vendor

Marston et al. (2011); Benlian & Hess (2011)

Large amount of computing power in short amount of time (analyzing terabytes of data in a period of

Marston et al. (2011); Benlian & Hess (2011)

minutes) Ability to request more computing resource in minutes with minimal service provider interaction

Marston et al. (2011); Benlian & Hess (2011); Iyer & Henderson (2010)

IT services in countries that would traditionally lack resources for deployment of IT service

Marston et al. (2011)

Adaptive structure shared by different end users in different ways with different mediums

Marston et al. (2011); Benlian & Hess (2011); Iyer & Henderson (2010)

Offers mobile interactivity Marston et al. (2011); Iyer & Henderson (2010) Massively scalable services (SaaS, PaaS, IaaS) Marston et al. (2011); Benlian & Hess (2011);

Mantena et al. (2012); Iyer & Henderson (2010a); Bardhan et al. (2007); Benlian et al. (2011); Vouk (2008); Rhoton (2011)

Ability to choose between public, private or hybrid Marston et al. (2011); Iyer & Henderson (2010); Bardhan et al. (2010)

On demand access to information Marston et al. (2011); Benlian & Hess (2011); Iyer & Henderson (2010); Bardhan et al. (2010); Benlian et al. (2011); Buyya et al. (2009); Armbrust et al. (2009)

Reduced maintenance, upgrades and development with vendor (focus on core competencies)

Marston et al. (2011); Benlian & Hess (2011)

Offers green practices Marston et al. (2011); Alford & Morton (2009); Benlian & Hess (2011); Whitten et al. (2010); Iyer & Henderson (2010)

Ability to verify history/location or application of an item through documentation

Iyer & Henderson (2010)

Payment choices (flat, pay per use, two tier) Alford & Morton (2009); Buyya et al. (2009)

Table 2. EDI Benefits Literature Performance Business/Supply chain performance Cantor & Macdonald (2009); Machuca & Barajas

(2004); Rosenzweig & Roth (2007); Sanders (2007); Zhu & Kraemer (2005) (2002); Lee et al. (1997a,b)

Flexibility/Convenience Faster information delivery Hill & Scudder (2002); Sheombar (1992); Jayaram

& Vickery (1998); Narasimhan & Carter (1998); Boyer & Pagell. (2000); Sanders (2008); Ragatz et al. (1997); Gunasekaran & Ngai (2005)

Frequency of information flow Hill & Scudder (2002); Sheombar (1992); Jayaram & Vickery (1998); Narasimhan & Carter (1998); Holland et al. (1992); Barratt & Oke (2007); Rosenzweig et al. (2003); Barratt & Barratt (2011)

Ease of information flow Hill & Scudder (2002); Sheombar (1992); Jayaram & Vickery (1998); Narasimhan & Carter (1998); Boyer & Pagell. (2000); Sanders (2008); Ragatz et al. (1997); Gunasekaran & Ngai (2005)

After conducting a literature review of benefits and risks associated with both EDI’s impact on collaboration and cloud computing’s potential impact on collaboration we found that a variety of benefits and risks coincide between the two. EDI is an information technology involved in direct routing of information from one computer to another requiring standardized business transactions (Hill and Scudder, 2002; Strader et al.,1998; Walton and Maruchek, 1997). Cloud computing technology, on the other hand, develops from research in virtualization, distributed, utility and grid computing (Vouk, 2008) and is a service based emerging information technology that offers on demand access to shared computing resources through the internet (Lee and Mautz, Jr., 2012). We collected research from both web-based and non web-based EDI, but concentrate our discussion on the more frequently used web-based EDI.

Cloud computing and EDI present both flexibility and convenience for collaboration in the supply chain. This includes faster information delivery, greater frequency of information flow and ease of information flow. While cloud computing and web based EDI can promote all three levels of collaboration, cloud computing has flexibility options that allow a user to build collaborative relationships more efficiently than EDI users.

First in terms of flexibility and convenience: cloud computing users, unlike EDI users, can choose to rent from a third party as well as choose from a variety of massively scalable services. These include software as a service (SaaS), infrastructure as a service (IaaS), and platform as a service (PaaS). Each of these services have varied degrees of collaborative benefits associated with them. For example, all three provide the user with the ability to use large amounts of computing power in a short period of time. Previous research indicates that cloud users are able to analyze terabytes of data in a period of minutes which is a substantial increase in speed of information flow over traditional EDI (Benlian and Hess, 2011; Marston et al., 2011). This is particularly useful in information centralization and vendor managed inventory collaboration that requires large orders and distribution data to be processed in a short period of time.

Further cloud computing offers mobile interactivity and the ability to share information and collaborate with supply chain partners using a variety of mediums (Benlian and Hess, 2011; Iyer and Henderson, 2010; Marston et al., 2011) unlike EDI applications which still require a common platform on either end (i.e. common ERP systems) (Monczka et al., 2011, p. 709) While speed and flexibility in delivery may improve information flow between supply chain partners it may not impact the actual relationship between supply chain partners and ultimately may not impact BI collaboration.

Based on social network theory stronger ties require higher levels of collaboration. These levels of collaboration in turn require consistent communication and a rich relationship between two partners (Hendricks and Singhal, 2003). Cloud computing promotes flexibility in its pricing strategy (pay per use, flat fee and or two-tier pay per use and flat fee) and choice between a public cloud open to a variety of users, a private cloud password sensitive between limited users or a hybrid cloud which entails security benefits of a private but also the economies of scale of a public cloud (Alford and Morton, 2009; Marston et al., 2011). This flexibility allows for users to foster greater relationships by providing a cloud infrastructure that fits not only their service and security needs but also those of their supply chain partners. By focusing on the needs of both, the user has the potential to create an environment that provides for richer and higher levels of collaboration. Cloud computing also offers the opportunity for users to verify information sharing through online documentation (Iyer and Henderson, 2010). This is

especially important in BI collaboration which involves forecasting and a high level of trust in supply chain partners’ data. Trust is enhanced through consistent communication, however the ability to verify this communication is a step forward in enhancing trust between supply chain partners which goes above information sharing and leads to greater collaboration over time (Hendricks and Singhal, 2003).

While the cloud does have service opportunities that can impact the relationships between supply chain partners it is important to express that security is still a concern for current cloud users. The concerns result from both the recency of the information technology as well as riskiness of storing data in an easily accessible medium (Rochwerger et al., 2009). It is not uncommon for recent information technologies to have greater perceived security risk than their more established predecessors (Gauzente. 2004). Yet perceived risk can be reduced by both compatibility and usefulness (Bettman, 1973; Holak and Lehmann, 1990; Karahanna et al., 2006) both of which are benefits of cloud computing. Further, the variety of services offered by the cloud including privatization and verification of information sharing have the potential to reduce these security concerns which may hinder higher levels of collaboration between partners. Thus, current research suggests that although security is still a concern, cloud computing offers a variety of services that have the potential to reduce concerns over security (Vouk, 2008).

Despite the potential that cloud computing holds in optimizing collaboration, research in cloud computing is still in its infancy. We have so far provided a discussion of the benefits of cloud computing that enhances three levels of collaboration. Next we describe our multi-agent simulation that uses artificial intelligence to simulate collaboration at three levels and its impact on fill rate.

4. Methodology

4.1. Simulation

Simulation models are used when certain characteristics of the supply chain cannot easily be modeled with analytical tools such as regression, queuing and optimization or when stochastic variables are present (Riddalls et al., 2000). In this study we simulate the impact of collaboration on a four tier supply chain which we consider a complex adaptive system (CAS). This is defined by complexity theory as a system with a large amount of variables and interacting forces that are difficult to understand or optimize by traditional top down or bottom up approaches (Holland et al., 1992) and incorporates agents that learn and adapt based on a dynamic environment (Juarrero, 2000).

We specifically use agent-based simulation which is a powerful approach to understanding complex adaptive systems (Tesfatsion, 2003). An agent is a self-contained software program capable of controlling its own decision making and acting based on its perception of its environment, in order to fulfill one or more goals. The agent has the following main behavior attributes: autonomous, cooperative (social), reactive, proactive, and learning. Being autonomous means that the agent can act without intervention by other entities (humans or computer systems), and can exercise control over one’s own action/algorithms. Agents can also interact with other agents via some kind of agent communication language (social). Being reactive means that agents can perceive the environment and are able to respond in a timely fashion to changes. Further being proactive refers to agents that do not simply act in response to the environment but are part of a more complex goal-oriented behavior. Finally, learning means that agents can change their behavior based on their previous experience.

On the other hand, an agent is a software entity that has a set of protocols which govern the

operations of the manufacturing entity, a knowledge base, an inference mechanism and an explicit model

of the problem to solve (Chen and Krass., 2001). Agents communicate and negotiate with the other agents, perform the operations based on the local available information and may pursue their local goals. This definition has both technical and organizational aspects. Technically, agents possess sufficient knowledge and inferential capability to behave in a manner that would be classified as “intelligent” if performed by a person. Organizationally, agents are entrusted with sufficient authority to make commitments for users. This enables them to represent their principals and adhere to the same corporate rules, policies and procedures required to be followed by people in the organization.

Since we use a four-tier supply chain we simulate using multi-agent based simulation. A multi-

agent system is one that consists of a number of agents that take specific roles and interact with one-another to solve problems that are beyond the capabilities or knowledge of any individual agent. These interactions can vary from simple information interchanges, to, cooperation, coordination and negotiation in order to manage interdependent activities. Multi-agent systems are ideal for representing problems that include many problem-solving methods, multiple viewpoints, and multiple entities. In these domains, multi-agent systems offer the advantages of distributed and concurrent problem solving along with the advantages of sophisticated schemes for interaction. Agent technology and multi-agent system can also be useful in developing highly complex systems. The flexibility provided by multi-agent systems allows us to realistically simulate a complex and dynamic supply chain environment where decentralization, collaboration and intelligence are essential characteristics (Calheiros et al., 2011; Foster et al., 2008). Further, by assuming that the supply chain system is represented as a chronological sequence of events, in which each event occurs at an instant in time and marks a change of the state in the system, we utilize the discrete event simulation.

Overall, computer simulation is a widely used method in Operations and Supply Chain literature

to measure a variety of topics. Right below systems dynamics, agent based simulation is one of the most popular simulation techniques in Operations Management, (Jahangirian et al., 2010).In supply chain management it is particularly useful because unlike system dynamics it can be used for long term analyses to represent several players in an industry treated as separate agents with separate strategic behaviors (Albino et al., 2007; Jahangirian et al., 2010; Schwartz, 2000). However, a disadvantage of using just agent based simulation, especially in examining supply chains using social network theory, is its inability to mirror all forms of collaboration including two way interactions among multiple entities exchanging real time business intelligence information. Therefore, in addition to agent based simulation we also use artificial intelligence to mirror all forms and levels of collaboration among supply chain entities. To do this we use SeSAm (Shell for Simulation Agent Systems). This software is characterized by its ability to allow modeling, simulation as well as the evaluation of the simulation runs. The behavior of the individual agents can be defined by state diagrams (reasoning engines) with a graphical user interface. Each of the states can be described with actions characterized by programming code fragments. A single agent can have several state diagrams, which run in parallel during simulation. Furthermore, the communication among agents follows FIPA and setup based on JADE.

4.2. Simulation Design

Figure 2 presents the simulated supply chain structure based on a multi-tier supply chain with each tier representing several suppliers, manufacturers, wholesalers, distributors, retailers and consumers. In this model we assume agents in adjacent tiers have consistent flow of materials and/or information.

4.2.1. Customers In this model, the customer's requirements are the information sent from the customer to the retailers. Signifying a discrete event, the customers come to local retailers and place an order. Customer demand is fulfilled from the retailers’ stock. When demand exceeds the end-product stock, the order is backordered

and delivered to customers as soon as it becomes available. We will use a discrete event generator to simulate the actual demand for each retailer. The model imitates the uncertainty in customer demand through the use of fuzzy functions which will be further explained. A triangular function with three random parameters is used to imitate the customer's requirement for each retailer.

4.2.2. Manufactories

Figure 3 presents the logic flow of manufactory agents. Several categories of failures (i.e. machine breakdowns, material shortages and adjustments) were included as random events associated with a given probability. The cycle time of the machine for a product is set on a fixed rate. The factory runs sixteen hours per day, seven days per week. 4.2.3. Distributors/Wholesalers Figure 4 shows the logic flow of distributors and wholesalers. Inventory is controlled based on a periodic review policy. Order quantities are based on a predetermined level known as the order-up-to level

(Bertazzi, Paletta et al., 2002). In this simulation, complete back-ordering is assumed. That is, if an order from the downstream agent exceeds an upstream agent’s inventory, the whole order quantity is backordered. When the backordered quantity becomes available, it is sent to the production facility in the next delivery period. This delivery process continues until the whole ordered quantity is delivered to the production facility. Furthermore, replenishment quantities for each inventory are received with a lead time which we specify as an uncertainty issue.

4.2.4. Multi-Agent Negotiation

Negotiation is a discussion among conflicting parties with the aim of reaching agreement about a divergence of interests (Davis, 1993). Used as a coordination mechanism to find an acceptable agreement between partners or collectively search for a coordinated solutions, it may involve two parties (bilateral) or more (multilateral) as well as one issue (single issue) or many issues (multi-issue). In this study, communication among agents follows the appropriate protocol, which defines the rules governing the interaction.

Figure 5 shows the logic flow of collaborative planning, forecasting and replenishment. In forecasting, when an agent receives information using cloud computing, they will forecast information based on history experience and exception criteria. If a change is determined as an exception, the agent will submit a change requirement to the order forecast and adjust his or her own plan.

4.2.5. Uncertainty in Supply Chains

A substantial amount of supply chain literature focuses on uncertainty in customer demand (Van der Vorst and Beulens, 2002). However, along with uncertainty in customer demand there is also uncertainty in supply (Davis, 1993; Viswanathan and Piplani, 2001). In some cases, quantity and quality of raw materials or products delivered from a supplier vary. In addition, lead time which includes order processing, a production and transportation time is often difficult to specify accurately. Thus, in our simulation we consider all three sources of uncertainty.

Uncertainties in SCM parameters and control problems are usually treated as stochastic processes and described by probability distributions. However, there are situations where all these requirements are not satisfied and, therefore, the conventional probabilistic reasoning methods are not appropriate. For

example, in the case of launching a new product there may be a lack of evidence available or limited confidence. In these situations, uncertainties in parameters can be specified based on managerial experience and subjective judgment. It may be convenient to express these uncertainties using various imprecise linguistic expressions; for example, customer demand D is about dm, but definitely not less than dl and not greater than du, external supplier SR is very reliable, in terms of percentage of a raw material order that can be delivered, or lead time L is most likely to be in the interval [ll′ , lu′]. Fuzzy sets are found to be useful in representing these approximate qualifiers, due to their conceptual and computational simplicity. The typical membership functions that can represent fuzzy customer demand, fuzzy external supplier reliability and the fuzzy lead time mentioned above are shown in Figure 6. They can be derived from subjective manager belief.

Choosing fuzzy sets to describe imprecise SCM parameters usually means that it is done before

seeing any data. When real data become available, one can model these values as relative frequencies and probability distributions. 4.2.6. Performance Measures

In this study, we consider system level quantitative performance measures, using fill rate as proxy of system service level. Borrowing from previous literature we define the fill rate performance measure as the ratio between the number of satisfied orders and the total number of orders (Dong and Chen, 2005; Sridharan and LaForge, 1989). 4.2.7. Scenario Design In order to examine the impact of collaboration (via cloud computing) on supply chain performance we simulate four scenarios to denote the different levels of collaboration: base scenario (without information sharing), information centralization (one way information sharing), Vendor management inventory and continuous replenishment (real time two-way information sharing), and BI level collaboration in which more data is exchanged and full collaboration takes place (i.e. joint forecasting).

4.2.7.1. Base Scenario: Without Information Sharing In the base scenario, we assume that there is no cloud computing used to facilitate information sharing and collaboration. Each agent makes dependent inventory decisions based solely on historic order record without any information from other members in the supply chain. Another assumption that we make is all agents base their replenishment on a 4-week moving average. Finally, we assume information will take time to transfer, and upstream agents will not begin to process an order until the next period.

4.2.7.2. Scenario I: Information Centralization Information centralization is the most basic level of collaboration in which retailers broadcast the market consumption (approximated as their sales) to the rest of the supply chain. It is a multicasting process of the market consumption information in real time. In this study we consider information centralization as one way communication where retailers push information to other supply chain partners.

In this scenario, the information about an order from customers will be sent to the other agents

immediately. All of the agents make their replenishment plan based on the actual market consumption. While there is no information delay, all agents still need to consider lead time.

4.2.7.3. Scenario II: Vendor Managed Inventory (VMI)

In this scenario, lower tier agents do not need to place orders because upper tier agents use information centralization to decide when to replenish them. Thus, the uncertainty of both supplier reliability and lead time are alleviated because the upper tier agents can estimate their own lead time with more accuracy than lower tier agents.

4.2.7.4. Scenario III: BI level In this scenario, we establish higher requirements for information sharing. Agents electronically exchange a series of written comments and supporting data, which include past sales trends, scheduled promotions, and forecasts. Unlike previous scenarios, BI level collaboration shares more information than only operations information. This allows the participants to coordinate joint forecasts. Unlike one way information sharing, partner agents interact and work together towards a similar goal (Angerhofer and Angelides, 2006). In BI collaboration full disclosure of information and an element of trust is vital for facilitating cohesive collaboration (Ashleigh and Nandhakumar, 2007). Since disclosure of information and trust between supply chain partners exist, uncertainties are reduced.

5. Results and Discussion As shown in Table 3, we define sixteen parameters before running each scenario. Our supply chain design is based on a virtual supply chain with four retailers, three distributors, two wholesalers and two manufactories. Table 3. Parameters

Parameter name Generator Base scenario Scenario I Scenario II Scenario III

Supply chain structure environment

Simulation days environment 365 365 365 365

Order period(days) environment 7 7 NA NA

Initial Inventory (Unit) environment 50 50 50 50

Minimum Stock (Unit) environment 20 20 20 20

Maximum Stock(Unit) environment 80 80 80 80

Inventory Cost($) environment 0.5 0.5 0.5 0.5

Backorder Cost($) environment 1 1 1 1

Fixed Shipping Cost ($) environment 50 50 50 50

Shipping Cost per Unit ($)

environment 0.5 0.5 0.5 0.5

Customer demand distribution

Customer 𝑑𝑙 = 5,

𝑑𝑚 = 10,

𝑑𝑢 = 15

𝑑𝑙 = 5,

𝑑𝑚 = 10,

𝑑𝑢 = 15

𝑑𝑙 = 5,

𝑑𝑚 = 10,

𝑑𝑢 = 15

𝑑𝑙 = 5,

𝑑𝑚 = 10,

𝑑𝑢 = 15

Supplier reliability distribution

Distributor,

wholesaler,

manufactory

R=60 R=70 R=70 R=80

Lead time distribution Distributor,

wholesaler,

manufactory

𝑙𝑙 = 1,

𝑙𝑙′ = 2,

𝑙𝑢′ = 3,

𝑙𝑢 = 4

𝑙𝑙 = 1,

𝑙𝑙′ = 2,

𝑙𝑢′ = 3,

𝑙𝑢 = 4

𝑙𝑙 = 1,

𝑙𝑙′ = 2,

𝑙𝑢′ = 3,

𝑙𝑢 = 4

𝑙𝑙 = 0.5,

𝑙𝑙′ = 1,

𝑙𝑢′ = 1.5,

𝑙𝑢 = 2

Promotion plan retailers NA NA NA ON

Manufacturing capability

manufactory NA NA NA ON

Supply lot size 100 NA NA NA

We evaluate performance after a fifty-two week yearly cycle. Customers randomly choose one of

four retailers and a triangular distribution (5, 10,15) is used to represent the customer demand per day which is typical of a four-tier supply chain customer demand.

Under each scenario, the inventory level and order information are recorded for each respective agent. The inventory, backorder and shipping costs are also updated in real time. After a fifty-two week year, the simulation will stop and performance measures are recorded.

Table 4. Cost and Fill rate in four scenarios Scenarios Measure Mean Standard Deviation Base scenario

Cost in a week $1,416.80 $318.08 Average cost per unit $5.09 $1.57 Fill rate 80.93% 7.23%

scenario I Cost in a week $1,118.60 $166.72 Average cost per unit $4.02 $1.05 Fill rate 86.61% 6.55%

scenario II Cost in a week $1,059.80 $87.71

Average cost per unit $3.80 $0.72 Fill rate 86.09% 5.30%

scenario III Cost in a week $745.78 $52.33 Average cost per unit $2.67 $0.56 Fill rate 93.60% 2.54%

Based on the results shown in figures 7 and 8 and table 4, the fill rate increased 12.67% from the base scenario (without information sharing) to scenario III (BI level collaboration). Additionally, BI level collaboration fill rate had a 6.99% and 7.51% improvement in fill rate over scenario I (information centralization) and scenario II (VMI) respectively. BI level collaboration (via cloud computing) also had significant reductions in cost per week from the base scenario ( $2.42 difference), scenario I ($1.35 difference), and scenario II ($1.13 difference). Although scenario II’s fill rate had a slight decrease (0.52%) from scenario I, this was attributable to a promotion event simulated in week 28 that reduced fill rate slightly.

Overall, our results support that higher levels of collaboration positively impact fill rate. Additionally higher levels of collaboration (via cloud computing) can substantially reduce cost which is often an impediment to maintaining high collaboration levels. Our model and simulation provide insight into the positive implications of higher levels of collaboration impacted through cloud computing technology. Although, certain actions (i.e. promotional campaigns) can ultimately reduce fill rate despite the level of collaboration or cloud computing use, the change is minimal.

These results also support social network theory, by suggesting stronger ties (i.e. BI level collaboration ) versus weaker ties (no information sharing and information centralization) can impact cohesiveness and reduce uncertainty thereby enhancing service level fill rate. While cloud computing provides an efficient, useful and cost effective means of communication, organizations need a level of trust and similar goals to further these benefits.

6. Conclusion

We have attempted to address two research questions: (1) what are the direct benefits of cloud computing on collaboration and how do they differ from EDI? And (2) how does the four levels of collaboration impact fill rate?

In addressing the first question we assessed both direct and indirect benefits of cloud computing as well as the more traditional EDI. From the literature analysis we found both to have similar benefits in terms of faster information delivery, frequency of information flow and ease of information flow. However, cloud computing surpasses EDI in terms of flexibility and convenience offered through massively scalable services (SaaS, PaaS, IaaS), varied and combined payment arrangements (flat, pay per use, two tier) and ability to privatize on different levels(private, public or hybrid cloud). Additionally, unlike EDI which often requires a common platform the cloud can be accessed from a variety of mediums and has the ability to compute a large amount of information in a short period of time. This ability to scale the cloud according to not only one organization’s needs but to a variety of organizations in the supply chain helps to build higher collaborative environments than one way or two way information sharing.

Further, cloud computing offers opportunities to privatize information reducing uncertainty in security issues and creating a foundation for inter organizational trust required for higher levels of collaboration.

In addressing the second question we distinguished collaboration from information sharing by focusing on the three levels of collaboration. First is information centralization or one way information sharing which does not require the use of cloud computing infrastructure. Second is VMI and continuous replenishment program collaboration or two way information sharing which may not require the use of cloud computing but may improve efficiency if the cloud is used. Finally, we describe BI level collaboration (collaborative planning, forecasting and replenishment) where organizations participate in joint forecasting and perpetual sharing of information. Often the third level of collaboration requires alignment of incentives, consistent communication, security and trust among supply chain partners. This will be further enhanced through the use of cloud computing over more traditional methods including EDI, which again requires common platforms and has less scalable opportunities.

Our results provide evidence that fill rate increases with higher levels of collaboration. That is as the level of collaboration increases, so does the percentage of demand that is filled by inventory on hand. The only exception to this was scenario II involving VMI and continuous replenishment or two way information sharing. Scenario II had a fill rate slightly below that of scenario I or one way information sharing (a 0.52% difference). This unexpected finding was caused by a simulated event around week 28 where the retailer participated in a promotion. Since the upstream members in the supply chain are unable to forecast this, they continue to send the same amount of units to the retailers. This results in a decrease in the fill rate as the retailers promotion increases demand without adequate supply of product. Despite this perpetual problem in a typical supply chain the cost still was significantly lower in scenario II than in scenario I elicited through the use of cloud computing. Further the change is fill rate was minimal in comparison to the cost savings percentage change (5.47% decrease)

The results of this study provide support for not only cloud computing’s impact on collaboration but how higher levels of collaboration can impact fill rate, a common measurement of service level in organizations. Organizations can use this simulation as a model to optimize service level as well as consider cloud computing as a legitimate tool for enhancing collaboration. Additionally, this paper has provided a multi-agent simulation assessing the impact of various levels of collaboration on fill rate. Future research can be aimed at analyzing potential mediating or moderating variables that may have caused the discrepancy of fill rate between each scenario. Additionally future research can assess stock out probability and how collaboration impacts this other common measurement of service levels in organizations. Given the lack of foundational research in cloud computing technology, we hope that this paper provides a step forward in assessing new and emerging applications of cloud computing within organizations and supply chain dynamics.

Appendix

Figure 2. Supply chain structure

Cloud Computing Collaboration

Information Centralization Vendor Managed Inventory & Continuous

Replenishment Program Business Intelligence Collaboration

Fill Rate + +

Figure 1. Cloud computing, collaboration and fill rate: a model

Demand from customers

Retailer 1

Retailer 2

Retailer 3

Retailer 4

Distributor 1

Distributor 2

Distributor 3

Wholesaler 1

Wholesaler 2

Manufactory 1

Manufactory 2

Figure 3. Manufactory behavior modeling

Figure 4. Distributor and wholesaler behavior modeling

Figure 5. Collaboration forecasting modeling

Figure 6. Supply Chain Uncertainty Modeling

Figure 7. Total cost

Figure 8. Fill rate

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