SCM-APO--DP-overviewPPT课件

,APOOverviewInternalTrainingDemandPlanningOverview,March2003,TrainingAgenda,AdvancedPlanner&OptimizerOverviewDemandPlanningOverviewSupplyNetworkPlanningOverviewProductionPlanning&DetailedSchedulingOverviewGlobalAvailable-to-PromiseOverviewAPOIntegration&CIFOverviewAPOImplementationConsiderations,Objectives,MainGoalsofThisSectionTounderstandDemandPlanningasanaccurateforecastingtoolintheAPOcontext.ToknowDemandPlanningmainfeatures,inconcrete:Itsarchitecture,datastorageandrepresentationattributesItsmaintools(PlanningToolbox,PlanningEnvironment,AccuracyAnalysis)ThedifferentforecastingmethodsavailableTovisualizehowDPappliestoarealcase(SaraLee).TobeawareofmainconsiderationsandcomplexityfactorswhenimplementingDemandPlanning.TogetfamiliarwiththelookofDPanditsbasicfunctionsthroughademoandpractisingwithsimpleexercises.,Contents,DemandPlanningFeatures&CapabilitiesCaseStudy:SaraLeeKeyAspectstoConsiderWhenImplementingDPDPDemo:AcceleratedSupplyChainIntegrationAPOTemplateDPExercises,DemandPlanningAccurateForecastingAtoolkitofstatisticalforecastingtechniquesTightlylinkedtotheR/3SystemandtheSAPBWSAP(datacanbeautomaticallytransferred)Treeselectionanddrill-downcapabilitiesfacilitatesnavigationthroughmultidimensionaldatastructuresUsestheAlertMonitortoreportexceptions,DemandPlanningFeaturesandCapabilities,PlannersKnowledgeTask-specificplanningtoolsFlexibleviewsGraphicsPromotionalplanningLifecyclemanagementCannibalizationAccuracyreporting,StatisticalMethodsMulti-modelapproachAveragemodelsExponentialsmoothingCausalfactorsTrenddampeningModelcombinationPickbest,DemandPlanningDataMart,AnticipationofFutureDemand,InformationCollaborativeforecastsOrder&shipmentactuals&historyCostPOSdataNielsen/IRIdata.,DemandPlanningFeaturesandCapabilities,CUSTOMER,YEARS,MONTHS,WEEKS,DAYS,HOURS,QUARTERS,SELL,HOLD,MOVE,MAKE,DESIGN,B2BExchanges,ContractManufacturers,3PLs/4PLs,ChannelPartnersB2BExchanges,BUY,SUPPLIER,ProductionActivityControl,OrderManagement,Procurement,ManufacturingExecutionSystem,LoadPlanning,TransportPlanning,DistributionRequirementsPlanning,MaterialsPlanning,ProductionPlanning,SupplyDemandMatching,ProductAllocation,SalesForecasting,InventoryTargetSetting,SupplyContractNegotiations,NetworkSourcing,CustomerServiceTerritoryPlanning,AvailabletoPromise,In-transit&On-handInventoryTracking,MaterialInventoryTracking,NewProductDevelopment,LogisticsNetworkDesign,DetailedProductionScheduling,MaterialRequirementsPlanning,APODemandPlanningwithinSupplyChainPlanning,DemandPlanningAPODP,SAPAPODemandPlanningArchitectureDemandPlanningiscomposedofthreelayers:GraphicaluserinterfacePlanningandanalysisengineDatamart,DemandPlanningFeaturesandCapabilities,SAPAPODemandPlanningArchitecture(continued)PerformanceisofvitalimportanceinanydemandplanningsolutionifusersaretofullybenefitfromavailableinformationDParchitectureincludesseveralfeaturestoensurehighperformance:DedicatedserverMultidimensionaldatamartbasedonthestarschemathatsupportsefficientuseofstoragespaceandofCPUcycles,minimizingqueryresponsetimeBatchforecastingsodonotimpedeonlineperformanceThesizeoftheinformationtreateddependson:Numberofcharacteristics:manycharacteristicswilllettheusermoreflexibilitytodefinetheplanninglevelandtoreviewtheinformationbutitmakesthesystemworksslowerNumberofkeyfigures:manykeyfigureswillgivetheuseralotofinformationrelatedtoforecastbutitmakesthesystemworksslowerNumberofcharacteristiccombinations:thetimeconsumingforanycalculation(e.g.macros)dependsdirectlyonthenumberofcharacteristiccombinationsNumberofplanningversions:twoplanningversionsneedsdoublecapacitythanoneTypeandnumberoftemporalperiods,DemandPlanningFeaturesandCapabilities,DemandPlanningFeaturesandCapabilities,DataStorageandRepresentationMultidimensionalDataStorageinthedatamartallowsto:ViewdataandplanfrommanydifferentperspectivesDrilldownfromoneleveltothenextInfoCubes:AmultidimensionaldatastructureTheprimarycontainerofdatausedinplanning,analysisandreportingContainstwotypesofdata,keyfiguresandcharacteristics(ordimensions):Keyfiguresarequantifiablevalues(e.g.salesinunits,orders,shipments,POS)Characteristicsordimensionsdeterminetheorganizationallevelsatwhichyoudoaggregationandreporting(ductsandcustomers)InfoCubesalsosharemasterdataanddescriptivetext,whicharestoredindifferenttablesTheOnlineAnalyticalProcessingprocessor:Modelsthebusinessrulesconsideringtheaggregationalbehaviorofkeyfigures(e.g.salessummedbyproductandtime)Guaranteesthatallbusinessrulesaremetandthecomputedviewspresentvalidresults,DataStorageandRepresentation(continued)Hierarchiesaremodeledascombinationsofcharacteristicvalues(ductaregroupedintoproductfamilyhierarchies)usingproportionalandtemporalfactors,inordertobeusedasthebasisforaggregation,disaggregationanddrillingdown.TheDPplanninglevelisbasedonthecharacteristicsdefinition.InordertobemoreintegratedwithR/3data,thedimensionsandcharacteristicsareusuallybasedonR/3hierarchies:ProductdimensionandcharacteristicsareusuallybasedonR/3producthierarchyCustomerdimensionandcharacteristicsareusuallybasedonaR/3customerhierarchyGeographicdimensionandcharacteristicsareusuallybasedonthesupplynetwork,DemandPlanningFeaturesandCapabilities,DataStorageandRepresentation(continued)TimeSeriesManagement:Basedoncatalogs:timeseriesdatawithrelatedattributes(motionalpatternsandlifecycles)SAPDPallowstoreusetimeseriessavingtimeandensuringconsistency(e.g.reuseapastpromotionalpatterntoestimatetheimpactofasimilarfuturepromotion)NotesManagementmaintainsallnotesenteredbyplannerstocreateanaudittrailofalldemandplanningactivities,whichisspeciallyhelpfulwhenmultiplesourcesandpeopleareinvolved(suchasinconsensusforecasting),DemandPlanningFeaturesandCapabilities,PlanningEnvironmentDPsrichplanningandforecastingfunctionsarebasedontheStatisticalForecastingToolboxandtheBusinessPlanningLibrary.Thesefunctionsinclude:Aggregatefunctions(sum,weightedsum,average)Disaggregatefunctions(quotas,proportionalandequaldistribution)Comparisonfunctions(difference,ratio,percent,percentdifference,shareandcorrelation)Financialfunctions(conversionfromunitsintorevenue,currencyconversionandbusinessperiodconversion)Time-seriesfunctions(time-phased,average,andweightedaverageoftimeseries),APlanningBookisaneasy-to-usetreecontrolforselectingdataandaframewithagridandagraphicaldatadisplay:Preconfiguredplanningbooksforpromotionalplanning,causalanalysis,statisticalforecasting,lifecyclemanagement,etcThesecanbeusedasguidesforcustomizedplanningbooks,DemandPlanningFeaturesandCapabilities,PlanningEnvironment(continued)YoucanuseAdvancedMacrosto:CalculatedeviationsMakeautomaticcorrectionsCalculatesalesbudgetsDefineyourownexceptionalsituationsLaunchstatusqueriesAdvancedMacrosmodelsthecalculationsbasedontheindividualbusinesstaskstoperformprincipally:BuildamacroconsistingofoneormorestepsControlhowmacrostepsareprocessedandhowresultsarecalculatedUseawiderangeoffunctionsandoperationsDefineoffsetssothattheresultinoneperiodisdeterminedbyavalueinthepreviousperiodRestricttheexecutionofamacrotoaspecificperiodorperiodsWritemacroresultstoarow,acolumnoracellCreatecontext-specificanduser-specificplanningviewsTriggeranalertintheAlertMonitortoinformofparticularbusinesssituationsIntegrationwithMicrosoftExcel,DemandPlanningFeaturesandCapabilities,StatisticalForecastingToolboxAToolboxofallpractical,provenforecastingmethodsTimeSeriesModels:Usespastsalestoidentifylevel,trend,andseasonalpatternsasabasisforcreatingfutureprojectionsNavemodels,movingaverage,simplelinearregression,Brownsexponentialsmoothing,Holt-Winters,Box-JenkinsStochasticModels:AccurateforecastwithsporadicdemandpatternCrostonmodelusesexponentialsmoothingtoestimate:ThesizeofdemandduringperiodsinwhichdemandoccurThedemandfrequencyFinalforecastaredeterminedbydistributingthesizeofdemandaccordingtothedemandfrequency,DemandPlanningFeaturesandCapabilities,StatisticalForecastingToolbox(continued)MultipleLinearRegression:TechniqueforestimatingtherelationshipbetweenpastsalesandothercausalfactorsVarietyofoptionstomodellinearandnon-lineartrends:SeasonalpatternsLifecyclepatternsDummyvariablesandtimelagsCorrelationanalysiscorrectsvariablesPick-the-Best,appliesthebestmethodamong:Alloftheavailableforecastingmethods,orTheplanner-specifiedforecastingmethodsS-ShapedCurvessupportscompletelifecycleforecasting(introduction/growthandend-of-lifephases)LogisticandexponentialfunctionsFirstestimationbasedonsimilarproductsAdjustedovertimewhensaleshistoryisavailable,DemandPlanningFeaturesandCapabilities,CausalAnalysisIncludesallsignificantcausalfactors(price,numberofdisplays,numberofstores,temperature,workingdays)inthemodelsanddeterminehowtheyaffectcustomersbehaviorSimulatesalesdevelopmentaccordingtothemixofcausalfactors(what-ifanalysis,marketingmixplanning)Multiplelinearregressiontomodeltheimpactofcausalfactors,DemandPlanningFeaturesandCapabilities,Multi-TierForecastingIntegratessell-indata(likePOSdata)intotheprocessofforecastingsell-throughdata(likeshipments)CausalmodelbasedonsignificantcausalfactorstoforecastPOSSecondcausalmodelisusedtoforecastshipments:UsespastPOSdataandthePOSforecastasthemaincausalfactorTakesthetimelagbetweenPOSandshipmentsintoaccountConsidersothercausalfactors(forwardbuys,tradepromotions),POSData,Manufacturing,Retailer,SalesHistory,Consumer,Promotion,Advertisement,time,Consumerdemand+Replenishmentleadtime+Forwardbuying=RetailerDemand,DemandPlanningFeaturesandCapabilities,DataAnalysisIdentifiesmissingvaluesandoutliersinthedatatoimprovethequalityofthestatisticalforecast.Throughtheoutlier,anautomaticcorrectionofhistoricaldataisdonetakingintoconsiderationout-of-rangedatathatmaydisturbtheidentificationofhistoricalpatternIdentifiesstructuralchangesin“established”patterns:Level,trend,andamplitudechangesChangefromunstabletostablebehaviorAutomaticdetectionviatrackingsignalsAutomaticoutlierdetection&correctionManualintervention,DemandPlanningFeaturesandCapabilities,PromotionPlanningImpactofpromotionsmustbeprojectedseparatelyfromstandardforecastcomponentsthatarebasedonhistoricalsalesdataTakespricesintoaccountwhendoingprofitabilityanalysisforpromotionalcalendarsReportingcapabilitiesallowtotrackpromotionalactivitiesandrelatedcostsArchivesapromotionpatterninapromotioncatalog,soitcanbereusedSeveraltechniquesforestimatingtheeffectofapromotion,Promotion,Planner,DemandPlanningFeaturesandCapabilities,LifeCycle-ManagementADemandPlanningandSupplyNetworkPlanningbothcomponentsfunctionPlanningstrategiesforaproductdependonthestageofitslifecycle:Shouldtheproductbeintroduced,andwhen?Howshouldaproductbepromotedduringthedifferentstages?Shouldtheproductbedeleted,andwhen?Shouldasuccessorproductbeintroduced?Shouldare-launchbestartedforaproduct,andwhen?Whatisthecannibalizationeffectofanewproductwithexistingproducts?Etc.DPcanrepresentthelaunch,growthanddiscontinuationphasesbyusingphase-in,phase-outandlikemodelingprofiles(orcombiningthem):Aphase-inprofilereducesdemandhistorybyeverincreasingpercentagesduringaspecificperiodorperiods(simulatingupwardsalescurvelaunchandgrowthphases)Aphase-outprofilereducesdemandforecastofaproductbyeverdecreasingpercentages(simulatingdownwardsalescurvediscontinuationphase)Likemodelingcreatesaforecastusingthehistoricaldataonaproductwithasimilardemandbehavior(newproductsandproductswithshortlifecycles),ProductLaunch,Aggregate,EndofLife,DemandPlanningFeaturesandCapabilities,Consensus-BasedForecastingSAPDPsupportsconsensus-basedSales&OperationsPlanning(S&OP)MultidimensionaldatastructureoftheInfoCubesenablestocreatemultipleplans:ProductlevelsforMarketingSalesareasandaccount/channelforSalesDistributioncentersandplantsforOperationsBusinessunitsforFinanceSynchronizesmultipleplansintooneConsensusPlanthatdrivesbusinessCompositeForecastingreconcilesandcombinesdifferentplansonsamelevelandmulti-levels,DemandPlanningFeaturesandCapabilities,Forecast,1n,.,Combine&Reconcile,SalesForecast,MarketingForecast,ForecastAccuracyAnalysis&AlertMonitorForecastaccuracyreporting:HelpstoassesstheaccuracyofpastforecastsIntegratesthisknowledgeintoprojectionsforthefutureStoresaseriesofforecastsforaparticularperiodandcompareseachdeviationofthisseriestotheactualvaluesforthesameperiod(meanabsolutedeviation,errortotal,meanpercentageerror,)Reportsshoeforecasterrorsatanylevelanddimension:ActualversusforecastActualversustime-laggedforecastActualversusdifferentplanningversionsActualversusbudgetAlertMonitorinformsinrealtimeviae-mailorexceptionmessageifanexceptionoccursExceptionconditionscanbedefinedbasedonthresholdsforspecialstatisticsandtrackingsignalsReportscanbesorted:ByforecasterrorRestrictthemtoproductswithaforecasterrorgreaterthanaspecifiedthreshold,DemandPlanningFeaturesandCapabilities,AdvantagesofSAPAPODemandPlanningGlobalserverwithaBWinfrastructureIntegratedexceptionhandling,creationofuserdefinedalertsIntegrationwithProductionPlanning(S&OPscenario)MainmemorybasedplanningFlexiblenavigationintheplanningtable,variabledrilldownExtensiveforecastingtechniquePromotionplanningandevaluationCollaborativeplanningviatheinternetSupportsSalesBillsofMaterial(BOMs),DemandPlanningFeaturesandCapabilities,Contents,DemandPlanningFeatures&CapabilitiesCaseStudy:SaraLeeKeyAspectstoConsiderWhenImplementingDPDPDemo:AcceleratedSupplyChainIntegrationAPOTemplateDPExercises,CaseStudy:SaraLee,IntroductionMainobjectivesofDemandPlanningforSaraLee:S&OPpurposes:ProvidetheessentialinputforS&OPmonthlycycle(forecast)andcreateconsensuswithintheOpCo.DemandForecastshouldcontaintherequireddetailinordertocomparewithBusiness/SalestargetsSupplyPlanningpurposes:ProvideupdatedforecastfromdifferentOpCos(inweeklybuckets)toSupplyPlanninginordertobaseSupplyPlanningonconsolidatedforecastfromeachOpCoBenefitsofDemandPlanningforSaraLee:ImprovethecommunicationandtransparencyfromallOpCostoCoEProvidetoSupplyPlanningshortandlongtermvolumeestimationforcapacityplanningCreateconsensusintheOpCo(togetherwithS&OP)UnderstandingthedemandofeachOpCothroughdeepanalysis(KPIs,marketintelligence,)Movefrom”Reactionon”toward”PlanActivities”ImprovedcustomerservicelevelLowerobsoleteandsafetystocks,CaseStudy:SaraLee,ProjectApproachAtemplatehasbeendevelopedinordertoalign,coverandsupportalltheprocessesperformedintheSaraLeeOpcosinEurope.Indifferentphases,theOpcoswillstarttousethenewtemplate,changingtheiractualproceduresand/orsystems(localroll-outs).Therewillbeacentralteamresponsibleofmaintainingthebasicandcommonapplications.Ineveryroll-outalocalteamwillbeassignedtocheckthattherequirementsoftheOpcoarecovered,toconductthetrainings,etc.Communicationbetweenlocalandcentralteams:Eitherinthecentralandinthelocalteams,therewillbeamemberresponsibleofthecommunicationbetweenthem.Thecommunicationlinkwillbeone-to-one.CUSTOMIZING:Thelocalteamwillaskthecentralforcustomizingnewstructures.Everylocalroll-outwillhaveadifferentcopyofthe“ImplementationGuidelines”.GAPS:Thelocalteamwilldetectfunctionalitynotcoveredbythetemplate,then,thesegapsmustbewrittendowninadocumentcalled“EuRoPefit”.Bothteamswillhaveameetingtodeterminehoweachissueinthe“EuRoPefit”mustbesolved.,Initialtraining(centraltolocal),EuRoPefitsessions(local),EuRoPefitanalysis(central&local),GAPestimation(central),GAPsapproval(projectmanagement),CentralGAPsdesign-Templatedevelopment-(central),LocalGAPsdesign(local),CaseStudy:SaraLee,ProjectApproach(continued)Procedureforthe“EuRoPefit”AnalysisandDevelopment:,CaseStudy:SaraLee,DemandPlanningProcessesDemandPlanningProcessesaredividedintothreecycles:AOP/Outlookgeneration:ProvidevolumestakenfromAPODPasastartingpointfortheAOP/OutlookgenerationMonthlycycle:UpdateDemandForecastforthefollowing24fiscalperiodsandprovideittotheSalesandOperationsPlanningmonthlycycle(tocreateaconsensusandrunSupplyPlanning).Weeklycycle:Reviewcurrentmonthforecasttoidentifysupplyrisks,adviseSalesandMarketingoftheserisksandchangetheforecastwhichappliestoaperiodoutsideoftheSupplyPlanningfrozenperiod.,Tactical,Operational,MonthlyCycle,WeeklyCycle,Strategic,AOPgeneration,CaseStudy:SaraLee,DemandPlanningProcesses(continued)AOP/Outlookgeneration:APOForecastvolumecanbeusedasastartingpointforAOPgeneration.VolumesaresenttoR/3whereitisconvertedintovalue.Volume/valueadjustmentsaredoneinR/3AOPvolumeissentbacktoAPOforSupplyPlanningpurposesandKPIanalysis,CO-PA(R/3),APO,CO-PA(R/3),APO,VolumesfromAPODP,Convertvolumetovalue,AdjustVolume,RunSNPwithAdjustedvolume,VolumeadjustedafterSNP,Convertvolumetovalue,AdjustVolume,FinalAOPvolumesenttoAPO,InterfaceSAP-APO,SendAdjustedVolumetoAPO,DemandPlanning,Finance,SupplyPlanning,Finance,Responsible,Process,CaseStudy:SaraLee,DemandPlanningProcesses(continued)MonthlyCycle:DemandPlanningcanbeconsideredasasub-processoftheSalesandOperationsPlanning,CaseStudy:SaraLee,DemandPlanningProcesses(continued)MonthlyCycle:RollingforecastformonthMtoM+24ispreparedbyDemandPlanners:InsecondlastweekofmonthM-1,BasedonthehistoryaccumulateduntilmonthM-2,CaseStudy:SaraLee,DemandPlanningProcesses(continued)MonthlyCycle:Demandplannerswillprovideeverymontharollingforecastforthefollowing24fiscalperiods.Therewillbesomedifferencesbetweenthefirst6monthsandtheremaining12months:First6months:Presentedinweeksifneeded(inAPODPnotmuchextraworkisneeded)Forecastbasedoncleanhistory+promotionsLast18months(S&OPrequirementforlongtermcapacitychecking):PresentedinmonthsForecastasextrapolationofnon-cleanedhistory,CaseStudy:SaraLee,DemandPlanningProcesses(continued)WeeklyProcess:Processmodeloverview:Theprocessconsistsofreviewingtheconsumptionoftheforecastwithinthecurrentmonth,facilitatingdecisionmakingoncriticalexceptions(e.g.potentialstockstorage)Thiswillbemadebyexceptionbasedonthefollowingsources:Consumptionofforecastaftertheweeklyupload(Monday-Tuesday)Dailystock-outreportcomingfromR/3OrdertoCash(CDP)willdevelopATPbasedon:Physicalstock+IncomingstockPromised(reserved)stock,CaseStudy:SaraLee,DataStructureSCPdatastructurearebasedonCDPhierarchies:HierarchiesdefinedtakingintoaccounttheglobalEuRoPesolutionEasytointegratewithCDPCDPisresponsiblefordefiningthecontentofeachofthelevelofthehierarchiesPlanninglevelsaregroupedindimensions.Dimensionsdonothaveanyfunctionalimpact,