第六章供应链中的需求预测

第七章DemandForecasting

inaSupplyChainLearningObjectivesUnderstandtheroleofforecastingforbothanenterpriseandasupplychain.Identifythecomponentsofademandforecast.Forecastdemandinasupplychaingivenhistoricaldemanddatausingtime-seriesmethodologies.Analyzedemandforecaststoestimateforecasterror.RoleofForecasting

inaSupplyChainThebasisforallplanningdecisionsinasupplychainUsedforbothpushandpullprocessesProductionscheduling,inventory,aggregateplanningSalesforceallocation,promotions,newproductionintroductionPlant/equipmentinvestment,budgetaryplanningWorkforceplanning,hiring,layoffsAllofthesedecisionsareinterrelatedCharacteristicsofForecastsForecastsarealwaysinaccurateandshouldthusincludeboththeexpectedvalueoftheforecastandameasureofforecasterrorLong-termforecastsareusuallylessaccuratethanshort-termforecastsAggregateforecastsareusuallymoreaccuratethandisaggregateforecastsIngeneral,thefartherupthesupplychainacompanyis,thegreateristhedistortionofinformationitreceivesComponentsandMethodsCompaniesmustidentifythefactorsthatinfluencefuturedemandandthenascertaintherelationshipbetweenthesefactorsandfuturedemandPastdemandLeadtimeofproductreplenishmentPlannedadvertisingormarketingeffortsPlannedpricediscountsStateoftheeconomyActionsthatcompetitorshavetakenComponentsandMethodsQualitativePrimarilysubjectiveRelyonjudgmentTimeSeriesUsehistoricaldemandonlyBestwithstabledemandCausalRelationshipbetweendemandandsomeotherfactorSimulationImitateconsumerchoicesthatgiverisetodemandComponentsofanObservationObserveddemand(O)= systematiccomponent(S) +randomcomponent(R)Systematiccomponent–expectedvalueofdemandLevel(currentdeseasonalizeddemand)Trend(growthordeclineindemand)Seasonality(predictableseasonalfluctuation)Randomcomponent–partofforecastthatdeviatesfromsystematiccomponentForecasterror–differencebetweenforecastandactualdemandBasicApproachUnderstandtheobjectiveofforecasting.Integratedemandplanningandforecastingthroughoutthesupplychain.Identifythemajorfactorsthatinfluencethedemandforecast.Forecastattheappropriatelevelofaggregation.Establishperformanceanderrormeasuresfortheforecast.Time-SeriesForecastingMethodsThreewaystocalculatethesystematiccomponentMultiplicativeS=levelxtrendxseasonalfactorAdditiveS=level+trend+seasonalfactorMixedS=(level+trend)xseasonalfactorStaticMethodswhereL = estimateoflevelatt=0T = estimateoftrendSt = estimateofseasonalfactorforPeriodtDt = actualdemandobservedinPeriodtFt = forecastofdemandforPeriodtTahoeSaltYearQuarterPeriod,tDemand,Dt121 8,000132 13,000143 23,000214 34,000225 10,000236 18,000247 23,000318 38,000329 12,0003310 13,0003411 32,0004112 41,000Table7-1TahoeSaltFigure7-1EstimateLevelandTrendPeriodicityp=4,t=3TahoeSaltFigure7-2TahoeSaltFigure7-3AlinearrelationshipexistsbetweenthedeseasonalizeddemandandtimebasedonthechangeindemandovertimeEstimatingSeasonalFactorsFigure7-4EstimatingSeasonalFactorsAdaptiveForecastingTheestimatesoflevel,trend,andseasonalityareadjustedaftereachdemandobservationEstimatesincorporateallnewdatathatareobservedAdaptiveForecastingwhereLt = estimateoflevelattheendofPeriodt

Tt = estimateoftrendattheendofPeriodt

St = estimateofseasonalfactorforPeriodt

Ft = forecastofdemandforPeriodt(madePeriodt–1orearlier)Dt = actualdemandobservedinPeriodt

Et = Ft–Dt=forecasterrorinPeriodtStepsinAdaptiveForecastingInitializeComputeinitialestimatesoflevel(L0),trend(T0),andseasonalfactors(S1,…,Sp)ForecastForecastdemandforperiodt+1EstimateerrorComputeerrorEt+1=Ft+1–Dt+1ModifyestimatesModifytheestimatesoflevel(Lt+1),trend(Tt+1),andseasonalfactor(St+p+1),giventheerrorEt+1MovingAverageUsedwhendemandhasnoobservabletrendorseasonalitySystematiccomponentofdemand=levelThelevelinperiodtistheaveragedemandoverthelastNperiodsLt=(Dt+Dt-1+…+Dt–N+1)/NFt+1=LtandFt+n=LtAfterobservingthedemandforperiodt+1,revisetheestimatesLt+1=(Dt+1+Dt+…+Dt-N+2)/N,Ft+2=Lt+1MovingAverageExampleAsupermarkethasexperiencedweeklydemandofmilkofD1=120,D2=127,D3=114,andD4=122gallonsoverthepastfourweeksForecastdemandforPeriod5usingafour-periodmovingaverageWhatistheforecasterrorifdemandinPeriod5turnsouttobe125gallons?MovingAverageExampleL4=(D4+D3+D2+D1)/4=(122+114+127+120)/4=120.75ForecastdemandforPeriod5F5=L4=120.75gallonsErrorifdemandinPeriod5=125gallonsE5=F5–D5=125–120.75=4.25ReviseddemandL5=(D5+D4+D3+D2)/4=(125+122+114+127)/4=122SimpleExponentialSmoothingUsedwhendemandhasnoobservabletrendorseasonalitySystematiccomponentofdemand=levelInitialestimateoflevel,L0,assumedtobetheaverageofallhistoricaldataSimpleExponentialSmoothingRevisedforecastusingsmoothingconstant0<a<1GivendataforPeriods1tonCurrentforecastThusSimpleExponentialSmoothingSupermarketdataE1=F1–D1=120.75–120=0.75Trend-CorrectedExponentialSmoothing(Holt’sModel)AppropriatewhenthedemandisassumedtohavealevelandtrendinthesystematiccomponentofdemandbutnoseasonalitySystematiccomponentofdemand=level+trendTrend-CorrectedExponentialSmoothing(Holt’sModel)ObtaininitialestimateoflevelandtrendbyrunningalinearregressionDt=at+bT0=a,L0=bInPeriodt,theforecastforfutureperiodsisFt+1=Lt+TtandFt+n=Lt+nTtRevisedestimatesforPeriodtLt+1=aDt+1+(1––a)(Lt+Tt)Tt+1=b(Lt+1–Lt)+(1–b)TtTrend-CorrectedExponentialSmoothing(Holt’sModel)MP3playerdemandD1=8,415,D2=8,732,D3=9,014,D4=9,808,D5=10,413,D6=11,961a=0.1,b=0.2UsingregressionanalysisL0=7,367andT0=673ForecastforPeriod1F1=L0+T0=7,367+673=8,040Trend-CorrectedExponentialSmoothing(Holt’sModel)RevisedestimateL1=aD1+(1––a)(L0+T0)=0.1x8,415+0.9x8,040=8,078T1=b(L1–L0)+(1–b)T0=0.2x(8,078––7,367)+0.8x673=681WithnewL1F2=L1+T1=8,078+681=8,759ContinuingF7=L6+T6=11,399+673=12,072Trend-andSeasonality-CorrectedExponentialSmoothingAppropriatewhenthesystematiccomponentofdemandisassumedtohavealevel,trend,andseasonalfactorSystematiccomponent=(level+trend)xseasonalfactorFt+1=(Lt+Tt)St+1andFt+l=(Lt+lTt)St+lTrend-andSeasonality-CorrectedExponentialSmoothingAfterobservingdemandforperiodt+1,reviseestimatesforlevel,trend,andseasonalfactorsLt+1=a(Dt+1/St+1)+(1––a)(Lt+Tt)Tt+1=b(Lt+1–Lt)+(1––b)TtSt+p+1=g(Dt+1/Lt+1)+(1––g)St+1a=smoothingconstantforlevelb=smoothingconstantfortrendg=smoothingconstantforseasonalfactorWinter’’sModelL0=18,439T0=524S1=0.47,S2=0.68,S3=1.17,S4=1.67F1=(L0+T0)S1=(18,439+524)(0.47)=8,913TheobserveddemandforPeriod1=D1=8,000ForecasterrorforPeriod1=E1=F1–D1=8,913–8,000=913Winter’’sModelAssumea=0.1,b=0.2,g=0.1;reviseestimatesforlevelandtrendforperiod1andforseasonalfactorforPeriod5L1=a(D1/S1)+(1––a)(L0+T0)=0.1x(8,000/0.47)+0.9x(18,439+524)=18,769T1=b(L1–L0)+(1––b)T0=0.2x(18,769–18,439)+0.8x524=485S5=g(D1/L1)+(1––g)S1=0.1x(8,000/18,769)+0.9x0.47=0.47F2=(L1+T1)S2=(18,769+485)0.68=13,093TimeSeriesModelsForecastingMethodApplicabilityMovingaverageNotrendorseasonalitySimpleexponentialsmoothingNotrendorseasonalityHolt’smodelTrendbutnoseasonalityWinter’smodelTrendandseasonalityMeasuresofForecastErrorDecliningalphaSelectingtheBestSmoothingConstantFigure7-5SelectingtheBestSmoothingConstantFigure7-6ForecastingDemandatTahoeSaltMovingaverageSimpleexponentialsmoothingTrend-correctedexponentialsmoothingTrend-andseasonality-correctedexponentialsmoothingForecastingDemandatTahoeSaltFigure7-7ForecastingDemandatTahoeSaltMovingaverageL12=24,500F13=F14=F15=F16=L12=24,500s=1.25x9,719=12,148ForecastingDemandatTahoeSaltFigure7-8ForecastingDemandatTahoeSaltSingleexponentialsmoothingL0=22,083L12=23,490F13=F14=F15=F16=L12=23,490s=1.25x10,208=12,761ForecastingDemandatTahoeSaltFigure7-9ForecastingDemandatTahoeSaltTrend-CorrectedExponentialSmoothingL0=12,015andT0=1,549L12=30,443andT12=1,541F13=L12+T12=30,443+1,541=31,984F14=L12+2T12=30,443+2x1,541=33,525F15=L12+3T12=30,443+3x1,541=35,066F16=L12+4T12=30,443+4x1,541=36,607s=1.25x8,836=11,045ForecastingDemandatTahoeSaltFigure7-10ForecastingDemandatTahoeSaltTrend-andSeasonality-CorrectedL0=18,439T0=524S1=0.47S2=0.68S3=1.17S4=1.67L12=24,791T12=532F13=(L12+T12)S13=(24,791+532)0.47=11,940F14=(L12+2T12)S13=(24,791+2x532)0.68=17,579F15=(L12+3T12)S13=(24,791+3x532)1.17=30,930F16=(L12+4T12)S13=(24,791+4x532)1.67=44,928s=1.25x1,469=1,836ForecastingDemandatTahoeSaltForecastingMethodMADMAPE(%)TSRangeFour-periodmovingaverage 9,719 49–1.52to2.21Simpleexponentialsmoothing 10,208 59–1.38to2.15Holt’smodel 8,836 52–2.15to2.00Winter’smodel 1,469 8–2.74to4.00Table7-2TheRoleofITinForecastingForecastingmoduleiscoresupplychainsoftwareCanbeusedtobestdetermineforecastingmethodsforthefirmandbyproductcategoriesandmarketsRealtimeupdateshelpfir