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

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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.

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

  1. 1. Arab Academy For Science And Technology And MaritimeTransportCollege of International Transport And LogisticsStudying The Effect of Inventory Policy andSupply Chain Structure on Bullwhip EffectThesis SubmittedIn Partial fulfilment of the Requirement for theBachelor of Science (BSC)ByAbdel-Kader MohamedMohamed Ahmed SholkamyMohamed Ibrahim SamhanOla Emad Yassin SakranSarah Lotfi Mohamed GaafarSupervised By:Dr. Mohammed El BeheiryDr. Khaled Seif El-Molouk2012
  2. 2. DeclarationWe hereby certify that this material, which we now submit for assessment on theprogram of study leading to the award of Bachelor of logistics is entirely our ownwork, that we have exercised reasonable care to ensure that the work is original, anddoes not to the best of our knowledge breach any law of copyright, and has not beentaken from the work of others save and to the extent that such work has been cited andacknowledged within the text of our work.Signed By: Abdel-Kader MohamedMohamed Ahmed SholkamyMohamed Ibrahim SamhanOla Emad Yassin SakranSarah Lotfi Mohamed GaafarDate: 18th of February 2012
  3. 3. IAcknowledgmentFirst 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 puttheir names on the project, but it will not be well-mannered to not mention them. Theyinspired us greatly to work in this project. Their willingness to motivate us contributedtremendously to our project. Without their encouragement and guidance this project wouldnot have materialized.We would like to show our appreciation to Dr. Hamdy Barghout. I cant say thank youenough for his tremendous support and help. We feel motivated and encouraged every timewe attend his meeting.Besides, we would like to thank Mr. Karim Selaawi for his efforts with us and provide for usvaluable information as the guidance of our project.The guidance and support received from all the members who contributed and who arecontributing to this project, was vital for the success of the project. We are grateful for theirconstant support and help.Finally, an honorable mention goes to our families and friends for their understandings andsupports on us in completing this project. Without helps of the particular that mentionedabove, we would face many difficulties while doing this.
  4. 4. IIAbstractBullwhip effect is of the phenomenon that affects the performance of any supply chain. Thecauses of bullwhip effect are divided into two main categories primary causes (triggers of thebullwhip effect) and secondary causes (amplifiers of the bullwhip effect). Two of the mainsecondary causes are the inventory policy and the supply chain structure. In this thesis asimulation model is built for two supply chains structures, each composed of three tierswhere the retailer(s) and the distribution centre adopting (Q,r) policy. Applying differentvalues of the order quantities at the retailer(s) and the distribution centre using the simulationto find out how the bullwhip effect is affected by these values. Results show that thebullwhip is highly affected by changing the order quantities or the supply chain structure.
  5. 5. IIISummaryMany academics and researchers view the supply chain management as the integration ofpreviously known knowledge areas such as inventory management, facility location, transportmanagement ... etc. Yet, this integration helped in the discovering of new phenomena andissues which have great effect on the supply chain performance. One of the most importantphenomena is the Bullwhip Effect. Some can trace the first appearance of the bullwhip effectin the literature to Jay Forrester work (1961), he developed a simulation of multiechlonsystem (the term supply chain was not used yet) and found that the demand variabilityincreases from down echelons to the upper ones. He was able to record the phenomena andcalled it Forrester effect but no investigation was made about the harms, causes andcountermeasures. In late 80s a focus is made on the bullwhip effect and many efforts startedto 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 inventorypolicy and the supply chain structure. A simulation model will be built for a serial supplychain consisting of three members, retailer, distribution centre and a factory. The demand atthe retailer is Poisson distributed and no lead time is considered. The inventory policy at theretail and the distribution centre is (Q, r) policy, the values of the order quantities at theretailer and the distribution centre will be varied and the bullwhip effect will be measured tofind the effect of changing the order quantities on the bullwhip effect. Then the some of thetested order quantities will be tested on another structure. The second structure will be onefactory, 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 aformulation of the problem which will be investigated is explained.Chapter One: Contains literature review about the bullwhip effect, with the main researchesdone 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 helpin 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 futureare given.Bibliography: all references which are read during the course of preparing this thesis arelisted.
  6. 6. IVList of figuresFIGURE 1.1: TRADITIONAL FLOWS WITHIN SUPPLY CHAINS................................................................................................................3FIGURE 1.2: SUPPLY CHAIN STAGES......................................................................................................................................................4FIGURE 1.3: THE BULLWHIP EFFECT GRAPH..........................................................................................................................................5FIGURE 1.4: BEER GAME ......................................................................................................................................................................7FIGURE 1.6: ORDER BATCHING ..........................................................................................................................................................11FIGURE 1.7: PRICE FLUCTUATION ......................................................................................................................................................12FIGURE 1.8: INPUT DEMAND AND OUTPUT ORDERS FOR A SUPPLY CHAIN MEMBER.......................................................................17FIGURE 2.1: SYSTEMATIC SIMULATION APPROACH...........................................................................................................................26FIGURE 2.2: THE SIMULATION PROCEDURE .......................................................................................................................................30FIGURE 3.1: BUILD MENU IN THE PRO-MODEL ....................................................................................................................................40FIGURE 3.2: THE LOGIC BUILDER IN PRO-MODEL.............................................................................................................................41FIGURE 3.3: THE STRUCTURE OF THE SUPPLY CHAIN (MODEL 1).....................................................................................................42FIGURE 3.4: THE ENTITIES OF THE SUPPLY CHAIN IN THE PRO-MODEL (MODEL 1).........................................................................43FIGURE 3.5: LOCATIONS TABLE OF THE SUPPLY CHAIN IN THE PRO-MODEL (MODEL 1).................................................................44FIGURE 3.6: ARRIVALS TABLE OF THE SUPPLY CHAIN IN THE PRO-MODEL (MODEL 1)...................................................................44FIGURE 3.7: THE PROCESS TABLE OF THE SUPPLY CHAIN IN PRO-MODEL (MODEL 1) ....................................................................45FIGURE 3.8: THE OPERATION BOX OF THE SUPPLY CHAIN IN THE PRO-MODEL (MODEL1) .............................................................45FIGURE 3.9: ROUTING TABLE AND MOVE LOGIC BOX OF THE SUPPLY CHAIN IN THE PRO-MODEL (MODEL1)...............................46FIGURE 3.11: THE SUPPLY CHAIN MAP OF (MODEL 2)......................................................................................................................48FIGURE 3.13: LOCATIONS TABLE OF THE SUPPLY CHAIN IN THE PRO-MODEL (MODEL 2)...............................................................51FIGURE 314: ARRIVALS TABLE OF THE SUPPLY CHAIN IN THE PRO-MODEL (MODEL 2)..................................................................51FIGURE 3.14: THE PROCESS TABLE OF THE SUPPLY CHAIN IN PRO-MODEL (MODEL 2) ..................................................................52FIGURE 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 .................................................................................................................................57FIGURE 4.1: BULLWHIP AT DIFFERENT RETAILER POLICIES AND THE MEAN DEMAND AT THE RETAILER EQUAL 2............................57(A) DISTRIBUTION CENTER (900,100) POLICY....................................................................................................................................58(B) DISTRIBUTION CENTER (1200, 100) POLICY .................................................................................................................................59(C) DISTRIBUTION CENTER (1800, 100) POLICY .................................................................................................................................59(D) DISTRIBUTION CENTER (2500, 100) POLICY .................................................................................................................................60FIGURE 4.2: BULLWHIP AT DIFFERENT RETAILER POLICIES AND THE MEAN DEMAND AT THE RETAILER EQUAL 4............................60(A) DISTRIBUTION CENTER (900,100) POLICY....................................................................................................................................61(B) DISTRIBUTION CENTER (1200, 100) POLICY .................................................................................................................................61(C) DISTRIBUTION CENTER (1800, 100) POLICY .................................................................................................................................62(D) DISTRIBUTION CENTER (2500, 100) POLICY .................................................................................................................................62FIGURE 4.3: BULLWHIP AT DIFFERENT RETAILER POLICIES AND THE MEAN DEMAND AT THE RETAILER EQUAL 8............................62FIGURE 4.4: EXAMPLE OF VARIABILITY AT RETAILS AND DISTRIBUTION CENTER AT DIFFERENT ORDER QUANTITIES. .....................63FIGURE 4.5: BULLWHIP EFFECT AT DIFFERENT RETAILERS ORDERING POLICIES ...............................................................................65FIGURE 4.6: BULLWHIP AT DIFFERENT RETAILER POLICIES AND THE MEAN DEMAND AT THE RETAILER EQUAL 2............................65FIGURE 4.7: BULLWHIP AT DIFFERENT RETAILER POLICIES AND THE MEAN DEMAND AT THE RETAILER EQUAL 4............................68FIGURE 4.8: BULLWHIP AT DIFFERENT RETAILER POLICIES AND THE MEAN DEMAND AT THE RETAILER EQUAL 8............................69
  7. 7. Vlist of tablesTABLE 2.1: DATA REQUIREMENTS FOR SUPPLY CHAIN MODEL ...........................................................................................................38TABLE 4.1: POISSON (2)......................................................................................................................................................................55TABLE 4.2: POISSON (4)......................................................................................................................................................................58TABLE 4.3: POISSON (8)......................................................................................................................................................................60TABLE 4.4: BULLWHIP EFFECT AT DIFFERENT RETAILERS ORDERING POLICIES ................................................................................64TABLE 4.5: TABLE FOR POISSON (2) LARGE STRUCTURE ..................................................................................................................65TABLE 4.6: TABLE FOR POISSON (4) LARGE STRUCTURE ..................................................................................................................68TABLE 4.7: TABLE FOR POISSON (8) LARGE STRUCTURE ..................................................................................................................69
  8. 8. VITABLE OF CONTENTSAcknowledgment ............................................................................................................................... IAbstract............................................................................................................................................. IISummary.......................................................................................................................................... IIIList of figures....................................................................................................................................IVlist of tables.......................................................................................................................................VIntroduction: ..................................................................................................................................... 1Chapter 1 .......................................................................................................................................... 3Literature Review.............................................................................................................................. 31.1 Introduction:.......................................................................................................................... 31.2 The Bullwhip Effect: ............................................................................................................... 51.3 Bullwhip Definition: ............................................................................................................... 51.4 Bullwhip History:.................................................................................................................... 61.5 Causes of the Bullwhip effect:................................................................................................ 71.5.1 Primary cause:................................................................................................................ 81.5.2 Secondary causes:........................................................................................................ 101.6 Harms of Bullwhip Effect:..................................................................................................... 151.7 Countermeasures to the Bullwhip Effect: ............................................................................. 161.8 The Quantification of the Bullwhip Effect:............................................................................ 161.9 Thesis Objectives: ................................................................................................................ 18Chapter 2 ........................................................................................................................................ 19Simulation....................................................................................................................................... 192.1 Simulation: .......................................................................................................................... 192.2 The Role of Simulation:........................................................................................................ 192.3 Simulation Process:.............................................................................................................. 242.4 Systematic Simulation Approach:......................................................................................... 252.5 Steps in Simulation Study:.................................................................................................... 262.5.1 Problem Formulation ....................................................................................................... 272.5.2 Setting Study Objectives:.................................................................................................. 272.5.3 Conceptual Modelling: ..................................................................................................... 272.5.4 Data Collection:................................................................................................................ 282.5.5 Model Building:................................................................................................................ 282.5.6 Model Verification: .......................................................................................................... 28
  9. 9. VII2.5.7 Model Validation:............................................................................................................. 292.5.8 Model Analysis:................................................................................................................ 292.6 Study Documentation: ......................................................................................................... 302.7 A Simulation Report Includes The Following Elements:......................................................... 312.8 Analytical or Simulation-Based Models: ............................................................................... 322.9 Characteristics of a Simulation Model:................................................................................. 332.10 Objectives of supply chain Simulation:................................................................................. 342.11 Types of simulation:............................................................................................................. 342.12 Data Requirements for Supply Chain Modelling: .................................................................. 36Chapter 3 ........................................................................................................................................ 39Pro-Model and Developed Models .................................................................................................. 393.1 Introduction:........................................................................................................................ 393.2 Typical Applications For Using Pro-Model Include: ............................................................... 393.3 Using Pro-Model:................................................................................................................. 403.4 Building Models:.................................................................................................................. 403.5 Logic Builder:....................................................................................................................... 403.6 Model 1 (Small):................................................................................................................... 423.7 Model 2 (Large):................................................................................................................... 48Chapter 4 ........................................................................................................................................ 55Results &discussion......................................................................................................................... 554.1 Design of experiments: ........................................................................................................ 554.2 Results:................................................................................................................................ 554.2.1 The effect of changing the retailers demand mean.......................................................... 624.2.2 The effect of increasing Qr on the distribution center variability....................................... 634.2.3 The effect of constant Qr on the distribution center variability......................................... 634.2.4 The effect changing the order quantities on bullwhip effect............................................. 644.3 Results and discussion of the second structure .................................................................... 65Chapter Five.................................................................................................................................... 72Conclusion and Recommendations.................................................................................................. 725.1 Conclusions:......................................................................................................................... 725.2 Recommendation: ............................................................................................................... 72Reference........................................................................................................................................ 74
  10. 10. 1Introduction:A supply chain involves, directly or indirectly, parties in order to meet a customer needs andwants. The supply chain not only includes the manufacturer and suppliers, but alsotransporters, warehouses, retailers, and customers. Within each organization, such asmanufacturer, the supply chain includes all functions involved in receiving and meeting thecustomer 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 topurchase for example Pampers. The supply chain begins with the customer and their need forthis product. The next stage of this supply chain is the Wal-Mart retail store that the customervisits. Wal-Mart stocks its shelves using inventory that may have been supplied from afinished-goods warehouse that Wal-Mart manages or from a distributor using trucks suppliedby 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 ofsuppliers who may themselves have been supplied by lower tier suppliers. For example,packaging material may come from Tenneco packaging while Tenneco receives rawmaterials to manufacture the packaging from other suppliers.The supply chain is involves three main flows: flow of information, product, and fundsbetween different stages. In our example, Wal-Mart provides the product, pricing andavailability information, to the customer. The customer transfers funds to Wal-Mart bybuying the product. Wal-Mart is the main point-of-information sales data as well asreplenishment order via trucks back to the store. Wal-Mart transfers funds to the distributorafter the replenishment. The distributor also provides pricing information and sends deliveryschedules to Wal-Mart. Similar information, material, and fund flows take place across theentire supply chain.Forrester (1961) initiated the analysis of the demand variability amplification and pointed outthat it is a consequence of industrial dynamics or time varying behaviour of industrialorganizations.According to Forrester’s effect, or the ―acceleration principle‖, a 10 percent change in therate of sale at the retail level can result in up to a 40 percent change in demand for themanufacturer. Remedy for this effect is to understand the system as a whole and to makemodifications in behavioural practice. John Sterman (1989) described a classroom gameknown 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 bothorders and inventory upstream. Sterman attributed this amplified order variability to players’irrational behaviour or misconceptions about inventory and demand information. The playersin the supply chain completely ignore the pipeline inventory when they are making theirordering decisions.
  11. 11. 2They failed to account for the long time lags between placing and receiving orders and end upwith poor decisions. Richard Metters (1997) conducted a study to determine the significanceof 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 incorrectdemand updating and forward buying.b) Seasonality is induced week by week on a monthly basis caused by orderbatching.Profitability is examined under heavy, moderate and no demand seasonality. It is concludedthat eliminating the bullwhip effect can increase product profitability by 10-30%, and thepotential profit increases from dampening the monthly seasonal changes outweigh those thatare associated with weekly seasonality. Lee et al. (1997) have proposed four sources of thebullwhip effect - demand signal processing, rationing game, order batching and pricevariations. Simple mathematical models are developed to demonstrate that the amplifiedorder variability is an outcome of the rational and optimizing behaviour of the supply chainmembers.Strategies that can be implemented to reduce the distortion are also discussed. (E.g. avoidmultiple demand forecasts updates, eliminate gaming in shortage situations, break orderbatches, and stabilize prices)Chen et al. (2000) focused on determining the impact of demand forecasting on the bullwhipeffect and quantifying the increase in variability at each stage of the supply chain. Thevariance of the orders placed by the retailer relative to the variance of the demand faced bythe retailer is determined.Chen et al. (2000) also analysed the impact of centralized customer demand information onthe bullwhip effect. It is demonstrated that centralizing the demand information will certainlyreduce the magnitude of the bullwhip effect, but it will not completely eliminate the increasein variability. Dejonckheere et al. (2002) analysed the bullwhip effect induced by forecastingalgorithms in order-up-to policies and suggested a new general replenishment rule that canreduce variance amplification significantly.Order-up-to policies whose order-up-to levels will be updated by means of exponentialsmoothing, moving averages and demand signal processing are compared. In order-up-tosystems, the bullwhip effect is guaranteed when forecasting is necessary. Bullwhip generatedby moving average forecasting in order-up-to model is much less than that generated byexponential forecasts and demand signal processing.A general replenishment rule capable of smoothing ordering patterns, even when demand hasto be forecasted is proposed. The crucial difference with the order-up-to policies is that netstock and on order inventory discrepancies are only fractionally taken into account.
  12. 12. 3Chapter 1Literature Review1.1 Introduction:A supply chain involved, directly or indirectly, parties in order to meet a customer needs andwants. The supply chain is involves three main flows: flow of information, product, andfunds as shown in figure 1.1 between different stages. For example, Wal-Mart provides theproduct, pricing and availability information, to the customer. The customer transfers fundsto Wal-Mart by buying the product. Wal-Mart is the main point-of-information sales data aswell as replenishment order via trucks back to the store. Wal-Mart transfers funds to thedistributor after the replenishment. The distributor also provides pricing information andsends delivery schedules to Wal-Mart. Similar information, material, and fund flows takeplace across the entire supply chain.Figure 1.1: Traditional Flows within Supply ChainsThis example shows that the customer is an integral part of the supply chain. The primarypurpose from the existence of any supply chain is to satisfy customer needs, and for thecompany is to gain profits. The term supply chain conjures up images of product or supplymoving 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 thischain. 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
  13. 13. 4distributors. Thus, most supply chains are actually networks. It may be more accurate to usethe term supply network or supply web to describe the structure of most supply chains.Figure 1.2: supply chain stagesA typical supply chain may involve a variety of stages. These supply chain stages include:• Customers• Retailers• Wholesalers/Distributors• Manufacturers• Component/Raw material suppliersEach stage need not be presented in a supply chain. The appropriate design of the supplychain will depend on both the customer’s needs and the roles of the stages involved. In somecases, 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 companyL.L. Bean, manufacturers do not respond to customer orders directly. In this case, L.L. Beanmaintains an inventory or product from which they fill customer orders. Compared to theDell supply chain, the L.L. Bean supply chain contains an extra stage (the retailer, L.L. Beanitself) between the customer and the manufacturer. In the case of other retail stores, thesupply chain may also contain a wholesaler or distributor between the store and themanufacturer.
  14. 14. 51.2 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 forcoordination 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 controland delivery planes of individual members in the supply chain. In this research we study thedemand information flow in the supply chain and report the variability in orders between themembers of supply chain. An important phenomenon observed in supply chain practices isthat the variability of an upstream members demand is greater than that of the downstreammember. This effect was found by logistics executives at Procter & Gamble (P&G) andcalled the "bullwhip effect‖. This phenomenon can be described as the variance of productionexceeding the variance of sales under the optimal behavior. The basically, the bullwhip effectis largely caused by demand single processing, order batching, price variation, and rationingand gaming and can be reduced through information sharing. To eliminate this effect reducedelays and collapsing all cycle time between members.Companies have to invest in extra capacity to meet the high variable demand. This capacity isthen 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 variabledemand 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 variabledemand also increases lead-times. These inflated lead-times lead to increased stocks andbullwhip effects. Thus the bullwhip effect can be quite exasperating for companies; theyinvest 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 withproducts 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 supplyand demand. Observed across most industries, the bullwhip phenomenon creates large swingsFigure 1.3: the bullwhip effect graph
  15. 15. 6in demand on the supply chain resulting from relatively small, but unplanned, variations inconsumer 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 immediateneed.1.4 Bullwhip History:Not long ago, logistics executives at Procter & Gamble (P&G) examined the order patternsfor one of their best-selling products, Pampers. Its sales at retail stores were fluctuating, butthe variability’s were certainly not excessive. However, as they examined the distributorsorders, the executives were surprised by the degree of variability. When they looked at P&Gsorders of materials to their suppliers, such as 3M, they discovered that the swings were evengreater. At first glance, the variability’s did not make sense. While the consumers, in thiscase, the babies, consumed diapers at a steady rate, the demand order variability’s in thesupply chain were amplified as they moved up the supply chain. P&G called thisphenomenon the "bullwhip" effect. (In some industries, it is known as the "whiplash" or thewhipsaw" effect.)When Hewlett-Packard (HP) executives examined the sales of one of its printers at a majorreseller, they found that there were, as expected, some fluctuations over time. However, whenthey examined the orders from the reseller, they observed much bigger swings. Also, to theirsurprise, they discovered that the orders from the printer division to the companys integratedcircuit division had even greater fluctuations.What happens when a supply chain is plagued with a bullwhip effect that distorts its demandinformation as it is transmitted up the chain? In the past, without being able to see the sales ofits products at the distribution channel stage, HP had to rely on the sales orders from theresellers to make product forecasts, plan capacity, control inventory, and scheduleproduction. Big variations in demand were a major problem for HPs management. Thecommon symptoms of such variations could be excessive inventory, poor product forecasts,insufficient or excessive capacities, poor customer service due to unavailable products orlong backlogs, uncertain production planning (i.e., excessive revisions), and high costs forcorrections, such as for expedited shipments and overtime. HPs product division was avictim 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 redefinehow the grocery supply chain should work. One motivation for the initiative was theexcessive amount of inventory in the supply chain. Various industry studies found that thetotal supply chain, from when products leave the manufacturers production lines to whenthey arrive on the retailers shelves, has more than 100 days of inventory supply. Distortedinformation has led every entity in the supply chain – the plant warehouse, a manufacturersshuttle warehouse, a manufacturers market warehouse, a distributors central warehouse, thedistributors regional warehouses, and the retail stores storage space – to stockpile because ofthe high degree of demand uncertainties and variability’s. Its no wonder that the ECR reportsestimated a potential $30 billion opportunity from streamlining the inefficiencies of thegrocery supply chain.
  16. 16. 71.5 Causes of the Bullwhip effect:The best way to understand of the bullwhip effect is the well-known "beer game." The BeerDistribution 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 makeorder decisions based only on orders from the next downstream player. Participants take therole 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: Minimizingcosts for capital employed in stocks while avoiding out-of-stock situations. The surprisingresults of the simulation explain inefficiencies of supply chains known as the bullwhip effect.Figure 1.4: Beer GameIn contrast, we show that the bullwhip effect is a consequence of the players rationalbehavior within the supply chains infrastructure. This important distinction implies thatcompanies wanting to control the bullwhip effect have to focus on modifying the chainsinfrastructure 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 updating5. Order batching6. Price fluctuation7. Rationing and shortage gaming8. Inventory policies.
  17. 17. 8Figure 1.5: The Causes of Bullwhip EffectEach of the five forces in concert with the chains infrastructure and the order managersrational decision making creates the bullwhip effect. Understanding the causes helpsmanagers design and develop strategies to counter it.These causes can be classified into primary causes which trigger the bullwhip effect andsecondary 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 thesupply chain away from the retail, wholesaler, distributor and manufacturer are not allowedto communicate and order decisions are only based on the downstream orders. Each week thecustomer places demand with the wholesaler who fulfils the order from his inventory. Thewholesaler requests an order from the distributor who gets his supply from the manufacturerwho 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 ordersfrom the next downstream parterres. The ordering patterns are not known and not stable. Thevariability’s of an upstream site are always greater than those of the downstream site. Thismisinterpretation 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 timeincrease the variability will increase but if the order variability increase informallythroughout the entire supply chain, then that will make no impact in bullwhip effects, theincrease of remanufacturing lead time increase the bullwhip effects. There is some otherimportant information such as:1. Lower safety stock.2. Reduction in_ out of stock loss.
  18. 18. 93. Improvement in customer service level.Lead time also depends on inventory, ordering, and replenishment policies used and thecoordination 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 mitigaterisk 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. Safetystock is held when there is uncertainty in the demand level or lead time for the product, itserves as an insurance against stock outs. The amount of safety stock an organization choosesto keep on hand can dramatically affect their business. Too much safety stock can result inhigh holding costs of inventory. In addition, products which are stored for too long a time canspoil, expire, or break during the warehousing process. Too little safety stock can result inlost sales and, thus, a higher rate of customer turnover. As a result, finding the right balancebetween too much and too little safety stock is essential.Safety stocks are mainly used in a manufacturing strategy. This strategy is employed whenthe 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 thecustomer demand. Indeed, the Production Planning is based on a forecast, which is differentfrom the real demand. By absorbing these variations, safety stock improves the customerservice level.To reduce safety stock, these include better use of technology, increased collaboration withsuppliers, and more accurate forecasting in a lean supply environment, lead times arereduced, which can help minimize safety stock levels thus reducing the likelihood and impactof stock outs, an Enterprise Resource Planning system can also help an organization to reduceits 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 customersatisfaction that is, the feeling that a product or service has met the customer expectation. Itsimportance varies by products, industry and customer; defective or broken merchandise canbe exchanged, often only with a receipt and within a specified time frame. Retail stores oftenhave a desk or counter devoted to dealing with returns, exchanges and complaints, or willperform related functions at the point of sale, the perceived success of such interactions beingdependent on employees From the point of view of an overall sales process engineeringeffort, customer service plays an important role in an organizations ability to generateincome and revenue. From that perspective, customer service should be included as part of anoverall approach to systematic improvement. A customer service experience can change theentire perception a customer has of the organization.Companies should have to provide better customer service. Executives should know thecompetitors; customer service is a very critical component in achieving and maintaining ahigh level of customer satisfaction. When pressures move the organization to meet onlyperformance goals and measurements such as overhead absorption, labor efficiency, purchaseprice variance.
  19. 19. 101.5.2Secondary causes:a) Demand Forecast Updating:Every company in a supply chain usually does product forecasting for its productionscheduling, capacity planning, inventory control, and material requirements planning.Forecasting is often based on the order history from the companys immediate customers. Theoutcomes of the beer game are the consequence of many behavioral factors, such as theplayers’ perceptions and mistrust. An important factor is each players thought process inprojecting the demand pattern based on what will be observed. When downstream operationplaces an order, the upstream manager processes that order (information) as a signal aboutfuture product demand. Based on this signal, the upstream manager readjusts the demandforecasts and, in turn, the orders placed with the suppliers of the upstream operation. Wecontend 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 themanager of the previous site constitute your demand. If you are also using exponentialsmoothing to update your forecasts and safety stocks, the orders that you place with yoursupplier will have even bigger swings. For an example of such fluctuations in demand, theorders placed by the dealer to the manufacturer have much greater variability than theconsumer demands. Because the amount of safety stock contributes to the bullwhip effect, itis intuitive that, when the lead times between the resupply of the items along the supply chainare longer, the fluctuation is even more significant.b) Order Batching:In a supply chain, each company places orders with an upstream organization using someinventory monitoring or control. Demands come in, depleting inventory, but the companymay not immediately place an order with its supplier. It often batches or accumulatesdemands before issuing an order. There are two forms of order batching: periodic orderingand 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 thesupplier cannot handle frequent order processing because the time and cost of processing anorder can be substantial. P&G estimated that, because of the many manual interventionsneeded 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 theyrun their material requirements planning (MRP) systems.Consider a company that orders once a month from its supplier. The supplier faces a highlystream of orders. There is a spike in demand at one time during the month, followed by nodemands for the rest of the month. Of course, this variability is higher than the demands thecompany itself faces. Periodic ordering amplifies variability and contributes to the bullwhipeffect. One common obstacle for a company that wants to order frequently is the economicsof transportation.
  20. 20. 11There 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 asupplier. Sometimes, suppliers give their best pricing for FTL orders. For most items, a fulltruckload could be a supply of a month or more. Full or close to full truckload ordering wouldthus 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 allcustomers order cycles were spread out evenly throughout the week, the bullwhip effectwould be minimal. The periodic surges in demand by some customers would be insignificantbecause not all would be ordering at the same time. Unfortunately, such an ideal situationrarely exists. Orders are more likely to be randomly spread out or, worse, to overlap. Whenorder cycles overlap, most customers that order periodically do so at the same time. As aresult, the surge in demand is even more pronounced, and the variability from the bullwhipeffect is at its highest.Figure 1.6: Order Batchingc) Price Fluctuation :Estimates indicate that 80 percent of the transactions between manufacturers and distributorsin the grocery industry were made in a "forward buy" arrangement in which items werebought in advance of requirements, usually because of a manufacturers attractive price offer.Forward buying constitutes $75 billion to $100 billion of inventory in the grocery industry.
  21. 21. 12Manufacturers and distributors periodically have special promotions like price discounts,quantity discounts, coupons, rebates, and so on. All these promotions result in pricefluctuations. Additionally, manufacturers offer trade deals (e.g., special discounts, priceterms, and payment terms) to the distributors and wholesalers, which are an indirect form ofprice discounts.When the products price returns to normal, the customer stops buying until it has depleted itsinventory As a result, the customers buying pattern does not reflect its consumption pattern,and the variation of the buying quantities is much bigger than the variation of theconsumption rate the bullwhip effect.When high-low pricing occurs, forward buying may well be a rational decision. If the cost ofholding 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 shouldorder optimally to take advantage of the low price opportunities.Figure 1.7: Price Fluctuationd) 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 receive50 percent of what they order.Knowing that the manufacturer will ration when the product is in short supply, customersexaggerate their real needs when they order. Later, when demand cools, orders will suddenly
  22. 22. 13disappear and cancellations pour in. This seeming overreaction by customers anticipatingshortages results when organizations and individuals make sound, rational economicdecisions and "game" the potential rationing. The effect of "gaming" is that customers ordersgive the supplier little information on the products real demand, a particularly vexingproblem for manufacturers in products early stages. The gaming practice is very common. Inthe 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 candeliver, and then cancel all other duplicate orders.More recently, Hewlett-Packard could not meet the demand for its LaserJet III printer andrationed the product. Orders surged, but HP managers could not discern whether the ordersgenuinely reflected real market demands or were simply phantom orders from resellers tryingto get better allocation of the product. When HP lifted its constraints on resupply of the LaserJets, many resellers canceled their orders.HPs costs in excess inventory after the allocation period and in unnecessary capacityincreases were in the millions of dollars. During the Christmas shopping seasons in 1992 and1993, Motorola could not meet consumer demand for handsets and cellular phones, forcingmany distributors to turn away business. Distributors like Air Touch Communications and theBaby Bells, anticipating the possibility of shortages and acting defensively, drastically overordered 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 realizedthat the dealers were swamped with inventory and new orders for phones were not as healthybefore, Motorolas stock tumbled almost 10 percent.e) Inventory Policy:There are many different types of replenishment policies, the most commonly used are: theperiodic review, the continuous review, order-up-to policy, base stock replenishment policyand reorder point- order quantity policy. Given the common practice in retailing to replenishinventories frequently, daily and the tendency of manufacturers to produce to demand, afocus will be made in this analysis on periodic review, base-stock, or order- up-toreplenishment policies. Standard Base-Stock Replenishment Policy (S,R):The standard periodic review base stock replenishment policy is the replenishment policy atthe end of every review period, the retailer tracks his inventory position, which is the sum ofthe inventory on order ( items order but not arrived yet due to the lead time) minus thebacklog (demand that couldn’t be fulfilled and still has to be delivered). A replenishmentorder 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 correlatedto 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 mayimpose high capacity and inventory costs on the manufacturer. The manufacturer not only
  23. 23. 14prefers a level production schedule, the smoothed demand also allows him to minimize hisraw materials inventory cost. Therefore, we discuss a smoothing replenishment policy that isable 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 withsuppliers 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 downto zero. In view of instantaneous replenishment of stock the level of inventory jumps to theoriginal level from zero level.In real life situations one never encounters a zero lead time. There is always a time lag fromthe date of placing an order for material and the date on which materials are received. As aresult the reorder point is always higher than zero, and if the firm places the order when theinventory reaches the reorder point, the new goods will arrive before the firm runs out ofgoods to sell. The decision on how much stock to hold is generally referred to as the orderpoint 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 whichis the Inventory needed during the lead time (i.e., the difference between the order date andthe receipt of the inventory ordered) and the safety stock which is the minimum level ofinventory 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. Insummary, 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 receivinginventory, 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 riskof stock out, resulting in possible customer dissatisfaction and lost sales, and the increasedcosts 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 andthe 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 materialsbe made when the level of inventory is just adequate to meet the needs of production duringlead-time.
  24. 24. 151.6 Harms of Bullwhip Effect:Bullwhip effect in supply chains has led to the distortion of demand information; the harm isdone both at the micro and macro levels:At the micro level, the existence of the bullwhip effect in supply chain will bring a doubleloss for companies include efficiency and profitability .Firstly, the product stock is to adaptthe demand change to set up, the excessive demand fluctuation caused to supply in chainsexcessive stock directly, has taken enterprises fund massively, formed the high quota theinventory cost, brought the pressure for enterprises production and operating activities.Secondly, because the demand uncertainty increased, the difficulty of the enterprise’ perfectforecast to the demand is also enlarged. And in the supply the possibility which the backordering and out of stock is increasing, all of these lead to reduce the level of customerservice.Third, the demand distortion also affects enterprises production. Because of the distortiondemand information misleading, the productive plans have to revise frequently, producescannot advance continually. Therefore the production cost and the physical distribution costis increasing also.At macro level, the bullwhip effect will cause the economic resource the blind flowing andthe low efficiency disposition .Bullwhip effect is a classic "market failure‖ phenomenon,because the upstream industry received the demand information deviated from the truedemand, it may lead to over-investment or investment shortage. The capital entersexcessively means the competition aggravating and the income drop, ultimately hurting thedevelopment of the industry itself. Therefore causing the financial systems hidden dangerand bring the risk of the macroeconomic movement. Excessive Inventory:As forecast inaccuracies become amplified up the supply chain, it can result in a highlyinaccurate demand forecast being made by the producer. As a result, the producer may end upproducing more of the product than the market is actually willing to accept. This means thatthe producer will have produced too many units. This can be disastrous in some cases, as itmay not be possible to offload the products for a profit. The products will likely be sold at adeep discount to secondary markets (for example, companies that purchase wholesaleoverstocks). In a worst-case scenario, it could result in having an excess of products that mustsimply be destroyed. Inefficient Production:The bullwhip effect can lead to inefficient production. This happens when the producer doesnot have accurate demand data and cannot accurately produce the required amount of productahead of time and cannot schedule production in an efficient way. This can lead to a reactiveproduction, where the producer does not produce enough and then must rush to producemore. This is extremely inefficient because it means that rather than operating at a constantrate, the producer is alternating between times where it is producing nothing and times whereit is at maximum capacity. Increases of Cost
  25. 25. 16The most important effect that the bullwhip effect has is that it increases costs (sometimesdramatically). 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 theirproducts). Stock-outs result in lost revenues from sales that are missed. They can cause costlylosses to a companys reputation and they can result in the competition gaining yourcustomers. Also, inefficient production can be much more costly because it requires hiringand training extra staff, paying overtime wages and may require sourcing materials from thequickest (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 toapply countermeasures to overcome it. Here are some of these solutions: Countermeasures to demand forecast inaccuracies - Lack of demand visibility can beaddressed by providing access to point of sale (POS) data. Single control ofreplenishment or Vendor Managed Inventory (VMI) can overcome exaggerated demandforecasts. Long lead times should be reduced where economically advantageous. Countermeasures to order batching - High order cost is countered with Electronic DataInterchange (EDI) and computer aided ordering (CAO). Full truck load economics arecountered with third-party logistics and assorted truckloads. Correlated ordering iscountered with regular delivery appointments. More frequent ordering results in smallerorders and smaller variance. However, when an entity orders more often, it will not see areduction 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 ordecrease; see the standard loss function in the Inventory Management section. Countermeasures to shortage gaming - Proportional rationing schemes are counteredby allocating units based on past sales. Ignorance of supply chain conditions can beaddressed by sharing capacity and supply information. Unrestricted ordering capabilitycan be addressed by reducing the order size flexibility and implementing capacityreservations. For example, one can reserve a fixed quantity for a given year and specifythe quantity of each order shortly before it is needed, as long as the sum of the orderquantities equals to the reserved quantity. Countermeasures to fluctuating prices - High-low pricing can be replaced witheveryday low prices (EDLP). Special purchase contracts can be implemented in order tospecify ordering at regular intervals to better synchronize delivery and purchase. Free return policies are not addressed easily. Often, such policies simply must beprohibited 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 definitioncaptures the distortion of information flow that goes upstream (the downstream stage’s orderis the demand input to the upstream stage).
  26. 26. 17The 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 notavailable, is inferred from the sales and inventory data. This definition essentially capturesthe distortion of material flow that goes downstream. The bullwhip measurements based onthese two definitions are usually good approximation to each other (as material flow more orless follows information flow), but they different in concept.Four measuring quantities to quantify the bullwhip effect, where the four measures ofbullwhip 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, M1Is the standard deviation of the quantities ordered by this member from its next upperstream member. So M1 = STD (Q). Second measure: Coefficient of Variation, M2Is the ratio between the standard deviation of the quantities ordered by this member fromits next upper stream member to the mean of these quantities. So M2 = STD (Q)/Q. Third measure: Variances Ratio, M3It is the ratio between the variance of the quantities ordered by a certain SC member fromits next upper stream member to the variance of the input demand. So M3 = Var (Q)/Var(d). Fourth measure: Coefficient of Variation Ratio, M4It is ratio between standard deviation of the quantities ordered by this member from itsnext upper stream member to the mean of these quantities divided by the ratio betweenstandard 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: AggregationTo measure the bullwhip effect correctly one has to be aware of different dataaggregation level, aggregation could be made across echelon(s) or products, whichmeans either to get one measure for the bullwhip for each echelon in the supply chainso treating it as a serial supply chain or if there are more than one product theseproducts are aggregated and treated as one product.
  27. 27. 18 Incomplete data.The aggregation may be able to be done across both echelons and products. Theydemonstrated using random generated data that different aggregation methods can leadto totally different values for the bullwhip measure. There may be a conceptualimbalance between incoming demand and outgoing demand; furthermore theinformation on those values may be incomplete. Filtering of causes.To analyzed act on the bullwhip effect, one has to understand the causes of the demandvariations at hand.1.9 Thesis Objectives:From the previous review it is clear that the causes of bullwhip effect drew great attention ofmany 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 thesupply chain members are following (Q,r) policy. To find quantitatively the effect of the supply chain structure on the bullwhipeffect.
  28. 28. 19Chapter 2Simulation2.1 Simulation:Simulation is one of the most powerful tools available to decision-makers responsible for thedesign and operation of complex processes and systems. It makes possible the study, analysisand evaluation of situations that would not be otherwise possible. In an increasinglycompetitive World, simulation has become an indispensable problem solving methodologyfor engineers, designers and managers.We will define simulation as the process of designing a model of a real system andconducting experiments with this model for the purpose of understanding the behavior of thesystem and /or evaluating various strategies for the operation of the system. Thus it is criticalthat the model be designed in such a way that the model behavior mimics the responsebehavior of the real system to events that take place over time.The terms model and system are key components of our definition of simulation. By a modelwe mean a representation of a group of objects or ideas in some form other than that of theentity itself. By a system we mean a group or collection of interrelated elements thatcooperate to accomplish some stated objective. One of the real strengths of simulation is thefact that we can simulate systems that already exist as well as those that are capable of beingbrought into existence, i.e. those in the preliminary or planning stage of development. (RobertE. Shannon 1998)2.2 The Role of Simulation:It is necessary to clarify the role that simulation plays in modern industrial and businessfirms. In this section we clarify the role of simulation by first justifying the use of simulationboth technically and economically and then presenting the spectrum of simulationapplications to various industries in the manufacturing and service sectors. It is also worthmentioning that using simulation in industrial and business application is the most commonbut not the only field in which simulation is utilized; it is also used for educational andlearning purposes, training, virtual reality applications, movies and animation production, andcriminal justice, among others.
  29. 29. 202.2.1 Simulation JustifiedThe question of why and when to simulate is typical of those that cross the minds ofpractitioners, engineers, and managers. We simply simulate because of simulationcapabilities that are unique and powerful in system representation, performance estimation,and improvement. Simulation is often the analysts’ refuge when other solution tools, such asmathematical models, fail or become extremely difficult to approximate the solution to acertain problem. Most real-world processes in production and business systems are complex,stochastic, and highly nonlinear and dynamic, which makes it almost impossible to presentthem using physical or mathematical models. Attempts to use analytical models inapproaching 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 cannotbe solved by other means, such as mathematical and calculus- based methods. Theperformance of a real system is a complicated function of the design parameters, and ananalytical closed-form expression of the objective function or the constraints may not exist. Asimulation model can therefore be used to replace the mathematical formulation of theunderlying system. With the aid of the model and rather than considering every possiblevariation of circumstances for the complex problem, a sample of possible execution paths istaken 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 asqueuing systems, inventory models, and Markovian models, which are commonly used toanalyze production and business systems, often fail to provide statistics on systemperformance when real-world conditions intensify to overwhelm and exceed the systemapproximating assumptions. Examples include entities whose arrival at a plant or bank is nota Poisson process, and the flow of entities is based on complex decision rules understochastic variability within availability of system resources. Decision support is anothercommon justification of simulation studies.Obviously, engineers and managers want to make the best decisions possible, especiallywhen encountering critical stages of design, expansion, or improvement projects where thereal system has not yet been built. By carefully analyzing the hypothetical system withsimulation, designers can avoid problems with the real system when it is built. Simulationstudies at this stage may reveal insurmountable problems that could result in projectcancellation, and save cost, effort, and time. Such savings are obtained since it is alwayscheaper 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.
  30. 30. 21The economic justification of simulation often plays a role in selecting simulation as asolution tool in design and improvement studies. Although simulation studies might be costlyand time consuming in some cases, the benefits and savings obtained from such studies oftenrecover the simulation cost and avoid much further costs. Simulation costs are typically theinitial simulation software and computer cost, yearly maintenance and upgrade cost, trainingcost, engineering time cost, and other costs: for traveling, preparing presentations withmultimedia tools, and so on. Such costs are often recovered with the first two or threesuccessful simulation projects. Further, the cost and time of simulation studies are oftenreduced by analyst experience and become minuscule compared to the long-term savingsfrom increasing productivity and efficiency.2.2.2 Simulation ApplicationsA better answer to the question ―why simulate?‖ can be reached by exploring the widespectrum of simulation applications to all aspects of science and technology. This spectrumstarts by using simulation in basic sciences to estimate the area under a curve, evaluatingmultiple integrals, and studying particle diffusion, and continuer by utilizing simulation inpractical situations and designing queuing systems, communication networks, economicforecasting, biomedical systems, and war strategies and tactics.Today, simulation is being used for a wide range of applications in both manufacturing andbusiness operations. As a powerful tool, simulation models of manufacturing systems areused:• 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 automatedstorage 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 mainlineFor business operations, simulation models are also being used for a wide range ofapplications:• To determine the number of bank tellers, which results in reducing customer waitingtime by a certain percentage.• To design distribution and transportation networks to improve the performance oflogistic and vending systems• To analyze a company’s financial system
  31. 31. 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 timeand schedule the working pattern of the medical staff• To assess the impact of government regulations on different public services at boththe municipal and national levels• To test the feasibility of different product development processes and to evaluate theirimpact on company’s budget and competitive strategy• To design communication systems and data transfer protocolsTo reach the goals of the simulation study, certain elements of each simulated system oftenbecome the focus of a simulation model. Modeling and tracking such elements provideattributes and statistics necessary to design, improve, and optimize the underlying systemperformance.2.2.3 Simulation PrecautionsLike any other engineering tool, simulation has limitations. Such limitations should be dealtwith as a motivation and should not discourage analysts and decision makers. Knowinglimitations of the tool in hand should emphasize using it wisely and motivate the user todevelop creative methods and establish the correct assumptions that benefit from thepowerful simulation capabilities and preclude simulation limitations from being a dampingfactor.However, certain precautions should be considered in using simulation to avoid the potentialpitfalls of simulation. Examples of issues that we should pay attention to when consideringsimulation include the following:1. The simulation analyst or decision maker should be able to answer the question of whennot to simulate. A lot of simulation studies are considered to be design overkill whenconducted for solving problems of relative simplicity.Such problems can be solved using engineering analysis, common sense, ormathematical models. Hence, the only benefit from approaching simple systems withsimulation is being able to practice modeling and to provide an animation of the targetedprocess.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 decisionmakers think of simulation study as model-building time and cost.Although model building is a critical phase of a simulation study, it often consumes lesstime and cost than does experimental design or data collection.3. The skill and knowledge of the simulation analyst. Being an engineer is almost essentialfor simulation practitioners because of the type of analytical, statistical, and systemanalyses skills required for conducting simulation studies.4. Expectations from the simulation study should be realistic and not overestimated.
  32. 32. 23A lot of professionals think of simulation as a ―crystal ball‖ through which they canpredict and optimize system behavior. It should be clear to the analyst that simulationmodels themselves are not system optimizers. While it should be asserted that simulationis just a tool and an experimental platform, it should also be emphasized that combiningsimulation with appropriate statistical analyses, experimental design, and efficient searchengine can lead to invaluable system information that benefits planning, design, andoptimization.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, andassumptions.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, theanalyst should decide on the amount of detail incorporated into the simulation model.Some study objectives can be reached with macro-level modeling, whereas others requiremicro-level modeling. There is no need for the analyst to exhaust his or her modelingskills trying to incorporate details that are irrelevant to simulation objectives. Instead, themodel should be focused on providing the means of system analysis that yields resultsdirectly relevant to study objectives.7. Model validation and verification is not a trivial task. As discussed later, modelvalidation 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. Modelverification, on the other hand, focuses on making sure that the model behaviorresembles the intended behavior of the actual simulated system. Both practices determinethe degree of model reliability and require the analyst to be familiar with the skills ofmodel testing and the structure and functionality of the actual system.8. The results of simulation can easily be misinterpreted. Hence, the analyst shouldconcentrate efforts on collecting reliable results from the model through proper settingsof run controls (warm-up period, run length, and number of replications) and on usingthe proper statistical analyses to draw meaningful and accurate conclusions from themodel. Typical mistakes in interpreting simulation results include relying on a short runtime (not asteady-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 whileignoring the variability encompassed into response values.9. Simulation inputs and outputs should be communicated clearly and correctly to allparties of a simulation study. System specialists such as process engineers and systemmanagers need to be aware of the data used and the model logic in order to verify themodel and increase its realistic representation.Similarly, the results of the simulation model should be communicated to get feedbackfrom parties on the relevancy and accuracy of results.10. The analyst should avoid using incorrect measures of performance when building andanalyzing model results. Model performance measures should be programmed correctly
  33. 33. 24into the model and should be represented by statistics collected from the model. Suchmeasures should also represent the type of information essential to the analyst anddecision maker to draw conclusions and inferences about model behavior.11. The analyst should avoid the misuse of model animation. In fact, animation is animportant simulation capability that provides engineers and decision makers with a greattool for system visualization and response observance. Hence, it is true that ―a picture isworth a thousand words.‖ Such a tool is also useful for model debugging, validation andverification, and presentation to executives and customers. However, a lot of peoplemisuse model animation and rely on their observation to draw conclusions as to modellong-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 notnecessarily reflect its long-term behavior. Instead, model statistics are a betterrepresentation of model response.12. The analyst needs to get the support of upper management and decision makers to makea simulation study fruitful and successful.13. Finally, the analyst should select the appropriate simulation software tools that fit theanalyst’s knowledge and expertise and that are capable of modeling the underlyingsystem and providing the simulation results required.The criteria for selecting the proper simulation software tools are available in theliterature and are not the focus of this book. It should be known, however, that simulationpackages vary in their capabilities and inclusiveness of different modeling systems andtechniques, such as conveyor systems, power and free systems, automated guided vehiclesystems, kinematics, automated storage and retrieval systems, human modelingcapabilities, 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 isreferred to in the context of a simulation process. The details of such a process often dependon the project objectives, the simulation software used, and even on the way the team handlessimulation modeling. Although the tactics of such a process often varies from one applicationto another and from one simulation project to another, the overall structure of the simulationprocess is common. It is necessary to follow a systematic method when performing thesimulation process. This chapter is focused on analyzing the various aspects of the simulationprocess, the process followed by a complete simulation study.There are three purpose of simulation:2.3.1 Simulation-Based System DesignThe focus here is on an event-driven or transaction-based process and service design ratherthan the product design (see, e.g., Yang and El-Haik, 2003). Process compatibility studies canbenefit greatly from simulation-based design. Applications of simulation projects include awide spectrum of projects, such as the development of new facilities, major expansions of amanufacturing system, a new clinic, a new bank, a new vehicle program, and a newtransportation network.
  34. 34. 252.3.2 Problem Solving Simulation:Production and business systems often face challenges that affect their operation andperformance. The impact of such challenges varies from reduced efficiency or frequentdelays and failures to major shutdowns and catastrophes. Hence, a big portion of the work ofproduction managers, system engineers, and operations managers is often focused onmonitoring the system performance, tackling operational problems, and attempting to preventfuture problems. In addition, many of these problems may be concluded from customercomplains, 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 certainlevel of performance depends on effort in establishing and implementing plans for continuousimprovement. Companies do not always wait until a problem arises to take correction andimprovement actions. Managers and engineers often believe that there is always a window forimprovement in the way that companies produce products or provide services. Through thiswindow, system managers and planners often foresee opportunities for making the systembetter 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 aswell as for assessing the performance of each alternative. Hence, a complete simulation studyoften includes problem definition; setting simulation objectives; developing a conceptualmodel; specifying model assumptions; collecting pertinent model data; building, verifying,and validating the simulation model; analyzing model outputs; and documenting the projectfindings.2.4 Systematic Simulation Approach:The approach followed for applying each of the three categories of simulation studiesdiscussed 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 studyeffectively. This approach consists of common stages for performing the simulation study, asshown in Figure 6.6. The approach shown in the figure is a typical engineering methodologyfor the three categories of simulation studies (i.e., system design, problem solving, andsystem improvement) that puts the simulation process into the context of engineering solutionmethods. Engineers and simulation analysts often adopt and use such an approach implicitlyin real-world simulation studies without structuring it into stages and steps. The approach isan 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.
  35. 35. 26Figure 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, andconducting simulation experiments. This procedure is a detailed translation of the systematicsimulation approach presented in Figure 6.6. Figure 6.7 is a flowchart of the step-by-stepsimulation procedure.The simulation systematic approach shown in Figure 6.7 represents the engineeringframework of the simulation study. The steps may vary from one analyst to another becauseof factors such as the nature of the problem and the simulation software used. However, thebuilding blocks of the simulation procedure are typically common among simulation studies.The simulation procedure, often represented by a flowchart, consists of the elements and thelogical sequence of the simulation study. It also includes decision points through which theconcept and model are checked, validated, and verified. Iterative steps may be necessary toadjust and modify the model concept and logic. Finally, the procedure shows steps that canbe executed in parallel with other steps.
  36. 36. 272.5.1 Problem FormulationThe simulation study should start with a concise definition and statement of the underlyingproblem. The problem statement includes a description of the situation or the system of thestudy and the problem that needs to be solved. Formulating the problem in terms of an overallgoal and a set of constraints provides a better representation of the problem statement. Athorough understanding of the elements and structure of the system under study often helps indeveloping 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. Suchobjectives represent the criteria through which the overall goal of the study is achieved. Studyobjectives simply indicate questions that should be answered by the simulation study.Examples include determining current-state performance, testing design alternatives, studyingthe impact of speeding up the mainline conveyor, and optimizing the number of carriers in amaterial-handling system.2.5.3 Conceptual Modelling:Developing a conceptual model is the process through which the modeler abstracts thestructure, functionality, and essential features of a real-world system into a structural andlogical representation that is transferable into a simulation model. The model concept can bea simple or a complex graphical representation, such as a block diagram, a flowchart, or aprocess map that depicts key characteristics of the simulated system, such as inputs, elements,parameters, logic, flow, and outputs. Such a representation should eventually beprogrammable and transferable into a simulation model using available simulation softwaretools. Thus, a successful model concept is one that takes into consideration the method oftransferring each abstracted characteristic, building each model element, and programmingthe conceptual logic using the software tool.
  37. 37. 282.5.4 Data Collection:Simulation models are data-driven computer programs that receive input data, execute thelogic designed, and produce certain outputs. Hence, the data collection step is a keycomponent of any simulation study. Simulation data can, however, be collected in parallel tobuilding a model using the simulation software. This is recommended since data collectionmay be time consuming in some cases, and building the model structure and designing modellogic can be independent of the model data. Default parameters and generic data can be usedinitially until the system data are collected.2.5.5 Model Building:Data collection and model building often consume the majority of the time required forcompletion of a simulation project. To reduce such time, the modeler should start building thesimulation model while data are being collected. The conceptual model can be used toconstruct 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 sequenceof the simulation procedure. Constructing model components, entity flow, and logic dependsmostly on the model concept and is in most cases independent of model data. Once the modelis 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 themodel simultaneously reduces significantly the overall duration of the simulation study andprovides 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 theintended logical design used in building the model. Although, model logic can be definedusing different methods and can be implemented using different programming techniques,execution of the logic when running the model should reflect the initial design of theprogrammer or modeler. Different methods are used for debugging logical (programming)errors as well as errors in inputting data and setting model parameters. Corrected potentialcode and data discrepancies should always be verified by careful observation of changes inmodel behavior.To verify a model, we simply check whether the model is doing what it is supposed to do. Forexample, does the model read the input data properly? Does the model send the right part tothe right place? Does the model implement the production schedule prescribed? Docustomers in the model follow the queuing discipline proposed? Does the model provide theright output?And so on. Other verification techniques include applying rules of common sense, watchingthe model animation periodically during run time, examining model outputs, and askinganother modeler to review the model and check its behavior. The observations made by otheranalysts are valuable since the model builder will be more focused on the programming
  38. 38. 29details and less focused on the implication of different programming elements. When themodel logic is complex, more than one simulation analyst may have to work on building themodel.2.5.7 Model Validation:Model validation is the process of checking the accuracy of the model representation to thereal-world system that has been simulated. It is simply about answering the followingquestion: Does the model behave similarly to the simulated system? Since the model will beused to replace the actual system in experimental design and performance analysis, can werely in its representation of the actual system? Knowing that the model is only anapproximation of the real-world system, key characteristics of actual system behavior shouldbe captured in the model, especially those related to comparing alternatives, drawinginferences, and making decisions. Hence, necessary changes and calibrations that are made tothe model to better represent the actual system should be returned to the model concept. Themodel concept represents the modeler’s abstraction of the real-world system structure andlogic. Thus, if the model were not fully valid, the model concept needs to be enhanced andthen translated into the simulation model. Several techniques are usually followed bymodelers to check the validity of the model before using it for such purposes. Examplesinclude 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, andso on, vis-à-vis the real-world system, and matching the results of the model statistics tothose of actual system performance measures.Cross-validation using actual system results and running certain what-if scenarios can also beused to check model validity. For example, last year’s throughput data can used is to validatethe throughput number produced by the model for the same duration and under similarconditions. We can also double the cycle time of a certain operation and see if the systemthroughput produced is affected accordingly or if the manufacturing lead time data reflect thisincrease in cycle time.2.5.8 Model Analysis:Having a verified and validated simulation model provides analysts with a great opportunitysince it provides a flexible platform on which to run experiments and to apply various typesof 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 ina relatively short time. Hence, it takes only a few minutes to run multiple simulationreplications for long periods of time in most simulation environments.
  39. 39. 302.6 Study Documentation:The final step in a simulation study is to document the study and report its results. Properdocumentation is crucial to the success of a simulation study. The simulation process oftenincludes communicating with many sides, writing complex logic, encountering enormousamounts of data, conducting extensive experimentation, and going through several progressreviews and milestones. Thus, without proper documentation, the analyst loses track of andcontrol over the study and cannot deliver the required information or meet the studyexpectations. This often results in an inaccurate simulation model with poor results, inabilityto justify model behavior and explain model results, and loss of others’ confidence in studyfindings and recommendations.Figure 2.2: The Simulation Procedure
  40. 40. 312.7 A Simulation Report Includes The Following Elements:1. The System Being Simulateda. Backgroundb. System descriptionc. System design2. The Simulation Problema. Problem formulationb. Problem assumptionsc. Study objectives3. The Simulation Modela. Model structureb. Model inputsc. Model assumptions4. Simulation Resultsa. Results summaryb. Results analysis5. Study Conclusiona. Study findingb. Study recommendations6. Study Supplementsa. Drawings and graphsb. Input datac. Output datad. Experimental designe. Others

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