Thesis appendix Final Print

SUPPLY CHAIN MANAGEMENT OF PERISHABLE ITEMS: CHANNEL DYNAMICS AND INTEGRATED DECISION MAKING

APPENDIX - TABLE OF CONTENTS

A SYSTEM DYNAMICS

A.1 System Dynamics Basics ..................................................................................................A-1 A.2 The Methodology..............................................................................................................A-1 A.3 Software for System Dynamics Modeling ........................................................................A-6 A.4 Concluding Remarks.........................................................................................................A-6

B MONGINIS CASE

B.1 Introduction....................................................................................................................... B-1 B.2 History...............................................................................................................................B-2 B.3 The Opportunity Recognition Phase ................................................................................. B-3 B.4 Development Stage - Franchisee Network and Freshness ................................................ B-4 B.5 The Growth Stage - Expansion ......................................................................................... B-6 B.6 Saturation .......................................................................................................................... B-7

B.6.1 Customer....................................................................................................................... B-8 B.6.2 Supplier......................................................................................................................... B-8 B.6.3 Factory.......................................................................................................................... B-9 B.6.4 Franchisee .................................................................................................................. B-10 B.6.5 Management ............................................................................................................... B-11 B.6.6 Market......................................................................................................................... B-11 B.6.7 Information System ..................................................................................................... B-12

B.7 What Next? ..................................................................................................................... B-13 C MATHEMATICAL DERIVATIONS

C.1 Factory Production for Generalized Model....................................................................... C-1 C.1.1 Holding Inventory at the Factory.................................................................................. C-1 C.1.2 Derivation of PQ .......................................................................................................... C-2 C.1.3 Derivation of ........................................................................................................ C-3 NPQ

D CAKE GAME

D.1 The Game..........................................................................................................................D-1 D.1.1 Game Instructions ....................................................................................................D-1 D.1.2 Game Software .........................................................................................................D-2

D.2 Experimental Design.........................................................................................................D-3 D.3 Ithink Model......................................................................................................................D-4

E SUPPLY CHAIN SYSTEM DYNAMICS MODELS

E.1 Supply Chain Capacity Augmentation.............................................................................. E-1 E.2 Supply Chain Models........................................................................................................ E-1

Appendix A

A SYSTEM DYNAMICS A.1 System Dynamics Basics System dynamics provides a set of tools and techniques to develop models of organizational systems,

to gauge the impact of policy alternatives via sensitivity and what-if types of analyses. The field

originated from the work of Forrester [1961] and was initially known as ‘Industrial Dynamics’.

System dynamics has its origin in control engineering and management, the approach uses a

perspective based on information feedback and delays to understand the dynamic behaviour of

complex physical, biological, and social systems. This linkage between structure and behaviour is the

guiding principle. System dynamics can help companies gain insights into underlying mechanics that

determine the behavioural dynamics of organizations. This, in turn, can help improve decision-

making in today's integrated value chain. Senge [1992] enriched the discipline by propounding the

concept of ‘System Archetypes’, generic structures observable in business systems demonstrating

qualitatively similar behaviour helped to evolve generic strategies to address each archetype. Senge’s

book gave fresh impetus to the practice of the field and increased its visibility in the practicing

management community. The availability of visual modeling and simulation software also

contributed significantly in making the methodology popular. In the four decades since its inception,

the methodology has been applied to problems that vary widely in scope (single organization to

national and economies), in business processes modeled (supply chain management, project

management, service delivery, IT infrastructure and strategic planning) and business types

(manufacturing, service, research and development, health care, insurance, military and government).

The purpose of this overview is to present only the basics of this discipline. Dutta and Roy [2002]

provide a primer on system dynamics and this document borrows heavily from that article. One can

refer to Richardson [1996] and Sterman [2000] for further details on system dynamics modeling.

A.2 The Methodology The philosophy of system dynamics modeling is founded on three principles:

1. Structure determines behaviour — structure refers to the complex inter-linkages among different

parts of organization and includes human decision-making processes. An example of this is a

A-1

Appendix A: System Dynamics

supply chain, which involves complex interaction of the components (customer, retailer,

wholesaler, distributor, factory, and raw material supplier) through order and material flows and

decisions made about these flows.

2. The structure of organizational systems often involves ‘soft’ variables — e.g. perceptions of

quality, user satisfaction, morale, etc. A supply chain structure includes how each agent forms

perceptions about the future behaviour of its customer. The mental models of people play a

crucial role in determining the dynamic behaviour of organizational systems.

3. Significant leverage can be obtained from understanding the mental model and changing it.

Modeling in system dynamics starts with identification of the reference mode behaviour — time

dependent behaviour of one or two important variables of the system, the dynamics of which the

model would try to explain.

The next step involves creating a causal loop diagram, a pictorial representation of the underlying

structure that is thought to explain the reference mode behaviour. Typically, modelers and subject

matter experts work jointly to evolve a causal loop diagram. In doing so, they have to resolve

differences in their individual mental maps and arrive at a shared understanding of the underlying

causes of the reference mode behaviour. The causal loop diagram in Figure A.1 shows the circular

relationship between the flow and the accumulation.

State ofSystem

ManagementPolicy, Target etc

Action

+/-

+/-

Figure A.1: Casual loop diagram

On the diagram, each arrow represents a cause and effect relationship. The polarity of the link (+/-)

indicates the direction of change that a change in the cause induces in the effect. A positive sign

indicates change in the same direction (increase/decrease induces increase/decrease) while a negative

A-2

Appendix A: System Dynamics sign indicates change in the opposite direction (increase/decrease induces decrease/increase). The pair

of parallel lines on the links indicates time delay between cause and effect. It's easy to see that this

structure models situations where management decision controls a flow thereby changing the level of

accumulation and so on, giving rise to a sequence of decisions over time that determine the dynamic

behaviour of the system.

Depending on the polarities of causal links present, a feedback loop as shown in Figure A.2 can

generate one of two types of effects — a snowball effect, one in which a change in state generates

action that causes a bigger change in the state, or a balancing effect where a change in state generates

action to absorb the change. In the parlance of system dynamics, these two loops are termed as

reinforcing or balancing loops, respectively.

CashBalance

InterestRate

InterestEarned

+

+

Inventory

TargetInventory

Production

-

+

Figure A.2: Feedback loop: Reinforcing loop (left) and Balancing loop (right)

A reinforcing loop generates exponential growth behaviour. A balancing loop stabilizes the system

around a target state. In some cases, depending on loop conditions, a balancing loop can generate

oscillations around the target state. In a typical system, the presence of a number of such feedback

loops of either type generates the complex dynamics of the system. For illustration in Figure A.3 we

present a firm that is experiencing growth in a particular market. In this example, the firm uses its

sales force to get orders from the market. As sales persons get orders, a part of the revenue earned is

allotted to support the sales staff salary. As more orders get booked, the company hires more staff.

This is a reinforcing loop that pushes for growth in the firm's sales force. However, the stream of

orders booked increases the order backlog and progressively pushes production capability to the limit

until the delivery delay no longer remains a ratio between order backlog and production rate

permitted by capacity. This delay cause deterioration in sales force effectiveness (they find it harder

to sell) and reduce orders booked. This balancing loop limits the growth of the firm.

A-3

Appendix A: System Dynamics

Budget alloted tosales force

SalesForce

OrdersBooked

SalesEffectiveness

OrderBacklog

DeliveryDelay

+

+

+

+

+

+

-

Figure A.3: Feedback sales loop

From the study of these two loops we can intuitively say that while the orders received by the firm

increases initially, it ultimately reaches stability. Based on this understanding, management can

decide to design an appropriate policy by which the firm augments its capacity at appropriate times

and continues to grow to the full potential market. By showing the feedback loops, the modeler

provides a structural explanation of the mechanics underlying the system's dynamic behaviour. The

causal links are drawn based on existing theory, results of correlation study or hypotheses about the

relationship between cause and effect.

In the next step of model building, the stock and flow structure of the system is drawn based on the

causal loop diagram. The stock and flow structure shows stocks, flow controllers and decision

structures within the system. Conserved physical flows connect stocks in the diagram. Information

flows drive different physical flows. The standard notations, equations and representation followed in

STELLA™ software which is used to develop system dynamic models are given below.

= Stock or Level (State) Variable

= Converter or Auxiliary Variable

= Decision Process Diamond

= Source or Sink

~ = Look up Table or Graphical Function

= Connector or Wire Arrow

= Flow or Rate Variable

A-4

Appendix A: System Dynamics The stock and flow diagram for the sales system discussed earlier is shown in Figure A.4.

SalesForce

Hiring

OrderBacklog

OrderBooking

Deliv eries

Deliv eryDelay

SalesEf f ectiv eness

Budget Allotedf or Sales Force

Figure A.4: Stock and flow diagram for sales system

The arrows drawn with regulating valves indicate physical flows. Rectangles (Order Backlog, Sales

Staff) indicate accumulations or stocks. The valves (Order Bookings, Deliveries, Hiring) on the

physical flows control flows in and out of stock. In system dynamics parlance they are termed as flow

variables. Circles (Budget Allotted for Sales Force) indicate converters that are used to capture

decision rules or perform intermediate computation. Thin arrows represent information flows

connecting converters with stocks.

The stock and flow structure of the system is simply shorthand for the underlying mathematical

representation of the system. Each stock is an integration of flows affecting it. Table A.1 shows the

equations for the generic stock and flow diagram. For e.g. the inventory ‘X’ is affected by the goods

receipts ‘dx’. This in turn is dependent on the orders placed ‘Y’ which again depends on the inventory

in the system ‘X’.

Table A.1: Stock and flow diagram and corresponding system equations

( )( )

( ) (0)

( )

( ) ( )

X t dx dt X

dx f Y t

Y t g X t

= ⋅ +

=

=

∫

XdX

Y

For the purpose of simulation, the system equations are expressed as difference equations wherein

over a time period ‘dt’, the value of stock changes by ‘dt’ times the net flow into the stock. A flow is

expressed as a function of one or more stocks and converters. Each converter represents the decision

rule that is dependent on the current state. Associated delays or attenuation, if any, are represented

A-5

Appendix A: System Dynamics appropriately. Software packages available today are capable of automatically creating the underlying

mathematical representation from the graphical stock and flow structure.

Simulating the system equations over time with assumed initial values for system variables generates

the dynamic behaviour of the system. At this point elaborate tests are performed to validate the model

for adequacy of problem boundary coverage and reproduction of reference mode behaviour. A

validated model is used for performing different kinds of analysis like sensitivity analysis and what-if

analysis to support decision making about a future course of action. One important analysis involves

experimental identification of feedback loops that dominate the dynamics at different points of time.

Termed as loop dominance analysis, this provides further insight into the structure of the system and

leads to design of policy structures that result in favorable dynamics.

A.3 Software for System Dynamics Modeling STELLA1, VENSIM2 and POWERSIM3 are the three most popular commercially available software

for developing and testing system dynamics models. All of these are comparable in terms of their

capability to create the model graphically, simulate the same and perform sensitivity analysis. The

basic capabilities include

o Drawing the model using a graphics users interface

o Writing the underlying system equation in a user friendly manner

o Simulating the same with different values of simulation intervals

o Publishing the results both as table and graph

o Performing sensitivity analysis and publishing comparison of run results

A.4 Concluding Remarks System dynamics helps convert a mental model to a formal model on which rigorous tests can be

performed to gain new insights. Since its appearance forty five years ago, it has grown considerably

and is now a mature discipline equipped with tools and techniques necessary to solve complex real

world problems. Additional and latest information can be obtained from System Dynamics Society4,

the official forum of system dynamics practitioners. It also publishes a quarterly journal titled System

Dynamics Review and holds annual meeting for exchange of ideas.

1 http://www.hps-inc.com/2 http://www.vensim.com/3 http://www.powersim.com/4 http://www.systemdynamics.org/

A-6

http://www.hps-inc.com/

http://www.vensim.com/

http://www.powersim.com/

http://www.systemdynamics.org/

Appendix B

B MONGINIS CASE B.1 Introduction It was 9:00 in the morning and Alok Dey, General Manager, Switz Foods Private Limited (SFPL),

was back in his favourite office chair sipping a cup of hot coffee. But the chair was no longer that

comfortable as it once used to be, for he saw another competitor outlet open on his way to the office.

A few words of last evening from his finance manager, Sarkar echoed in his ears

The growth in profits for this year (2004-05) will be less than expected. This is the first time in the last 5 years we are not able to meet our numbers!

Thoughts began to flow as to how the company had grown over the years. SFPL had reached the

stature of being the best in cakes and bakery business. His team had worked hard in establishing the

delivery of right products, in the right quantity, at the right time with optimal cost. But, Dey knew

that the times ahead would be hard with increasing competition from domestic players and entry of

global heavy weights in the food business. He was now deliberating on how to meet the newer

challenges. Meetings, discussions, debates, market reports had led his team to two possible solutions.

The first was to reduce cost by decreasing the uncertainties in the system and the second was to

increase volumes by entering new markets.

“Monginis, The Cake Shop” was the name by which Kolkatans knew SFPL, which was jointly owned

by Mr. T. F. Khorakiwala and Mr. Arnab Basu. It manufactured and distributed savouries, pastries,

cakes, birthday gateaux, cookies, breads and other bakery items. Products were divided into two main

categories: (1) Pastries and Cakes, and (2) Savouries. As a craft bakery, Monginis needed manual

skills to give the desired shapes to its hot selling cakes. The products reached the customers through

the extensive network of franchisee outlets all over the city. Switz paid appropriate royalty to

Monginis Foods Limited, Mumbai for using its brand name.

The bakery industry in Kolkata was highly competitive with numerous players coming into the

market. Monginis led the market followed by Sugar & Spice. Other players in the industry were

Kathleen, Flurys, Upper Crust, Cakes ‘N’ Bakes, Hot Breads, Ambrosia, Modern Bakery and Kookie

B-1

Appendix B: Monginis Case Jar (see Exhibit B.1 for market share of dominant players). Kookie Jar, Upper Crust and Flurys

catered to upper segment of the market and the rest catered to middle and lower segments.

SFPL was an entrepreneurial organisation though it did not have intrapreneurial experts. This was

evident in its innovative ways of doing business right since inception. It was one of the first

organisations to cover the whole of Calcutta (now Kolkata) under a single distribution network. It was

the first organisation in the industry to start selling savouries through franchisee network at the time

when savouries were sold through sweetmeat shops in Calcutta. The way it created its franchisee

network was highly innovative as well. The policy of taking back the leftovers at the end of the day

with no extra cost to the franchisee ensured high sales. This also ensured that only fresh products

reached the customer. It realised the importance of having a sound distribution network for perishable

commodities and it accordingly invested heavily in logistics. The organisation also realised the

importance of adopting latest technologies in order to keep pace with the changing environment. It

deployed latest machines in production and worked on automating the production process.

B.2 History Khorakiwalas bought a small chain of bakery - Monginis in Goa in 1978, which was to become a big

brand name in the bakery business 20 years later. On acquisition, the business was moved to the

lucrative market in Bombay (now Mumbai). Cakes were perceived as western food and hence the

growth in the initial years did not meet expectations. Khorakiwalas were looking for avenues to

expand the business. Saudi Arabia was seen as a good proposition as they already had familial ties

there. It was also a lucrative market. They established Al Mintakh Sweets and Pastries in

collaboration with a local partner. The business flourished and more units were opened in Oman and

U.A.E., and it grew into a big company. The whole group was named as Switz Corporate with

presence in more than 10 countries and head office in Dubai. Meanwhile, Bombay business also

picked up. Monginis started a chain of retail outlets in Bombay to cater to customer needs in various

parts of the city.

SFPL is part of Switz India Limited, which is a part of Switz Corporate Group. Mr. Arnab Basu who

was instrumental in starting the operations in Calcutta, handled Switz operations in India. The group

had seven factories in different parts of the country and manufactured bakery products. SFPL was one

of them, which manufactured the highly perishable products that have a very short shelf life, ranging

from one to three days. Over the years, SFPL had come up with innovative strategies to stay ahead of

competition and carved out a name for itself in the hearts of the Kolkatans. Monginis was one of

easiest and fastest brand recall among all generations and sections of city in bakery industry.

B-2

Appendix B: Monginis Case B.3 The Opportunity Recognition Phase Mr. T. F. Khorakiwala had about twenty years of experience in bakery business and had a special

expertise in trading of bakery machinery. Basu, a friend of T. F. Khorakiwala, was working as a

probationary officer in the State Bank of India when he left his job to join Khorakiwalas in Bombay

in late seventies. He also had experience with Hindustan Lever Limited in marketing division. Soon,

Basu was sent to Saudi Arabia to handle operations of Switz Corporate. Under Basu’s leadership

Switz grew at a rapid pace. However, because of the growing unrest in Middle East during late 80’s,

Basu decided to come back to India and settle in Calcutta, his hometown. He had many productive

years left in him and looked forward to start a business. The only business he could think of was

bakery. By this time, he knew the intricacies of the business and had mastered the trade. Basu

contacted Khorakiwalas in Bombay who gave him green signal to sell Monginis products in Calcutta.

The products sold were mainly packaged cakes with relatively longer shelf life of one to two months.

The products were initially transported from Bombay and sold in Calcutta. It took almost 10-15 days

for transporting these products. This started in 1988 and continued for next three years.

However, Calcuttans did not have great liking for packaged cakes. They wanted something fresh.

Fresh cakes were not available in most parts of city as almost all good bakeries like Nahoum’s,

Firpo’s and Flury’s were located in central Calcutta. To get a fresh cake, customer had to travel to

central Calcutta. A cake meant for special occasion required an additional trip to place an order. What

further complicated the problem was that cakes often decorated with creams required careful

handling, maintenance of temperature and proper transportation facilities because of its perishable

nature. The difficulties arose due to packing and stacking constraints. Basu realised the gaps in

market in terms of fresh cakes not being available to all Calcuttans and the problems they had to face

in transporting the cakes to their homes. Basu decided to do something about it. Dey recalled those

findings:

All those who wanted to buy good cakes had to travel all the way to central Calcutta. We realised that there existed an opportunity. There were many people who having bought a cake from the Park Street area1 finally landed home with a spoiled one. Also, an average Calcuttan could not afford these highly priced cakes. The fact that people had to travel to central Calcutta made the cakes even more expensive.

The duo realised that selling cakes at locations close to the customers would help plug the gaps and

simultaneously solve problems of perishability and transportation. The customer would get fresh

1 A central location in Calcutta, which flourished during British rule in India and stayed in limelight post

independence.

B-3

Appendix B: Monginis Case cakes. She would not have to travel all the way to central Calcutta to buy a good cake. What it

required was manufacturing of products within city limits and selling the cakes at or near customer’s

doorsteps. This arrangement would shift liability of transporting cakes from customers to the

company. Basu, who had already seen all this with Switz, decided that it would be a good idea to

manufacture the products in Calcutta and sell them through exclusive franchisee network rather than

asking for products from Mumbai.

B.4 Development Stage - Franchisee Network and Freshness The decision was taken by the management to provide cakes near customer’s residence. But

implementing the idea was difficult on account of limited resources. Banks and other financial

institutions did not come forward to extend help. Finance was managed primarily from personal

savings and contacts of Basu. Finding competent personnel to manage the business was another

problem. Basu handpicked a few of the best minds from different fields to be part of top management

team. Mr. Dey, an alumnus of Indian Institute of Technology was hired for technical support; Mr.

Maitra who was working with ITC (the tobacco giant in India) was hired to head marketing. Others

included Mr. Acharya, Mr. Saha and Mr. Ghosh, all considered best in their respective fields,

materials management, finance and administration. Scarcity of resources and uncertainty of demand

in new business meant that it would be smart to start small. A small production facility was built at

Kasba Industrial Estate2 and SFPL was born in 1991.

The top management mission was to give the best products to customers at reasonable prices. A

commitment to ‘best quality’ was made right from day one. The best of raw material and machinery

were used for production. Raw materials from suppliers went through rigorous testing before being

accepted. Top priority was given to cleanliness and hygiene in the factory. Cakes were given different

shapes depending on the requirements of the customers. The artisans manually provided the finishing

touches after products passed through machines. For this very reason, SFPL called itself a craft

bakery where the entire process of cake preparation could not be replaced by machines.

Once the production began, the next vital step was to take the cakes to customers. For this very

reason, the outlets were named ‘Monginis – Your Friendly Neighbourhood Cake Shop’. Initially, it

was difficult to attract potential franchisees to open an outlet for bakery items as the concept of

franchisees in bakery did not exist. Bakery was associated with bread and tiffin cakes and was looked

down upon as not so profitable and traditional. It was considered to be a business of relatively 2 A special area on the fringes of Calcutta developed by the provincial government for promotion of small and

medium scale enterprises.

B-4

Appendix B: Monginis Case uneducated. SFPL did not have the necessary capital to build franchisee shops of its own. Kolkata at

that time had high level of unemployment. Maitra remembers those days:

We targeted unemployed and relatively less educated youth. But they had to be street smart. Our target person had to have a house with a room on ground floor, which could be used as a shop to sell Monginis products.

This was a win-win strategy as SFPL did not have adequate resources and youth did not have jobs.

This innovative strategy was successful and the company could attract its first franchisee in Dhakuria,

a place in south Calcutta. The franchisee was given commission proportionate to sales achieved at the

end of the day.

The organisation was new and had just started to grow when unforeseen problems surfaced. Due to

demand and production fluctuations, there were stock-outs on few days and leftovers on others. The

franchisee was sceptical about the unsold goods and did not want to take any risk of incurring losses.

This hurt the growth and sales did not increase. Management attributed it to the franchisee’s fear of

running into losses due to leftovers. To allay the fears, the company decided that all the unsold

products at the end of the day would be taken back with no cost to the franchisee. With this, the fear

of loss due to unsold items was removed. Monginis wanted to be recognised as a company that was

associated with freshness. Through the policy of freshness, it achieved its objective of capturing

customers mind space. Maitra recalls:

Customers started to perceive freshness and Monginis together, and we were the first in Calcutta to have this policy of taking back the leftovers at the end of the day.

However, the policy of taking back the leftovers had a caveat attached to it in the sense that before the

end of a day the franchisee, based on his demand estimates, would place the order for next day.

Orders were delivered to him the next morning. The orders were of two types (1) Normal orders and

(2) Special orders. The leftover from the normal orders was taken back but Switz decided not to take

back leftover from special orders. The rationale for this decision was that special orders were received

from customers based on actual demand and there was practically no risk of loss to franchisee.

Sometimes production department had to go out of the way and produce special orders in the night

shift so that delivery could be made to customers the next morning, in time. Monginis followed the

policy of honouring special orders at all costs.

Selling reasonably priced fresh cakes through middle class neighbourhood shops was an immediate

success. Customers liked the idea of fresh cakes being available at their doorsteps. By the end of first

year Monginis recorded sales of Rs. 6 lacs at 1991-92 prices. Saha in an interview said:

B-5

Appendix B: Monginis Case

We were able to rope in two franchisees during the first year of this decision. We won over the franchisee, who was earlier sceptical about the losses due to leftovers, as he generated sufficient monthly commission.

The whole process of taking orders, planning for production, delivering products and keeping track of

activities at franchisee end was handled manually. Some departments felt that the manual systems

took a significant amount of time in planning the requirements of day-to-day operations. To handle

these complaints, production department decided to go for an information system (IS) package that

could help it in its decision-making of how much to produce in batches. SFPL installed its first

computer in 1993 and used LOTUS®3 as a database to maintain franchisee information, the details of

orders placed and the credit balances.

B.5 The Growth Stage - Expansion Monginis saw a huge jump in its sales after first year. While sales increased to Rs. 250 lacs at the end

of 1994-95, the number of outlets had reached 23. There was a huge demand for its products, the year

on year rate of growth shot up exponentially. Existing infrastructure soon came to be perceived as a

bottleneck. Frequently, the production fell short of the demand because of limited facilities. The

existing production area required expansion. SFPL expanded its facility at Kasba with latest machines

and increased manpower. With this, it was able to reach more customers and the sales began to grow

at a fast pace (see Exhibit B.2).

In 1993-94, Switz became the first bakery to sell savouries through its network of franchisees. By

then, the other bakeries had also started selling cakes through small franchisee network. SFPL

increased number of variants in both the product categories and also expanded its franchisee network

(see Exhibit B.3). By 2004, entire Kolkata was covered under one big franchisee network. Monginis

shops could be seen in all parts of the city, including suburban areas. To service all these franchisees

at right time and in right conditions, Switz had established the biggest fleet of vans in cake industry in

Kolkata. These vans were specially designed to carry perishable products. However, very soon, Switz

realised that transportation was not its core competence and outsourced its logistics to Mahindra and

Mahindra in 1999.

Success brought its own problems and challenges. Other players started imitating Monginis strategies

to succeed in the market. It was easy as there were low entry barriers. Anyone who knew how to bake

good cakes could enter the market. But as Ghosh said:

3 Lotus used to be the registered trademark of electronic spreadsheet software, manufactured and distributed by

the Lotus Development Corporation.

B-6

Appendix B: Monginis Case

We did not react much to the competitors. We just focused on providing quality and fresh products to the customers.

The limited ways in which Monginis reacted to the competition, besides increasing the product

variants was by collaborating with other organisations that marketed complimentary products. It

collaborated with Pepsi Foods, Kwality Ice Cream (India) and Biskfarm to stock soft drinks, ice

creams and biscuits at its franchisees. These were products that could be consumed along with

pastries and savouries.

Urged by intense competition and the fast-changing dynamic environment, Monginis carried out a

significant innovation at the end of 1999. The company initiated a series of changes to reengineer its

production processes. It also promoted the establishment of Dream Bake Private Limited for the

production of packaged cakes, which was a separate venture. As per top management, SFPL also

changed its organisational structure from hierarchical to flat and flexible.

Despite all this, one problem, which still worried SFPL management, was that there always existed a

gap between demand and production because of the variations at both ends. The organisation was

trying to bridge the gap between demand and supply by proper information flow along the supply

chain. This was a complex task as each department had acquired systems that best suited its need of

data analysis. This made compatibility of information difficult at organisational level, across

departments. For example, the production department used Excel, despatch department that handled

both the order processing and finished products had Access database, and finance department had

purchased Tally software. Apart from this the operating systems in use were different. The orders

were received over phone, noted down on paper, and then entered into the system for further

processing. This led to isolated pools of information, some of which was redundant and on several

occasions led to inconsistencies.

B.6 Saturation

The number of Monginis franchisee outlets had risen to 102 by December 2004. The nearest

competitor was far behind with only 68 outlets. The operations covered entire metropolitan area of

Kolkata, which made further expansion difficult. Management became aware that further penetration

might lead to channel conflict. The sales were estimated to touch Rs. 2300 lacs by the end of the

financial year in March 2005. SFPL, at the same time was aware of the threat posed by the entry of

global heavyweights. Over last four to five years, global players like Domino’s, Pizza Hut, Barista,

Café Coffee Day had entered fast food market in Kolkata. These joints were specifically attracting

B-7

Appendix B: Monginis Case youngsters from middle class who earlier went to Monginis. This had led to increased competition

especially in savouries.

Thus a saturating market, increased competition, lower margins were becoming major concerns for

the organisation. The management formed a team to investigate the issues and come up with action

plan for future. The managers of each functional department were made part of this cross-functional

team. The team decided to look into every aspect of the business howsoever small it was, with focus

on cost cutting and reduction of uncertainties. It also wanted to explore the possibilities of reaching

new markets. Maitra started the proceedings of the first meeting:

If we go at this rate our profits are sure to plummet. We need to reduce the leftovers at all costs. Also increasing the in house process efficiency and effectiveness should be high on our agenda.

The team was aware that the proper flow of material and information was vital to reduce the

uncertainties in the system. The team drew a macro view of supply chain structure (see Exhibit B.4).

To analyse the task at hand, they classified information flow at the following levels: customer,

franchisee, management, factory and supplier (see Exhibit B.5 for daily activity flow). The team

prepared excerpts in each category.

B.6.1 Customer The team started with focus on importance of customer to the business:

Customer is the king in business and should get the highest service. ‘Give him what he wants, where he wants and the price at which he wants’ has been the driving force in the organisation.

Customers’ views about Monginis in terms of price and image were summarised (see Exhibit B.6).

On retrieving the historical data the team found that the customers’ eating habits at Monginis cake

shop varied, based on day of the week, date of the month, the outlet location, occasions, festivals etc.

The variations were significant. Maitra estimated stock-outs to be around 5%, but was of the opinion

that the customers usually went in for the items available on the shelf. It was also found that the

ambience and the decor at the franchisee outlets also had a significant impact on the buying habits of

the customer.

B.6.2 Supplier Flour, Sugar, Egg and Fat were the important raw materials. Most of the orders with the suppliers

were pre-negotiated, but some were negotiated as and when demand arose. SFPL followed the ‘order

B-8

Appendix B: Monginis Case up to level’ inventory policy and held stock that was sufficient for about a week. The other raw

materials like cocoa products, spices etc., were ordered in lots that lasted for about a fortnight. The

supplier received the orders for fresh vegetables and meat the previous night, which was delivered the

next morning. It was the responsibility of the supplier to deliver this material to the factory premises.

After the quality inspection process, the accepted materials were updated in stock and the rejected

materials were sent back to the supplier. The ordering process followed was manual and stock

keeping was performed every alternate day.

B.6.3 Factory The production at SFPL was divided into two shifts viz. day and night. The orders for day1

(tomorrow) were received on evening of day0 (today). However, to optimally utilize the production

capacity, certain portion of day1 demand was produced in the day shift of day0. Production normally

took place in accordance with the production work order, which was given by the Production head,

after looking at daily sales plan given by the Operations head. The orders were received via phone,

fax or through sales representatives and then entered into the system. The information was then

processed to obtain the production required for the day. Once all the orders for the next day were

received, the factory evaluated deficits. The company followed a policy to meet the special orders at

all costs. During the day production, the items were first assigned to special orders. The deficit items

were taken for production in the night shift only if it was above a certain threshold, which was set by

the Production head. Threshold was usually based on feasible batch size for that particular item.

Each product category was divided into different items and each item had a number of variants. For

example a pastry could be round, rectangular or heart shaped, could have different toppings, made

with or without egg, and be of different weights. Similarly, a savoury could be vegetarian or non-

vegetarian, have different flavours and have variants based on the gravy fillings. The production

therefore was formula specific and was called ‘recipe’. The entire production process was sub-divided

into separate processes. The team drew the production process flow for pastries and savouries (see

Exhibit B.7). Even if there was higher demand from franchisee for normal order, the production

department had final say on the amount that it would produce. Production department would decide,

mainly, based on production capacity, stock availability, and manpower. After the production process

was over, the products were put in crates and transferred to the finished goods godown for final

despatch. The despatch took place in accordance with the delivery schedule, as defined in the sales

order. The details were updated in the Excel sheet and stored for future reference.

B-9

Appendix B: Monginis Case Top management exercised various controls to reduce gap between demand and supply. Despite this,

there was always a position where one was greater than other. Some of the factors responsible for the

gap were festivals, strikes, cricket matches, rains and heat. Though the company had a method of

dealing with the gap, but it was not very efficient. All franchisees were divided into three categories

namely A, B and C based on amount of leftovers returned. The A category was the one where the

franchisee made a good forecast and usually returned minimal unsold items. Category C was the other

extreme. If production was greater than the demand then the extra products were distributed among

franchisees based on their category. The A category franchisees get maximum, B category less than

that and C category do not get any. Similarly, in case of shortage, the A category franchisees were

given what they had asked for, B category were given little less than demanded and C category were

given the minimal.

The team had a fresh look at the recent months’ orders and supply. One of the problems was that it

was difficult to calculate cost for each item because there was no appropriate bill of material. The

processes were mostly integrated and there were no accounts of the losses that occurred on its way

from raw material to finished stock. Apportioning the cost to each item was difficult and judgement

was used to do so (see Exhibit B.8).

B.6.4 Franchisee The franchisee network had grown far and wide, and covered the entire Kolkata metropolitan area

(see Exhibit B.3). The outlets gave normal order to the factory, in the evening. These orders were

placed over phone. The sales persons would also transfer the special order information to the factory.

The franchisee received the products in the morning and same time returned the leftovers of the

previous day. The payment of the sold products was also made on spot.

To get a first hand account of the happenings at the franchisee the team made a few visits to the

franchisee shops at different points of time. At about 7:30 in the evening, in a Park Street outlet it was

found that there was no stock for many items. A customer was overheard saying that he had to miss

his favourite patties, three days in a row. The stocks at many other outlets were also significantly less

during evenings. A look at past orders revealed that the amount sent to the franchisee was less than

that asked for in most of the cases. The management was aware of this but did not want to risk

sending more products and incur increased loss due to leftovers. The team was looking for an

appropriate method, which could help it predict as best as possible the amount of each item to be

produced and shipped to the franchisee. The objective was to increase the sales of each franchisee and

minimise the leftover at the end of everyday.

B-10

Appendix B: Monginis Case B.6.5 Management The management policies had been successful because of the professionalism adopted in running the

business. It followed centralized decision making for managing its supply chain. Ghosh said:

We face a lot of challenges with perishability and logistics, and learn new things every day. We work on a T+1 day trading cycle when even the stock market follows a T+3 days trade settlement cycle!

Over time, the management took significant decisions by going in for capacity augmentation,

investment in new machinery, promoting new businesses etc. They wondered if installing new

information systems could integrate business globally. Management felt that they lacked the

computing infrastructure to perform detailed analysis and come up with: optimal inventory of raw

materials, production batch sizes and schedules, franchisee sales, market growth etc. Team was

looking for standard software and packages that could talk to each other and aid the management of

Switz Corporate. Excise duty was another area of concern for the management as it was raised back

to 16% (see Exhibit B.9). As the items produced were price sensitive, management was thinking

about negotiating excise duty rates with the government through mutual cooperation of all players in

the industry.

B.6.6 Market The market for bakery products was growing (see Exhibit B.10). There was a good demand for cakes

and bakery products in smaller towns. Maitra felt that this market could be tapped. He believed that

this was the best way to increase the revenues of the organisation. He was of the opinion that ten big

towns in West Bengal after Kolkata could accommodate at least three franchisee outlets each. This

was potentially a big market. These cities had a large middle class, which Monginis could target. The

market seemed all the more lucrative given the fact that most of MNC food joints were unlikely to

move to these locations in near future. Two alternatives were discussed on how to cater to this

market.

The first alternative was to start production facilities in these towns. This would entail manufacturing

of quality products, which would be comparable to the ones produced and sold in Kolkata. The

problem with this was to get expert artisans, since these people might be difficult to find in these

small towns, as per Maitra. Another problem with this alternative was that new plant and machinery

would need to be installed. This could increase costs and hamper feasibility of business in these areas

at the current level of technology.

B-11

Appendix B: Monginis Case The second option was to transport products manufactured in Kolkata to these cities. The decision

could dilute company’s image about freshness, the key plank of the organisation, if products were not

supplied within right time and in right condition. All the products manufactured by SFPL were highly

perishable with shelf life of twelve to thirty hours and required special handling, packaging and

transportation. In addition, the products had to be stored at right temperature during transportation,

which varied from under ten degree Celsius to above sixty degree Celsius.

B.6.7 Information System A prominent characteristic of SFPL value chain was that it depended significantly on coordination

among various departments. But it did not have an integrated IT system that could help development

of IT strategy, manage supply chain operations, and support and maintain infrastructure. Distinct

operating systems (Windows 98/2000/XP, Unix, Windows NT) and databases (MS Access, Sybase)

existed simultaneously in the company, resulting in data isolation and inconsistency. Isolated systems

had the risk of information conflicts and functional redundancy. The current systems lacked the

capability to manage, control and support multiple sites, and the ability to adapt to dynamic

environment. The team felt that an integrated IS package was need of the hour. It was also aware that

the management would take into account its findings to see what benefits would accrue by going for

integrated IS.

The initiative of planning for new information system was prompted by limitations in the existing

systems as they were neither able to keep up with the evolving needs due to organisational expansion

nor did they satisfy the increasing demands for information sharing and data analysis. The existing

system did not have the capability to learn and make recommendations regarding the ordering

quantities at the outlets for the different products. The focus of team meetings was on reviewing the

right packages and clarifying the business necessities that called for installing the package. The main

reason was to enhance the efficiency of data acquisition and make accurate and timely information

available to everyone in the organisation. The team expected IS plans to achieve significant

improvement in productivity, reduction in manpower and eliminate manual delays and errors. This

was part of a larger attempt towards reducing costs and increasing market share and profitability.

They expected the software to improve their critical business processes such as planning, production

management, inventory control and faster decision making. Potential benefits could include

breakthrough reductions in working capital, information about customer wants and needs, the ability

to view and manage the extended enterprise of suppliers, alliances and customers as an integrated

whole. However, these systems were also expensive, complex and notoriously difficult to implement.

B-12

Appendix B: Monginis Case Over the last two months, Dey had been spending long hours with his team collecting the facts of the

organisation and drawing conclusions. He was not sure if there was a good solution to the enormous

task at hand. Product variety, stock-outs, leftovers, collaborations, franchisee contracts, enterprise

system deployment, new markets, competition, transportation and the list could go on. The situation

was confusing. He sometimes felt that it would be a tightrope walk to address these questions, while

at other times he felt that the company was doing well and they just needed to maintain it.

B.7 What Next? Having finished the coffee, Dey reviewed his schedule for the day. At 10:00 he had a meeting with

Maitra to review the recommended markets for SFPL to enter. He was somewhat concerned about

entering new regions for it demanded significant investments. At 11:00 he planned to sit with Saha to

finalize the production policies and the vision to achieve lean manufacturing. He was not sure if he

had to recommend significant changes to the management. At 2:00 he would talk to some franchisees

over phone. These people represented Monginis face to the customer and their input was vital. At

3:00 he had an appointment with the suppliers and was confident of getting their approval for the

proposed change in ordering policy.

Dey decided to keep his focus on the session coming up tomorrow with the Board, where he had to

put forward the current challenges faced by the organisation and also suggest solutions. At the end of

the day, he needed to finalize the report and prepare the presentation scheduled for next day. He

wondered if he was in for another sleepless night. The final touches to the information system

requirements for the organisation and the strategy that should be adopted by SFPL was still pending.

He knew that whatever he suggested required rigorous validation in terms of benefits. The system

implementation would call for changes in organisation restructuring which could mean redundancy of

some posts, change in processes, and most importantly - heavy investments.

Monginis had initiated many innovations in the past. Is another innovation round the corner?

B-13

Appendix B: Monginis Case Exhibit B.1: Market Share of Bakery (Cakes/Savouries/Pastries/Gateaux) Industry

Year 2004

Company % Monginis 46 Sugar & Spice 24 Kathleen 12 Flurys 9 Others 9

Source: SFPL Estimates Note: Only branded players considered Sweetmeat shops and artisanals not included Exhibit B.2: Revenue Statement Rs. in Lacs Cakes/Pastries/Gateaux Savouries

Year Sales Costs Leftover% Sales Costs Leftover%

1991-92 6.16 7.48 - - - - 1992-93 53.28 56.60 - - - - 1993-94 81.47 80.06 1.52 41.79 41.07 9.38 1994-95 171.42 168.03 1.58 80.69 79.09 10.61 1995-96 326.95 323.70 1.43 146.88 145.42 10.08 1996-97 591.77 576.52 1.23 220.96 215.26 10.45 1997-98 667.10 657.89 2.06 516.23 509.10 8.43 1998-99 936.52 928.95 1.74 468.39 464.60 11.04 1999-00 978.69 966.97 1.19 528.31 521.98 6.98 2000-01 752.09 743.20 1.50 637.63 630.09 5.61 2001-02 713.46 687.83 1.83 694.42 669.47 5.96 2002-03 885.29 845.83 1.76 869.15 830.40 5.66 2003-04 1,071.81 1,014.87 1.81 913.68 865.14 6.72 2004-05 (E) 1,225.00 1,163.75 2.78 1,075.00 1,021.25 6.14

Source: SFPL Note: Leftover % = Leftover quantity * price * 100 / Total Sales Cost includes the leftover (that is scrape) loss

B-14

Appendix B: Monginis Case Exhibit B.3: Growth in Franchisee Outlets

Outlet Spread in Kolkata Metropolitan Region, December 2004

Year No. of Outlets

1991-92 - 1992-93 2 1993-94 5 1994-95 23 1995-96 33 1996-97 39 1997-98 50 1998-99 56 1999-00 62 2000-01 71 2001-02 79 2002-03 87 2003-04 96 2004-05(E) 103

Source: SFPL Note: Figures in bracket

indicate revenue percentage from that region

Exhibit B.4: Schematic View of Monginis Supply Chain

BUDGE BUDGE

BEHALA

BAURIPUR

GARIA

KALIGHAT

BBD BAG

HOWRAH 6 (3%) SALT LAKE

14 (18%)

DUM DUM

BARAKPUR

KONNA NAGAR

HOOGLY 5 (03%)

KALYANI

24 SOUTH PARGANAS 5 (7%)

SOUTH KOLKATA 17 (17 %)

CENTRAL KOLKATA 26 (22 %)

NORTH KOLKATA 21 (23%)

MAP OF KOLKATA

NORTH

24 NORTH PARGANAS 8 (07%)

(NOT TO SCALE)

PurchaseDept.

Stage 1 Stage 2 Stage LGodown/DespatchSection

Factory

1

2

N

1

2

M

Supplier Franchisee

Production

Centralized Supply Chain

Information Flow

Material Flow

PurchaseDept.

Stage 1 Stage 2 Stage LGodown/DespatchSection

Factory

1

2

N

1

2

M

Supplier Franchisee

Production

Centralized Supply Chain

Information Flow

Material Flow Source: Drawn as per our discussion with SFPL Management

B-15

Appendix B: Monginis Case Exhibit B.5: Activity Flow Diagram Customer Shops SupplierManagement Factory

RM Orders

RM Supply

Day ProductionOrder

Deficit

Night Production

Allocation

Sales

Return

Customer Shops SupplierManagement Factory

RM Orders

RM Supply

Day ProductionOrder

Deficit

Night Production

Allocation

Sales

Return

Source: Drawn as per our discussion with SFPL Management Exhibit B.6: Monginis in Customers View Prices of Monginis Cakes Vs Competitors %

More expensive 4 Slightly expensive 29 At par 49 Slightly cheaper 3 Don’t Know/Can’t Say 15

Source: ACNielsen ORG-MARG Report, January 2005 Image of Monginis Cake Vs Competitors

Figures in %

Quality Taste PackagingBetter than Competitors 77 76 72 Same as Competitors 11 11 10 Worse than Competitors 1 1 3 Don’t Know/Can’t Say 11 12 15

Source: ACNielsen ORG-MARG Report, January 2005

B-16

Appendix B: Monginis Case Exhibit B.7: Production Process Generic Process Flowchart of Pastries/Gateaux

Sponge PreparationFlour SiftingFlour Sifting

Egg BreakingEgg Breaking

Other R.M.Other R.M.

BatchingBatching MixingMixing DepositingDepositing

Preparation of Mould

Preparation of MouldCoolingCooling

Fat LaminationFat Lamination

Sponge shifted to Pastry

Department


DepartmentCuttingCutting Butter Cream

LayeringButter Cream

LayeringButter Cream Preparation

Butter Cream Preparation

Butter Cream (WIP)

Butter Cream (WIP)

Color PremixColor Premix

Direct R.M.Direct R.M.Top DecorationTop Decoration

Baking inOvenBaking inOven MouldingMoulding

Individual Piece Cutting


Finished ProductFinished Product CraftingCrafting CapsulingCapsuling

Sponge PreparationFlour SiftingFlour Sifting

Egg BreakingEgg Breaking


BatchingBatching MixingMixing DepositingDepositing

Preparation of Mould

Preparation of MouldCoolingCooling

Fat LaminationFat Lamination


Department


DepartmentCuttingCutting Butter Cream

LayeringButter Cream

LayeringButter Cream Preparation

Butter Cream Preparation

Butter Cream (WIP)

Butter Cream (WIP)

Color PremixColor Premix

Direct R.M.Direct R.M.Top DecorationTop Decoration

Baking inOvenBaking inOven MouldingMoulding



Finished ProductFinished Product CraftingCrafting CapsulingCapsuling

Source: SFPL Production Documents Note: RM – Raw Material, WIP – Work In Progress Generic Process Flowchart for Savouries

Dough PreparationFlour SiftingFlour Sifting


BatchingBatching KneadingKneading SheetingSheeting

Fat LaminationFat LaminationSheetingSheetingFinished DoughFinished Dough FoldingFolding

Spray of Egg / Milk Water

Spray of Egg / Milk Water Baking in OvenBaking in Oven Cooling &

CountingCooling & Counting

Sheeting & Cutting

Sheeting & Cutting

Filling depositionand Folding


Kitchen DepartmentKitchen Department

Dough DividingDough Dividing

FillingFilling

Finished ProductFinished Product

Dough PreparationFlour SiftingFlour Sifting


BatchingBatching KneadingKneading SheetingSheeting

Fat LaminationFat LaminationSheetingSheetingFinished DoughFinished Dough FoldingFolding

Spray of Egg / Milk Water

Spray of Egg / Milk Water Baking in OvenBaking in Oven Cooling &

CountingCooling & Counting

Sheeting & Cutting

Sheeting & Cutting



Kitchen DepartmentKitchen Department

Dough DividingDough Dividing

FillingFilling

Finished ProductFinished Product

Source: SFPL Production Documents

B-17

Appendix B: Monginis Case Exhibit B.8: Representative List of Products

Item Name Retailer Producer Prodn. Avg. Order Qty. % Var. Prodn. SP SP Cost Special Normal Normal Qty. Qty. Cakes/Pastries

Milk Cake 6.00 5.00 4.50 148 1,152 11.24 1,301 Choco Muffin 6.00 5.00 4.50 97 1,235 12.10 1,329 Crown Cake 5.00 4.20 4.00 145 1,115 12.19 1,260 Butter Scotch 6.00 5.00 4.00 130 1,794 10.31 1,891 Strawberry 6.00 5.00 4.00 33 1,342 14.13 1,258 Vanilla 8.00 6.80 6.00 13 528 26.13 566 Choco Chips 8.00 6.80 6.00 76 1,503 5.48 1,495 Choco Delite 8.00 6.80 6.00 36 1,242 8.69 1,088 Truffle Pastry 10.00 8.50 7.50 84 1,008 26.33 1,199 Toffee Pastry 5.00 4.20 3.50 90 1,318 6.16 1,435

Savouries

Panir Kachouri 6.00 5.00 4.00 293 2,277 5.84 2,748 Fish Kachouri 10.00 8.50 8.00 94 1,402 7.41 1,482 Mistisukh 5.00 4.20 3.50 632 1,133 9.14 1,765 Veg. Patties 7.00 6.00 5.00 2,398 3,736 4.88 5,629 Chicken Patties 10.00 8.50 8.00 1,386 4,356 7.83 5,153 Chicken Titbit 6.00 5.00 4.00 823 2,423 4.27 3,346 Veg. Pizza 8.00 6.80 6.00 83 1,374 3.86 1,456 Chicken Pizza 12.00 10.20 9.00 53 1,650 5.53 1,703 Fish Spring Roll 14.00 12.00 10.00 90 1,350 8.77 1,352 Cream Roll 10.00 8.50 7.50 332 1,053 8.00 1,102 Chicken Spring Roll 12.00 10.20 9.00 23 1,156 7.67 1,199 Veg. Manchurian 12.00 10.20 8.50 513 1,196 4.46 1,704 Chicken Croissant 12.00 10.20 9.00 57 1,337 6.26 1,298 Fish Titbit 6.00 5.00 4.00 122 1,145 5.36 1,253 Fish Chop 6.00 5.00 4.50 112 1,614 4.36 1,754

Source: SFPL Note: Price and Cost are in Rs.; SP - Selling Price Prices given are for standard products. Price varies based on size, shape etc. Production cost is an approximate estimate as it is produced in batches High sales (~Rs. 4 lacs) occur during special occasions like Diwali, Id, Christmas etc. Low sales (~Rs. 0.5 lacs) occur during strikes, bundh etc. The high and low sales days are not accounted while taking average List of representative items (chosen based on highest quantity sold), is not exhaustive % Variation Normal Order = Variation in normal order quantity * 100 / Normal order quantity The average order and production quantity shown in the table are daily figures, for 2004 Production is carried out in morning and night shifts Product Variety - Cakes/Pastries/Gateaux = 1305, Savouries = 59 Daily Avg. Production product variety - Cakes/Pastries/Gateaux = 180, Savouries = 30 Plant utilization is around 85%

B-18

Appendix B: Monginis Case Exhibit B.9: Statement Showing Effect of Indirect Taxes on Performance Rs. in Lacs

2001-02 2002-03 2003-04 2004-05 2005-06 Audited Audited Audited Projected Budgeted

Excise Duty Rate 16% 16% 8% 16% 16% Net Sales 1,254.71 1,546.17 1,792.10 2,011.79 2,493.79 Sales Tax / VAT 120.21 166.55 187.83 209.59 326.08 Excise Duty 80.60 98.34 65.08 121.37 176.77 Profit After Tax 50.58 78.21 105.49 107.00 105.00

Source: SFPL Note: Savouries have excise exemption Switz brand is excise exempted till Rs. 100 lacs of sales Exhibit B.10: Estimated Market Growth

% Value Growth % Volume Growth 2003-04 1999-04 2003-04 1999-04

Bakery Products

CAGR CAGR Bread 7.2 9.2 5.4 5.8 Pastries - - - - Cakes 9.6 10.5 7.4 7.8 Baked goods 7.4 9.3 5.5 5.9 Savoury 6.8 7.3 5.1 5.1 Biscuits 7.0 7.3 5.4 5.9 Others 7.3 8.7 5.5 5.9

Source: Euromonitor, December 2004

B-19

Appendix C

C MATHEMATICAL DERIVATIONS C.1 Factory Production for Generalized Model Let n denote the number of replenishments to the retailer during one production run and denote the

last additional time period (a fraction of

lt

Rt ) of production run. This is shown in Figure C.1 which

represents one production setup lasting for R Fn n retailer replenishments.

Factory (Production)

tR Time

IF(t)

0

1

2

n

tltF= ntR+tl

(nR/nF)tR

qR

Figure C.1: Inventory level at factory

C.1.1 Holding Inventory at the Factory Let denote the finished goods inventory level at the factory after iiQ th replenishment. Let denote

the total inventory held during that period. Since the deterioration rate is constant we can assume that

a fraction of this inventory,

iA

iAθ is lost due to deterioration.

Enumerating this after each replenishment i

i = 1, 1 1R RPt q Q Aθ− − =

C-1

Appendix C: Mathematical Derivations i = 2, 1 2R RQ Pt q Q A2θ+ − − =

i = n-1, 2 1n R R n nQ Pt q Q A 1θ− −+ − − = −

n

i = n, 1n R R nQ Pt q Q A θ− + − − = (C1)

i = n+1, 1 1n l R n nQ Pt q Q A θ+ ++ − − =

i = n+2, 1 2n R n nQ q Q A 2θ+ +− − = +

i = R Fn n , ( ) ( ) ( )1R F R F R FRn n n n n nQ q Q A θ− − − =

The ending inventory ( ) 0

R Fn nQ = . Adding all the (C1) equations yields

( )( 1 2 ... ...R F

R RR l R F R n n n

F F

n nnPt Pt q Pt q A A A An n )θ+ − = − = + + + + + (C2)

The total inventory on hold over the period ( )R F Rn n t is

( )1 21... ...

R F

Rn Fn n

F

nA A A A A q qnθ R

⎡ ⎤= + + + + + = −⎢ ⎥

⎣ ⎦ (C3)

C.1.2 Derivation of PQ The finished product inventory level at the factory can be written as

( )( ), 0 1 1;F

P Ri

dI tP I t t t i n

dtθ= − ≤ ≤ ≤ ≤ + (C4)

Solving the differential equation yields

1( ) 1i

t tF i

PI t e Q eθ θ

θ−

−⎡ ⎤= − +⎣ ⎦ (C5)

Enumerating over each replenishment i

i = 1, 1 1 0( ) 1 t t

FPI t Q e Q eθ θ

θ−⎡ ⎤= = − +⎣ ⎦

i = 2, 2 11 t tPQ e Q eθ θ

θ−⎡ ⎤= − +⎣ ⎦ (C6)

i = n-1, 1 21 t tn n

PQ e Q eθ θ

θ−

− −⎡ ⎤= − +⎣ ⎦

i = n, 11 t tn n

PQ e Q eθ θ

θ−

−⎡ ⎤= − +⎣ ⎦

The starting inventory . Solving equations (0 0Q = C6) gives

C-2

Appendix C: Mathematical Derivations

( )1R Rt ntn

P DQ e eθ θ

θ θ−⎡ ⎤= −⎢ ⎥⎣ ⎦

− (C7)

Using (C5) and (C7), the inventory at the factory when the production stops after F Rt nt t= + l time

units is

( )1 1l lR Rt tt ntP

P P DQ e e e eθ θθ θ

θ θ θ− −⎡ ⎤⎡ ⎤= − + − −⎢ ⎥⎣ ⎦ ⎣ ⎦

(C8)

C.1.3 Derivation of NPQ From literature, for constant deterioration rate it is known that

Ending inventory = Opening inventory * ( )1 tθ− (C9)

Enumerating over each period i

i = n+1, ( )1 1 R lt tn NPQ Q θ −+ = −

i = n+2, ( )(2 1 1 ) Rtn n RQ Q q θ+ += − − (C10)

i = R Fn n , ( ) ( )( )( )1 1 R

R F R F

tRn n n nQ Q q θ−= − −

Solving equations (C10) gives

( )( )( )

1 1

1 1

RR

F

R l R

nn t

nR

NP t t t

qQθ

θ θ

⎛ ⎞− −⎜ ⎟⎝ ⎠

− −

⎡ ⎤− −⎢ ⎥= ⎢ ⎥− − −⎢ ⎥⎣ ⎦

1 (C11)

C-3

Appendix D

D CAKE GAME D.1 The Game The “Cake Game” is similar to the famous “Beer Game” but with additional constraint of

perishability. The items arriving at each echelon are assumed to be fresh but can last a maximum

period of four weeks. If the items are not consumed within this period it gets outdated and has a cost

attached to it. Unmet demand results in lost sales at the retailer whereas it results in backorder at all

other echelons. To keep the rules of the game simple we have costs associated with only lost sales /

backorder and outdates. It is charged at one monetary unit per time period. The team with the least

supply chain cost is declared as the winner. The game board is shown in Figure D.1.

RETAILER

WHOLESALER

DISTRIBUTOR

FACTORY

Order

Production

FinishedGoods

Order Order Order Order Order Order

GoodsGoodsGoodsGoodsGoodsGoods

TopLeft

TopRight

BottomLeft

BottomRight

RETAILER

WHOLESALER

DISTRIBUTOR

FACTORY

Order

Production

FinishedGoods



RETAILER

WHOLESALER

DISTRIBUTOR

FACTORY

Order

Production

FinishedGoods



TopLeft

TopRight

BottomLeft

BottomRight

Figure D.1: Game board

D.1.1 Game Instructions On announcement of current week, do the following steps and note down the activities in the game

record sheet shown in Table D.1.

Step 1: Move the leftover items that have expired (last week T+1 column) to Spoilt Units

Step 2: Open the goods receipt (bottom right). Update the Start Inv column

Starting Inventory = Goods received + Last weeks Ending Inventory – Spoilt Units

D-1

Appendix D: Cake Game Step 3: Open the order card (top left). Update the Orders to satisfy column

Order to satisfy = Order received + Last weeks backlog

Step 4: Advance the top right and bottom left card by one position

Step 5: Determine your shipment and write it on the card and place it at the bottom left

Shipment = Minimum

Step 6: Calculate backlog and update the column

Backlog (if positive) = Order to satisfy – Start Inv

Step 7: End Inventory = Start Inv – Shipments

Update Columns T+1 to T+4

Inventory with expiry date Week (T) T + 4 T + 3 T + 2 T + 1

End Inv

Last Week Current Week

Step 8: Determine the order to be placed (last column) and place it on the top right

Table D.1: Tabulation worksheet

D.1.2 Game Software To reduce the computing complexities of the subjects so that they can focus on the ordering decisions,

software of the game was developed using Microsoft Excel. All calculations are inbuilt to compute

the cost of the entire supply chain. The only decision that needs to be made by the subject is the

ordering decision.

D-2

Appendix D: Cake Game D.2 Experimental Design Design of Experiments (DOE) results in eight different runs. The performance parameters for each

echelon of the supply chain are tabulated in Table D.2 by averaging the replications of six simulation

runs for each case.

Table D.2: Average of six replications of each simulation run for the different players of the supply chain

Run Min order

Max order

Range order

Min inv

Max inv

Range inv

Min order time

Max order time

Min inv

time

Max inv

time

Max spoilt

Max spoilt time

Retailer 1 0.83 11.50 10.67 -9.50 24.17 33.67 22.83 8.50 13.83 23.17 4.83 24.172 0.00 10.17 10.17 -4.17 20.00 24.17 39.17 11.83 49.00 24.83 2.83 29.333 0.33 12.17 11.83 -12.50 25.33 37.83 23.17 7.83 14.83 23.17 5.33 24.174 0.00 10.00 10.00 -5.00 20.33 25.33 38.83 12.33 49.00 25.00 2.83 29.335 0.83 11.50 10.67 -9.50 24.17 33.67 23.17 8.50 13.83 23.17 N/A N/A 6 0.00 10.17 10.17 2.17 20.67 18.50 31.00 11.83 10.67 26.17 N/A N/A 7 0.33 12.17 11.83 -12.50 25.33 37.83 23.17 7.83 14.83 23.33 N/A N/A 8 0.00 10.00 10.00 -0.17 21.17 21.33 31.17 12.33 11.33 26.00 N/A N/A

Wholesaler 1 0.00 16.83 16.83 -21.67 30.33 52.00 22.33 8.50 13.00 22.50 8.00 23.832 0.00 12.67 12.67 -6.17 23.67 29.83 34.17 10.33 30.17 22.83 4.33 24.003 0.00 19.17 19.17 -26.67 30.67 57.33 23.17 9.17 13.50 23.00 8.33 24.004 0.00 12.83 12.83 -9.33 24.67 34.00 33.00 10.33 12.17 22.17 4.50 24.505 0.00 16.83 16.83 -21.67 34.50 56.17 22.33 8.50 13.00 25.17 N/A N/A 6 0.00 12.67 12.67 -5.17 26.00 31.17 26.67 10.33 11.50 28.67 N/A N/A 7 0.00 19.17 19.17 -26.67 37.83 64.50 23.00 9.17 13.67 25.67 N/A N/A 8 0.00 12.83 12.83 -9.33 27.00 36.33 26.00 10.33 12.17 29.00 N/A N/A

Distributor 1 0.00 25.67 25.67 -36.17 36.00 72.17 22.33 9.67 12.00 21.50 9.33 23.672 0.00 16.83 16.83 -11.83 31.83 43.67 26.67 10.33 10.50 20.50 7.33 23.173 0.00 31.83 31.83 -46.33 40.67 87.00 20.00 11.00 13.00 20.83 11.00 22.334 0.00 18.67 18.67 -17.67 35.50 53.17 25.67 10.17 11.50 21.00 8.50 23.175 0.00 25.67 25.67 -36.00 43.83 79.83 22.33 9.67 12.00 30.33 N/A N/A 6 0.00 16.83 16.83 -11.83 34.33 46.17 23.50 10.33 10.50 31.17 N/A N/A 7 0.00 31.83 31.83 -46.33 44.33 90.67 20.00 11.00 13.00 28.83 N/A N/A 8 0.00 18.67 18.67 -17.67 38.33 56.00 23.67 10.17 11.50 34.67 N/A N/A

Factory 1 0.00 41.33 41.33 -39.00 55.67 94.67 18.33 10.67 11.17 18.17 18.50 21.502 1.33 24.00 22.67 -14.00 45.00 59.00 20.33 10.83 9.67 20.67 11.83 21.673 0.00 55.50 55.50 -55.33 80.83 136.17 15.17 12.17 12.50 18.50 32.83 17.834 0.00 29.00 29.00 -20.67 49.33 70.00 20.83 11.17 10.67 20.00 14.83 22.335 0.00 41.33 41.33 -39.17 59.67 98.83 18.33 10.67 11.17 22.83 N/A N/A 6 0.00 24.00 24.00 -13.67 49.83 63.50 20.00 10.83 9.67 30.00 N/A N/A 7 0.00 55.50 55.50 -55.00 86.17 141.17 15.17 12.17 12.50 22.83 N/A N/A 8 0.00 29.00 29.00 -20.50 57.50 78.00 19.67 11.17 10.67 34.67 N/A N/A

D-3

Appendix D: Cake Game D.3 Ithink Model Simulation model is built using ithink software package. A representative model for the wholesaler of

a non perishable item supply chain is presented in Figure D.2. The same model is replicated at the

other echelons. The demand at the retailer will be from the customer and the supplier dispatch at the

factory will be the ordering decision by the factory itself.

Order Backlog 3

In Transit 2 Local Inv entory 2

Order Backlog 2

Expected Demand 2

Inv entoryAdjustment Time 2

Order decision 2 Order up to lev el 2

Backlogchange 2

Expectation change 2

Expectation Adjustment Time 2

SupplierDispatch 2

arriv al 2 Shipment 2

Net inv entory 2

ShipmentRequirement 2

Demand 2

Inv entory Position 2

Tansit lead time 2

Order decision

Shipment 3

Stage 2: Wholesaler

Figure D.2: Representative diagram of the wholesaler for a non-perishable product

D-4

Appendix E

E SUPPLY CHAIN SYSTEM DYNAMICS MODELS E.1 Supply Chain Capacity Augmentation The factory capacity augmentation for a short lifecycle perishable product was analyzed in section

6.4. The graph and metrics designed for monitoring the performance of the supply chain under

different input conditions and various information processing policies are provided in Figure E.1. The

figure is a screen shot from ithink software.

Figure E.1: Monitor (screen shot) for evaluating the various policies

E.2 Supply Chain Models The strategic models for the supply chain presented in Chapter 6 have been developed using system

dynamics modeling methodology. The complete models are provided in this section. First the causal

loop diagram for the holistic supply chain is provided in Figure E.2. The later figures represent the

stock and flow diagrams for the various sectors of the supply chain.

E-1

Chapter E: Supply Chain System Dynamics Models

AcqusitionLag

SupplierSpoilage

SupplierInventoryAcquistion

RateSupplierBacklog

SupplierShipment

+

+ +-

+

-

Factory RMInventory

Factory RMSpoilage

Factory RMOrder

FactoryProduction

Factory FGInventory

Factory FGSpoilage

FactoryShipment

OrderBacklog

ProductionOrder

RetailSpoilage

RetailerInventoryRetail

Orders

RetailSales

+-

++

+

+

++

+

+

-+

+

-

+

++

+

Capacitytilization

onCapacity

CapacityExpansion

InvestmentPolicy

Delivery DelayRecognized U

Producti ++

+

+

+

-

Demand

Price

Market Share

CompetitorMarket Share

CompetitorPrice

CompetitorCapacityUtilization

CompetitorDemand

IndustryDemand

CompetitorCapacity

+

+

-

+

+

+

+

+-

+

+

+

-

+

+

-

Factory FGInventory

Discrepancy -+

-

RetailerInventory

Discrepancy -+

< roductionCapacity>P

-

-

Factory RMInventory

Discrepancy -+

-

SupplierInventory

Discrepancy-

-

+

MARKET

SUPPLIER

FACTORY RM

RETAILER

FACTORY FG

FACTORY CAPACITY AUGMENTATION

Figure E.2: Simplified causal loop diagram for the supply chain model

E-2


Supply Line SL

StockS

Order RateOR

Acquisition Rate AR

Consumption C

SpoilageL

Adjustment f orStock AS

Acquistion Lag ACL

Adjustment f orSupply Line ASL

DesiredSupply Line

SL'

DesiredStock S'

Stock AdjustmentTime SAT

ExpectedConsmptionRate ECRDesired

AcquisitionRate DAR

IndicatedOrders IO

Supply LineAdjustment Time

SLAT

Av erageLif e AL

IndicatedOrders IO 2

ExpectedAcquistionLag EAL

Supply ProcurementVariation


Perishabity

Supply Line SL 2

Stock S 2

Order RateOR 2

Acquisition Rate AR 2

Consumption C 2

Spoilage L2

Adjustment f orStock AS 2

Acquistion Lag ACL 2

Adjustment f orSupply Line ASL 2

DesiredSupply Line

SL' 2

DesiredStock S' 2

Stock AdjustmentTime SAT 2

ExpectedConsmptionRate ECR 2Desired

AcquisitionRate DAR 2



SLAT 2

Av erageLif e AL 2

ExpectedAcquistionLag EAL 2

Consumption C

Perishabity 2

MeanCy cleTime

MeanCy cleTime 2

Supplier Factory Raw Material Inv entory

Figure E.3: Stock and flow diagram for supplier and factory raw material inventory

We have used the anchor and adjustment inventory policy at every echelon. The orders computed at each echelon are placed with the upstream

supplier. The demand from the downstream is met through the echelon inventory. Demand that exceeds stock, results in lost sales. Spoilage occurs

from the stock at all levels of the supply chain. The stock and flow diagram of the raw material inventory management at the supplier and factory

is shown in Figure E.3.

E-3


Consumption C 2

Consumption C 3

Supply Line SL 3

Stock S 3

Order RateOR 3


Consumption C 3

Spoilage L3




DesiredSupply Line

SL' 3

DesiredStock S' 3






SLAT 3

Av erageLif e AL 3



Demand

Perishabity 3

Supply Line SL 4

Stock S 4

Order RateOR 4


Consumption C 4

Spoilage L4




DesiredSupply Line

SL' 4

DesiredStock S' 4






SLAT 4

Av erageLif e AL 4


Production Capacity

Perishabity 4

MeanCy cleTime 3

MeanCy cleTime 4

Factory Finished GoodsRetailer

Figure E.4: Stock and flow diagram for finished goods at the factory and retailer

The ordering policy for the finished goods is same as that used for the raw materials. The model structure of finished goods at the factory and

retailer is shown in Figure E.4. The expected consumption rate is a smoothed variable of the spoilage and consumption and represents the likely

future echelon demand.

E-4


Forcst RS

Relativ ePrice

Demand

Consumption C 4

Order RateOR 3

Consumption C 4

Stock S 4


Supply Line SL 3


Market shareCompetitor

Market Share

~Relativ e

attractiv eness

Demand

Forcst RS

IF ofTSF

CompetitorDemand

IndustryDemand

Production Capacity

Production Capacityon Order IF of

PCOO

Production CapacityAcquired

CapacityExpansion

CapacityUtilization

CompetitorCapacityUtilization

Production Capacity

CompetitorCapacity

~Price

~ CompetitorPrice

Test Input TIInitial Consumption

ExpectedDemand

IF of ED

OF of ED

TotalShortf all

Upward Past Max

DelayTime

TotalSales

IF ofTSF

Shortage

IF of TS

Deliv eryDelay

Recognized

Deliv eryDelay

Traditional

Deliv ery DelayManagement Goal

TDDT

Deliv ery DelayOperating Goal

Deliv eryDelay Bias

DesiredInv entory

Cov erage

OF of ED

Inv estmentPolicy

Inv estmentDelay Bias

Inv estmentPolicy

PCDP

Upward

Timehorizon

Forcst RO

Inf ormationStructureChange

DiscreteAugmentation

Forcst RO

UsePOSData

OF of ED

timeto adjust

ProductionCapacityRelease

Production Capacity

API

Total Exp Demand

IF to TED

ExpectedDemand

+

ExpDemAdj

AtomicPattern

Indicator

MarketCapacity Augmentation

Heuristic

Forecasting

Loo…

Figure E.5: Model for factory capacity augmentation, forecasting, heuristics, market and loop dominance

E-5

Chapter E: Supply Chain System Dynamics Models The atomic pattern indicator of the variable of interest (production capacity in our case) is computed to determine the loop dominance. By

deactivating the candidate loops’ one at a time, one can determine the time intervals when the different loops dominate the behaviour of the

interest variable. Figure E.5 also presents the stock and flow structure for factory capacity augmentation, forecasting techniques, heuristics

employed by the management and market dynamics.

Spoilage

Min order

IF ofmin order

IndicatedOrders IO

Max order

IF ofmax order

min order time

IF ofmin order time

max order time

IF ofmax order time

StockS

Min inv

IF ofmin inv

Max inv

IF ofmax inv

min inv time

IF ofmin inv time

max inv time

IF ofmax inv time

Max spoilage

IF ofmax spoilage

max spoilage time

IF ofmax spoilage time

Inv Range

OrderAmplitude

Stockout

IF ofStockout

Sales

IF ofSales

Consumption CIndicatedOrders IO 2 ITR

+

Av g Inv

Stockout percent

Totaldemand

IF oftotal demand

TotalSpoilage

IF of spoilage

SpoilageSpoilagePercent

Av ailability %

Echelon Summary

Figure E.6: Model to compute performance metrics at each echelon

At each echelon the representation to determine the performance metrics are shown in Figure E.6. This includes among many others, determining

the maximum and minimum inventory, spoilage, stock-outs and the time of its apex and nadir.

E-6

CURRENT LIST OF PUBLICATION Conference Proceedings o Narasimha Kamath B. and Rahul Roy, “Supply chain structure design for a short lifecycle

product: A loop dominance based analysis”, Hawaii International Conference on System Sciences (HICSS-38), Hawaii, USA, January 3-6, 2005.

o Narasimha Kamath B. and Rahul Roy, “A system dynamics framework for analysis of ordering

policies: Application to supply chain management in perishable goods industry”, Conference on System Dynamics (CSD 2005), Tezpur, India, November 4-5, 2005.

Case o Narasimha Kamath B., Munish Thakur, Rahul Roy and Subir Bhattacharya, “Switz Foods

Calcutta: Surviving Perishability”, European Case Clearing House, 2006 (Forthcoming). This case was selected as one of the best cases at “Case Chase – Entrepreneurship Competition”, ISB Hyderabad, India, April 14-16, 2005.

Working Papers o Narasimha Kamath B. and Rahul Roy, “Capacity augmentation of a supply chain for a short

lifecycle product: A systems dynamics framework”, IIM Calcutta WPS-553/2005, Under review after first revision at European Journal of Operational Research.

o Narasimha Kamath B. and Subir Bhattacharya, “Cost minimization of a multi-echelon supply

chain for a perishable product”, IIM Calcutta WPS-546/2005, Under review at International Journal of Production Economics.

o Narasimha Kamath B. and Subir Bhattacharya, “An integrated model for a perishable item in a

multi-echelon supply chain”, Under review at European Journal of Operational Research.