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Channel Dynamics and Integrated Decision Making
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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
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
Sponge shifted to Pastry
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
Individual Piece Cutting
Finished ProductFinished Product CraftingCrafting CapsulingCapsuling
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
Sponge shifted to Pastry
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
Individual Piece Cutting
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
Other R.M.Other R.M.
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
Filling depositionand Folding
Kitchen DepartmentKitchen Department
Dough DividingDough Dividing
FillingFilling
Finished ProductFinished Product
Dough PreparationFlour SiftingFlour Sifting
Other R.M.Other R.M.
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
Filling depositionand Folding
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
Order Order Order Order Order Order
GoodsGoodsGoodsGoodsGoodsGoods
RETAILER
WHOLESALER
DISTRIBUTOR
FACTORY
Order
Production
FinishedGoods
Order Order Order Order Order Order
GoodsGoodsGoodsGoodsGoodsGoods
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
Chapter E: Supply Chain System Dynamics Models
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
IndicatedOrders IO 3
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
IndicatedOrders IO 2
Supply LineAdjustment Time
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
Chapter E: Supply Chain System Dynamics Models
Consumption C 2
Consumption C 3
Supply Line SL 3
Stock S 3
Order RateOR 3
Acquisition Rate AR 3
Consumption C 3
Spoilage L3
Adjustment f orStock AS 3
Acquistion Lag ACL 3
Adjustment f orSupply Line ASL 3
DesiredSupply Line
SL' 3
DesiredStock S' 3
Stock AdjustmentTime SAT 3
ExpectedConsmptionRate ECR 3Desired
AcquisitionRate DAR 3
IndicatedOrders IO 3
Supply LineAdjustment Time
SLAT 3
Av erageLif e AL 3
IndicatedOrders IO 4
ExpectedAcquistionLag EAL 3
Demand
Perishabity 3
Supply Line SL 4
Stock S 4
Order RateOR 4
Acquisition Rate AR 4
Consumption C 4
Spoilage L4
Adjustment f orStock AS 4
Acquistion Lag ACL 4
Adjustment f orSupply Line ASL 4
DesiredSupply Line
SL' 4
DesiredStock S' 4
Stock AdjustmentTime SAT 4
ExpectedConsmptionRate ECR 4Desired
AcquisitionRate DAR 4
IndicatedOrders IO 4
Supply LineAdjustment Time
SLAT 4
Av erageLif e AL 4
ExpectedAcquistionLag EAL 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
Chapter E: Supply Chain System Dynamics Models
Forcst RS
Relativ ePrice
Demand
Consumption C 4
Order RateOR 3
Consumption C 4
Stock S 4
Acquisition Rate AR 3
Supply Line SL 3
Adjustment f orSupply Line ASL 2
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.