第六章供应链中的需求预测.ppt

第七章 DemandForecastinginaSupplyChain LearningObjectives Understandtheroleofforecastingforbothanenterpriseandasupplychain Identifythecomponentsofademandforecast Forecastdemandinasupplychaingivenhistoricaldemanddatausingtime seriesmethodologies Analyzedemandforecaststoestimateforecasterror RoleofForecastinginaSupplyChain ThebasisforallplanningdecisionsinasupplychainUsedforbothpushandpullprocessesProductionscheduling inventory aggregateplanningSalesforceallocation promotions newproductionintroductionPlant equipmentinvestment budgetaryplanningWorkforceplanning hiring layoffsAllofthesedecisionsareinterrelated CharacteristicsofForecasts ForecastsarealwaysinaccurateandshouldthusincludeboththeexpectedvalueoftheforecastandameasureofforecasterrorLong termforecastsareusuallylessaccuratethanshort termforecastsAggregateforecastsareusuallymoreaccuratethandisaggregateforecastsIngeneral thefartherupthesupplychainacompanyis thegreateristhedistortionofinformationitreceives ComponentsandMethods CompaniesmustidentifythefactorsthatinfluencefuturedemandandthenascertaintherelationshipbetweenthesefactorsandfuturedemandPastdemandLeadtimeofproductreplenishmentPlannedadvertisingormarketingeffortsPlannedpricediscountsStateoftheeconomyActionsthatcompetitorshavetaken ComponentsandMethods QualitativePrimarilysubjectiveRelyonjudgmentTimeSeriesUsehistoricaldemandonlyBestwithstabledemandCausalRelationshipbetweendemandandsomeotherfactorSimulationImitateconsumerchoicesthatgiverisetodemand ComponentsofanObservation Observeddemand O systematiccomponent S randomcomponent R Systematiccomponent expectedvalueofdemandLevel currentdeseasonalizeddemand Trend growthordeclineindemand Seasonality predictableseasonalfluctuation Randomcomponent partofforecastthatdeviatesfromsystematiccomponentForecasterror differencebetweenforecastandactualdemand BasicApproach Understandtheobjectiveofforecasting Integratedemandplanningandforecastingthroughoutthesupplychain Identifythemajorfactorsthatinfluencethedemandforecast Forecastattheappropriatelevelofaggregation Establishperformanceanderrormeasuresfortheforecast Time SeriesForecastingMethods ThreewaystocalculatethesystematiccomponentMultiplicativeS levelxtrendxseasonalfactorAdditiveS level trend seasonalfactorMixedS level trend xseasonalfactor StaticMethods TahoeSalt Table7 1 TahoeSalt Figure7 1 EstimateLevelandTrend Periodicityp 4 t 3 TahoeSalt Figure7 2 TahoeSalt Figure7 3 Alinearrelationshipexistsbetweenthedeseasonalizeddemandandtimebasedonthechangeindemandovertime EstimatingSeasonalFactors Figure7 4 EstimatingSeasonalFactors AdaptiveForecasting Theestimatesoflevel trend andseasonalityareadjustedaftereachdemandobservationEstimatesincorporateallnewdatathatareobserved AdaptiveForecasting StepsinAdaptiveForecasting InitializeComputeinitialestimatesoflevel L0 trend T0 andseasonalfactors S1 Sp ForecastForecastdemandforperiodt 1EstimateerrorComputeerrorEt 1 Ft 1 Dt 1ModifyestimatesModifytheestimatesoflevel Lt 1 trend Tt 1 andseasonalfactor St p 1 giventheerrorEt 1 MovingAverage UsedwhendemandhasnoobservabletrendorseasonalitySystematiccomponentofdemand 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 1 MovingAverageExample AsupermarkethasexperiencedweeklydemandofmilkofD1 120 D2 127 D3 114 andD4 122gallonsoverthepastfourweeksForecastdemandforPeriod5usingafour periodmovingaverageWhatistheforecasterrorifdemandinPeriod5turnsouttobe125gallons MovingAverageExample L4 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 4 122 SimpleExponentialSmoothing UsedwhendemandhasnoobservabletrendorseasonalitySystematiccomponentofdemand levelInitialestimateoflevel L0 assumedtobetheaverageofallhistoricaldata SimpleExponentialSmoothing Revisedforecastusingsmoothingconstant0 a 1 GivendataforPeriods1ton Currentforecast Thus SimpleExponentialSmoothing Supermarketdata E1 F1 D1 120 75 120 0 75 Trend CorrectedExponentialSmoothing Holt sModel AppropriatewhenthedemandisassumedtohavealevelandtrendinthesystematiccomponentofdemandbutnoseasonalitySystematiccomponentofdemand level trend Trend 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 Tt Trend 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 8 040 Trend 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 12 072 Trend andSeasonality CorrectedExponentialSmoothing Appropriatewhenthesystematiccomponentofdemandisassumedtohavealevel trend andseasonalfactorSystematiccomponent level trend xseasonalfactorFt 1 Lt Tt St 1andFt l Lt lTt St l Trend andSeasonality CorrectedExponentialSmoothing Afterobservingdemandforperiodt 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 smoothingconstantforseasonalfactor Winter sModel 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 8 000 913 Winter sModel Assumea 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 13 093 TimeSeriesModels MeasuresofForecastError Decliningalpha SelectingtheBestSmoothingConstant Figure7 5 SelectingtheBestSmoothingConstant Figure7 6 ForecastingDemandatTahoeSalt MovingaverageSimpleexponentialsmoothingTrend correctedexponentialsmoothingTrend andseasonality correctedexponentialsmoothing ForecastingDemandatTahoeSalt Figure7 7 ForecastingDemandatTahoeSalt MovingaverageL12 24 500F13 F14 F15 F16 L12 24 500s 1 25x9 719 12 148 ForecastingDemandatTahoeSalt Figure7 8 ForecastingDemandatTahoeSalt SingleexponentialsmoothingL0 22 083L12 23 490F13 F14 F15 F16 L12 23 490s 1 25x10 208 12 761 ForecastingDemandatTahoeSalt Figure7 9 ForecastingDemandatTahoeSalt Trend 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 11 045 ForecastingDemandatTahoeSalt Figure7 10 ForecastingDemandatTahoeSalt Trend 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 1 836 ForecastingDemandatTahoeSalt Table7 2 TheRoleofITinForecasting ForecastingmoduleiscoresupplychainsoftwareCanbeusedtobestdetermineforecastingmethodsforthefirmandbyproductcategoriesandmarketsRealtimeupdateshelpfirmsrespondquicklytochangesinmarketplaceFacilitatedemandplanning RiskManagement Errorsinforecastingcancausesignificantmis