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1Chapter5:
RiskPooling&Forecasting1Dr.YANGRuina1Chapter5:RiskPooling&For12AgendaRiskPoolingCase1:ACMEForecasting2AgendaRiskPooling23RiskPoolingDemandvariabilityisreducedifoneaggregatesdemandacrosslocations.Morelikelythathighdemandfromonecustomerwillbeoffsetbylowdemandfromanother.Reductioninvariabilityallowsadecreaseinsafetystockandthereforereducesaverageinventory.3RiskPoolingDemandvariabilit3供應(yīng)鏈管理(英文課件)Chapter5-Risk-Pooling-and-Forecasting4供應(yīng)鏈管理(英文課件)Chapter5-Risk-Pooling-and-Forecasting56AcmeRiskPoolingCaseElectronicequipmentmanufactureranddistributor2warehousesfordistributioninMassachusettsandNewJersey(partitioningthenortheastmarketintotworegions)Customers(retailers)receivingitemsfromwarehouses(eachretailerisassignedawarehouse)WarehousesreceivematerialfromChicagoCurrentrule:97%servicelevelEachwarehouseoperatetosatisfy97%ofdemand(3%probabilityofstock-out)6AcmeRiskPoolingCaseElectro67Replacethe2warehouseswithasinglewarehouse(locatedsomesuitableplace)andtrytoimplementthesameservicelevel97%NewIdea7Replacethe2warehouseswith78HistoricalDataAslow-movingproduct8HistoricalDataAslow-moving89SummaryofHistoricalData9SummaryofHistoricalData910InventoryLevels10InventoryLevels1011SavingsinInventoryAverageinventoryforProductA:AtNJwarehouseisabout88unitsAtMAwarehouseisabout91unitsInthecentralizedwarehouseisabout132unitsAverageinventoryreducedbyabout36percentAverageinventoryforProductB:AtNJwarehouseisabout15unitsAtMAwarehouseisabout14unitsInthecentralizedwarehouseisabout20unitsAverageinventoryreducedbyabout43percent11SavingsinInventoryAverage11DiscussionQuestionAnalyzethestrengthsandweaknessesofthecurrentdistributionsystemandthenewdistributionsystem.(e.g.deliveryleadtime,totalinventoryinvestment)DiscussionQuestionAnalyzethe1213Centralizinginventoryreducesbothsafetystockandaverageinventoryinthesystem.
--
Reallocationofitemsfromonemarkettoanothereasilyaccomplishedincentralizedsystems.Notpossibletodoindecentralizedsystemswheretheyservedifferentmarkets.CriticalPoints13CriticalPoints1314Thehigherthecoefficientofvariation,thegreaterthebenefitfromriskpooling.
--Thehigherthevariability,thehigherthesafetystockskeptbythewarehouses.Thevariabilityofthedemandaggregatedbythesinglewarehouseislower.
CriticalPoints14Thehigherthecoefficiento1415Thebenefitsfromriskpoolingdependonthebehaviorofthedemandfromonemarketrelativetodemandfromanother.
--RiskpoolingbenefitsarehigherinsituationswheredemandsobservedatwarehousesarenegativelycorrelatedCriticalPoints15Thebenefitsfromriskpooli15供應(yīng)鏈管理(英文課件)Chapter5-Risk-Pooling-and-Forecasting1617Centralizedvs.DecentralizedSystemsSafetystock:lowerwithcentralizationServicelevel:higherservicelevelforthesameinventoryinvestmentwithcentralizationOverheadcosts:higherindecentralizedsystemCustomerleadtime:responsetimeslowerinthedecentralizedsystemTransportationcosts:notclear.Consideroutboundandinboundcosts.17Centralizedvs.Decentraliz1718ContentsofForecastingIntroductionForecastEvaluationSubjectiveMethodsObjectiveMethods--CausalModels--TimeSeriesModelsSummary
18ContentsofForecastingIntro1819LearningObjectivesofForecastingUnderstandcommonlyusedforecastingtechniques
LearntoevaluateforecastsLearntochooseappropriateforecastingtechniques19LearningObjectivesofForec1920IntroductiontoForecasting20IntroductiontoForecasting2021IntroductiontoForecasting21IntroductiontoForecasting2122longtermDemandfulfillmentPurchasingProductioncontrolAggregateplanningDemandforecastingInventorymanagementOperationsschedulingDistributionplanningTransportplanningFulfillmentimplementationDistributionnetworkdesignSupplyChainManagement
ProductdevelopmentmediumtermshorttermDistributionFacilitylocationand
layoutManufacturingSupplynet-
workdesignPartner
selectionProduct
portifolioDerivative
product
developmentAdaptionsCurrent
product
supportMaterials
orderingSupplycontract
designDemandforecastingisthestartingpointofallplanningandcontrol!22longDemandPurchasingProducti2223CharacteristicsofForecastTheforecastisalwayswrongItisdifficulttomatchsupplyanddemandThelongertheforecasthorizon,theworsetheforecast(Timehorizon)ItisevenmoredifficultifoneneedstopredictcustomerdemandforalongperiodoftimeAggregateforecastsaremoreaccurateChoosingappropriateaggregationlevels,timehorizons,andforecastingtechniquesiscrucial23CharacteristicsofForecast2324AGoodForecastisMorethanaSingleNumber24AGoodForecastisMorethan2425Long-termForecastsareAlwaysWrong25Long-termForecastsareAlwa2526WhatMakesaGoodForecast?26WhatMakesaGoodForecast?2627TWOFORECASTS1617181920Aug-02Sep-02Oct-02Nov-02Dec-02SalesWhichforecastisbetter?Howcanweevaluatetheforecastingperformance?Forecastquality27TWOFORECASTS1617181920Aug-02728ForecastErrors28ForecastErrors2829EvaluationofForecastAccuracy29EvaluationofForecastAccur2930MeasuringForecastAccuracy—Forecast130MeasuringForecastAccuracy—3031MeasuringForecastAccuracy—Forecast231MeasuringForecastAccuracy—3132EvaluationsofTwoForecasts32EvaluationsofTwoForecasts3233BiasinForecast33BiasinForecast3334BiasinForecast34BiasinForecast3435ReasonsforBiasinForecast
Lineartrendornon-lineartrendSeasonalityExternalfactors,suchaspromotionandadvertisementIfrelevantelementsarenotconsideredintheforecast,theforecastcanbecomebiased.Theseelementscaninclude:35ReasonsforBiasinForecast3536QualitativeMethodsQualitativeMethodsSalesForceCompositePanelofExpertsMarketResearchDelphiMethodApplicationUsedtogenerateforecastsifhistoricaldataarenotavailable(e.g.,introductionofnewproduct)Usedtomodifyforecastsgeneratedbyotherapproaches(e.g.,consideringinformationnotincludedinquantitativemethods)36QualitativeMethodsQualitati3637SalesForceEstimate
RationaleSalesforceisclosetocustomerandhasgoodinformationonfuturedemandsApproach
Membersofsalesforceperiodicallyreporttheirestimates.TheseestimatesarethenaggregatedtogeneratetheoverallforecastMainadvantagesSalesforceknowscustomerwellSalesterritoriesaretypicallydividedbydistrict/region.Salesforecastscanbebrokendowncorrespondingly37SalesForceEstimateRationa3738SalesForceEstimateBiasofsalesforce
-Mighthaveincentivestooverestimatesalesorunderestimatesales
-MightnaturallybeoptimisticorpessimisticSalesforcedoesnotalwayshaveallinformationnecessarytogenerateforecast
-Featuresofproductslaunchedinfuture
-PreferencesofcustomersinnewmarketsegmentsTypicalapplicationMaindrawbacksShort-termandmedium-termdemandforecasting38SalesForceEstimateTypical3839ExecutiveOpinionRationaleUpper-levelmanagementhasbestinformationonlatestproductdevelopmentsandfutureproductlaunchesApproachSmallgroupofupper-levelmanagerscollectivelydevelopforecastsCombineknowledgeandexpertisefromvariousfunctionalareasPeoplewhohavebestinformationonfuturedevelopmentsgeneratetheforecastsMainadvantages39ExecutiveOpinionRationaleAp3940ExecutiveOpinionExpensiveNoindividualresponsibilityforforecastqualityRiskthatfewpeopledominatethegroup
TypicalapplicationsMaindrawbacksShort-termandmedium-termdemandforecasting40ExecutiveOpinionTypicalapp4041MarketResearchRationaleUltimately,consumersdrivedemandApproachDetermineconsumerinterestsbycreatingandtestinghypothesesthroughdata-gatheringsurveys:
DesignquestionnaireSelectcustomersample
Conductsurvey(e.g.,telephone,mail,orinterview)
Analyzeinformationandgenerateforecast41MarketResearchRationaleAppr4142MarketResearchExpensiveRequireconsiderableknowledgeandskillsSometimesvaliditynotguaranteedduetolowresponserates:Formailedquestionnairesresponserateoften<30%TypicalapplicationSystematicandfact-basedapproachExcellentaccuracyforshort-termforecastsGoodaccuracyformedium-termforecastsMainadvantagesMaindrawbacksShort-termandmedium-termdemandforecasting42MarketResearchTypicalappli4243DelphiMethodRationaleAnonymouswrittenresponsesencouragehonestyandavoidthatagroupofexpertsaredominatedbyonlyafewmembersApproachCoordinator
sendsinitial
questionnaireEachexpertwritesresponse(anonymous)CoordinatorperformsanalysisCoordinator
sendsupdatedquestionnaireConsensusreached?CoordinatorsummarizesforecastNoYes43DelphiMethodRationaleApproa4344DelphiMethodSlowprocessExpertsarenotaccountablefortheirresponsesLittleevidencethatreliablelong-termforecastscanbegeneratedwithDelphiorothermethods
Long-termforecastingTechnologyforecastingGenerateconsensusCanforecastlong-termtrendwithoutavailabilityofhistoricaldataMainadvantagesMaindrawbacksTypicalapplication44DelphiMethodGenerateconse4445ObjectiveForecastingMethods45ObjectiveForecastingMethod4546CausalModelsCausalModelsLinearRegressionNon-linearRegressionApplicationUsedtoforecasttheperformance(demand,profit,etc.)ofabusinessinvestmentbasedontheobserveddataofexistingandsimilarbusinessactivities46CausalModelsCausalApplicati4647ASimpleExample47ASimpleExample4748LinearRegression:ObjectiveObjectiveIdeaFindalinearfunctionthatrepresentsthepredictedvariableyasafunctionofpredictivevariablesx1,x2,…,xmandbestfitstheobserveddata48LinearRegression:Objective4849494950505051515152525253EXAMPLE:m=1(1)a=1,796x2,710–132x35,29012x1,796–132x132
=50.6b=12x35,290–132x2,71012x1,796–132x132
=15.9CoefficientsObserveddataandanalysis53EXAMPLE:m=1(1)a=1,7965354EXAMPLE:m=1(2)PopulationDemandy(x)=a+bx=50.6+15.9xQuestion:Whatdemandwouldweexpectfrominvestinginabusinesswithanearbypopulation10thousand?Answer:y(10)=54EXAMPLE:m=1(2)Population54555555565656575757585858595959606060616161626262636363646464656565666666676767686868696969供應(yīng)鏈管理(英文課件)Chapter5-Risk-Pooling-and-Forecasting7071717172727273737374747475757576767677777778787879797980808081818182828283838384848485858586868687878788888889898990909091919192929293OtherFactorsofForecasting93OtherFactorsofForecasting9394TheMostAppropriateTechniquePurposeoftheforecastHowwilltheforecastbeused?Dynamicsofsystemforwhichforecastwillbemade.Howaccurateisthepasthistoryinpredictingthefuture?94TheMostAppropriateTechniq9495DeterminetheMostAppropriateTechnique95DeterminetheMostAppropria9596SummaryDemandplanning/forecastingisthestartingpointofallplanningTheperformanceofforecastingapproachcanbeevaluatedbasedonvariousmetrics
-MAD
-MSE
-MAPEVariousforecastingapproachesexist.Whichoneisappropriatedependsonthesituation.
-
Qualitativemethods,
-Causalmodels,or
-Time-seriesmodels96SummaryDemandplanning/forec9697Chapter5:
RiskPooling&Forecasting97Dr.YANGRuina1Chapter5:RiskPooling&For9798AgendaRiskPoolingCase1:ACMEForecasting2AgendaRiskPooling9899RiskPoolingDemandvariabilityisreducedifoneaggregatesdemandacrosslocations.Morelikelythathighdemandfromonecustomerwillbeoffsetbylowdemandfromanother.Reductioninvariabilityallowsadecreaseinsafetystockandthereforereducesaverageinventory.3RiskPoolingDemandvariabilit99供應(yīng)鏈管理(英文課件)Chapter5-Risk-Pooling-and-Forecasting100供應(yīng)鏈管理(英文課件)Chapter5-Risk-Pooling-and-Forecasting101102AcmeRiskPoolingCaseElectronicequipmentmanufactureranddistributor2warehousesfordistributioninMassachusettsandNewJersey(partitioningthenortheastmarketintotworegions)Customers(retailers)receivingitemsfromwarehouses(eachretailerisassignedawarehouse)WarehousesreceivematerialfromChicagoCurrentrule:97%servicelevelEachwarehouseoperatetosatisfy97%ofdemand(3%probabilityofstock-out)6AcmeRiskPoolingCaseElectro102103Replacethe2warehouseswithasinglewarehouse(locatedsomesuitableplace)andtrytoimplementthesameservicelevel97%NewIdea7Replacethe2warehouseswith103104HistoricalDataAslow-movingproduct8HistoricalDataAslow-moving104105SummaryofHistoricalData9SummaryofHistoricalData105106InventoryLevels10InventoryLevels106107SavingsinInventoryAverageinventoryforProductA:AtNJwarehouseisabout88unitsAtMAwarehouseisabout91unitsInthecentralizedwarehouseisabout132unitsAverageinventoryreducedbyabout36percentAverageinventoryforProductB:AtNJwarehouseisabout15unitsAtMAwarehouseisabout14unitsInthecentralizedwarehouseisabout20unitsAverageinventoryreducedbyabout43percent11SavingsinInventoryAverage107DiscussionQuestionAnalyzethestrengthsandweaknessesofthecurrentdistributionsystemandthenewdistributionsystem.(e.g.deliveryleadtime,totalinventoryinvestment)DiscussionQuestionAnalyzethe108109Centralizinginventoryreducesbothsafetystockandaverageinventoryinthesystem.
--
Reallocationofitemsfromonemarkettoanothereasilyaccomplishedincentralizedsystems.Notpossibletodoindecentralizedsystemswheretheyservedifferentmarkets.CriticalPoints13CriticalPoints109110Thehigherthecoefficientofvariation,thegreaterthebenefitfromriskpooling.
--Thehigherthevariability,thehigherthesafetystockskeptbythewarehouses.Thevariabilityofthedemandaggregatedbythesinglewarehouseislower.
CriticalPoints14Thehigherthecoefficiento110111Thebenefitsfromriskpoolingdependonthebehaviorofthedemandfromonemarketrelativetodemandfromanother.
--RiskpoolingbenefitsarehigherinsituationswheredemandsobservedatwarehousesarenegativelycorrelatedCriticalPoints15Thebenefitsfromriskpooli111供應(yīng)鏈管理(英文課件)Chapter5-Risk-Pooling-and-Forecasting112113Centralizedvs.DecentralizedSystemsSafetystock:lowerwithcentralizationServicelevel:higherservicelevelforthesameinventoryinvestmentwithcentralizationOverheadcosts:higherindecentralizedsystemCustomerleadtime:responsetimeslowerinthedecentralizedsystemTransportationcosts:notclear.Consideroutboundandinboundcosts.17Centralizedvs.Decentraliz113114ContentsofForecastingIntroductionForecastEvaluationSubjectiveMethodsObjectiveMethods--CausalModels--TimeSeriesModelsSummary
18ContentsofForecastingIntro114115LearningObjectivesofForecastingUnderstandcommonlyusedforecastingtechniques
LearntoevaluateforecastsLearntochooseappropriateforecastingtechniques19LearningObjectivesofForec115116IntroductiontoForecasting20IntroductiontoForecasting116117IntroductiontoForecasting21IntroductiontoForecasting117118longtermDemandfulfillmentPurchasingProductioncontrolAggregateplanningDemandforecastingInventorymanagementOperationsschedulingDistributionplanningTransportplanningFulfillmentimplementationDistributionnetworkdesignSupplyChainManagement
ProductdevelopmentmediumtermshorttermDistributionFacilitylocationand
layoutManufacturingSupplynet-
workdesignPartner
selectionProduct
portifolioDerivative
product
developmentAdaptionsCurrent
product
supportMaterials
orderingSupplycontract
designDemandforecastingisthestartingpointofallplanningandcontrol!22longDemandPurchasingProducti118119CharacteristicsofForecastTheforecastisalwayswrongItisdifficulttomatchsupplyanddemandThelongertheforecasthorizon,theworsetheforecast(Timehorizon)ItisevenmoredifficultifoneneedstopredictcustomerdemandforalongperiodoftimeAggregateforecastsaremoreaccurateChoosingappropriateaggregationlevels,timehorizons,andforecastingtechniquesiscrucial23CharacteristicsofForecast119120AGoodForecastisMorethanaSingleNumber24AGoodForecastisMorethan120121Long-termForecastsareAlwaysWrong25Long-termForecastsareAlwa121122WhatMakesaGoodForecast?26WhatMakesaGoodForecast?122123TWOFORECASTS1617181920Aug-02Sep-02Oct-02Nov-02Dec-02SalesWhichforecastisbetter?Howcanweevaluatetheforecastingperformance?Forecastquality27TWOFORECASTS1617181920Aug-0123124ForecastErrors28ForecastErrors124125EvaluationofForecastAccuracy29EvaluationofForecastAccur125126MeasuringForecastAccuracy—Forecast130MeasuringForecastAccuracy—126127MeasuringForecastAccuracy—Forecast231MeasuringForecastAccuracy—127128EvaluationsofTwoForecasts32EvaluationsofTwoForecasts128129BiasinForecast33BiasinForecast129130BiasinForecast34BiasinForecast130131ReasonsforBiasinForecast
Lineartrendornon-lineartrendSeasonalityExternalfactors,suchaspromotionandadvertisementIfrelevantelementsarenotconsideredintheforecast,theforecastcanbecomebiased.Theseelementscaninclude:35ReasonsforBiasinForecast131132QualitativeMethodsQualitativeMethodsSalesForceCompositePanelofExpertsMarketResearchDelphiMethodApplicationUsedtogenerateforecastsifhistoricaldataarenotavailable(e.g.,introductionofnewproduct)Usedtomodifyforecastsgeneratedbyotherapproaches(e.g.,consideringinformationnotincludedinquantitativemethods)36QualitativeMethodsQualitati132133SalesForceEstimate
RationaleSalesforceisclosetocustomerandhasgoodinformationonfuturedemandsApproach
Membersofsalesforceperiodicallyreporttheirestimates.TheseestimatesarethenaggregatedtogeneratetheoverallforecastMainadvantagesSalesforceknowscustomerwellSalesterritoriesaretypicallydividedbydistrict/region.Salesforecastscanbebrokendowncorrespondingly37SalesForceEstimateRationa133134SalesForceEstimateBiasofsalesforce
-Mighthaveincentivestooverestimatesalesorunderestimatesales
-MightnaturallybeoptimisticorpessimisticSalesforcedoesnotalwayshaveallinformationnecessarytogenerateforecast
-Featuresofproductslaunchedinfuture
-PreferencesofcustomersinnewmarketsegmentsTypicalapplicationMaindrawbacksShort-termandmedium-termdemandforecasting38SalesForceEstimateTypical134135ExecutiveOpinionRationaleUpper-levelmanagementhasbestinformationonlatestproductdevelopmentsandfutureproductlaunchesApproachSmallgroupofupper-levelmanagerscollectivelydevelopforecastsCombineknowledgeandexpertisefromvariousfunctionalareasPeoplewhohavebestinformationonfuturedevelopmentsgeneratetheforecastsMainadvantages39ExecutiveOpinionRationaleAp135136ExecutiveOpinionExpensiveNoindividualresponsibilityforforecastqualityRiskthatfewpeopledominatethegroup
TypicalapplicationsMaindrawbacksShort-termandmedium-termdemandforecasting40ExecutiveOpinionTypicalapp136137MarketResearchRationaleUltimately,consumersdrivedemandApproachDetermineconsumerinterestsbycreatingandtestinghypothesesthroughdata-gatheringsurveys:
DesignquestionnaireSelectcustomersample
Conductsurvey(e.g.,telephone,mail,orinterview)
Analyzeinformationandgenerateforecast41MarketResearchRationaleAppr137138MarketResearchExpensiveRequireconsiderableknowledgeandskillsSometimesvaliditynotguaranteedduetolowresponserates:Formailedquestionnairesresponserateoften<30%TypicalapplicationSystematicandfact-basedapproachExcellentaccuracyforshort-termforecastsGoodaccuracyformedium-termforecastsMainadvantagesMaindrawbacksShort-termandmedium-termdemandforecasting42MarketResearchTypicalappli138139DelphiMethodRationaleAnonymouswrittenresponsesencouragehonestyandavoidthatagroupofexpertsaredominatedbyonlyafewmembersApproachCoordinator
sendsinitial
questionnaireEachexpertwritesresponse(anonymous)CoordinatorperformsanalysisCoordinator
sendsupdatedquestionnaireConsensusreached?CoordinatorsummarizesforecastNoYes43DelphiMethodRationaleApproa139140DelphiMethodSlowprocessExpertsarenotaccountablefortheirresponsesLittleevidencethatreliablelong-termforecastscanbegeneratedwithDelphiorothermethods
Long-termforecastingTechnologyforecastingGenerateconsensusCanforecastlong-termtrendwithoutavailabilityofhistoricaldataMainadvantagesMaindrawbacksTypicalapplication44Delphi
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