Bullwhip Effect·Refers to the phenomenon where order variability increases as theordersmoveupstreaminthesupplychain·Bullwhipcosts:settingupand shuttingdownmachines,: idling and overtime in the workload,. hiring and firing of the workforce,:excessiveupstreaminventory,.difficultyinforecastingandscheduling,:systemsnervousness,and:poorsupplier/customerrelationships
Bullwhip Effect • Refers to the phenomenon where order variability increases as the orders move up stream in the supply chain • Bullwhip costs • setting up and shutting down machines, • idling and overtime in the workload, • hiring and firing of the workforce, • excessive upstream inventory, • difficulty in forecasting and scheduling, • systems nervousness, and • poor supplier/customer relationships •
.Causes.Irrational-overlook the inventory-on-order (the orders placed but not yetreceived),:Rational (Leeetal.,1997)demandsignalprocessing·batchordering,.pricefluctuation,and·shortagegaming
• Causes • Irrational – overlook the inventory-on-order (the orders placed but not yet received), • Rational (Lee et al., 1997) • demand signal processing, • batch ordering, • price fluctuation, and • shortage gaming
·Remedies:Informationsharing:Vendormanaged inventory (VMI):Lead timereduction:Centralized inventoryplanning·Capacityflexibility
• Remedies • Information sharing • Vendor managed inventory (VMI) • Lead time reduction • Centralized inventory planning • Capacity flexibility
·Methodologies used to studybullwhip effect:Empirical:Experimental:Analytical:Simulation
• Methodologies used to study bullwhip effect • Empirical • Experimental • Analytical • Simulation
: An empirical study by Shan et al. (2014):Withinafirm:Quarterlydata onover1200 companies listed onthe Shanghai and Shenzhenstockexchangesfrom2002to2009:DatapreprocessingBullwhipratio=Volatilityofproductionovervolatilityof demand:Independent Variables include inventorydays, seasonalityratio, AR(1)coefficientfor demand shock,profit margin, firm size,and days accountspayable·Regressionmodel
• An empirical study by Shan et al. (2014) • Within a firm • Quarterly data on over 1200 companies listed on the Shanghai and Shenzhen stock exchanges from 2002 to 2009 • Data preprocessing • Bullwhip ratio = Volatility of production over volatility of demand • Independent Variables include inventory days, seasonality ratio, AR(1) coefficient for demand shock, profit margin, firm size, and days accounts payable • Regression model
·Elements of bullwhip models:Supplychainnetworkstructure:Demand model:Forecastingmethod:Leadtime·Replenishmentpolicy.Measures of bullwhip effect
• Elements of bullwhip models • Supply chain network structure • Demand model • Forecasting method • Lead time • Replenishment policy • Measures of bullwhip effect
·Bullwhip model 1-Chen et al. (2000): 1-1 serial: AR(1) demand:Simplemoving averageforecasting:Fixedleadtime.Orderuptoreplenishmentpolicy:Bullwhipratio=Var(order)/Var(demand)
• Bullwhip model 1 – Chen et al. (2000) • 1-1 serial • AR(1) demand • Simple moving average forecasting • Fixed lead time • Order up to replenishment policy • Bullwhip ratio = Var(order)/Var(demand)
Majorfindings:The difference between thevariability in the centralized and decentralizedsupplychainsincreasesaswemoveupthesupplychain.·Centralizing customerdemand information can significantly reducethebullwhip..The bullwhip effect can be reduced,but not completelyeliminated, bycentralizingdemandinformation
• Major findings • The difference between the variability in the centralized and decentralized supply chains increases as we move up the supply chain. • Centralizing customer demand information can significantly reduce the bullwhip. • The bullwhip effect can be reduced, but not completely eliminated, by centralizing demand information
· Bullwhip model 2 - Liao and Chang (2010):3-echelonserieschain:M3competitiondemanddatawithunknownmodels:5forecastingmethodswithAcO,tunedparameters:3levelsofconstantleadtimes:2inventorypolicies:(s,S)and(r,Q)withAcOtunedparameters.Total supplychaininventorycost (asameasureofbullwhipeffect)
• Bullwhip model 2 - Liao and Chang (2010) • 3-echelon series chain • M3 competition demand data with unknown models • 5 forecasting methods with ACOR tuned parameters • 3 levels of constant lead times • 2 inventory policies: (s, S) and (r, Q) with ACOR tuned parameters • Total supply chain inventory cost (as a measure of bullwhip effect)
·MajorfindingsThedampedPegelforecastingmethodisthebestintermsofpredictionerrorsbecauseitoutperformsothersinthreeoffivemeasures,followedbythesimpleexponential smoothingthatwins one of theremainingtwoandtiesthedampedPegelinone;.The supplychain inventory cost increases with increasing leadtimeand echelon levelofthesupplychainwhenthe(s,S)policyisused,butnotthe(r,Q)policy;? The (r, Q) inventory policy generaliy incurs lower supply chain inventory cost than the(s, s) policy;:Sharingdemand information reduces inventory costand thereduction ishigherfor(s, S) than for (r, Q);.Thebest demand forecasting method forminimizinginventory cost varieswiththeinventorypolicy used and leadtime;andThecorrelationbetweenforecasting errorsand inventorycosts is eithernegligibleorminimal
• Major findings • The damped Pegel forecasting method is the best in terms of prediction errors because it outperforms others in three of five measures, followed by the simple exponential smoothing that wins one of the remaining two and ties the damped Pegel in one; • The supply chain inventory cost increases with increasing lead time and echelon level of the supply chain when the (s, S) policy is used, but not the (r, Q) policy; • The (r, Q) inventory policy generally incurs lower supply chain inventory cost than the (s, S) policy; • Sharing demand information reduces inventory cost and the reduction is higher for (s, S) than for (r, Q); • The best demand forecasting method for minimizing inventory cost varies with the inventory policy used and lead time; and • The correlation between forecasting errors and inventory costs is either negligible or minimal