供应链需求预测的方法

DemandForecastinginaSupplyChain,7-1,2,大綱,預測在供應鏈的角色預測的特性主要企業預測項目預測的方法與組成時間序列預測預測誤差的衡量指標執行預測的建議CPFR,3,預測在供應鏈的角色,ThebasisforallstrategicandplanningdecisionsinasupplychainExamples:Production:scheduling,inventory,aggregateplanningMarketing:salesforceallocation,promotions,newproductionintroductionFinance:plant/equipmentinvestment,budgetaryplanningPersonnel:workforceplanning,hiring,layoffsAllofthesedecisionsareinterrelated,4,預測的特性,Forecastsarealwayswrong.Shouldincludeexpectedvalueandmeasureoferror.Long-termforecastsarelessaccuratethanshort-termforecasts(forecasthorizonisimportant)Aggregateforecastsaremoreaccuratethandisaggregateforecasts,5,主要企業預測項目,市場需求量母體數預測單位需求量預測驅動變數預測市場佔有率預測企業銷售量預測單價預測生命週期預測,6,預測的方法,主觀法(subjectivemethods)預測人員依個人主觀的判斷進行預測常應用在缺乏歷史資料時透過專家進行主觀預測草根法GrassrootsBottomup市調法MarketresearchLong-rangeNewproductsales歷史類推法Historicalanalogy類似的產品經驗類推DelphiMethod以問卷方式蒐集專家意見以進行預測經由問卷溝通，專家間無直接互動以避免主控性以統計量收斂為停止指標,7,預測的方法,客觀法(objectivemethods)以歷史資料為基礎進行預測TimeSeries(外插法)假設過去之需求資料是未來需求良好指標下，使用歷史資料進行預測，適合當需求環境穩定、無劇烈變動時進行Causal(因果關係法)假設需求與環境中某些因素是高度相關，藉由發現需求與環境因素的相關性去估計未來的需求TransferFunctionModel(轉換函數模式)結合TimeSeries與Causal兩者，經由解釋變數與應變數之歷史資料產生轉換函數，再將解釋變數之預測值代入轉換函數產生應變數之預測值ARIMAT、SARIMAT,8,需求資料的組成,Observeddemand(O)=Systematiccomponent(S)+Randomcomponent(R),Systematiccomponent:ExpectedvalueofdemandRandomcomponent:ThepartoftheforecastthatdeviatesfromthesystematiccomponentForecasterror:differencebetweenforecastandactualdemand,9,需求資料組成的關係類型,相乘系統部分水準趨勢季節性因素相加系統部分水準趨勢季節性因素混合系統部分（水準趨勢）季節性因素,10,時間序列預測,Forecastdemandforthenextfourquarters.,11,時間序列預測,12,預測的方法,StaticAdaptiveMovingaverageSimpleexponentialsmoothingHoltsmodel(withtrend)Wintersmodel(withtrendandseasonality),13,預測的流程,UnderstandtheobjectivesofforecastingIntegratedemandplanningandforecastingIdentifymajorfactorsthatinfluencethedemandforecastUnderstandandidentifycustomersegmentsDeterminetheappropriateforecastingtechniqueEstablishperformanceanderrormeasuresfortheforecast,14,時間序列預測,GoalistopredictsystematiccomponentofdemandMultiplicative:(level)(trend)(seasonalfactor)Additive:level+trend+seasonalfactorMixed:(level+trend)(seasonalfactor)StaticmethodsAdaptiveforecasting,15,靜態法,Assumeamixedmodel:Systematiccomponent=(level+trend)(seasonalfactor)Ft+l=L+(t+l)TSt+l=forecastinperiodtfordemandinperiodt+lL=estimateoflevelforperiod0T=estimateoftrendSt=estimateofseasonalfactorforperiodtDt=actualdemandinperiodtFt=forecastofdemandinperiodt,16,靜態法,EstimatinglevelandtrendEstimatingseasonalfactors,17,範例資料分析,產品之需求有季節性的現象每年度之第二季為全年度需求最低之時需求皆是從每年度之第二季遞增至下年度之第一季此需求變化呈現週期現象，每個週期為一年三個週期的需求水準有逐漸上升的趨勢,18,LevelandTrend因子的估計,Beforeestimatinglevelandtrend,demanddatamustbedeseasonalizedDeseasonalizeddemand=demandthatwouldhavebeenobservedintheabsenceofseasonalfluctuationsPeriodicity(p)thenumberofperiodsafterwhichtheseasonalcyclerepeatsitselffordemandatTahoeSalt(Table7.1,Figure7.1)p=4,19,去季節因子的需求資料,Dt-(p/2)+Dt+(p/2)+S2Di/2pforpevenDt=(sumisfromi=t+1-(p/2)tot+1+(p/2)SDi/pforpodd(sumisfromi=t-(p/2)tot+(p/2),p/2truncatedtolowerinteger,20,去季節因子的需求資料,Fortheexample,p=4isevenFort=3:D3=D1+D5+Sum(i=2to4)2Di/8=8000+10000+(2)(13000)+(2)(23000)+(2)(34000)/8=19750D4=D2+D6+Sum(i=3to5)2Di/8=13000+18000+(2)(23000)+(2)(34000)+(2)(10000)/8=20625,21,去季節因子的需求資料,ThenincludetrendDt=L+tTwhereDt=deseasonalizeddemandinperiodtL=level(deseasonalizeddemandatperiod0)T=trend(rateofgrowthofdeseasonalizeddemand)Trendisdeterminedbylinearregressionusingdeseasonalizeddemandasthedependentvariableandperiodastheindependentvariable(canbedoneinExcel)Intheexample,L=18,439andT=524,22,需求的時間序列(Figure7.3),23,估計季節因子,UsethepreviousequationtocalculatedeseasonalizeddemandforeachperiodSt=Dt/Dt=seasonalfactorforperiodtIntheexample,D2=18439+(524)(2)=19487D2=13000S2=13000/19487=0.67Theseasonalfactorsfortheotherperiodsarecalculatedinthesamemanner,24,估計季節因子(Fig.7.4),25,估計季節因子,Theoverallseasonalfactorfora“season”isthenobtainedbyaveragingallofthefactorsfora“season”Iftherearerseasonalcycles,forallperiodsoftheformpt+i,1ip,theseasonalfactorforseasoniisSi=Sum(j=0tor-1)Sjp+i/rIntheexample,thereare3seasonalcyclesinthedataandp=4,soS1=(0.42+0.47+0.52)/3=0.47S2=(0.67+0.83+0.55)/3=0.68S3=(1.15+1.04+1.32)/3=1.17S4=(1.66+1.68+1.66)/3=1.67,26,預測未來需求,Usingtheoriginalequation,wecanforecastthenextfourperiodsofdemand:F13=(L+13T)S1=18439+(13)(524)(0.47)=11868F14=(L+14T)S2=18439+(14)(524)(0.68)=17527F15=(L+15T)S3=18439+(15)(524)(1.17)=30770F16=(L+16T)S4=18439+(16)(524)(1.67)=44794,27,動態預測法,Theestimatesoflevel,trend,andseasonalityareadjustedaftereachdemandobservationGeneralstepsinadaptiveforecastingMovingaverageSimpleexponentialsmoothingTrend-correctedexponentialsmoothing(Holtsmodel)Trend-andseasonality-correctedexponentialsmoothing(Wintersmodel),28,動態預測模式的符號說明,Ft+1=(Lt+lT)St+1=forecastforperiodt+linperiodtLt=EstimateoflevelattheendofperiodtTt=EstimateoftrendattheendofperiodtSt=EstimateofseasonalfactorforperiodtFt=Forecastofdemandforperiodt(madeperiodt-1orearlier)Dt=ActualdemandobservedinperiodtEt=ForecasterrorinperiodtAt=Absolutedeviationforperiodt=|Et|MAD=MeanAbsoluteDeviation=averagevalueofAt,29,動態預測的基本步驟,Initialize:Computeinitialestimatesoflevel(L0),trend(T0),andseasonalfactors(S1,Sp).Thisisdoneasinstaticforecasting.Forecast:Forecastdemandforperiodt+1usingthegeneralequationEstimateerror:ComputeerrorEt+1=Ft+1-Dt+1Modifyestimates:Modifytheestimatesoflevel(Lt+1),trend(Tt+1),andseasonalfactor(St+p+1),giventheerrorEt+1intheforecastRepeatsteps2,3,and4foreachsubsequentperiod,30,移動平均法,UsedwhendemandhasnoobservabletrendorseasonalitySystematiccomponentofdemand=levelThelevelinperiodtistheaveragedemandoverthelastNperiods(theN-periodmovingaverage)CurrentforecastforallfutureperiodsisthesameandisbasedonthecurrentestimateofthelevelLt=(Dt+Dt-1+Dt-N+1)/NFt+1=LtandFt+n=LtAfterobservingthedemandforperiodt+1,revisetheestimatesasfollows:Lt+1=(Dt+1+Dt+Dt-N+2)/NFt+2=Lt+1,31,移動平均法,FromTahoeSaltexample(Table7.1)Attheendofperiod4,whatistheforecastdemandforperiods5through8usinga4-periodmovingaverage?L4=(D4+D3+D2+D1)/4=(34000+23000+13000+8000)/4=19500F5=19500=F6=F7=F8Observedemandinperiod5tobeD5=10000Forecasterrorinperiod5,E5=F5-D5=19500-10000=9500Reviseestimateoflevelinperiod5:L5=(D5+D4+D3+D2)/4=(10000+34000+23000+13000)/4=20000F6=L5=20000,32,簡單指數平滑法,UsedwhendemandhasnoobservabletrendorseasonalitySystematiccomponentofdemand=levelInitialestimateoflevel,L0,assumedtobetheaverageofallhistoricaldataL0=Sum(i=1ton)Di/nCurrentforecastforallfutureperiodsisequaltothecurrentestimateofthelevelandisgivenasfollows:Ft+1=LtandFt+n=LtAfterobservingdemandDt+1,revisetheestimateofthelevel:Lt+1=aDt+1+(1-a)LtLt+1=Sum(n=0tot+1)a(1-a)nDt+1-n,33,簡單指數平滑法,FromTahoeSaltdata,forecastdemandforperiod1usingexponentialsmoothingL0=averageofall12periodsofdata=Sum(i=1to12)Di/12=22083F1=L0=22083Observeddemandforperiod1=D1=8000Forecasterrorforperiod1,E1,isasfollows:E1=F1-D1=22083-8000=14083Assuminga=0.1,revisedestimateoflevelforperiod1:L1=aD1+(1-a)L0=(0.1)(8000)+(0.9)(22083)=20675F2=L1=20675Notethattheestimateoflevelforperiod1islowerthaninperiod0,34,HoltsModel,AppropriatewhenthedemandisassumedtohavealevelandtrendinthesystematiccomponentofdemandbutnoseasonalityObtaininitialestimateoflevelandtrendbyrunningalinearregressionofthefollowingform:Dt=at+bT0=aL0=bInperiodt,theforecastforfutureperiodsisexpressedasfollows:Ft+1=Lt+TtFt+n=Lt+nTt,35,HoltsModel,Afterobservingdemandforperiodt,revisetheestimatesforlevelandtrendasfollows:Lt+1=aDt+1+(1-a)(Lt+Tt)Tt+1=b(Lt+1-Lt)+(1-b)Tta=smoothingconstantforlevelb=smoothingconstantfortrendExample:TahoeSaltdemanddata.Forecastdemandforperiod1usingHoltsmodel(trendcorrectedexponentialsmoothing)Usinglinearregression,L0=12015(linearintercept)T0=1549(linearslope),36,HoltsModel,Forecastforperiod1:F1=L0+T0=12015+1549=13564Observeddemandforperiod1=D1=8000E1=F1-D1=13564-8000=5564Assumea=0.1,b=0.2L1=aD1+(1-a)(L0+T0)=(0.1)(8000)+(0.9)(13564)=13008T1=b(L1-L0)+(1-b)T0=(0.2)(13008-12015)+(0.8)(1549)=1438F2=L1+T1=13008+1438=14446F5=L1+4T1=13008+(4)(1438)=18760,37,WintersModel,Appropriatewhenthesystematiccomponentofdemandisassumedtohavealevel,trend,andseasonalfactorSystematiccomponent=(level+trend)(seasonalfactor)AssumeperiodicitypObtaininitialestimatesoflevel(L0),trend(T0),seasonalfactors(S1,Sp)usingprocedureforstaticforecastingInperiodt,theforecastforfutureperiodsisgivenby:Ft+1=(Lt+Tt)(St+1)andFt+n=(Lt+nTt)St+n,38,WintersModel,Afterobservingdemandforperiodt+1,reviseestimatesforlevel,trend,andseasonalfactorsasfollows:Lt+1=a(Dt+1/St+1)+(1-a)(Lt+Tt)Tt+1=b(Lt+1-Lt)+(1-b)TtSt+p+1=g(Dt+1/Lt+1)+(1-g)St+1a=smoothingconstantforlevelb=smoothingconstantfortrendg=smoothingconstantforseasonalfactor,39,WintersModel,Example:TahoeSaltdata.Forecastdemandforperiod1usingWintersmodel.Initialestimatesoflevel,trend,andseasonalfactorsareobtainedasinthestaticforecastingcase,40,WintersModel,L0=18439T0=524S1=0.47,S2=0.68,S3=1.17,S4=1.67F1=(L0+T0)S1=(18439+524)(0.47)=8913Theobserveddemandforperiod1=D1=8000Forecasterrorforperiod1=E1=F1-D1=8913-8000=913Assumea=0.1,b=0.2,g=0.1;reviseestimatesforlevelandtrendforperiod1andforseasonalfactorforperiod5L1=a(D1/S1)+(1-a)(L0+T0)=(0.1)(8000/0.47)+(0.9)(18439+524)=18769T1=b(L1-L0)+(1-b)T0=(0.2)(18769-18439)+(0.8)(524)=485S5=g(D1/L1)+(1-g)S1=(0.1)(8000/18769)+(0.9)(0.47)=0.47F2=(L1+T1)S2=(18769+485)(0.68)=13093,41,預測誤差,類型偏差(bias)與隨機誤差(randomerror)偏差的原因未涵蓋正確之變數錯誤的變數關係錯誤的趨勢線錯誤的季節需求修正未發現的長期趨勢,42,預測誤差的衡量指標,Forecasterror=Et=Ft-Dt