《供應鏈管理(第7版英文版)》教學課件CH7

SupplyChainManagement:Strategy,Planning,andOperationSeventhEditionChapter7DemandForecastinginaSupplyChainCopyright?2019,2016,2013PearsonEducation,Inc.AllRightsReservedLearningObjectives7.1Understandtheroleofforecastingforbothanenterpriseandasupplychain.7.2Identifythecomponentsofademandforecastandsomebasicapproachestoforecasting.7.3Forecastdemandusingtime-seriesmethodologiesgivenhistoricaldemanddatainasupplychain.7.4Analyzedemandforecaststoestimateforecasterror.7.5UseExceltobuildtime-seriesforecastingmodels.RoleofForecastinginaSupplyChainThebasisforallplanningdecisionsinasupplychainUsedforbothpushandpullprocessesProductionscheduling,inventory,aggregateplanningSalesforceallocation,promotions,newproductionintroductionPlant/equipmentinvestment,budgetaryplanningWorkforceplanning,hiring,layoffsAllofthesedecisionsareinterrelatedCharacteristicsofForecastsForecastsarealwaysinaccurateandshouldthusincludeboththeexpectedvalueoftheforecastandameasureofforecasterrorLong-termforecastsareusuallylessaccuratethanshort-termforecastsAggregateforecastsareusuallymoreaccuratethandisaggregateforecastsIngeneral,thefartherupthesupplychainacompanyis,thegreateristhedistortionofinformationitreceivesSummaryofLearningObjective1(1of2)Forecastingisakeyinputforvirtuallyeverydesignandplanningdecisionmadeinasupplychain.Itisimportanttorecognizethatallforecastsarelikelytobewrong.Thus,anestimationofforecasterrorisessentialtoeffectivelyusetheforecast.Reducingtheforecasthorizon(byreducingtheleadtimeoftheassociateddecision)andaggregationaretwoeffectiveapproachestodecreaseforecasterror.SummaryofLearningObjective1(2of2)Arelativelyrecentphenomenon,however,istocreatecollaborativeforecastsforanentiresupplychainandusetheseasthebasisfordecisions.Collaborativeforecastinggreatlyincreasestheaccuracyofforecastsandallowsthesupplychaintomaximizeitsperformance.Withoutcollaboration,supplychainstagesfartherfromdemandwilllikelyhavepoorforecaststhatwillleadtosupplychaininefficienciesandalackofresponsiveness.ComponentsandMethods(1of2)CompaniesmustidentifythefactorsthatinfluencefuturedemandandthenascertaintherelationshipbetweenthesefactorsandfuturedemandPastdemandLeadtimeofproductreplenishmentPlannedadvertisingormarketingeffortsPlannedpricediscountsStateoftheeconomyActionsthatcompetitorshavetakenComponentsandMethods(2of2)QualitativePrimarilysubjectiveRelyonjudgmentTimeSeriesUsehistoricaldemandonlyBestwithstabledemandCausalRelationshipbetweendemandandsomeotherfactorSimulationImitateconsumerchoicesthatgiverisetodemandComponentsofAnObservationObserveddemand(O)=systematiccomponent(S)

+randomcomponent(R)Systematiccomponent–expectedvalueofdemandLevel(currentdeseasonalizeddemand)Trend(growthordeclineindemand)Seasonality(predictableseasonalfluctuation)Randomcomponent–partofforecastthatdeviatesfromsystematicpartForecasterror–differencebetweenforecastandactualdemandFiveImportantPointsintheForecastingProcessUnderstandtheobjectiveofforecasting.Integratedemandplanningandforecastingthroughoutthesupplychain.Identifythemajorfactorsthatinfluencethedemandforecast.Forecastattheappropriatelevelofaggregation.Establishperformanceanderrormeasuresfortheforecast.SummaryofLearningObjective2Demandconsistsofasystematicandarandomcomponent.Thesystematiccomponentmeasurestheexpectedvalueofdemand.Therandomcomponentmeasuresfluctuationsindemandfromtheexpectedvalue.Thesystematiccomponentconsistsoflevel,trend,andseasonality.Levelmeasuresthecurrentde-seasonalizeddemand.Trendmeasuresthecurrentrateofgrowthordeclineindemand.Seasonalityindicatespredictableseasonalfluctuationsindemand.Thegoalofforecastingistoestimatethesystematiccomponentandthesize(notdirection)oftherandomcomponent(intheformofaforecasterror).Goodforecastingrequiresaclearunderstandingoftheobjectiveoftheforecastandshouldbeintegratedacrossthesupplychain.Time-SeriesForecastingMethodsThreewaystocalculatethesystematiccomponentMultiplicativeS=level×trend×seasonalfactorAdditiveS=level+trend+seasonalfactorMixedS=(level+trend)×seasonalfactorStaticMethodsSystematiccomponent=(level+trend)×seasonalfactorWhereL = estimateoflevelatt=0T = estimateoftrendSt = estimateofseasonalfactorforPeriodtDt = actualdemandobservedinPeriodtFt = forecastofdemandforPeriodtTahoeSalt(1of5)Table7-1QuarterlyDemandforTahoeSaltYearQuarterPeriod,tDemand,Dt121 8,000132 13,000143 23,000214 34,000225 10,000236 18,000247 23,000TahoeSalt(2of5)Table7-1[continued]YearQuarterPeriod,tDemand,Dt318 38,000329 12,0003310 13,0003411 32,0004112 41,000TahoeSalt(3of5)Figure7-1QuarterlyDemandatTahoeSaltDeseasonalizedemandandrunlinearregressiontoestimatelevelandtrend.Estimateseasonalfactors.EstimateLevelandTrend(1of2)Periodicityp=4,t=3EstimateLevelandTrend(2of2)TahoeSalt(4of5)Figure7-2ExcelWorkbookwithDeseasonalizedDemandforTahoeSaltTahoeSalt(5of5)Figure7-3DeseasonalizedDemandforTahoeSaltAlinearrelationshipexistsbetweenthedeseasonalizeddemandandtimebasedonthechangeindemandovertimeEstimatingSeasonalFactors(1of3)Figure7-4DeseasonalizedDemandandSeasonalFactorsforTahoeSaltEstimatingSeasonalFactors(2of3)EstimatingSeasonalFactors(3of3)AdaptiveForecasting(1of2)Theestimatesoflevel,trend,andseasonalityareupdatedaftereachdemandobservationEstimatesincorporateallnewdatathatareobservedAdaptiveForecasting(2of2)WhereLt=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=levelThelevelinperiodtistheaveragedemandoverthelastN

periodsAfterobservingthedemandforperiodt

+1,revisetheestimatesMovingAverageExample(1of2)AsupermarkethasexperiencedweeklydemandofmilkofD1=120,D2=127,D3=114,andD4=122gallonsoverthepastfourweeksForecastdemandforPeriod5usingafour-periodmovingaverageWhatistheforecasterrorifdemandinPeriod5turnsouttobe125gallons?MovingAverageExample(2of2)ForecastdemandforPeriod5 F5=L4=120.75gallonsErrorifdemandinPeriod5=125gallons E5=F5–D5=120.75–125=–4.25ReviseddemandSimpleExponentialSmoothing(1of3)UsedwhendemandhasnoobservabletrendorseasonalitySystematiccomponentofdemand=levelInitialestimateoflevel,L0,assumedtobetheaverageofallhistoricaldataSimpleExponentialSmoothing(2of3)GivendataforPeriods1tonCurrentforecastRevisedforecastusingsmoothingconstant(0<α<1)ThusSimpleExponentialSmoothing(3of3)SupermarketdataF1

=L0

=120.75E1

=F1?D1

=120.75?120=0.75L1=αD1+(1?α)L0=0.1×120+0.9×120.75=120.68Trend-CorrectedExponentialSmoothing(Holt’sModel)(1of4)AppropriatewhenthedemandisassumedtohavealevelandtrendinthesystematiccomponentofdemandbutnoseasonalitySystematiccomponentofdemand=level+trendTrend-CorrectedExponentialSmoothing(Holt’sModel)(2of4)ObtaininitialestimateoflevelandtrendbyrunningalinearregressionDt

=at+bT0=a,L0=bInPeriodt,theforecastforfutureperiodsisFt+1

=Lt+Tt

andFt+n=Lt+nTtRevisedestimatesforPeriodtTrend-CorrectedExponentialSmoothing(Holt’sModel)(3of4)SmartphoneplayerdemandD1=8,415,D2=8,732,D3=9,014,D4=9,808,D5=10,413,D6=11,961,α=0.1,β=0.2UsingregressionanalysisL0=7,367andT0=673ForecastforPeriod1F1=L0+T0=7,367+673=8,040Period1errorE1=F1–D1=8,040–8,415=–375Trend-CorrectedExponentialSmoothing(Holt’sModel)(4of4)RevisedestimateWithnewL1F2=L1+T1=8,078+681=8,759ContinuingF7=L6+T6=11,399+673=12,072Trend-andSeasonality-CorrectedExponentialSmoothing(1of2)Appropriatewhenthesystematiccomponentofdemandhasalevel,trend,andseasonalfactorSystematiccomponent=(level+trend)×seasonalfactorTrend-andSeasonality-CorrectedExponentialSmoothing(2of2)Afterobservingdemandforperiodt+1,reviseestimatesforlevel,trend,andseasonalfactorsα

=smoothingconstantforlevelβ

=smoothingconstantfortrendγ

=smoothingconstantforseasonalfactorWinter’sModel(1of3)L0=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’sModel(2of3)Assumeα

=0.1,β

=0.2,γ

=0.1;reviseestimatesforlevelandtrendforperiod1andforseasonalfactorforPeriod5Winter’sModel(3of3)ForecastdemandforPeriod2F2

=(L1+T1)S2=(18,769+485)(0.68)=13,093TimeSeriesModelsForecastingMethodApplicabilityMovingaverageNotrendorseasonalitySimpleexponentialsmoothingNotrendorseasonalityHolt’smodelTrendbutnoseasonalityWinter’smodelTrendandseasonalitySummaryofLearningObjective3Time-seriesmethodsforforecastingarecategorizedasstaticoradaptive.Instaticmethods,theestimatesofparametersarenotupdatedasnewdemandisobserved.Staticmethodsincluderegression.Inadaptivemethods,theestimatesareupdatedeachtimeanewdemandisobserved.Adaptivemethodsincludemovingaverages,simpleexponentialsmoothing,Holt’smodel,andWinter’smodel.Movingaveragesandsimpleexponentialsmoothingarebestusedwhendemanddisplaysneithertrendnorseasonality.Holt’smodelisbestwhendemanddisplaysatrendbutnoseasonality.Winter’smodelisappropriatewhendemanddisplaysbothtrendandseasonality.MeasuresofForecastError(1of2)Forecasterrorscontainvaluableinformationandmustbeanalyzedfortworeasons:ManagersuseerroranalysistodeterminewhetherthecurrentforecastingmethodispredictingthesystematiccomponentofdemandaccuratelyAllcontingencyplansmustaccountforforecasterrorMeasuresofForecastError(2of2)SummaryofLearningObjective4Forecasterrormeasurestherandomcomponentofdemand.Thismeasureisimportantbecauseitrevealshowinaccurateaforecastislikelytobeandwhatcontingenciesafirmmayhavetoplanfor.TheM

S

E,M

A

D,andM

A

P

Eareusedtoestimatethesizeofthefore-casterror.ThebiasandT

Sareusedtoestimateiftheforecastconsistentlyover-orunder-forecastsorifdemandhasdeviatedsignificantlyfromhistoricalnorms.SelectingtheBestSmoothingConstant(1of2)Figure7-5SelectingSmoothingConstantbyMinimizingM

S

ESelectingtheBestSmoothingConstant(2of2)Figure7-6SelectingSmoothingConstantbyMinimizingM

A

DForecastingDemandatTahoeSalt(1of10)MovingaverageSimpleexponentialsmoothingTrend-correctedexponentialsmoothingTrend-andseasonality-correctedexponentialsmoothingForecastingDemandatTahoeSalt(2of10)Figure7-7TahoeSaltForecastsUsingFour-PeriodMovingAverageForecastingDemandatTahoeSalt(3of10)MovingaverageL12=24,500F13=F14=F15=F16=L12=24,500σ

=1.25×9,719=12,148ForecastingDemandatTahoeSalt(4of10)Figure7-8TahoeSaltForecastsUsingSimpleExponentialSmoothingForecastingDemandatTahoeSalt(5of10)Simpleexponentialsmoothingα

=0.1L0=22,083L12=23,490F13=F14=F15=F16=L12=23,490σ=1.25×

10,208=12,761ForecastingDemandatTahoeSalt(6of10)Figure7-9Trend-CorrectedExponentialSmoothingForecastingDemandatTahoeSalt(7of10)Trend-CorrectedExponentialSmoothingL0=12,015andT0=1,549L12=30,443andT12=1,541F13=L12+T12=30,443+1,541=31,984F14=L12+2T12=30,443+2×1,541=33,525F15=L12+3T12=30,443+3×

1,541=35,066F16=L12+4T12=30,443+4×

1,541=36,607σ

=1.25×

8,836=11,045ForecastingDemandatTahoeSalt(8of10)Figure7-10Trend