Studying the effect of inventory policy and supply chain structure on bullwhip effect

Arab Academy For Science And Technology And Maritime
Transport
College of International Transport And Logistics
Studying The Effect of Inventory Policy and
Supply Chain Structure on Bullwhip Effect
Thesis Submitted
In Partial fulfilment of the Requirement for the
Bachelor of Science (BSC)
By
Abdel-Kader Mohamed
Mohamed Ahmed Sholkamy
Mohamed Ibrahim Samhan
Ola Emad Yassin Sakran
Sarah Lotfi Mohamed Gaafar
Supervised By:
Dr. Mohammed El Beheiry
Dr. Khaled Seif El-Molouk
2012

Declaration
We hereby certify that this material, which we now submit for assessment on the
program of study leading to the award of Bachelor of logistics is entirely our own
work, that we have exercised reasonable care to ensure that the work is original, and
does not to the best of our knowledge breach any law of copyright, and has not been
taken from the work of others save and to the extent that such work has been cited and
acknowledged within the text of our work.
Signed By: Abdel-Kader Mohamed
Mohamed Ahmed Sholkamy
Mohamed Ibrahim Samhan
Ola Emad Yassin Sakran
Sarah Lotfi Mohamed Gaafar
Date: 18th of February 2012

I
Acknowledgment
First and foremost, we would like to thank our supervisors of this project, Dr. Mohamed El-
Beheiry and Dr. Khaled Seif for the valuable guidance and advice. They didn’t want to put
their names on the project, but it will not be well-mannered to not mention them. They
inspired us greatly to work in this project. Their willingness to motivate us contributed
tremendously to our project. Without their encouragement and guidance this project would
not have materialized.
We would like to show our appreciation to Dr. Hamdy Barghout. I can't say thank you
enough for his tremendous support and help. We feel motivated and encouraged every time
we attend his meeting.
Besides, we would like to thank Mr. Karim Selaawi for his efforts with us and provide for us
valuable information as the guidance of our project.
The guidance and support received from all the members who contributed and who are
contributing to this project, was vital for the success of the project. We are grateful for their
constant support and help.
Finally, an honorable mention goes to our families and friends for their understandings and
supports on us in completing this project. Without helps of the particular that mentioned
above, we would face many difficulties while doing this.

II
Abstract
Bullwhip effect is of the phenomenon that affects the performance of any supply chain. The
causes of bullwhip effect are divided into two main categories primary causes (triggers of the
bullwhip effect) and secondary causes (amplifiers of the bullwhip effect). Two of the main
secondary causes are the inventory policy and the supply chain structure. In this thesis a
simulation model is built for two supply chains structures, each composed of three tiers
where the retailer(s) and the distribution centre adopting (Q,r) policy. Applying different
values of the order quantities at the retailer(s) and the distribution centre using the simulation
to find out how the bullwhip effect is affected by these values. Results show that the
bullwhip is highly affected by changing the order quantities or the supply chain structure.

III
Summary
Many academics and researchers view the supply chain management as the integration of
previously known knowledge areas such as inventory management, facility location, transport
management ... etc. Yet, this integration helped in the discovering of new phenomena and
issues which have great effect on the supply chain performance. One of the most important
phenomena is the Bullwhip Effect. Some can trace the first appearance of the bullwhip effect
in the literature to Jay Forrester work (1961), he developed a simulation of multiechlon
system (the term supply chain was not used yet) and found that the demand variability
increases from down echelons to the upper ones. He was able to record the phenomena and
called it Forrester effect but no investigation was made about the harms, causes and
countermeasures. In late 80s a focus is made on the bullwhip effect and many efforts started
to be directed to investigate the causes, countermeasures and quantification.
In this thesis two of the main causes of the bullwhip effect will be investigated, the inventory
policy and the supply chain structure. A simulation model will be built for a serial supply
chain consisting of three members, retailer, distribution centre and a factory. The demand at
the retailer is Poisson distributed and no lead time is considered. The inventory policy at the
retail and the distribution centre is (Q, r) policy, the values of the order quantities at the
retailer and the distribution centre will be varied and the bullwhip effect will be measured to
find the effect of changing the order quantities on the bullwhip effect. Then the some of the
tested order quantities will be tested on another structure. The second structure will be one
factory, one distribution centre and two retailers.
This thesis is organized as follows:
Introduction: In the introduction a background about the problem will be given and a
formulation of the problem which will be investigated is explained.
Chapter One: Contains literature review about the bullwhip effect, with the main researches
done to determine the harms, causes, countermeasures and quantification of bullwhip effect.
Chapter Two: In this chapter a review of the simulation process and its usage as a tool help
in decision making.
Chapter Three: This chapter includes a demonstration of the simulation package used
(ProModel) and detailed illustration of the developed models.
Chapter Four: Include the reached results and decision of these results.
Chapter Five: In this chapter main conclusions are reached and recommendations for future
are given.
Bibliography: all references which are read during the course of preparing this thesis are
listed.

IV
List of figures
FIGURE 1.1: TRADITIONAL FLOWS WITHIN SUPPLY CHAINS................................................................................................................3
FIGURE 1.2: SUPPLY CHAIN STAGES......................................................................................................................................................4
FIGURE 1.3: THE BULLWHIP EFFECT GRAPH..........................................................................................................................................5
FIGURE 1.4: BEER GAME ......................................................................................................................................................................7
FIGURE 1.6: ORDER BATCHING ..........................................................................................................................................................11
FIGURE 1.7: PRICE FLUCTUATION ......................................................................................................................................................12
FIGURE 1.8: INPUT DEMAND AND OUTPUT ORDERS FOR A SUPPLY CHAIN MEMBER.......................................................................17
FIGURE 2.1: SYSTEMATIC SIMULATION APPROACH...........................................................................................................................26
FIGURE 2.2: THE SIMULATION PROCEDURE .......................................................................................................................................30
FIGURE 3.1: BUILD MENU IN THE PRO-MODEL ....................................................................................................................................40
FIGURE 3.2: THE LOGIC BUILDER IN PRO-MODEL.............................................................................................................................41
FIGURE 3.3: THE STRUCTURE OF THE SUPPLY CHAIN (MODEL 1).....................................................................................................42
FIGURE 3.4: THE ENTITIES OF THE SUPPLY CHAIN IN THE PRO-MODEL (MODEL 1).........................................................................43
FIGURE 3.5: LOCATIONS TABLE OF THE SUPPLY CHAIN IN THE PRO-MODEL (MODEL 1).................................................................44
FIGURE 3.6: ARRIVALS TABLE OF THE SUPPLY CHAIN IN THE PRO-MODEL (MODEL 1)...................................................................44
FIGURE 3.7: THE PROCESS TABLE OF THE SUPPLY CHAIN IN PRO-MODEL (MODEL 1) ....................................................................45
FIGURE 3.8: THE OPERATION BOX OF THE SUPPLY CHAIN IN THE PRO-MODEL (MODEL1) .............................................................45
FIGURE 3.9: ROUTING TABLE AND MOVE LOGIC BOX OF THE SUPPLY CHAIN IN THE PRO-MODEL (MODEL1)...............................46
FIGURE 3.11: THE SUPPLY CHAIN MAP OF (MODEL 2)......................................................................................................................48
FIGURE 3.13: LOCATIONS TABLE OF THE SUPPLY CHAIN IN THE PRO-MODEL (MODEL 2)...............................................................51
FIGURE 314: ARRIVALS TABLE OF THE SUPPLY CHAIN IN THE PRO-MODEL (MODEL 2)..................................................................51
FIGURE 3.14: THE PROCESS TABLE OF THE SUPPLY CHAIN IN PRO-MODEL (MODEL 2) ..................................................................52
FIGURE 3.15: ROUTING TABLE AND MOVE LOGIC BOX OF THE SUPPLY CHAIN IN THE PRO-MODEL (MODE2)...............................53
(A) DISTRIBUTION CENTER (900,100) POLICY....................................................................................................................................56
(B) DISTRIBUTION CENTER (1200, 100) POLICY .................................................................................................................................56
(C) DISTRIBUTION CENTER (1800, 100) POLICY .................................................................................................................................57
(D) DISTRIBUTION CENTER (2500, 100) POLICY .................................................................................................................................57
FIGURE 4.1: BULLWHIP AT DIFFERENT RETAILER POLICIES AND THE MEAN DEMAND AT THE RETAILER EQUAL 2............................57
FIGURE 4.4: EXAMPLE OF VARIABILITY AT RETAILS AND DISTRIBUTION CENTER AT DIFFERENT ORDER QUANTITIES. .....................63
FIGURE 4.5: BULLWHIP EFFECT AT DIFFERENT RETAILER'S ORDERING POLICIES ...............................................................................65

V
list of tables
TABLE 2.1: DATA REQUIREMENTS FOR SUPPLY CHAIN MODEL ...........................................................................................................38
TABLE 4.1: POISSON (2)......................................................................................................................................................................55
TABLE 4.2: POISSON (4)......................................................................................................................................................................58
TABLE 4.3: POISSON (8)......................................................................................................................................................................60
TABLE 4.4: BULLWHIP EFFECT AT DIFFERENT RETAILER'S ORDERING POLICIES ................................................................................64
TABLE 4.5: TABLE FOR POISSON (2) LARGE STRUCTURE ..................................................................................................................65

VI
TABLE OF CONTENTS
Acknowledgment ............................................................................................................................... I
Abstract............................................................................................................................................. II
Summary.......................................................................................................................................... III
List of figures....................................................................................................................................IV
list of tables.......................................................................................................................................V
Introduction: ..................................................................................................................................... 1
Chapter 1 .......................................................................................................................................... 3
Literature Review.............................................................................................................................. 3
1.1 Introduction:.......................................................................................................................... 3
1.2 The Bullwhip Effect: ............................................................................................................... 5
1.3 Bullwhip Definition: ............................................................................................................... 5
1.4 Bullwhip History:.................................................................................................................... 6
1.5 Causes of the Bullwhip effect:................................................................................................ 7
1.5.1 Primary cause:................................................................................................................ 8
1.5.2 Secondary causes:........................................................................................................ 10
1.6 Harms of Bullwhip Effect:..................................................................................................... 15
1.7 Countermeasures to the Bullwhip Effect: ............................................................................. 16
1.8 The Quantification of the Bullwhip Effect:............................................................................ 16
1.9 Thesis Objectives: ................................................................................................................ 18
Chapter 2 ........................................................................................................................................ 19
Simulation....................................................................................................................................... 19
2.1 Simulation: .......................................................................................................................... 19
2.2 The Role of Simulation:........................................................................................................ 19
2.3 Simulation Process:.............................................................................................................. 24
2.4 Systematic Simulation Approach:......................................................................................... 25
2.5 Steps in Simulation Study:.................................................................................................... 26
2.5.1 Problem Formulation ....................................................................................................... 27
2.5.2 Setting Study Objectives:.................................................................................................. 27
2.5.3 Conceptual Modelling: ..................................................................................................... 27
2.5.4 Data Collection:................................................................................................................ 28
2.5.5 Model Building:................................................................................................................ 28
2.5.6 Model Verification: .......................................................................................................... 28

VII
2.5.7 Model Validation:............................................................................................................. 29
2.5.8 Model Analysis:................................................................................................................ 29
2.6 Study Documentation: ......................................................................................................... 30
2.7 A Simulation Report Includes The Following Elements:......................................................... 31
2.8 Analytical or Simulation-Based Models: ............................................................................... 32
2.9 Characteristics of a Simulation Model:................................................................................. 33
2.10 Objectives of supply chain Simulation:................................................................................. 34
2.11 Types of simulation:............................................................................................................. 34
2.12 Data Requirements for Supply Chain Modelling: .................................................................. 36
Chapter 3 ........................................................................................................................................ 39
Pro-Model and Developed Models .................................................................................................. 39
3.1 Introduction:........................................................................................................................ 39
3.2 Typical Applications For Using Pro-Model Include: ............................................................... 39
3.3 Using Pro-Model:................................................................................................................. 40
3.4 Building Models:.................................................................................................................. 40
3.5 Logic Builder:....................................................................................................................... 40
3.6 Model 1 (Small):................................................................................................................... 42
3.7 Model 2 (Large):................................................................................................................... 48
Chapter 4 ........................................................................................................................................ 55
Results &discussion......................................................................................................................... 55
4.1 Design of experiments: ........................................................................................................ 55
4.2 Results:................................................................................................................................ 55
4.2.1 The effect of changing the retailer's demand mean.......................................................... 62
4.2.2 The effect of increasing Qr on the distribution center variability....................................... 63
4.2.3 The effect of constant Qr on the distribution center variability......................................... 63
4.2.4 The effect changing the order quantities on bullwhip effect............................................. 64
4.3 Results and discussion of the second structure .................................................................... 65
Chapter Five.................................................................................................................................... 72
Conclusion and Recommendations.................................................................................................. 72
5.1 Conclusions:......................................................................................................................... 72
5.2 Recommendation: ............................................................................................................... 72
Reference........................................................................................................................................ 74

1
Introduction:
A supply chain involves, directly or indirectly, parties in order to meet a customer needs and
wants. The supply chain not only includes the manufacturer and suppliers, but also
transporters, warehouses, retailers, and customers. Within each organization, such as
manufacturer, the supply chain includes all functions involved in receiving and meeting the
customer needs. These functions include, but are not limited to, new product development,
marketing, operations, distribution, finance, and customer service.
To understand the simple supply chain considers a customer walking into a Wal-Mart store to
purchase for example Pampers. The supply chain begins with the customer and their need for
this product. The next stage of this supply chain is the Wal-Mart retail store that the customer
visits. Wal-Mart stocks its shelves using inventory that may have been supplied from a
finished-goods warehouse that Wal-Mart manages or from a distributor using trucks supplied
by a third party. The distributor in turn is stocked by the manufacturer (say Procter & Gamble
[P&G] in this case). The P&G manufacturing plant receives raw material from a variety of
suppliers who may themselves have been supplied by lower tier suppliers. For example,
packaging material may come from Tenneco packaging while Tenneco receives raw
materials to manufacture the packaging from other suppliers.
The supply chain is involves three main flows: flow of information, product, and funds
between different stages. In our example, Wal-Mart provides the product, pricing and
availability information, to the customer. The customer transfers funds to Wal-Mart by
buying the product. Wal-Mart is the main point-of-information sales data as well as
replenishment order via trucks back to the store. Wal-Mart transfers funds to the distributor
after the replenishment. The distributor also provides pricing information and sends delivery
schedules to Wal-Mart. Similar information, material, and fund flows take place across the
entire supply chain.
Forrester (1961) initiated the analysis of the demand variability amplification and pointed out
that it is a consequence of industrial dynamics or time varying behaviour of industrial
organizations.
According to Forrester’s effect, or the ―acceleration principle‖, a 10 percent change in the
rate of sale at the retail level can result in up to a 40 percent change in demand for the
manufacturer. Remedy for this effect is to understand the system as a whole and to make
modifications in behavioural practice. John Sterman (1989) described a classroom game
known as the Beer Game where participants simulate a supply chain.
As the game proceeds, a small change in consumer demand is turned into wild swings in both
orders and inventory upstream. Sterman attributed this amplified order variability to players’
irrational behaviour or misconceptions about inventory and demand information. The players
in the supply chain completely ignore the pipeline inventory when they are making their
ordering decisions.

2
They failed to account for the long time lags between placing and receiving orders and end up
with poor decisions. Richard Metters (1997) conducted a study to determine the significance
of the detrimental effect of the amplified demand variability on profitability.
Two distinct experimental designs are considered:
a) Seasonality is induced month by month on an annual basis caused by incorrect
demand updating and forward buying.
b) Seasonality is induced week by week on a monthly basis caused by order
batching.
Profitability is examined under heavy, moderate and no demand seasonality. It is concluded
that eliminating the bullwhip effect can increase product profitability by 10-30%, and the
potential profit increases from dampening the monthly seasonal changes outweigh those that
are associated with weekly seasonality. Lee et al. (1997) have proposed four sources of the
bullwhip effect - demand signal processing, rationing game, order batching and price
variations. Simple mathematical models are developed to demonstrate that the amplified
order variability is an outcome of the rational and optimizing behaviour of the supply chain
members.
Strategies that can be implemented to reduce the distortion are also discussed. (E.g. avoid
multiple demand forecasts updates, eliminate gaming in shortage situations, break order
batches, and stabilize prices)
Chen et al. (2000) focused on determining the impact of demand forecasting on the bullwhip
effect and quantifying the increase in variability at each stage of the supply chain. The
variance of the orders placed by the retailer relative to the variance of the demand faced by
the retailer is determined.
Chen et al. (2000) also analysed the impact of centralized customer demand information on
the bullwhip effect. It is demonstrated that centralizing the demand information will certainly
reduce the magnitude of the bullwhip effect, but it will not completely eliminate the increase
in variability. Dejonckheere et al. (2002) analysed the bullwhip effect induced by forecasting
algorithms in order-up-to policies and suggested a new general replenishment rule that can
reduce variance amplification significantly.
Order-up-to policies whose order-up-to levels will be updated by means of exponential
smoothing, moving averages and demand signal processing are compared. In order-up-to
systems, the bullwhip effect is guaranteed when forecasting is necessary. Bullwhip generated
by moving average forecasting in order-up-to model is much less than that generated by
exponential forecasts and demand signal processing.
A general replenishment rule capable of smoothing ordering patterns, even when demand has
to be forecasted is proposed. The crucial difference with the order-up-to policies is that net
stock and on order inventory discrepancies are only fractionally taken into account.

3
Chapter 1
Literature Review
1.1 Introduction:
A supply chain involved, directly or indirectly, parties in order to meet a customer needs and
wants. The supply chain is involves three main flows: flow of information, product, and
funds as shown in figure 1.1 between different stages. For example, Wal-Mart provides the
product, pricing and availability information, to the customer. The customer transfers funds
to Wal-Mart by buying the product. Wal-Mart is the main point-of-information sales data as
well as replenishment order via trucks back to the store. Wal-Mart transfers funds to the
distributor after the replenishment. The distributor also provides pricing information and
sends delivery schedules to Wal-Mart. Similar information, material, and fund flows take
place across the entire supply chain.
Figure 1.1: Traditional Flows within Supply Chains
This example shows that the customer is an integral part of the supply chain. The primary
purpose from the existence of any supply chain is to satisfy customer needs, and for the
company is to gain profits. The term supply chain conjures up images of product or supply
moving from suppliers to manufacturers to distributors to retailers to customers along a chain.
It is important to visualize information, funds, and product flows along both directions of this
chain. The term supply chain may also imply that only one player is involved at each stage.
In reality, a manufacturer may receive material from several suppliers and then supply several

4
distributors. Thus, most supply chains are actually networks. It may be more accurate to use
the term supply network or supply web to describe the structure of most supply chains.
Figure 1.2: supply chain stages
A typical supply chain may involve a variety of stages. These supply chain stages include:
• Customers
• Retailers
• Wholesalers/Distributors
• Manufacturers
• Component/Raw material suppliers
Each stage need not be presented in a supply chain. The appropriate design of the supply
chain will depend on both the customer’s needs and the roles of the stages involved. In some
cases, such as Dell, a manufacturer may fill customer orders directly. Dell builds-to-order;
that is, a customer order initiates manufacturing at Dell. Dell does not have a retailer,
wholesaler, or distributor in its supply chain. In other cases, such as the mail order company
L.L. Bean, manufacturers do not respond to customer orders directly. In this case, L.L. Bean
maintains an inventory or product from which they fill customer orders. Compared to the
Dell supply chain, the L.L. Bean supply chain contains an extra stage (the retailer, L.L. Bean
itself) between the customer and the manufacturer. In the case of other retail stores, the
supply chain may also contain a wholesaler or distributor between the store and the
manufacturer.

5
1.2 The Bullwhip Effect:
In supply chains, every member needs to make forecast of its own production planning,
inventory control and material requirement planning. The one important mechanism for
coordination in a supply chain is the information flows among members of the supply chain.
These information flows have direct impact on the production scheduling inventory control
and delivery planes of individual members in the supply chain. In this research we study the
demand information flow in the supply chain and report the variability in orders between the
members of supply chain. An important phenomenon observed in supply chain practices is
that the variability of an upstream member's demand is greater than that of the downstream
member. This effect was found by logistics executives at Procter & Gamble (P&G) and
called the "bullwhip effect‖. This phenomenon can be described as the variance of production
exceeding the variance of sales under the optimal behavior. The basically, the bullwhip effect
is largely caused by demand single processing, order batching, price variation, and rationing
and gaming and can be reduced through information sharing. To eliminate this effect reduce
delays and collapsing all cycle time between members.
Companies have to invest in extra capacity to meet the high variable demand. This capacity is
then under-utilized when demand drops. Unit labor costs rise in periods of low demand, over-
time, agency and sub-contract costs rise in periods of high demand. The highly variable
demand increases the requirements for safety stock in the supply chain. Additionally,
companies may decide to produce to stock in periods of low demand to increase productivity.
If this is not managed properly this will lead to excessive obsolescence. Highly variable
demand also increases lead-times. These inflated lead-times lead to increased stocks and
bullwhip effects. Thus the bullwhip effect can be quite exasperating for companies; they
invest in extra capacity, extra inventory, work over-time one week and stand idle the next,
whilst at the retail store the shelves of popular products are empty, and the shelves with
products that aren’t selling are full. The figure 1.3 shows the Bullwhip effect.
1.3 Bullwhip Definition:
The bullwhip effect refers to an economic condition relating to materials or product supply
and demand. Observed across most industries, the bullwhip phenomenon creates large swings
Figure 1.3: the bullwhip effect graph

6
in demand on the supply chain resulting from relatively small, but unplanned, variations in
consumer demand that escalate with each link in the chain.
A series of events leads to variability in supplier demand up each level of the supply chain.
The bullwhip effect occurs when consumers purchase more than required for their immediate
need.
1.4 Bullwhip History:
Not long ago, logistics executives at Procter & Gamble (P&G) examined the order patterns
for one of their best-selling products, Pampers. Its sales at retail stores were fluctuating, but
the variability’s were certainly not excessive. However, as they examined the distributors'
orders, the executives were surprised by the degree of variability. When they looked at P&G's
orders of materials to their suppliers, such as 3M, they discovered that the swings were even
greater. At first glance, the variability’s did not make sense. While the consumers, in this
case, the babies, consumed diapers at a steady rate, the demand order variability’s in the
supply chain were amplified as they moved up the supply chain. P&G called this
phenomenon the "bullwhip" effect. (In some industries, it is known as the "whiplash" or the
whipsaw" effect.)
When Hewlett-Packard (HP) executives examined the sales of one of its printers at a major
reseller, they found that there were, as expected, some fluctuations over time. However, when
they examined the orders from the reseller, they observed much bigger swings. Also, to their
surprise, they discovered that the orders from the printer division to the company's integrated
circuit division had even greater fluctuations.
What happens when a supply chain is plagued with a bullwhip effect that distorts its demand
information as it is transmitted up the chain? In the past, without being able to see the sales of
its products at the distribution channel stage, HP had to rely on the sales orders from the
resellers to make product forecasts, plan capacity, control inventory, and schedule
production. Big variations in demand were a major problem for HP's management. The
common symptoms of such variations could be excessive inventory, poor product forecasts,
insufficient or excessive capacities, poor customer service due to unavailable products or
long backlogs, uncertain production planning (i.e., excessive revisions), and high costs for
corrections, such as for expedited shipments and overtime. HP's product division was a
victim of order swings that were exaggerated by the resellers relative to their sales; it, in turn,
created additional exaggerations of order swings to suppliers.
In the past few years, the Efficient Consumer Response (ECR) initiative has tried to redefine
how the grocery supply chain should work. One motivation for the initiative was the
excessive amount of inventory in the supply chain. Various industry studies found that the
total supply chain, from when products leave the manufacturers' production lines to when
they arrive on the retailers' shelves, has more than 100 days of inventory supply. Distorted
information has led every entity in the supply chain – the plant warehouse, a manufacturer's
shuttle warehouse, a manufacturer's market warehouse, a distributor's central warehouse, the
distributor's regional warehouses, and the retail store's storage space – to stockpile because of
the high degree of demand uncertainties and variability’s. It's no wonder that the ECR reports
estimated a potential $30 billion opportunity from streamlining the inefficiencies of the
grocery supply chain.

7
1.5 Causes of the Bullwhip effect:
The best way to understand of the bullwhip effect is the well-known "beer game." The Beer
Distribution Game is a simulation of a supply chain with four co-makers (retailer, wholesaler,
distributor and factory).The participants cannot communicate with each other and must make
order decisions based only on orders from the next downstream player. Participants take the
role of a co-maker and decide - based on their current stock situation and customer orders -
how much to order from their suppliers. All co-makers have a common goal: Minimizing
costs for capital employed in stocks while avoiding out-of-stock situations. The surprising
results of the simulation explain inefficiencies of supply chains known as the bullwhip effect.
Figure 1.4: Beer Game
In contrast, we show that the bullwhip effect is a consequence of the players' rational
behavior within the supply chain's infrastructure. This important distinction implies that
companies wanting to control the bullwhip effect have to focus on modifying the chain's
infrastructure and related processes rather than the decision makers' behavior.
The major causes of the bullwhip effect are:
1. Demand variability at the most downstream member of the supply chain.
2. Management misinterpretation of demand information.
3. Lead time of information and material.
4. Demand forecast updating
5. Order batching
6. Price fluctuation
7. Rationing and shortage gaming
8. Inventory policies.

8
Figure 1.5: The Causes of Bullwhip Effect
Each of the five forces in concert with the chain's infrastructure and the order managers'
rational decision making creates the bullwhip effect. Understanding the causes helps
managers design and develop strategies to counter it.
These causes can be classified into primary causes which trigger the bullwhip effect and
secondary causes which cause the existing bullwhip effect to be amplified.
1.5.1Primary cause:
a) Demand variability:
An unmanaged supply chain is not stable. Demand variability increases as one move up the
supply chain away from the retail, wholesaler, distributor and manufacturer are not allowed
to communicate and order decisions are only based on the downstream orders. Each week the
customer places demand with the wholesaler who fulfils the order from his inventory. The
wholesaler requests an order from the distributor who gets his supply from the manufacturer
who brews the beer.
b) Management misinterpretation of demand information:
From the beer game when the participant (customer, retailer, wholesalers, and suppliers)
cannot communicate with each other and must make order decisions based only on orders
from the next downstream parterres. The ordering patterns are not known and not stable. The
variability’s of an upstream site are always greater than those of the downstream site. This
misinterpretation is crate bullwhip.
c) Lead time of information and material:
Lead time is one of the most important causes of the bullwhip effects, when the lead time
increase the variability will increase but if the order variability increase informally
throughout the entire supply chain, then that will make no impact in bullwhip effects, the
increase of remanufacturing lead time increase the bullwhip effects. There is some other
important information such as:
1. Lower safety stock.
2. Reduction in_ out of stock loss.

9
3. Improvement in customer service level.
Lead time also depends on inventory, ordering, and replenishment policies used and the
coordination among the supply chain members.
 Lower Safety stock:
Safety stock is a term used to describe a level of extra stock that is maintained to mitigate
risk of shortfall in raw material or packaging due to uncertainties in supply and demands,
safety stock levels permit business operations to proceed according to their plans. Safety
stock is held when there is uncertainty in the demand level or lead time for the product, it
serves as an insurance against stock outs. The amount of safety stock an organization chooses
to keep on hand can dramatically affect their business. Too much safety stock can result in
high holding costs of inventory. In addition, products which are stored for too long a time can
spoil, expire, or break during the warehousing process. Too little safety stock can result in
lost sales and, thus, a higher rate of customer turnover. As a result, finding the right balance
between too much and too little safety stock is essential.
Safety stocks are mainly used in a manufacturing strategy. This strategy is employed when
the lead time of manufacturing is too long to satisfy the customer demand at the right cost,
quality, and waiting time. The main goal of safety stocks is to absorb the variability of the
customer demand. Indeed, the Production Planning is based on a forecast, which is different
from the real demand. By absorbing these variations, safety stock improves the customer
service level.
To reduce safety stock, these include better use of technology, increased collaboration with
suppliers, and more accurate forecasting in a lean supply environment, lead times are
reduced, which can help minimize safety stock levels thus reducing the likelihood and impact
of stock outs, an Enterprise Resource Planning system can also help an organization to reduce
its level of safety stock. Most ERP systems provide a type of Production Planning module.
 Improvement in customer service level:
Customer service is a series of activities designed to enhance the level of customer
satisfaction that is, the feeling that a product or service has met the customer expectation. Its
importance varies by products, industry and customer; defective or broken merchandise can
be exchanged, often only with a receipt and within a specified time frame. Retail stores often
have a desk or counter devoted to dealing with returns, exchanges and complaints, or will
perform related functions at the point of sale, the perceived success of such interactions being
dependent on employees From the point of view of an overall sales process engineering
effort, customer service plays an important role in an organization's ability to generate
income and revenue. From that perspective, customer service should be included as part of an
overall approach to systematic improvement. A customer service experience can change the
entire perception a customer has of the organization.
Companies should have to provide better customer service. Executives should know the
competitors; customer service is a very critical component in achieving and maintaining a
high level of customer satisfaction. When pressures move the organization to meet only
performance goals and measurements such as overhead absorption, labor efficiency, purchase
price variance.

10
1.5.2Secondary causes:
a) Demand Forecast Updating:
Every company in a supply chain usually does product forecasting for its production
scheduling, capacity planning, inventory control, and material requirements planning.
Forecasting is often based on the order history from the company's immediate customers. The
outcomes of the beer game are the consequence of many behavioral factors, such as the
players’ perceptions and mistrust. An important factor is each player's thought process in
projecting the demand pattern based on what will be observed. When downstream operation
places an order, the upstream manager processes that order (information) as a signal about
future product demand. Based on this signal, the upstream manager readjusts the demand
forecasts and, in turn, the orders placed with the suppliers of the upstream operation. We
contend that demand signal processing is a major contributor to the bullwhip effect.
One site up the supply chain, if you are the manager of the supplier, the daily orders from the
manager of the previous site constitute your demand. If you are also using exponential
smoothing to update your forecasts and safety stocks, the orders that you place with your
supplier will have even bigger swings. For an example of such fluctuations in demand, the
orders placed by the dealer to the manufacturer have much greater variability than the
consumer demands. Because the amount of safety stock contributes to the bullwhip effect, it
is intuitive that, when the lead times between the resupply of the items along the supply chain
are longer, the fluctuation is even more significant.
b) Order Batching:
In a supply chain, each company places orders with an upstream organization using some
inventory monitoring or control. Demands come in, depleting inventory, but the company
may not immediately place an order with its supplier. It often batches or accumulates
demands before issuing an order. There are two forms of order batching: periodic ordering
and push ordering.
Instead of ordering frequently, companies may order weekly, biweekly, or even monthly.
There are many common reasons for an inventory system based on order cycles. Often the
supplier cannot handle frequent order processing because the time and cost of processing an
order can be substantial. P&G estimated that, because of the many manual interventions
needed in its order, billing, and shipment systems, each invoice to its customers cost between
$35 and $75 to process. Many manufacturers place purchase orders with suppliers when they
run their material requirements planning (MRP) systems.
Consider a company that orders once a month from its supplier. The supplier faces a highly
stream of orders. There is a spike in demand at one time during the month, followed by no
demands for the rest of the month. Of course, this variability is higher than the demands the
company itself faces. Periodic ordering amplifies variability and contributes to the bullwhip
effect. One common obstacle for a company that wants to order frequently is the economics
of transportation.

11
There are substantial differences between full truckload (FTL) and less-than-truckload rates,
so companies have a strong incentive to fill a truckload when they order materials from a
supplier. Sometimes, suppliers give their best pricing for FTL orders. For most items, a full
truckload could be a supply of a month or more. Full or close to full truckload ordering would
thus lead to moderate to excessively long order cycles.
In push ordering, a company experiences regular surges in demand. The company has orders
"pushed" on it from customers periodically because salespeople are regularly measured,
sometimes quarterly or annually, which causes end of quarter or end of year order surges.
Salespersons that need to fill sales quotas may "borrow" ahead and sign orders prematurely.
When a company faces periodic ordering by its customers, the bullwhip effect results if all
customers' order cycles were spread out evenly throughout the week, the bullwhip effect
would be minimal. The periodic surges in demand by some customers would be insignificant
because not all would be ordering at the same time. Unfortunately, such an ideal situation
rarely exists. Orders are more likely to be randomly spread out or, worse, to overlap. When
order cycles overlap, most customers that order periodically do so at the same time. As a
result, the surge in demand is even more pronounced, and the variability from the bullwhip
effect is at its highest.
Figure 1.6: Order Batching
c) Price Fluctuation :
Estimates indicate that 80 percent of the transactions between manufacturers and distributors
in the grocery industry were made in a "forward buy" arrangement in which items were
bought in advance of requirements, usually because of a manufacturer's attractive price offer.
Forward buying constitutes $75 billion to $100 billion of inventory in the grocery industry.

12
Manufacturers and distributors periodically have special promotions like price discounts,
quantity discounts, coupons, rebates, and so on. All these promotions result in price
fluctuations. Additionally, manufacturers offer trade deals (e.g., special discounts, price
terms, and payment terms) to the distributors and wholesalers, which are an indirect form of
price discounts.
When the product's price returns to normal, the customer stops buying until it has depleted its
inventory As a result, the customer's buying pattern does not reflect its consumption pattern,
and the variation of the buying quantities is much bigger than the variation of the
consumption rate the bullwhip effect.
When high-low pricing occurs, forward buying may well be a rational decision. If the cost of
holding inventory is less than the price differential, buying in advance makes sense. In fact,
the high-low pricing phenomenon has induced a stream of research on how companies should
order optimally to take advantage of the low price opportunities.
Figure 1.7: Price Fluctuation
d) Rationing And Shortage Gaming:
When product demand exceeds supply, a manufacturer often rations its product to customers.
In one scheme, the manufacturer allocates the amount in proportion to the amount ordered.
For example, if the total supply is only 50 percent of the total demand, all customers receive
50 percent of what they order.
Knowing that the manufacturer will ration when the product is in short supply, customers
exaggerate their real needs when they order. Later, when demand cools, orders will suddenly

13
disappear and cancellations pour in. This seeming overreaction by customers anticipating
shortages results when organizations and individuals make sound, rational economic
decisions and "game" the potential rationing. The effect of "gaming" is that customers' orders
give the supplier little information on the product's real demand, a particularly vexing
problem for manufacturers in products early stages. The gaming practice is very common. In
the 1980s, on several occasions, the computer industry perceived a shortage of DRAM chips.
Orders shot up, not because of an increase in consumption, but because of anticipation.
Customers place duplicate orders with multiple suppliers and buy from the first one that can
deliver, and then cancel all other duplicate orders.
More recently, Hewlett-Packard could not meet the demand for its LaserJet III printer and
rationed the product. Orders surged, but HP managers could not discern whether the orders
genuinely reflected real market demands or were simply phantom orders from resellers trying
to get better allocation of the product. When HP lifted its constraints on resupply of the Laser
Jets, many resellers canceled their orders.
HP's costs in excess inventory after the allocation period and in unnecessary capacity
increases were in the millions of dollars. During the Christmas shopping seasons in 1992 and
1993, Motorola could not meet consumer demand for handsets and cellular phones, forcing
many distributors to turn away business. Distributors like Air Touch Communications and the
Baby Bells, anticipating the possibility of shortages and acting defensively, drastically over
ordered toward the end of 1994. Because of such overzealous ordering by retail distributors,
Motorola reported record fourth-quarter earnings in January 1995. Once Wall Street realized
that the dealers were swamped with inventory and new orders for phones were not as healthy
before, Motorola's stock tumbled almost 10 percent.
e) Inventory Policy:
There are many different types of replenishment policies, the most commonly used are: the
periodic review, the continuous review, order-up-to policy, base stock replenishment policy
and reorder point- order quantity policy. Given the common practice in retailing to replenish
inventories frequently, daily and the tendency of manufacturers to produce to demand, a
focus will be made in this analysis on periodic review, base-stock, or order- up-to
replenishment policies.
 Standard Base-Stock Replenishment Policy (S,R):
The standard periodic review base stock replenishment policy is the replenishment policy at
the end of every review period, the retailer tracks his inventory position, which is the sum of
the inventory on order ( items order but not arrived yet due to the lead time) minus the
backlog (demand that couldn’t be fulfilled and still has to be delivered). A replenishment
order is the then placed to raise the inventory position to an order up to or base stock level,
which determine the order quality.
Consequently the standard base-stock policy generates orders whose variability is correlated
to the variability of customer demand. Thus, when customer demand is wildly fluctuating,
this replenishment rule sends a highly variable order pattern to the manufacturer, which may
impose high capacity and inventory costs on the manufacturer. The manufacturer not only

14
prefers a level production schedule, the smoothed demand also allows him to minimize his
raw materials inventory cost. Therefore, we discuss a smoothing replenishment policy that is
able to reduce the variability of the orders transmitted upstream.
 Reorder Point – Ordered Quantity Policy (Q,R):
The reorder point ("ROP") is the level of inventory when an order should be made with
suppliers to bring the inventory up by the Economic order quantity ("EOQ").
The reorder point for replenishment of stock occurs when the level of inventory drops down
to zero. In view of instantaneous replenishment of stock the level of inventory jumps to the
original level from zero level.
In real life situations one never encounters a zero lead time. There is always a time lag from
the date of placing an order for material and the date on which materials are received. As a
result the reorder point is always higher than zero, and if the firm places the order when the
inventory reaches the reorder point, the new goods will arrive before the firm runs out of
goods to sell. The decision on how much stock to hold is generally referred to as the order
point problem, that is, how low should the inventory be depleted before it is reordered.
The two factors that determine the appropriate order point are the delivery time stock which
is the Inventory needed during the lead time (i.e., the difference between the order date and
the receipt of the inventory ordered) and the safety stock which is the minimum level of
inventory that is held as a protection against shortages due to fluctuations in demand.
Therefore:
Reorder Point = Normal consumption during lead-time + Safety Stock.
Several factors determine how much delivery time stock and safety stock should be held. In
summary, the efficiency of a replenishment system affects how much delivery time is needed.
Since the delivery time stock is the expected inventory usage between ordering and receiving
inventory, efficient replenishment of inventory would reduce the need for delivery time stock.
And the determination of level of safety stock involves a basic trade-off between the risk
of stock out, resulting in possible customer dissatisfaction and lost sales, and the increased
costs associated with carrying additional inventory.
Another method of calculating reorder level involves the calculation of usage rate per day,
lead time which is the amount of time between placing an order and receiving the goods and
the safety stock level expressed in terms of several days' sales.
Reorder level = Average daily usage rate x lead-time in days.
From the above formula it can be easily deduced that an order for replenishment of materials
be made when the level of inventory is just adequate to meet the needs of production during
lead-time.

15
1.6 Harms of Bullwhip Effect:
Bullwhip effect in supply chains has led to the distortion of demand information; the harm is
done both at the micro and macro levels:
At the micro level, the existence of the bullwhip effect in supply chain will bring a double
loss for companies include efficiency and profitability .Firstly, the product stock is to adapt
the demand change to set up, the excessive demand fluctuation caused to supply in chain's
excessive stock directly, has taken enterprise's fund massively, formed the high quota the
inventory cost, brought the pressure for enterprise's production and operating activities.
Secondly, because the demand uncertainty increased, the difficulty of the enterprise’ perfect
forecast to the demand is also enlarged. And in the supply the possibility which the back
ordering and out of stock is increasing, all of these lead to reduce the level of customer
service.
Third, the demand distortion also affects enterprise's production. Because of the distortion
demand information misleading, the productive plans have to revise frequently, produces
cannot advance continually. Therefore the production cost and the physical distribution cost
is increasing also.
At macro level, the bullwhip effect will cause the economic resource the blind flowing and
the low efficiency disposition .Bullwhip effect is a classic "market failure‖ phenomenon,
because the upstream industry received the demand information deviated from the true
demand, it may lead to over-investment or investment shortage. The capital enters
excessively means the competition aggravating and the income drop, ultimately hurting the
development of the industry itself. Therefore causing the financial system's hidden danger
and bring the risk of the macroeconomic movement.
 Excessive Inventory:
As forecast inaccuracies become amplified up the supply chain, it can result in a highly
inaccurate demand forecast being made by the producer. As a result, the producer may end up
producing more of the product than the market is actually willing to accept. This means that
the producer will have produced too many units. This can be disastrous in some cases, as it
may not be possible to offload the products for a profit. The products will likely be sold at a
deep discount to secondary markets (for example, companies that purchase wholesale
overstocks). In a worst-case scenario, it could result in having an excess of products that must
simply be destroyed.
 Inefficient Production:
The bullwhip effect can lead to inefficient production. This happens when the producer does
not have accurate demand data and cannot accurately produce the required amount of product
ahead of time and cannot schedule production in an efficient way. This can lead to a reactive
production, where the producer does not produce enough and then must rush to produce
more. This is extremely inefficient because it means that rather than operating at a constant
rate, the producer is alternating between times where it is producing nothing and times where
it is at maximum capacity.
 Increases of Cost

16
The most important effect that the bullwhip effect has is that it increases costs (sometimes
dramatically). This happens for a variety of reasons. When there is an inefficient production,
it means that stock-outs will occur (that is to say, those customers will not be able to get their
products). Stock-outs result in lost revenues from sales that are missed. They can cause costly
losses to a company's reputation and they can result in the competition gaining your
customers. Also, inefficient production can be much more costly because it requires hiring
and training extra staff, paying overtime wages and may require sourcing materials from the
quickest (rather than cheapest) supplier.
1.7 Countermeasures to the Bullwhip Effect:
While the bullwhip effect is a common problem, many leading companies have been able to
apply countermeasures to overcome it. Here are some of these solutions:
 Countermeasures to demand forecast inaccuracies - Lack of demand visibility can be
addressed by providing access to point of sale (POS) data. Single control of
replenishment or Vendor Managed Inventory (VMI) can overcome exaggerated demand
forecasts. Long lead times should be reduced where economically advantageous.
 Countermeasures to order batching - High order cost is countered with Electronic Data
Interchange (EDI) and computer aided ordering (CAO). Full truck load economics are
countered with third-party logistics and assorted truckloads. Correlated ordering is
countered with regular delivery appointments. More frequent ordering results in smaller
orders and smaller variance. However, when an entity orders more often, it will not see a
reduction in its own demand variance - the reduction is seen by the upstream entities.
Also, when an entity orders more frequently, its required safety stock may increase or
decrease; see the standard loss function in the Inventory Management section.
 Countermeasures to shortage gaming - Proportional rationing schemes are countered
by allocating units based on past sales. Ignorance of supply chain conditions can be
addressed by sharing capacity and supply information. Unrestricted ordering capability
can be addressed by reducing the order size flexibility and implementing capacity
reservations. For example, one can reserve a fixed quantity for a given year and specify
the quantity of each order shortly before it is needed, as long as the sum of the order
quantities equals to the reserved quantity.
 Countermeasures to fluctuating prices - High-low pricing can be replaced with
everyday low prices (EDLP). Special purchase contracts can be implemented in order to
specify ordering at regular intervals to better synchronize delivery and purchase.
 Free return policies are not addressed easily. Often, such policies simply must be
prohibited or limited.
1.8 The Quantification of the Bullwhip Effect:
There are two primary definitions of bullwhip effect measurement used:
The first one; originally described the bullwhip effect as a form of ―information distortion,‖
and measured it by comparing the order variance with the demand variance. This definition
captures the distortion of information flow that goes upstream (the downstream stage’s order
is the demand input to the upstream stage).

17
The second definition; used in most empirical studies, compares the variance of order receipts
(or shipments) with the variance of sales. In some cases, the order receipt information, if not
available, is inferred from the sales and inventory data. This definition essentially captures
the distortion of material flow that goes downstream. The bullwhip measurements based on
these two definitions are usually good approximation to each other (as material flow more or
less follows information flow), but they different in concept.
Four measuring quantities to quantify the bullwhip effect, where the four measures of
bullwhip effect are referred to as M1, M2, M3, and M4.
Figure 1.8: Input Demand and Output Orders for a Supply Chain Member.
 First measure: Standard Deviation, M1
Is the standard deviation of the quantities ordered by this member from its next upper
stream member. So M1 = STD (Q).
 Second measure: Coefficient of Variation, M2
Is the ratio between the standard deviation of the quantities ordered by this member from
its next upper stream member to the mean of these quantities. So M2 = STD (Q)/Q.
 Third measure: Variances Ratio, M3
It is the ratio between the variance of the quantities ordered by a certain SC member from
its next upper stream member to the variance of the input demand. So M3 = Var (Q)/Var
(d).
 Fourth measure: Coefficient of Variation Ratio, M4
It is ratio between standard deviation of the quantities ordered by this member from its
next upper stream member to the mean of these quantities divided by the ratio between
standard deviation of the input demand to the mean input demand. So M4 = [STD (Q)/Q]/
[STD (d)/d].
The following issues arise when measuring the bullwhip effect:
 Aggregation
To measure the bullwhip effect correctly one has to be aware of different data
aggregation level, aggregation could be made across echelon(s) or products, which
means either to get one measure for the bullwhip for each echelon in the supply chain
so treating it as a serial supply chain or if there are more than one product these
products are aggregated and treated as one product.

18
 Incomplete data.
The aggregation may be able to be done across both echelons and products. They
demonstrated using random generated data that different aggregation methods can lead
to totally different values for the bullwhip measure. There may be a conceptual
imbalance between incoming demand and outgoing demand; furthermore the
information on those values may be incomplete.
 Filtering of causes.
To analyzed act on the bullwhip effect, one has to understand the causes of the demand
variations at hand.
1.9 Thesis Objectives:
From the previous review it is clear that the causes of bullwhip effect drew great attention of
many researchers, yet to quantitative results were given about many of these causes.
Therefore the following objectives are to be achieved:
 To find quantitatively the effect of changing the order quantities when the
supply chain members are following (Q,r) policy.
 To find quantitatively the effect of the supply chain structure on the bullwhip
effect.

19
Chapter 2
Simulation
2.1 Simulation:
Simulation is one of the most powerful tools available to decision-makers responsible for the
design and operation of complex processes and systems. It makes possible the study, analysis
and evaluation of situations that would not be otherwise possible. In an increasingly
competitive World, simulation has become an indispensable problem solving methodology
for engineers, designers and managers.
We will define simulation as the process of designing a model of a real system and
conducting experiments with this model for the purpose of understanding the behavior of the
system and /or evaluating various strategies for the operation of the system. Thus it is critical
that the model be designed in such a way that the model behavior mimics the response
behavior of the real system to events that take place over time.
The term's model and system are key components of our definition of simulation. By a model
we mean a representation of a group of objects or ideas in some form other than that of the
entity itself. By a system we mean a group or collection of interrelated elements that
cooperate to accomplish some stated objective. One of the real strengths of simulation is the
fact that we can simulate systems that already exist as well as those that are capable of being
brought into existence, i.e. those in the preliminary or planning stage of development. (Robert
E. Shannon 1998)
2.2 The Role of Simulation:
It is necessary to clarify the role that simulation plays in modern industrial and business
firms. In this section we clarify the role of simulation by first justifying the use of simulation
both technically and economically and then presenting the spectrum of simulation
applications to various industries in the manufacturing and service sectors. It is also worth
mentioning that using simulation in industrial and business application is the most common
but not the only field in which simulation is utilized; it is also used for educational and
learning purposes, training, virtual reality applications, movies and animation production, and
criminal justice, among others.

20
2.2.1 Simulation Justified
The question of why and when to simulate is typical of those that cross the minds of
practitioners, engineers, and managers. We simply simulate because of simulation
capabilities that are unique and powerful in system representation, performance estimation,
and improvement. Simulation is often the analysts’ refuge when other solution tools, such as
mathematical models, fail or become extremely difficult to approximate the solution to a
certain problem. Most real-world processes in production and business systems are complex,
stochastic, and highly nonlinear and dynamic, which makes it almost impossible to present
them using physical or mathematical models. Attempts to use analytical models in
approaching real systems usually require many approximations and simplifying assumptions.
This often yields solutions that are unsuitable for problems of real-world applications.
Therefore, analysts often use simulation whenever they meet complex problems that cannot
be solved by other means, such as mathematical and calculus- based methods. The
performance of a real system is a complicated function of the design parameters, and an
analytical closed-form expression of the objective function or the constraints may not exist. A
simulation model can therefore be used to replace the mathematical formulation of the
underlying system. With the aid of the model and rather than considering every possible
variation of circumstances for the complex problem, a sample of possible execution paths is
taken and studied.
In short, simulation is often utilized when the behavior of a system is complex, stochastic
(rather than deterministic), and dynamic (rather than static). Analytical methods, such as
queuing systems, inventory models, and Markovian models, which are commonly used to
analyze production and business systems, often fail to provide statistics on system
performance when real-world conditions intensify to overwhelm and exceed the system
approximating assumptions. Examples include entities whose arrival at a plant or bank is not
a Poisson process, and the flow of entities is based on complex decision rules under
stochastic variability within availability of system resources. Decision support is another
common justification of simulation studies.
Obviously, engineers and managers want to make the best decisions possible, especially
when encountering critical stages of design, expansion, or improvement projects where the
real system has not yet been built. By carefully analyzing the hypothetical system with
simulation, designers can avoid problems with the real system when it is built. Simulation
studies at this stage may reveal insurmountable problems that could result in project
cancellation, and save cost, effort, and time. Such savings are obtained since it is always
cheaper and safer to learn from mistakes made with a simulated system (a computer model)
than to make them for real. Simulation can reduce cost, reduce risk, and improve analysts’
understanding of the system under study.

21
The economic justification of simulation often plays a role in selecting simulation as a
solution tool in design and improvement studies. Although simulation studies might be costly
and time consuming in some cases, the benefits and savings obtained from such studies often
recover the simulation cost and avoid much further costs. Simulation costs are typically the
initial simulation software and computer cost, yearly maintenance and upgrade cost, training
cost, engineering time cost, and other costs: for traveling, preparing presentations with
multimedia tools, and so on. Such costs are often recovered with the first two or three
successful simulation projects. Further, the cost and time of simulation studies are often
reduced by analyst experience and become minuscule compared to the long-term savings
from increasing productivity and efficiency.
2.2.2 Simulation Applications
A better answer to the question ―why simulate?‖ can be reached by exploring the wide
spectrum of simulation applications to all aspects of science and technology. This spectrum
starts by using simulation in basic sciences to estimate the area under a curve, evaluating
multiple integrals, and studying particle diffusion, and continuer by utilizing simulation in
practical situations and designing queuing systems, communication networks, economic
forecasting, biomedical systems, and war strategies and tactics.
Today, simulation is being used for a wide range of applications in both manufacturing and
business operations. As a powerful tool, simulation models of manufacturing systems are
used:
• To determine the throughput capability of a manufacturing cell or assembly line.
• To determine the number of operators in a labor-intensive assembly process
• To determine the number of automated guided vehicles in a complex material-
handling system
• To determine the number of carriers in an electrified monorail system
• To determine the number of storage and retrieval machines in a complex automated
storage and retrieval system
• To determine the best ordering policies for an inventory control system
• To validate the production plan in material requirement planning
• To determine the optimal buffer sizes for work-in-progress products
• To plan the capacity of subassemblies feeding a production mainline
For business operations, simulation models are also being used for a wide range of
applications:
• To determine the number of bank tellers, which results in reducing customer waiting
time by a certain percentage.
• To design distribution and transportation networks to improve the performance of
logistic and vending systems
• To analyze a company’s financial system

22
• To design the operating policies in a fast-food restaurant to reduce customer time-in-
system and increase customer satisfaction
• To evaluate hardware and software requirements for a computer network
• To design the operating policies in an emergency room to reduce patient waiting time
and schedule the working pattern of the medical staff
• To assess the impact of government regulations on different public services at both
the municipal and national levels
• To test the feasibility of different product development processes and to evaluate their
impact on company’s budget and competitive strategy
• To design communication systems and data transfer protocols
To reach the goals of the simulation study, certain elements of each simulated system often
become the focus of a simulation model. Modeling and tracking such elements provide
attributes and statistics necessary to design, improve, and optimize the underlying system
performance.
2.2.3 Simulation Precautions
Like any other engineering tool, simulation has limitations. Such limitations should be dealt
with as a motivation and should not discourage analysts and decision makers. Knowing
limitations of the tool in hand should emphasize using it wisely and motivate the user to
develop creative methods and establish the correct assumptions that benefit from the
powerful simulation capabilities and preclude simulation limitations from being a damping
factor.
However, certain precautions should be considered in using simulation to avoid the potential
pitfalls of simulation. Examples of issues that we should pay attention to when considering
simulation include the following:
1. The simulation analyst or decision maker should be able to answer the question of when
not to simulate. A lot of simulation studies are considered to be design overkill when
conducted for solving problems of relative simplicity.
Such problems can be solved using engineering analysis, common sense, or
mathematical models. Hence, the only benefit from approaching simple systems with
simulation is being able to practice modeling and to provide an animation of the targeted
process.
2. The cost and time of simulation should be considered and planned well.
Many simulation studies are underestimated in terms of time and cost. Some decision
makers think of simulation study as model-building time and cost.
Although model building is a critical phase of a simulation study, it often consumes less
time and cost than does experimental design or data collection.
3. The skill and knowledge of the simulation analyst. Being an engineer is almost essential
for simulation practitioners because of the type of analytical, statistical, and system
analyses skills required for conducting simulation studies.
4. Expectations from the simulation study should be realistic and not overestimated.

23
A lot of professionals think of simulation as a ―crystal ball‖ through which they can
predict and optimize system behavior. It should be clear to the analyst that simulation
models themselves are not system optimizers. While it should be asserted that simulation
is just a tool and an experimental platform, it should also be emphasized that combining
simulation with appropriate statistical analyses, experimental design, and efficient search
engine can lead to invaluable system information that benefits planning, design, and
optimization.
5. The results obtained from simulation models are as good as the model data inputs,
assumptions, and logical design. The commonly used phrase garbage-in-garbage-out
(GIGO) is very applicable to simulation studies.
Hence, special attention should be paid to data inputs selection, filtering, and
assumptions.
6. The analyst should pay attention to the level of detail incorporated in the model.
Depending on the objectives of the simulation study and the information available, the
analyst should decide on the amount of detail incorporated into the simulation model.
Some study objectives can be reached with macro-level modeling, whereas others require
micro-level modeling. There is no need for the analyst to exhaust his or her modeling
skills trying to incorporate details that are irrelevant to simulation objectives. Instead, the
model should be focused on providing the means of system analysis that yields results
directly relevant to study objectives.
7. Model validation and verification is not a trivial task. As discussed later, model
validation focuses on making sure that a model behaves as required by the model-
designed logic and that its response reflects the data used into the model. Model
verification, on the other hand, focuses on making sure that the model behavior
resembles the intended behavior of the actual simulated system. Both practices determine
the degree of model reliability and require the analyst to be familiar with the skills of
model testing and the structure and functionality of the actual system.
8. The results of simulation can easily be misinterpreted. Hence, the analyst should
concentrate efforts on collecting reliable results from the model through proper settings
of run controls (warm-up period, run length, and number of replications) and on using
the proper statistical analyses to draw meaningful and accurate conclusions from the
model. Typical mistakes in interpreting simulation results include relying on a short run
time (not a
steady-state response), including in the results biases caused by initial model conditions,
using the results of one simulation replication, and relying on the response mean while
ignoring the variability encompassed into response values.
9. Simulation inputs and outputs should be communicated clearly and correctly to all
parties of a simulation study. System specialists such as process engineers and system
managers need to be aware of the data used and the model logic in order to verify the
model and increase its realistic representation.
Similarly, the results of the simulation model should be communicated to get feedback
from parties on the relevancy and accuracy of results.
10. The analyst should avoid using incorrect measures of performance when building and
analyzing model results. Model performance measures should be programmed correctly

24
into the model and should be represented by statistics collected from the model. Such
measures should also represent the type of information essential to the analyst and
decision maker to draw conclusions and inferences about model behavior.
11. The analyst should avoid the misuse of model animation. In fact, animation is an
important simulation capability that provides engineers and decision makers with a great
tool for system visualization and response observance. Hence, it is true that ―a picture is
worth a thousand words.‖ Such a tool is also useful for model debugging, validation and
verification, and presentation to executives and customers. However, a lot of people
misuse model animation and rely on their observation to draw conclusions as to model
long-term behavior. Given that simulation models are stochastic and dynamic in nature,
it should be clear to the analyst that a model’s status at a certain time does not
necessarily reflect its long-term behavior. Instead, model statistics are a better
representation of model response.
12. The analyst needs to get the support of upper management and decision makers to make
a simulation study fruitful and successful.
13. Finally, the analyst should select the appropriate simulation software tools that fit the
analyst’s knowledge and expertise and that are capable of modeling the underlying
system and providing the simulation results required.
The criteria for selecting the proper simulation software tools are available in the
literature and are not the focus of this book. It should be known, however, that simulation
packages vary in their capabilities and inclusiveness of different modeling systems and
techniques, such as conveyor systems, power and free systems, automated guided vehicle
systems, kinematics, automated storage and retrieval systems, human modeling
capabilities, statistical tools, optimization methods, animation, and so on.
2.3 Simulation Process:
The set of techniques, steps, and logic followed when conducting a simulation study is
referred to in the context of a simulation process. The details of such a process often depend
on the project objectives, the simulation software used, and even on the way the team handles
simulation modeling. Although the tactics of such a process often varies from one application
to another and from one simulation project to another, the overall structure of the simulation
process is common. It is necessary to follow a systematic method when performing the
simulation process. This chapter is focused on analyzing the various aspects of the simulation
process, the process followed by a complete simulation study.
There are three purpose of simulation:
2.3.1 Simulation-Based System Design
The focus here is on an event-driven or transaction-based process and service design rather
than the product design (see, e.g., Yang and El-Haik, 2003). Process compatibility studies can
benefit greatly from simulation-based design. Applications of simulation projects include a
wide spectrum of projects, such as the development of new facilities, major expansions of a
manufacturing system, a new clinic, a new bank, a new vehicle program, and a new
transportation network.

25
2.3.2 Problem Solving Simulation:
Production and business systems often face challenges that affect their operation and
performance. The impact of such challenges varies from reduced efficiency or frequent
delays and failures to major shutdowns and catastrophes. Hence, a big portion of the work of
production managers, system engineers, and operations managers is often focused on
monitoring the system performance, tackling operational problems, and attempting to prevent
future problems. In addition, many of these problems may be concluded from customer
complains, market feedback, and actual sales numbers.
2.3.3 Continuous Improvement Simulation:
It is often asserted that the success of production and business systems in sustaining a certain
level of performance depends on effort in establishing and implementing plans for continuous
improvement. Companies do not always wait until a problem arises to take correction and
improvement actions. Managers and engineers often believe that there is always a window for
improvement in the way that companies produce products or provide services. Through this
window, system managers and planners often foresee opportunities for making the system
better and more prepared to face future challenges.
Simulation is used as an effective tool for diagnosing the system and defining problems,
challenges, and opportunities. It is also used for developing and evaluating alternatives as
well as for assessing the performance of each alternative. Hence, a complete simulation study
often includes problem definition; setting simulation objectives; developing a conceptual
model; specifying model assumptions; collecting pertinent model data; building, verifying,
and validating the simulation model; analyzing model outputs; and documenting the project
findings.
2.4 Systematic Simulation Approach:
The approach followed for applying each of the three categories of simulation studies
discussed in the last Section has a specific nature and requirements. For the three categories,
however, we can follow a generic and systematic approach for applying a simulation study
effectively. This approach consists of common stages for performing the simulation study, as
shown in Figure 6.6. The approach shown in the figure is a typical engineering methodology
for the three categories of simulation studies (i.e., system design, problem solving, and
system improvement) that puts the simulation process into the context of engineering solution
methods. Engineers and simulation analysts often adopt and use such an approach implicitly
in real-world simulation studies without structuring it into stages and steps. The approach is
an engineering methodology that consists of five iterative stages, as shown in Figure 6.6:
Identify the simulation problem, develop solution alternatives, evaluate solution alternatives,
select the best solution alternative, and implement the solution selected.

26
Figure 2.1: Systematic Simulation Approach.
2.5 Steps in Simulation Study:
In this section we present a procedure for conducting simulation studies in terms of a step-by-
step approach for defining the simulation problem, building the simulation model, and
conducting simulation experiments. This procedure is a detailed translation of the systematic
simulation approach presented in Figure 6.6. Figure 6.7 is a flowchart of the step-by-step
simulation procedure.
The simulation systematic approach shown in Figure 6.7 represents the engineering
framework of the simulation study. The steps may vary from one analyst to another because
of factors such as the nature of the problem and the simulation software used. However, the
building blocks of the simulation procedure are typically common among simulation studies.
The simulation procedure, often represented by a flowchart, consists of the elements and the
logical sequence of the simulation study. It also includes decision points through which the
concept and model are checked, validated, and verified. Iterative steps may be necessary to
adjust and modify the model concept and logic. Finally, the procedure shows steps that can
be executed in parallel with other steps.

27
2.5.1 Problem Formulation
The simulation study should start with a concise definition and statement of the underlying
problem. The problem statement includes a description of the situation or the system of the
study and the problem that needs to be solved. Formulating the problem in terms of an overall
goal and a set of constraints provides a better representation of the problem statement. A
thorough understanding of the elements and structure of the system under study often helps in
developing the problem statement.
2.5.2 Setting Study Objectives:
Based on the problem formulation, a set of objectives can be set to the simulation study. Such
objectives represent the criteria through which the overall goal of the study is achieved. Study
objectives simply indicate questions that should be answered by the simulation study.
Examples include determining current-state performance, testing design alternatives, studying
the impact of speeding up the mainline conveyor, and optimizing the number of carriers in a
material-handling system.
2.5.3 Conceptual Modelling:
Developing a conceptual model is the process through which the modeler abstracts the
structure, functionality, and essential features of a real-world system into a structural and
logical representation that is transferable into a simulation model. The model concept can be
a simple or a complex graphical representation, such as a block diagram, a flowchart, or a
process map that depicts key characteristics of the simulated system, such as inputs, elements,
parameters, logic, flow, and outputs. Such a representation should eventually be
programmable and transferable into a simulation model using available simulation software
tools. Thus, a successful model concept is one that takes into consideration the method of
transferring each abstracted characteristic, building each model element, and programming
the conceptual logic using the software tool.

28
2.5.4 Data Collection:
Simulation models are data-driven computer programs that receive input data, execute the
logic designed, and produce certain outputs. Hence, the data collection step is a key
component of any simulation study. Simulation data can, however, be collected in parallel to
building a model using the simulation software. This is recommended since data collection
may be time consuming in some cases, and building the model structure and designing model
logic can be independent of the model data. Default parameters and generic data can be used
initially until the system data are collected.
2.5.5 Model Building:
Data collection and model building often consume the majority of the time required for
completion of a simulation project. To reduce such time, the modeler should start building the
simulation model while data are being collected. The conceptual model can be used to
construct the computer model using assumed data until the data collected become available.
The overlap between model building and data collection does not affect the logical sequence
of the simulation procedure. Constructing model components, entity flow, and logic depends
mostly on the model concept and is in most cases independent of model data. Once the model
is ready, model input data and parameter settings can be inserted into the model later. Also,
since a large portion of a simulation study is often spent in collecting model data, building the
model simultaneously reduces significantly the overall duration of the simulation study and
provides more time for model analysis and experimentation.
2.5.6 Model Verification:
Model verification is the quality control check that is applied to the simulation model built.
Like any other computer program, the simulation model should perform based on the
intended logical design used in building the model. Although, model logic can be defined
using different methods and can be implemented using different programming techniques,
execution of the logic when running the model should reflect the initial design of the
programmer or modeler. Different methods are used for debugging logical (programming)
errors as well as errors in inputting data and setting model parameters. Corrected potential
code and data discrepancies should always be verified by careful observation of changes in
model behavior.
To verify a model, we simply check whether the model is doing what it is supposed to do. For
example, does the model read the input data properly? Does the model send the right part to
the right place? Does the model implement the production schedule prescribed? Do
customers in the model follow the queuing discipline proposed? Does the model provide the
right output?
And so on. Other verification techniques include applying rules of common sense, watching
the model animation periodically during run time, examining model outputs, and asking
another modeler to review the model and check its behavior. The observations made by other
analysts are valuable since the model builder will be more focused on the programming

29
details and less focused on the implication of different programming elements. When the
model logic is complex, more than one simulation analyst may have to work on building the
model.
2.5.7 Model Validation:
Model validation is the process of checking the accuracy of the model representation to the
real-world system that has been simulated. It is simply about answering the following
question: Does the model behave similarly to the simulated system? Since the model will be
used to replace the actual system in experimental design and performance analysis, can we
rely in its representation of the actual system? Knowing that the model is only an
approximation of the real-world system, key characteristics of actual system behavior should
be captured in the model, especially those related to comparing alternatives, drawing
inferences, and making decisions. Hence, necessary changes and calibrations that are made to
the model to better represent the actual system should be returned to the model concept. The
model concept represents the modeler’s abstraction of the real-world system structure and
logic. Thus, if the model were not fully valid, the model concept needs to be enhanced and
then translated into the simulation model. Several techniques are usually followed by
modelers to check the validity of the model before using it for such purposes. Examples
include checking the data used in the model and comparing them to the actual system data,
validating the model logic in terms of flow, sequence, routing, and decisions, scheduling, and
so on, vis-à-vis the real-world system, and matching the results of the model statistics to
those of actual system performance measures.
Cross-validation using actual system results and running certain what-if scenarios can also be
used to check model validity. For example, last year’s throughput data can used is to validate
the throughput number produced by the model for the same duration and under similar
conditions. We can also double the cycle time of a certain operation and see if the system
throughput produced is affected accordingly or if the manufacturing lead time data reflect this
increase in cycle time.
2.5.8 Model Analysis:
Having a verified and validated simulation model provides analysts with a great opportunity
since it provides a flexible platform on which to run experiments and to apply various types
of engineering analyses effectively. With the latest advances in computer speed and capacity,
even large-scale simulation models of intensive graphics can be run for several replications in
a relatively short time. Hence, it takes only a few minutes to run multiple simulation
replications for long periods of time in most simulation environments.

30
2.6 Study Documentation:
The final step in a simulation study is to document the study and report its results. Proper
documentation is crucial to the success of a simulation study. The simulation process often
includes communicating with many sides, writing complex logic, encountering enormous
amounts of data, conducting extensive experimentation, and going through several progress
reviews and milestones. Thus, without proper documentation, the analyst loses track of and
control over the study and cannot deliver the required information or meet the study
expectations. This often results in an inaccurate simulation model with poor results, inability
to justify model behavior and explain model results, and loss of others’ confidence in study
findings and recommendations.
Figure 2.2: The Simulation Procedure

31
2.7 A Simulation Report Includes The Following Elements:
1. The System Being Simulated
a. Background
b. System description
c. System design
2. The Simulation Problem
a. Problem formulation
b. Problem assumptions
c. Study objectives
3. The Simulation Model
a. Model structure
b. Model inputs
c. Model assumptions
4. Simulation Results
a. Results summary
b. Results analysis
5. Study Conclusion
a. Study finding
b. Study recommendations
6. Study Supplements
a. Drawings and graphs
b. Input data
c. Output data
d. Experimental design
e. Others

32
2.8 Analytical or Simulation-Based Models:
Analytical models presents a series of advantages that concisely describe the problem,
provide a closed series of solutions, allow an easy assessment of the impact caused by
changes in input on output measures, and offer the possibility of reaching an optimum
solution. Their main drawbacks relate to the assumptions made to describe a system as they
may not be very realistic and/or the mathematical formulae can be very complicated and
interfere with finding a solution.
Simulation models can describe highly complex systems and be used to either experiment
with systems that still not exist or experiment with existing systems without altering
them(this may also be done using analytical methods provided the system is not highly
complex). Among the drawbacks, one worthy of mention is that these models do not generate
a closed set of solutions. Each change made in the input variables requires a separate solution
series of runs. Complex simulation models may entail a long time to be constructed and run.
Furthermore, model validation may prove a difficult task (that is, correspondence with the
real system).
There are times when the combined use of both methods proves fruitful. The advantage of
this mixed, or hybrid, approach is that analytical models are able to produce optimum
solutions, whereas a suitable degree of realism and the accuracy of the system’s description
are reflected with simulation models. However, this combination has a disadvantage in that it
requires a greater level of familiarity with analytical models, and also more skill than if using
simulation models alone. (Daganzo, 2003)
The use of simulation-Based models for supply chain modeling, The Study on the supply
chain will be done by means of simulation when one or more several of the following
conditions apply (Shannon 1975)
• The problem has no mathematical formulation
• There is a mathematical model, but it has no analytical resolution methods.
• There is a model and methods, but the procedures are tedious, and simulation is
simpler and less costly.
• When the aim is to observe simulation history of the supply chain.
• It is impossible to experiment with a model before configuring the supply chain.
• It is impossible to experiment on the real supply chain.
• It is possible to experiment on the supply chain, but ethical reasons hinder this.
• When the aim is to observe very slow supply chain evolution by reducing the time
scale

Studying the effect of inventory policy and supply chain structure on bullwhip effect

Studying the effect of inventory policy and supply chain structure on bullwhip effect

Recomendados

Recomendados

Más contenido relacionado

Destacado

Destacado (17)

Similar a Studying the effect of inventory policy and supply chain structure on bullwhip effect

Similar a Studying the effect of inventory policy and supply chain structure on bullwhip effect (20)

Último

Último (20)

Studying the effect of inventory policy and supply chain structure on bullwhip effect