Bullwhip Effect, A Brief Review

transcript

International Review of Mechanical Engineering (I. RE. lií.E.). Vol. 2. n. IJanuary 2008

Bullwhip Effect: A Brief Review

S. Kumar, A. Haleem''

Abstract - This paper deals with an extensive literature review on hulhvhip effect in (he currentscenario. Bullwhip effect has become an important area of research in .supply chain management.A liieralnre review to understand process of bullwhip effect, its sources and measure.s along withrisk associated with, have been undertaken. Procedures for reducing bullwhip effect have beenalso discussed in a .supply chain configuration for collaboration. Bullwhip effect will be animportant parameter towards the success of retail houses. In order to deal wilh the issue differentsources of bullwhip effect are being identified. These sources are demand signal processing,rationing game, order batching and price fluctuation. Technique for benchmarking this effect isalso being explored. Copyright © 2008 Praise Worthy Prize S.r.l. - All rights reserved.

Keywords: Bullwhip effect. Supply Chain Management, Demand signal Processing, Rationinggame. Order Batching

I. IntroductionIn order to achieve an efficient supply chain, it is

necessary to integrate production, distribution andinventory. There are many researches dedicated to theintegration of production and distribution however;there have been only few publications concerning theoverall integration of production, distribution andinventory. For this Deterministic models andmathematical programming models have beenconstructed.

As the configuration of supply chain becomes morecomplicated and a lot of stochastic elements have to beconsidered in the supply chain, the difficulties toformulate optimization problems in supply chainmanagement are increasing thus new approaches arerequired. Among the publications on the stochasticmodel for supply chain management the distortion ofinformation causes a phenomenon known as bullwhip(or whiplash) effect (Lee, et al., 1997). Ihe bullwhipeffect refers to the phenomenon where the fluctuation ofinventory amplifies as one goes upstream the supplychain.

The study ofthe bullwhip effect has formed a fruitfulsub-area in the supply chain management research.Abundant literature indicates that the informationtransferred in the form of orders tends to be distortedand can misguide upstream members in their inventoryand production decisions. In particular, the variance oforders may be larger than that of sales, and thedistortion tends to increase as one move upstream. Thefirst formal description ofthe bullwhip phenomenon isusually attributed to (Forester, 1961. Sterman, 1989)further demonstrates and discusses this phenomenon inthe popular 'beer game". (Jacobs., 2000) developed aninternet version ofthe 'beer game'.

Economists also notice the bullwhip effect anddescribe the reason as a result of rational actions thatmanagers take to mitigate demand uncertainties and toavoid stock out and/or to smooth production. In recentwork, intensive investigations focus on investigating therelationship between demands and orders on differentinventory policies and special demand forms. (Li Ganget al. 2005).

II. Bullwhip F.ffect

The basic phenomenon ofthe bullwhip effect in thesupply chain and also in a multistage production systemis not new. Management scientists have known it forsometime. Forrester ( 1961 ) presented the first empiricalevidence of the existence of bullwhip eiTcct from asystem dynamics viewpoint. In supply chainmanagement, Sterman ( 1989) created a game to confirmthe existence of bullwhip elTect. which is knownnowadays as the MIT beer distribution game. The gamesimulate a supply chain consisting of members, thedecision of each player solely relies on orders, showedthai variance of orders amplify as one moves up in thesupply chain conforming the existence of bullwhipeffect. The game is played on a board that portrays theproduction and distribution of beer. Each team consistsof four sectors: Retailer. Wholesaler. Distributor, andFactory (R, W, D, F) arranged in a linear distributionchain. One or two people manage each sector. Penniesstand for cases of beer. A deck of cards representscustomer demand. Each simulated week, customer'spurchase from the retailer, who ships the beer requestedout of inventory. The retailer in turn orders from thewholesaler, who ships the beer requested out of theirown inventoiy. Likewise the wholesaler orders andreceives beer from the distributor, who in turn orders

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and receives beer from the factory, where the beer isbrewed. Al each stage there are shipping delays andorder processing delays. The players' objective is tominimize total team costs. (Nienhaus Joerg, ArneZiegenbein & Christoph Duijts, 2003).

Here, the effect is caused by the irrational behaviorof the decision makers (players) or by themisperceplions of feedback. Seeing these effects.,individual education Is proposed to diminish bullwhipeffect, however Lee et a!. (1997) argued the evenrational behavior of the decision making leads tobullwhip effect. Continuing the original arguments ofbeer distribution game, Jacobs (2000) proposed to playthe game using the Internet.

As the existence of bullwhip effect In the supplychain is proven, the sources of this phenomenon need tobe identified and the counter measures to diminish thiseffect need to be uncovered in the environment ofuncertainty. Various studies have been dedicated to thisarea. Kahn 1987 proved the bullwhip effect is alsoreduces when the retailers follows optimal inventoryand either when demand in each period is positivelyserially correlated or when the backlogging of excessdemand is permitted. This study explicitly considers thedynamic aspects of production / inventory requirement.

On the other hand, the bullwhip effect multistageproduction system, formerly known as demandamplification, was also evaluated over time. Manytechniques have been developed to diminish this effect.

The problem with the bullwhip effect is not only itsessence itself, i.e., demand becomes more variablealong the supply chain, but also the fact that it makesdemand less predictable at various places. Bothincreased variability and unpredictability can causeimportant financial costs due to higher inventory levelsand reduced agility. The bullwhip effect can beundesirable for the supplier because more volatileorders from the downstream stage can be very costly. Itcan make it more difTicult for the supplier to forecastdemand, leading to higher inventory and shortage costsfor the supplier. In addition, it can lead to largefluctuations in supplier production levels from period toperiod. (Julia Miyaoka, & Warren Hausman, 2004).

111. The Sources of the Bullwhip Effect

Researchers describe major four sources of thebullwhip effect from a managerial perspective, i.e.demand signal processing, the rationing game, orderbatching, and price variations (Lee et aL 1997, Cooper,M.C. et al, 1997, Dhahri Issan and Chabchoub Habib,2OO6.Chen et al, 2000, Xicolongzhang. 2004.GoranSvenson, 2003,Car!son Christer and Tuller Robert,1999-2000). Lee et al. (1997) identity four operationalcauses of the problem. Including errors in demandsignal processing, inventory rationing, order batching,and price variations, and recommend a number ofoperational strategies for dampening the cftcct. Thesecond category focuses on the behavioral causes of the

effect. Behavioral causes are usually studied in thelaboratory because it provides ways to eliminateoperational causes, which is impossible to do in thefield. The existence of the behavioral causes of theBullwhip effect has been demonstrated in a variety oflaboratory settings and by many different researchers,(Croson and Donohue 2003 and Croson et al. 2005).There is a large and growing literature on the Bullwhipeffect and its impact on supply cbain performance. Themagnification in demand is usually measured in termsof a change in the variance of orders placed at eachsupply chain level.

///. /. Demand Foreca.st Updating

Demand forecasts appear to be a major source of thebullwhip effect. The parties of the supply chain buildtheir forecasts on the historical demand patterns of theirimmediate customers. In this way, only the retailersbuild on the actual demand patterns of the customers,the other parties adjust to (unmotivated) fluctuations inthe ordering policies of those preceding them in thesupply chain. (Christer Carlsson. and Robert Fuller,1999-2000) Companies base their orders on forecasts,which are themselves based on their incoming orderswhile such forecasts are not perfectly accurate.Therefore, companies order more or less than what theyreally require to fulfill their demand. In other words,forecasting errors ampiily the variability of orders. Asolution proposed to this cause is information sharing:eacli client provides more complete intbrmation to itssupplier in order to allow the supplier to improve itsforecasting. Information sharing is already part ofindustry practices, such as VMI {Vendor-ManagedInventory), CRP (Continuous Replenishment Program),etc. (Moyaux Thierry, Brahim Chaib-draa, & SophieD'Amours. 2006).

111.2. Order Batching

Order batching refers to a company ordering a largequantity of a product in one week and not ordering anyfor many weeks. The main reason for a companyordering in batches is that it may prove to be less costlybecause of transportation costs or the company willreceive a discount if a large quantity is ordered in oneperiod. (Donnell T. O., L. Maguire, R. Mcivor and P.Humphreys. 2006).

/// . i . Price Fluctuation

The producers initiate and control the pricefluctu:itions for various reasons. Customers arc driven tobuy in larger quantities by attractive offers on quantitydiscounts, price discounts, coupons or rebates. Theirbehavior is quite rational. The problem introduced bythis behavior is that buying patterns will not reflectconsumption patterns anymore, customers buy inquantities which do not reflect their actual needs. This

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will amplify the bullwhip effect. The consequences arethat producers (rightfully) suffer: manufacturing is onovertime during campaigns, premium transportationrates are paid during peak seasons and products sufferdamages in overflowing storage spaces. (ChristerCarlsson, and Robert Full er, 1999-2000).

///. 4. Rationing and Shortage Gaming

The rationing and shortage gaming occurs whendemand exceeds supply. Ifthe manufacturers once havemet shortages with a rationing of customer deliveries,the customers will start to exaggerate their real needswhen there is a fear that supply will not cover demand.The bullwhip effect will ampliiy even further ifcustomers are allowed to cancel orders when their realdemand is satisfied. The gaming leaves littleinformation on real demand and will confuse thedemand patterns of customers. (Christer Carlsson, andRobert Fuller, 1999-2000). Also rationing schemes thatallocate limited production in proportion to the ordersplaced by retailers lead to a magnification of theBullwhip effect (Chopra and Meindl 2004). Rationingand short gaming can cause major problems, as whendemand is not as high, the orders will stop, andcancellations will begin to arise. (Donnell T.O', L.Maguire, R. Mcivor and P. Humphreys, 2006).

m.5. Misperception of Feedback

Sterman (1989) has noted that players in the beergame place orders in a non-optimal way because theydo not understand the whole dynamics in their supplychain. For example, they do not correctly interpret theirincoming orders, and in consequence, smooth theirorders when they should order more, because they donot understand that market consumption has increased.

111.6. Local Optimization without Global Vision

Several authors [Kahn. 1987. Naish. 1994] havenoted that companies maximize iheir own profit withouttaking into account the effect of their decisions on therest ofthe supply chain. It has been formally proven thatsome of these policies induce the Bullwhip efïect [Chenet al., 2000, Simchi-Levi et al.. 2006].

///. 7. Company Processes

Taylor and his colleagues [Taylor, 1999] propose twocauses of the bullwhip effect: variability in machinereliability and output, and variability in processcapability and subsequent product quality. In these twocauses, which ate summarized as Company processes.

Other factors leading to hullwhip effect are:insufficient market data, deficient information sharing,lack of cooperation and coordination, other uncertaintiesetc (Goran Svenson, 2003).

TABLEISOURCES OF BuLt.wnip EFFECT

Sources Description

Demand signalprocessing

RationingGame

Order Batching

PriceFluctuation

Misperceptionof" feedback

Localuplimizaliuiiwithuul globalvision

CompanyProcesses

Other ractors

•Demand fluctuation•Demand uncertainty•Updating Forecast•Use of unavailability forecastingtools.•Unavailability and Uncertaintyrelated to supplier,

•insufficient Capacity ofmanufacturing,•Pearofshortage,•Rational decision making process

•Order Cost• Batch size• Order cycle• Différent order policies ofdifferent members of supply chain.

• Raw material price fluctuation.• Final product price tlucluation.

Misinterpretation of MarketInlbniiation

Maxim Í7ation of organizationprolit without considenng rest ofthe supply chain.

Variability in machinereliability and output

Variability m processcapability and subsequentproduct qiialily

Goveminent policies,lack of co-ordination and co-operationkick of inibrmiition sliarmji dc

In nutshell, sources of bullwhip effect have beendivided into two categories (Wu, Diana Yan; Katok,Elena, 2005):• Categoiy I includes operational causes of the

problem including errors in demand signalprocessing, inventory rationing, order batching andprice variations.

• Category 11 has a focus on behavioral causes.It is a fact that these causes of the bullwhip effect

may be hard to tiionitor, and even harder to control inthe industi-y. We should also be aware of tbe fact thatthese causes may interact, and act in concert, and theresulting combined effects are not clearly understood.neither in theory nor in practice. It is probably the casethat the causes depend on the supply chain'sinfrastructure and the strategies used hy the variousactors.

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IV. Identifying and Measuring TheBnllwhip Effect

To measure the bullwhip efïect. one of theapproaches is to measure the amount of volatility aproduct contributes to the supply chain: a product facesvolatile demand from its customers but then imposes ¡tsown volatility on its suppliers, and if that volatility isgreater than lhe volatility of its demand, we say thebullwhip effect is present. To be specific, we say aproduct contributes to the bullwhip effect if the varianceof its production is greater than the variance of itsdemand, i.e., if its amplification ratio is greater thanone:

Amplification ratio =V [Production] / V [Demand]

where V [ ] is the variance operator.Our second approach to identify the bultwhip effect

is to compare demand volatility at different levels of thesupply chain. On lhe assumption that the bullwhip effectis present, we should observe that the variance of retaildemand is less than the variance of wholesale demand.which in turn is less than the variance of manufacturingdemand. Note we do not construct explicit linear supplychains, but "supply chains" are more like "supplywebs"., direct comparisons are problematic. As a result,we are more comfortable with an assignment of aproduct to one of three levels of the supply chain (retail,wholesale or manufacturing) and then a comparison ofdemand volatility at these ¡hree difTerent levels.(CachónGerald P,2005)

According to Lee et al. (1997b), the term "bullwhipeffect" has been first used by Procter and Gamble, whenthey experienced extensive demand amplifications fortheir diaper product Tampers". Lee et al. (1997a.1997b) describe the bullwhip effect as the result ofinformation distortion in a supply chain, wherecompanies upstream do not have information on actualconsumer demand. Consequently, their orderingdecisions are based on the incoming orders from thenext downstream company. A measure for this bullwhipeffect is the variability of upstream demand ± measuredby the standard deviation of demand relative to meandemand ± divided by the variability of downstreamdemand. A value for this measure greater than oneindicates amplified order variability. (Fransoo Jan C.and Woultcr Marc J.F.,200Ü).

V. Risks Associated With Bullwhip Effect

Now we call to examine how bullwhip impinges onsupply chain risk. Risk associated with bullwhip effectis concerned with financial loss. Unnecessaiy lossesshould be taken care of arising in volatile and uncertainsituation. Following adverse effects on supply chainperformance have been identified from literature(Carlsson. C and Fuller, R., 1999-2000. Dhahri Issamand Chabchaub 1 labib, 2006):

• Demand fluctuations• Excessive Inventory investments through out the

supply chain• Poor customer services due to shortages• Lost revenues• Sub standard productivity of capital invested in

operations• Capacity increase plans• Sub optimal transportation schemes• Missed production schedules due to false demand.

VI. Procednres for Reducing BullwhipEffect

Buliwhip effect can lead to serious consequences andcan affect the performance of supply chain adversely.Bullwhip effect is experienced by many industries likecomputer memory chips (Fisher. 1994). grocery (Fulleret ai, 1993), gasoline (Sterman. 2000). In order toreduce bullwhip effect it is relevant here to emphasizeupon reduced lead times, revision of reorderingproducers, restricting price fluctuations and integrationof planning and performance measurement (Lee andBilingtoii 1992. Towill 1996. Fransco and Wouters,2000, Goran Sevenson. 2003).

In a supply chain, a huge amount of information isbeing interchanged among its members containingbuliwhip effect using effective information sharing. Ingeneral, vertical information sharing, e.g., transmissionof point-of-sales data between a retailer and amanufacturer, has two effects, the "direct effect" on thepayoffs between the parties engaged in informationsharing, and the "indirect effect" of information sharingon olher competing ilrms. For example, knowing thatthe manufacturer receives some information from aretailer, other retailers may respond to the fact bychanging their strategies, and such reaction may causeadditional gains or losses to the parties directly engagedin information sharing.

.lohnson (1998) pointed out that there arc four waysto eliminate bullwhip effect: sharing information in theform of point-of-sale data, trying to develop channelalignment by exchanging decision rights, reducing leadtime, and eliminating forecast updating. Chen et a!,demonstrated that the bullwhip effect could be reducedpartially by centralizing demand information.Information sharing, particularly sharing information oninventory levels, has been ciied as a possiblecountermeasure to the bullwhip effect. From anoperational perspective, inventory information can beused to update demand forecasts and lessen the impactof demand-signaling errors and delays. In fact, suchinformation may even be helpful in supply chains wherethe demand distribution is known to all supply chainmembers and each member makes ordering decisionsbased on an order-up-to policy. From a behavioralperspective, inventoiy information can also provide ameans to affect behavior and. as a result, increase trust(or at least understanding) throughout the supply chain.

s. Kumar, A. Haleem

In an experimental setting based on the popular beerdistribution game, Croson and Donohue (2003) showedthat human decision makers in a four-member, serialsupply chain continued to exhibit bullwhip behavior intheir ordering patterns even when all the operationalcauses of the bullwhip were removed. They furtherfound that sharing everyone's inventory informationthroughout the entire supply chain significantlydampened order oscillations, although it did noteliminate the effect completely (Croson, R. andDonohue, K., 2005). Effective information sharing toreduce bullwhip effect is relevant to Indian FMCGsector also (Kumar Sanjay, Haleem Abid, 2007 a, b). Ithas been suggested that the value of information sharingcould be more significant in situations where there ismuch uncertainty concerning future demand, such asproduct introductions or promotions (Cachón andFisher, 2000). (I.ehtonen, Smaros & Holmström, 2004)

Advances in information system technology have hada huge impact on the evolution of supply chainmanagement. As a result of such technoiogicaladvances, supply chain partners can now work in tightcoordination to optimize the chain-wide performance,and the realized return may be shared among thepartners. A basic enabler for the coordination amongmembers of supply chain is information sharing, whichhas been greatly facilitated by the advances ininformation technology (Lee, H.L.. Padmanabhan, V.,and Whang, S., 2004) However, information sharingamong the members may pose a threat to theconfidentiality of the data.

Buliwhip effect may be eliminated by sharedknowledge with suppliers and customers, cooperationamong supply chain members, applications of internetenabled technology (Baljiko, 1999, Goran Sevenson,2003). Modeling of enablers for IT enablement ofsupply chains by using Interpretive structural modelingtechnique has been done by Jharkharia and Shankar,(2004).

The Bullwhip Effect can be diminished by reducingthe variability in the customer demand process and wecan reduce the variability of customer demand throughthe use of EDLP strategy. In EDLP, a product is offeredat a single consistent low price. By eliminating pricepromotions a retailer can eliminate many of thedramatic shifts in demand that occur along withpromotions that means EDLP strategy can lead to morestable or less variable customer demand patterns(Simchi-Levi David et al., 2006). Information lead timecan be reduced to the use of EDI as the lead timestypically include two components: Order Lead Timeand Information Lead Time (Simchi-Levi David et al..2006). Researchers have done lot of work on theutilization of EDI at the various stages of supply chainresulting into a substantial reduction of the BullwhipEffect on inventories. One of the barrier in using EDIhas always been the cost of implementation (MachucaJ.A.D. and Barajas R.P., 2004). The Bullwhip Elïectcan be eliminated by engaging in any of a number of

strategic partnerships. VMI is one of such option inwhich manufacturer manages the inventory of itsproduct at the retailer outlet. Therefore, in VMI themanufacturer does not rely on the order placed by theretailer and thus avoiding the Bullwhip Effect entirely(Simchi-Levi David et al., 2006). VMI has becomemore popular in the grocery sector in last fifteen yearsdue to success of retailers such as Wal-Mart (Andel,1996; Stalk et al., 1992; Disney S.M. and Towill D.R.,2003). There is substantial reduction in Bullwhip effecttypically halving the effect due to Vend or-ManagedInventory (VMl) (Disney and Towill, 2003). In VMI,the vendor is allowed to access customer salesinformation, inventory and demand information. Thevendor monitors the customer's inventory level and isresponsible and authorized to replenish the customer'sstock according to jointly agreed inventory controlprinciples and objectives. Vendor ManagedReplenishment (VMR) is often referred to as VendorManaged Inventory (VMI). (Holweg M. et al, 2005).Here vendor is not involved in planning process. Apartfrom collaboration in inventory replenishment andforecasting, there are more dimensions that one cancollaborate on such as promotions, new productintroduction etc. In vendor-managed replenishment, thetask of generating replenishment order is given to thesupplier who then takes the responsibility formaintaining the retailers inventory and service levels.

Computational Intelligence (Cl) techniques presentan intelligent approach to classical managementtechniques. Benefits out of using Cl techniques are(Donnellet. al., 2006):• More computationally powerful algorithms• Capability of handling complex situations• Better generalization and easy modification possible• More robust

Three main Cl techniques are: Fuzzy Logic,Artificial Neural Network and Genetic Algorithms.

VII. Conclusion

This paper attempts to classify the sources ofbullwhip effect such as demand signal processing;rationing game, order batching and price variation. Itfurther discusses identification and measuring thebullwhip effect in the supply chain. We describe the riskof bullwhip effect such as the possibility of bringingabout misfortune or loss. Here we conclude thatinformation sharing can reduce bullwhip effect andimplementing vendor managed itwentory andComputational Intelligence approaches.

References[I] Andel T (]'J9ft) 'Manajie liivenlorv' Ouu Inrurmaiion'.

'I ransport and Distribution, Vol 37. No 5. pp 54-58.[2] BaljkiiJ.L.. 1999. tixperts Warns Ol'Biiilwhip Bllect. hicclronic

Buyers Notice No 1170, 26'" July.|3] Cachón Cierald P, 2005. In Search 01" The tiullwhip Llïect,

Supply Chain Managemem An Inlcrnationül Journal, pp 7-y

s. Kumar. A. Haleem

[4] Cachou. G and Fislier. M. (2000) Supply chain inventorymanagement and the value of shared information. ManagementScience. Vol. 46, No. 8, p. 1032-1048.

15] Carlson Chrisler and Kuller Robert, 1999-2000, Soft Computing& Bullwhip Effect Economics & Complexity, Vol 2 , No 3,winter, pp-1-26.

J6| Chen ct al. 2000, Quaniifying The Bullwhip EITect In A SupplyChain: The Impact Of Forecasting, Lead fîmes AndInfonnation. Management Science, vol. 46 No. 3. pp 436443.

[7] Chopra, S. and Meindl, P , 2004, Supply chain Management!Strategy, Planning and Operation 2nd ed . Prentice-Ha!I:tinglewood ClitTs. NJ.

[8] Cooper, M C . D M Lambert. J D Pagh. 1997. Supply ChainManagement' More Than A New Name Kor Logistics,Inlemational Journal Of Logistics, International Journal OfLogistics Management 8 (I)

[9] Croson R. tJonoliuc K. 2003 The impact of POS data sharing onSupply chain management' an experimental study Productionand Operations Management 12: l - l !.

[lO] Croson. Rachel and Donohue. Karen Fall 2005 "Upstreamversus downstream information and its impact on ihe biiilwhipelTect". I'l' System Dynamics Review Volume 21 Number 3, Pg249-2«)

| l l | Dhahri Issam & Chabchoub Habib. 2006. Nonlinear GoalProgramming Models Quanlifymg The liullwhip VATcci InSupply Cham Based On ARIMA Parameters. European JournalOf Operational Research, xxx. unpublished, p-l I

[12] Donnell, T O . Maguire L.. Mcivor R.. Humphreys P., 2006.Minimi/ing The Bullwhip LiVect In A Supply Chain UsingOcnetic AlgorilhiTis. International Journal Of ProductionResearch, vol 44. no. 8, 1523-1543

[13| Disney S M.. Towill DR , 2003. On The Bullwhip EfTecI AndInventory Variance Produced By An Ordering Policy. OMEGA:The Internaticmal Journal Of Management Sciences 31(3). 157-167,

[14| Fisher. M.. 1994, National Bicycle. The Wharton Schooi Case,Philadelphia, PA

[15] Forester J.. 1961. Industrial Dynamics, MIT Press. Cambridge.MA

[16] Francsoo Jan C and Woulter Marc J F . 2000. Measuring TheBullwhip f'̂ fTecl In Sypply Chain, Supply Chain ManagementAn International Journal. pp79

[17] Fuller. J B . O' Conor, J , Rawlinson, R, 1993, TailoredLogistics: The Nest Advantage, Harvard Business Review 71(3). 87-98

118] Goran Sevenson, 2003. The Dullwhip Etiecl In IntraOrganizational Ilchelons, Iniemalional Journal of PhysicalDistribution And Logistics ManagemenI, vo! 33. no 2. pp 103-132.

[19] Holwag M et al, 2005, Supply Chain Collaboration MakingSense of The Strategie Continuum, European ManagementJournal, vol 23. No. 2. pp 170-181.

[20| Jacobs, F R. (2000). "Playing The Distribution Game over TheInternet, Production And Operation Management. Vol 9, No 1.pp31-39

[21] Jharkharia Sanjay and Shankar Ravi, 2004. IT Enablemcnt ofSupply Cham The Modeling linaWers. IJPPM, Voi 53 No 8. pp7OO-7'l2.

[22] Johnson, ME, (1998) Giving 'me what they want. Manage Rev87(10): 62-67

[23| Kahn. J . 1987. Inventories And The Volatility of Production.America Economic Review 77.667-679.

[24] Kumar Saiiiay. Haleem Abid. 2O(t7 a. "EUcctive InformationSharing for Reducing Bullwhip Effect: A Simulation basedStudy, Interinitional Journal ol" Compuler Science and SystemAnalysis. Vol l .No 2, PP 101-118

[25] Kumar Sanjay, llnlccm Abid. 2007 b. "Mixleling BullwhipEtTecl and Understanding ihe role of Information Sharing".G L O G i r r 07' an International Contcrcnce on Flexibility withBusiness Excellence in the Knowledge Ixonomy"

[26] Lee I! and Bilington C. 1942. Managing Supply ChamInventories Pitfalls And Opportunities, Sloan ManagementReview, vo! 33. No, 3, pp 65-73

[27] Lee H.I- Padmanabhan P. Whang S, 1997 a. InformationDistortion In A Supply Chain. The Bullwhip EITect ManagementScience 43, 543-558

128] Lee H.L. Padmanabhan P, Whang S, 1997 b. The BuüwhipEffect In A Supply Chain, Sloan Management Review 38 (3),93-102.

[30] Lehtonen. Johanna Smâros & Jan liolmström. "The Rflcct ofl.)enianij Visibility m Product Introductions". !'P 16th AnnualNOFOMA Conference, June 7-8. 20()4. Linköpmg. Sweden

[31) Li Gang. Wang Shouyang. Yan Hong and Yu Gang. 2005,Information Transformation In Supply Chain: A SimulationStudy. Computers And Operations Research, pp 707-710.

[32] Machuca J A D and Barajas R.P (2004) 'The Impact ofF.lectronic Data Interchange on Reducing Bullwhip Effect andSupply Chain Inventory Cost'. Transportation Research. Part E40. pp 209-228.

[34[ Moyaux. T . Chaib-draa. B . D'Amours. S.. 2006. "InformationSharing as a Coordmaiion Mcchantsm for Reducing theBullwhip effect in a Supply chain"

[35| Naish, H F. (1994). Production smoothing in the linearquadratic inventory model The Economic Journal, 104(425):K64-875.

¡36] Nienhaus J. et al. al,: Trends in Supply chain Management - Asurvey among more than 200European Companies. Centre forEnterprise Sciences (BWI), Swiss Fédérai Institute ofTechnology (ETIi) Zurich. Switzerland. 2003.

[37] Simchi-Levi, D ; Kaminsky. P . Simchi-Levi, 2006. E:Designing and Managing the Supply chain. Concepts, Strategies,and Case Studies. Irwin Mctiraw-ilill

[38] Stalk C... i-vans P. Shulman I.E. (1992) "Competing onCapabilities' The New Rules ot Corporate Strategy', Harvardßii.iines.\ Review, Vo\ 7t). No 2. pp 57-70.

[39[ Sterman. J . 2(K)0. Business Dynamics System Thinking andModeimg For A Complex World. Me Graw I lili

[40] Sterman. J.. 1989. Misperception s ol" Feedback in Dynamic

Decision Making Organizational Behavior and HumanDecision Processes 43(3), 301-335

[411 Taylor. D 1999. Measurement and analysis of demandamplification across the Supply chain The International Journalof Logistics Management, H)(2) 55-70

[42] Towiil D. 1946, Compression And The Supply Chain- A GuidedTerm. Supply Cham Management- An International Journal, voll .no I pp 15-27

]43] Wu. Diana Yan. Katok. Elena. 2005, Learning Communicationand The Bullwhip Etïeet. Journal of Operations Management.unpublished, pp-12

[44] Xicolongchang. 2004, F.tlects of Forecasling Methods onBullwhip EtVcct. International Journal of Production Economics,88 pp 15-27.

Authors' information'Department of Mcciianical and Automation Engineering.Guru Premsukh Memorial College of Engineering,Delhi- 110036. INDIA.Tel. o t r9 l - l 1-27207048-50E-mail, skbhardwai I971@vaiioo,com

'Coordinator MBA (Evening)Faculty ot" Engineering and Technology,Jamia Millia Islamia,New Delhi 110 025. INDIAlei O t r 9 l - | 1-26981268E-mail, haleem abidfi^emailcom

5. Kumar, A. Haleem

••< ¡¡ly Kumar is Head, Department of• hanical and Automation Engineering at

iiiMu Premsukh Memorial College ofF-ngineering, an All India Council of TechnicalEducation approved institution of New Delhi,Inciia. He is pursuing his research in the area ofiiiillwhip Effect He has done Masters in¡".ngineering Trom Delhi College of

Engineering, Delhi and Bachelor of Engineering (Hons ) fromNational Institute of Technology. Kurukshetra. He has IndustrialExperience in the development planning He has presented papers invarious National and international Conferetices.

Dr, Abid Haleem is a Professor of MechanicalEngineering and coordinator of MBA (evening)programme at Faculty of Engineering andTechnology, Jamia Millia Islainia (a centraluniversity hy an act of parliament). New Delhi,liulia Professor Haleem obtained his PhD fromMl (Delhi) in the area of'Policy Planning' He

I completed his graduation and post graduationdegrees in 'Mechanical Engineering" and

'Industrial Engineering' respectively. He was awarded Gold medal atpost graduation level. Professor Haleem has more than sixty fiveresearch papers to his credit, published in national and internationaljournals like Social Science Journal (USA), Eorlune Journal ofInternational Management, International .loumal of Economics and

Business, Indian Journal of Business and Economics, Global Journalof Flexible Systems Management Vision, Productivity. Pranjana etc.He has authored a book titled "Innovation, Flexibility and TechnologyTransfer", published by Tata McGraw Hill, India. He has experiencein industrial automation products of Rockwell Automation atMilwaukee, Wisconsin, USA and other international as well asnational organizations. Some of them are Department of InternationalDevelopment, liducational Consultants, Govemmeni of Orissa,ESCORTS Group, AICTE etc. He carries rich industrial experienceand has completed AICTE projects worth one million Indian rupees inthe field of industrial engineering. He has been the Secretary to GlobalInstitute o!' Flexible Systems Management. Presently, he is thePresident, Global School of Innovation and Technology Management.He is also an associate editor for Glogitl- an international journalenlisted with ProQuest and EBSCO He has extensive experience incoordinating different academic programs in the field of managementand technology.

Bullwhip Effect, A Brief Review

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