SCM-APO-DPoverview.ppt

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 DemandPlanning AccurateForecastingAtoolkitofstatisticalforecastingtechniquesTightlylinkedtotheR 3SystemandtheSAPBWSAP datacanbeautomaticallytransferred Treeselectionanddrill downcapabilitiesfacilitatesnavigationthroughmultidimensionaldatastructuresUsestheAlertMonitortoreportexceptions DemandPlanningFeaturesandCapabilities Planner sKnowledgeTask 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 DemandPlanningAPO DP 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 e g productsandcustomers InfoCubesalsosharemasterdataanddescriptivetext whicharestoredindifferenttablesTheOnlineAnalyticalProcessingprocessor Modelsthebusinessrulesconsideringtheaggregationalbehaviorofkeyfigures e g salessummedbyproductandtime Guaranteesthatallbusinessrulesaremetandthecomputedviewspresentvalidresults DataStorageandRepresentation continued Hierarchiesaremodeledascombinationsofcharacteristicvalues e g productaregroupedintoproductfamilyhierarchies usingproportionalandtemporalfactors inordertobeusedasthebasisforaggregation disaggregationanddrillingdown TheDPplanninglevelisbasedonthecharacteristicsdefinition InordertobemoreintegratedwithR 3data thedimensionsandcharacteristicsareusuallybasedonR 3hierarchies ProductdimensionandcharacteristicsareusuallybasedonR 3producthierarchyCustomerdimensionandcharacteristicsareusuallybasedonaR 3customerhierarchyGeographicdimensionandcharacteristicsareusuallybasedonthesupplynetwork DemandPlanningFeaturesandCapabilities DataStorageandRepresentation continued TimeSeriesManagement Basedoncatalogs timeseriesdatawithrelatedattributes e g promotionalpatternsandlifecycles SAPDPallowstoreusetimeseriessavingtimeandensuringconsistency e g reuseapastpromotionalpatterntoestimatetheimpactofasimilarfuturepromotion NotesManagementmaintainsallnotesenteredbyplannerstocreateanaudittrailofalldemandplanningactivities whichisspeciallyhelpfulwhenmultiplesourcesandpeopleareinvolved suchasinconsensusforecasting DemandPlanningFeaturesandCapabilities PlanningEnvironmentDP srichplanningandforecastingfunctionsarebasedontheStatisticalForecastingToolboxandtheBusinessPlanningLibrary 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 andseasonalpatternsasabasisforcreatingfutureprojectionsNa vemodels movingaverage simplelinearregression Brown sexponentialsmoothing 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 inthemodelsanddeterminehowtheyaffectcustomers behaviorSimulatesalesdevelopmentaccordingtothemixofcausalfactors 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 ManagementADemandPlanningandSupplyNetworkPlanningbothcomponents functionPlanningstrategiesforaproductdependonthestageofitslifecycle Shouldtheproductbeintroduced andwhen Howshouldaproductbepromotedduringthedifferentstages Shouldtheproductbedeleted andwhen Shouldasuccessorproductbeintroduced Shouldare launchbestartedforaproduct andwhen Whatisthecannibalizationeffectofanewproductwithexistingproducts Etc DPcanrepresentthelaunch growthanddiscontinuationphasesbyusingphase in phase outandlikemodelingprofiles orcombiningthem Aphase inprofilereducesdemandhistorybyeverincreasingpercentagesduringaspecificperiodorperiods simulatingupwardsalescurve launchandgrowthphases Aphase outprofilereducesdemandforecastofaproductbyeverdecreasingpercentages simulatingdownwardsalescurve discontinuationphase 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 ProvideupdatedforecastfromdifferentOpCo s inweeklybuckets toSupplyPlanninginordertobaseSupplyPlanningonconsolidatedforecastfromeachOpCoBenefitsofDemandPlanningforSaraLee ImprovethecommunicationandtransparencyfromallOpCo stoCoEProvidetoSupplyPlanningshortandlongtermvolumeestimationforcapacityplanningCreateconsensusintheOpCo 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 Incomingstock Promised reserved stock CaseStudy SaraLee DataStructureSCPdatastructurearebasedonCDPhierarchies HierarchiesdefinedtakingintoaccounttheglobalEuRoPesolutionEasytointegratewithCDPCDPisresponsiblefordefiningthecontentofeachofthelevelofthehierarchiesPlanninglevelsaregroupedindimensions Dimensionsdonothaveanyfunctionalimpact anditisonlyawayoforganisingtheinformationinthesystem IntheSCPEuRoPeSolutionitisplannedtouse3dimensions ProductCustomerandDemandorgan