SCM-APO-DPoverview

SAPSCM/APOEuropeanInitiativeAPOOverviewInternalTrainingDemandPlanningOverviewMarch2003-2-©Accenture2003TrainingAgendaAdvancedPlanner&OptimizerOverviewDemandPlanningOverviewSupplyNetworkPlanningOverviewProductionPlanning&DetailedSchedulingOverviewGlobalAvailable-to-PromiseOverviewAPOIntegration&CIFOverviewAPOImplementationConsiderations-3-©Accenture2003ObjectivesMainGoalsofThisSectionTounderstandDemandPlanningasanaccurateforecastingtoolintheAPOcontext.ToknowDemandPlanningmainfeatures,inconcrete:Itsarchitecture,datastorageandrepresentationattributesItsmaintools(PlanningToolbox,PlanningEnvironment,AccuracyAnalysis…)ThedifferentforecastingmethodsavailableTovisualizehowDPappliestoarealcase(SaraLee).TobeawareofmainconsiderationsandcomplexityfactorswhenimplementingDemandPlanning.TogetfamiliarwiththelookofDPanditsbasicfunctionsthroughademoandpractisingwithsimpleexercises.-4-©Accenture2003Contents1.DemandPlanningFeatures&Capabilities2.CaseStudy:SaraLee3.KeyAspectstoConsiderWhenImplementingDP4.DPDemo:AcceleratedSupplyChainIntegrationAPOTemplate5.DPExercises-5-©Accenture2003DemandPlanning–AccurateForecastingAtoolkitofstatisticalforecastingtechniquesTightlylinkedtotheR/3SystemandtheSAPBWSAP(datacanbeautomaticallytransferred)Treeselectionanddrill-downcapabilitiesfacilitatesnavigationthroughmultidimensionaldatastructuresUsestheAlertMonitortoreportexceptionsDemandPlanningFeaturesandCapabilitiesPlanner’sKnowledgeTask-specificplanningtoolsFlexibleviewsGraphicsPromotionalplanningLifecyclemanagementCannibalizationAccuracyreportingStatisticalMethodsMulti-modelapproachAveragemodelsExponentialsmoothingCausalfactorsTrenddampeningModelcombinationPickbestDemandPlanningDataMartAnticipationofFutureDemandInformationCollaborativeforecastsOrder&shipmentactuals&historyCostPOSdataNielsen/IRIdata...-6-©Accenture2003DemandPlanningFeaturesandCapabilitiesCUSTOMERYEARSMONTHSWEEKSDAYSHOURSQUARTERSSELLHOLDMOVEMAKEDESIGNB2BExchangesContractManufacturers3PLs/4PLsChannelPartnersB2BExchangesBUYSUPPLIERProductionActivityControlOrderManagementProcurementManufacturingExecutionSystemLoadPlanningTransportPlanningDistributionRequirementsPlanningMaterialsPlanningProductionPlanningSupplyDemandMatchingProductAllocationSalesForecastingInventoryTargetSettingSupplyContractNegotiationsNetworkSourcingCustomerServiceTerritoryPlanningAvailabletoPromiseIn-transit&On-handInventoryTrackingMaterialInventoryTrackingNewProductDevelopmentLogisticsNetworkDesignDetailedProductionSchedulingMaterialRequirementsPlanningAPODemandPlanningwithinSupplyChainPlanningDemandPlanningAPO–DP-7-©Accenture2003SAPAPODemandPlanningArchitectureDemandPlanningiscomposedofthreelayers:GraphicaluserinterfacePlanningandanalysisengineDatamartPlanningViewsGUIOLAPProcessorBusinessPlanningLibraryStatisticalForecastingToolboxPlanning&AnalysisEnginePlanningAreaTimeSeriesCatalogNotesDataMartDemandPlanningFeaturesandCapabilities-8-©Accenture2003SAPAPODemandPlanningArchitecture(continued)PerformanceisofvitalimportanceinanydemandplanningsolutionifusersaretofullybenefitfromavailableinformationDParchitectureincludesseveralfeaturestoensurehighperformance:DedicatedserverMultidimensionaldatamartbasedonthestarschemathatsupportsefficientuseofstoragespaceandofCPUcycles,minimizingqueryresponsetimeBatchforecastingsodonotimpedeonlineperformanceThesizeoftheinformationtreateddependson:Numberofcharacteristics:manycharacteristicswilllettheusermoreflexibilitytodefinetheplanninglevelandtoreviewtheinformationbutitmakesthesystemworksslowerNumberofkeyfigures:manykeyfigureswillgivetheuseralotofinformationrelatedtoforecastbutitmakesthesystemworksslowerNumberofcharacteristiccombinations:thetimeconsumingforanycalculation(e.g.macros)dependsdirectlyonthenumberofcharacteristiccombinationsNumberofplanningversions:twoplanningversionsneedsdoublecapacitythanoneTypeandnumberoftemporalperiodsDemandPlanningFeaturesandCapabilities-9-©Accenture2003DemandPlanningFeaturesandCapabilitiesDataStorageandRepresentationMultidimensionalDataStorageinthedatamartallowsto:ViewdataandplanfrommanydifferentperspectivesDrilldownfromoneleveltothenextInfoCubes:AmultidimensionaldatastructureTheprimarycontainerofdatausedinplanning,analysisandreportingContainstwotypesofdata,keyfiguresandcharacteristics(ordimensions):-Keyfiguresarequantifiablevalues(e.g.salesinunits,orders,shipments,POS…)-Characteristicsordimensionsdeterminetheorganizationallevelsatwhichyoudoaggregationandreporting(e.g.productsandcustomers)InfoCubesalsosharemasterdataanddescriptivetext,whicharestoredindifferenttablesTheOnlineAnalyticalProcessingprocessor:Modelsthebusinessrulesconsideringtheaggregationalbehaviorofkeyfigures(e.g.salessummedbyproductandtime)Guaranteesthatallbusinessrulesaremetandthecomputedviewspresentvalidresults-10-©Accenture2003DataStorageandRepresentation(continued)Hierarchiesaremodeledascombinationsofcharacteristicvalues(e.g.productaregroupedintoproductfamilyhierarchies)usingproportionalandtemporalfactors,inordertobeusedasth