demandplanning(供应链高级计划排程课件).ppt

,Demandplanning,2016-4,MichaelWagner,Introduction,Whyisdemandplanningnecessary?LargebenefitswhichareachievedbySupplyChainManagementareaccreditedtothereductionofinventories,esp.tothedecrementofsafetystocksWhatisthepurposeofdemandplanning?ThepurposeofDemandPlanningistoimprovedecisionsaffectingdemandaccuracyandthecalculationofbufferorsafetystockstoreachapredefinedservicelevel.Whatismainobstacles?processuncertainty(e.g.unreliableproductionprocesses,fluctuatinglead-timesetc.)demanduncertainty(differenceinplannedorestimateddemandandactualsales).,CONTENTS,Firstpart,Secondpart,Thirdpart,Lastpart,Ademandplanningframework,Statisticalforecastingtechniques,Incorporationofjudgmentalfactors,Additionalfeatures,Ademandplanningframework,Ademandplanningframework,productdimension:productproductgroupproductfamilyproductline;geographicdimension:customersalesregionDCregion/location;timedimension:differentbucketsize(daysweeksyears)andhorizon.,CONTENTS,Firstpart,Secondpart,Thirdpart,Lastpart,Ademandplanningframework,Statisticalforecastingtechniques,Incorporationofjudgmentalfactors,Additionalfeatures,Statisticalforecastingtechniques,time-series-analysis,causalmodels,Twobasicapproaches,Statisticalforecastingtechniques,Statisticalforecastingtechniques,time-series-analysis,causalmodels,Twobasicapproaches,Statisticalforecastingtechniques,1MovingAverageandSmoothingMethods,2RegressionAnalysis,3ARIMA/Box-Jenkins-method,Threemostfrequentlyusedforecastingmethods,Statisticalforecastingtechniques,1MovingAverage,Theparameterestimatefortheleveliscalculatedbyaveragingthepastndemandobservations.,SmoothingMethods,Theneedtocutthetime-seriesisavoidedbytheexponentialsmoothingmethod,becauseitassignsdifferentweightstoallobserveddemanddataandincorporatesthemintotheforecast,Statisticalforecastingtechniques,2RegressionAnalysis,Wheresignificantinfluenceofsomeknownfactorsispresent,itseemstobestraightforwardtousecausalmodelsintheforecastingprocess.Regressionanalysisisthestandardmethodforestimationofparametervaluesincausalmodels.,Statisticalforecastingtechniques,2RegressionAnalysis,Statisticalforecastingtechniques,3ARIMA/Box-Jenkins-method,theautoregressiveintegratedmovingaverage(ARIMA)modelsexplicitlyconsiderdependentdemands.Therefore,thesemethodsdontmakeassumptionsabouttheunderlyingdemandpattern,butcomposeafunctionfromdifferentbuilding-blockswhichfitstheobserveddatabest.,CONTENTS,Firstpart,Secondpart,Thirdpart,Lastpart,Ademandplanningframework,Statisticalforecastingtechniques,Incorporationofjudgmentalfactors,Additionalfeatures,Incorporationofjudgmentalfactors,Howisthesoftwareabletomakebetterforecaststhanahumanplannerwithyearsofexperienceindemandplanning?,Inthefollowingwedescribesomemethodsonhowtointegratestatisticalforecastingandstructuredjudgment.,CONTENTS,Firstpart,Secondpart,Thirdpart,Lastpart,Ademandplanningframework,Statisticalforecastingtechniques,Incorporationofjudgmentalfactors,Additionalfeatures,Additionalfeatures,SporadicDemand,LostSalesvs.Backorders,MeasuringForecastAccuracyandTriggeringExceptions,ModelSelectionandParameterEstimation,Life-Cycle-ManagementandPhase-in/Phase-out,SafetyStocks,Additionalfeatures,SporadicDemand,Wecallatime-seriessporadic(intermittent),ifnodemandisobservedinquitealotofperiods.Thosedemandpatternsespeciallyoccurforreplacementpartsorifonlyasmallpartofthedemandquantityisforecasted;forexamplethedemandforjeansinaspecificsizeononedayinaspecificstoremightbesporadic.,LostSalesvs.Backorders,Therearetwogenerallydifferentsolutionapproachesfortheproblemofforecastinginpresenceoflostsales:Thefirstonetriestocalculateavirtualdemandhistorywhichisbasedonthesaleshistoryandtheinformationonstock-outs.Analternativesolutiontothelost-salesproblemistheusageofsophisticatedstatisticalmethodswhichconsidertheobservedsalesasacensoredsampleofthedemandsample(seee.g.Nahmias(1997).,Additionalfeatures,MeasuringForecastAccuracyandTriggeringExceptions,Whydowemeasureforecastaccuracy?Firstofall,itisnotatoolforthecontrollertocheckthequalityofthedemandplannerswork.Itisratherabuildingblockinthedemandplanningprocess.Thedemandplannermightcheckwhetherthestatisticalmethodisappropriateforthetime-series,whetheradditionalhumanjudgmentpaysbackorwhetheritisusefultoincorporateinformationonpromotions.,Additionalfeatures,ModelSelectionandParameterEstimation,Theselectionofaforecastingmodelandtheestimationofnecessaryparametersareissueswhichareraisedintheimplementationphaseofdemandplanningorduringtheupdateofforecastingparameters.Thisupdateshouldbemademoreorlessregularly(e.g.everyyear)butnottoooften.,Life-Cycle-ManagementandPhase-in/Phase-out,Twomainapproachesareknowninpractice:Thefirstoneindexesthecompletetime-seriesanddeterminesthelife-cycle-factorwhichhastobemultipliedwiththeaveragedemandtogetthequantityforaspecificperiodinthelife-cycle(life-cycle-management).Thesecondapproach(phasingmethod)dividesthewholelife-cycleinthreephases.“phase-in”“constantdemandpattern”“phase-out”,Additionalfeatures,Safetystocks,Periodicreviewsystem:therisktimeequalsthesumofthereviewintervalandthereplenishmentlead-time:R=L+t.,Continuousreviewsystem:Therisktimeinacontinuousreviewsystemequalsonlythereplenishmentlead-timeL:R=L.,Additionalfeatures,Safetystocks,Butthatisonlyhalfofthesafetystockformula.Thesafetyfactorkrepresentsallotherdeterminantsofthesafetystock.Inthefollowingthedeterminantsandsomeoftheirvaluesareexplained,Servicel