第八章预测供应链需求.ppt

预测供应链需求 CR 2004 PrenticeHall Inc Chapter8 Ihopeyou llkeepinmindthateconomicforecastingisfarfromaperfectscience Ifrecenthistory sanyguide theexpertshavesomeexplainingtodoaboutwhattheytoldushadtohappenbutneverdid RonaldReagan 1984 产品计划三角形ProductinthePlanningTriangle CR 2004 PrenticeHall Inc 库存战略 预测 客户服务目标 采购和供应时间决策 存储基础知识 存储决策 产品 物流服务 订单管理和信息系统 库存决策 运输战略 运输基础知识 运输决策 选址战略 选址决策 网络规划流程 ForecastinginInventoryStrategy CR 2004 PrenticeHall Inc 供应链预测什么 Demand salesorrequirements需求 销售或请求Purchaseprices购买价格Replenishmentanddeliverytimes补给和交货时间 CR 2004 PrenticeHall Inc 8 1需求预测 1 需求的时间和空间特征 SpatialversusTemporalDemand 2 尖峰需求和规律性的需求 LumpyversusRegularDemand 3 派生需求和独立需求 DerivedversusIndependentDemand CR 2004 PrenticeHall Inc 典型时间序列模式TypicalTimeSeriesPatterns 随机Random 随机性或水平发展的需求 无趋势或季节性因素 CR 2004 PrenticeHall Inc 典型时间序列模式TypicalTimeSeriesPatterns 随机有趋势RandomwithTrend 随机性需求 上升趋势 无季节性因素 CR 2004 PrenticeHall Inc TypicalTimeSeriesPatterns RandomwithTrend Seasonal 随机性需求 有趋势 季节性因素 CR 2004 PrenticeHall Inc TypicalTimeSeriesPatterns Lumpy 尖峰需求模式 CR 2004 PrenticeHall Inc 8 2预测方法 1 定性方法Qualitative调查法Surveys专家系统Expertsystemsorrule based2 历史映射法 时间序列分析Historicalprojection 移动平均Movingaverage指数平滑Exponentialsmoothing3 因果或联想法Causalorassociative回归分析Regressionanalysis4 协同Collaborative 8 3对物流管理者有用的方法8 3 1 移动平均法MovingAverage Basicformula wherei timeperiodt currenttimeperiodn lengthofmovingaverageinperiodsAi demandinperiodi CR 2004 PrenticeHall Inc Example3 MonthMovingAverageForecasting Month i Demandfor month i Totaldemand duringpast3 months 3 month moving average 20 120 21 130 360 3 120 22 110 380 3 126 67 23 140 360 3 120 24 110 380 3 126 67 25 130 26 CR 2004 PrenticeHall Inc 加权移动平均WeightedMovingAverage period current in forecast period current in demand actual period next for forecast 0 30 to 0 01 usually constant smoothing where 1 formula smoothing exponential only level basic the to reduces which 1 1 1 1 then form in exponential are weights If 1 1 1 3 3 2 2 1 1 1 2 2 1 1 t t t t t t n t n t t t t n i i n n F A F F A F MA A A A A A MA w w where A w A w A w MA a a a a a a a a a a a a 8 3 2 指数平滑公式ExponentialSmoothingFormulas CR 2004 PrenticeHall Inc CR 2004 PrenticeHall Inc ExampleExponentialSmoothingForecasting Timeseriesdata 1 2 3 4 Last year 1200 700 900 1100 This year 1400 1000 Quarter Gettingstarted Assume 0 2 Averagefirst4quartersofdataanduseforpreviousforecast sayFo CR 2004 PrenticeHall Inc Example Cont d Beginforecasting Firstquarterof2ndyear Secondquarterof2ndyear CR 2004 PrenticeHall Inc Example Cont d Thirdquarterof2ndyear Summarizing 1 2 3 4 Last year 1200 700 900 1100 This year 1400 1000 Fore cast 1000 1080 1064 Quarter CR 2004 PrenticeHall Inc Example Cont d MeasuringforecasterrorasMAD绝对差 orRMSE std errorofforecast 标准差 CR 2004 PrenticeHall Inc Example Cont d UsingSFandassumingn 2 NoteTocomputeareasonableaverageforSF nshouldrangeoveratleastoneseasonalcycleinmostcases S F 408 Example Cont d Rangeoftheforecast F3 1064 Range Ifforecasterrorsarenormallydistributedandtheforecastisatthemeanofthedistribution i e aforecastconfidencebandcanbecomputed Theerrordistributionforthelevel onlymodelresultsis Biasshouldbe0orclosetoitinamodelofgoodfit CR 2004 PrenticeHall Inc 8 19 CR 2004 PrenticeHall Inc Example Cont d Fromanormaldistributiontable z 95 1 96 TheactualtimeseriesvalueYforquarter3isexpectedtorangebetween or264 Y 1864 CR 2004 PrenticeHall Inc 校正趋势CorrectingforTrendinES Thetrend correctedmodelisSt At 1 St 1 Tt 1 Tt St St 1 1 Tt 1Ft 1 St TtwhereSistheforecastwithouttrendcorrection Assuming 0 2 0 3 S 1 975 andT 1 0Forecastforquarter1ofthisyearS0 0 2 1100 0 8 975 0 1000T0 0 3 1000 975 0 7 0 8F1 1000 8 1008 Forecastforquarter2ofthisyearS0T0S1 0 2 1400 0 8 1000 8 1086 4T1 0 3 1086 4 1000 0 7 8 31 5F2 1086 4 31 5 1117 9Forecastforquarter3ofthisyearS2 0 2 1000 0 8 1086 4 31 5 1094 3T2 0 3 1094 3 1086 4 0 7 31 5 24 4F3 1094 3 24 4 1118 7 or1119 CR 2004 PrenticeHall Inc CorrectingforTrendinES Cont d CR 2004 PrenticeHall Inc CorrectingforTrendinES Cont d Summarizingwithtrendcorrection 1 2 3 4 Last year 1200 700 900 1100 This year 1400 1000 Fore cast 1008 1118 1119 Quarter CR 2004 PrenticeHall Inc Optimizing forES Minimizeaverageforecasterror 8 24 CR 2004 PrenticeHall Inc ControllingModelFitinES Trackingsignalmonitorsthefitofthemodeltodetectwhenthemodelnolongeraccuratelyrepresentsthedata wheretheMeanSquaredError MSE is Iftrackingsignalexceedsaspecifiedvalue controllimit revisesmoothingconstant s nisareasonablenumberofpastperiodsdependingontheapplication 8 25 8 3 3经典时间序列分解模型ClassicTimeSeriesDecompositionModel BasicformulationF T S C RwhereF 需求预测forecastT 趋势水平trendS 季节指数seasonalindexC 周期指数cyclicalindex usually1 R 残差指数residualindex usually1 Sometimeseriesdata 1 2 3 4 Lastyear 1200 700 900 1100 Thisyear 1400 1000 Quarter CR 2004 PrenticeHall Inc CR 2004 PrenticeHall Inc ClassicTimeSeriesDecompositionModel Cont d Trendestimation UsesimpleregressionanalysistofindthetrendequationoftheformT a bt Recallthebasicformulas and CR 2004 PrenticeHall Inc ClassicTimeSeriesDecompositionModel Cont d Redisplayingthedataforeaseofcomputation t Y Yt t 2 1 1200 1200 1 2 700 1400 4 3 900 2700 9 4 1100 4400 16 5 1400 7000 25 6 6 1000 6000 36 t 21 Y 6300 Yt 22700 t 2 91 ClassicTimeSeriesDecompositionModel Cont d Hence and then T 920 01 37 14t Forecastfor3rdquarterofthisyearis T 920 01 37 14 7 1179 99 CR 2004 PrenticeHall Inc CR 2004 PrenticeHall Inc ClassicTimeSeriesDecompositionModel Cont d ComputeseasonalindicesTheprocedureistoformaratioofactualdemandtotheestimateddemandforafullseasonalcycle 4quarters Onewayisasfollows t Y T Seasonal Index S t 1 1200 957 15 1 25 2 700 994 29 0 70 3 900 1031 43 0 87 4 1100 1068 57 1 03 T 920 01 37 14 1 957 15 St 1200 957 15 1 25 CR 2004 PrenticeHall Inc ClassicTimeSeriesDecompositionModel Cont d ComputeseasonalindicesSinceCandRindexvaluesareusually1 theadjustedseasonalforecastforthe3rdquarterofthisyearwouldbe F7 1179 99x0 87 1026 59 CR 2004 PrenticeHall Inc ClassicTimeSeriesDecompositionModel Cont d ForecastrangeThestandarderroroftheforecastis SF 预测的标准误差Yt 第t期的实际需求Ft 第t期的预测值N 预测期t的数量 CR 2004 PrenticeHall Inc ClassicTimeSeriesDecompositionModel Cont d Qtr t Y t T t S t F t 1 1 1200 957 15 1 25 2 2 700 994 29 0 70 3 3 900 1031 43 0 87 4 4 1100 1068 57 1 03 1 5 1400 1105 71 1 27 1404 25 2 6 1000 1142 85 0 88 1005 71 3 7 1179 99 1026 59 1105 71x1 27 1404 25 1142 85x0 88 1005 71 Tabledcomputations CR 2004 PrenticeHall Inc ClassicTimeSeriesDecompositionModel Cont d ThereisinadequatedatatomakeameaningfulestimateofSF However wewouldproceedasfollows Then Ft z SF Y Ft z SF Normally alargersamplesizewouldbeusedgivingapositivevalueforSF CR 2004 PrenticeHall Inc 8 3 4回归分析RegressionAnalysis 基本式BasicformulationF o 1X1 2X2 nXn ExampleBobbieBrooks amanufacturerofteenagewomen sclothes wasabletoforecastseasonalsalesfromthefollowingrelationshipF constant 1 no nonvendoraccounts 2 consumerdebtratio CR 2004 PrenticeHall Inc Salesperiod 1 Time period t 2 Sales Dt 000s 3 Dt t 4 t2 5 Trendvalue Tt 6 2 5 Seasonal index Forecast 000s Summer 1 9 458 9 458 1 12 053 0 78 Trans season 2 11 542 23 084 4 12 539 0 92 Fall 3 14 489 43 467 9 13 025 1 11 Holiday 4 15 754 63 016 16 13 512 1 17 Spring 5 17 269 86 345 25 13 998 1 23 Summer 6 11 514 69 084 36 14 484 0 79 Trans season 7 12 623 88 361 49 14 970 0 84 Fall 8 16 086 128 688 64 15 456 1 04 Holiday 9 18 098 162 882 81 15 942 1 14 Spring 10 21 030 210 300 100 16 428 1 28 Summer 11 12 788 140 668 121 16 915 0 76 Trans season 12 16 072 192 864 144 17 401 0 92 Fall 13 17 887 18 602 Holiday 14 18 373 20 945 Totals 78 176 723 1 218 217 650 RegressionForecastingUsingBobbieBrooksSalesData N 12 Dt t 1 218 217 t2 650 176 723 12 14 726 92 78 12 6 5 Regressionequationis Tt 11 567 08 486 13t Forecastedvalues 8 35 8 3 5组合模型预测CombinedModelForecasting Combinestheresultsofseveralmodelstoimproveoverallaccuracy ConsidertheseasonalforecastingproblemofBobbieBrooks Fourmodelswereused Threeofthemweretwoformsofexponentialsmoothingandaregressionmodel Thefourthwasmanagerialjudgementusedbyavicepresidentofmarketingusingexperience Eachforecastisthenweightedaccordingtoitsrespectiveerrorasshownbelow Calculationofforecastweights CR 2004 PrenticeHall Inc CombinedModelForecasting Combinestheresultsofseveralmodelstoimproveoverallaccuracy ConsidertheseasonalforecastingproblemofBobbieBrooks Fourmodelswereused Threeofthemweretwoformsofexponentialsmoothingandaregressionmodel Thefourthwasmanagerialjudgementusedbyavicepresidentofmarketingusingexperience Eachforecastisthenweightedaccordingtoitsrespectiveerrorasshownbelow Calculationofforecastweights CR 2004 PrenticeHall Inc CombinedModelForecasting Cont d WeightedAverageFallSeasonForecastUsingMultipleForecastingTechniques CR 2004 PrenticeHall Inc CR 2004 PrenticeHall Inc MultipleModelErrors 8 38 CR 2004 PrenticeHall Inc ActionsWhenForecastingisNotAppropriate SeekinformationdirectlyfromcustomersCollaboratewithotherchannelmembersApplyforecastingmethodswithcaution mayworkwhereforecastaccuracyisnotcritical DelaysupplyresponseuntildemandbecomesclearShiftdemandtootherperiodsforbettersupplyresponseDevelopquickresponseandflexiblesupplysystems CR 2004 PrenticeHall Inc 8 4物流管理者遇到的特殊预测问题 1 启动2 尖峰需求3 地区性预测4 预测误差 CR 2004 PrenticeHall Inc 协同预测CollaborativeForecasting 需求是块状或高度不确定DemandislumpyorhighlyuncertainInvolvesmultipleparticipantseachwithauniqueperspective twoheadsarebetterthanone 目标是减少预测误差Goalistoreduceforecasterror预测过程本质上是不稳定的Theforecastingprocessisinherentlyunstable CR 2004 PrenticeHall Inc CollaborativeForecasting DemandislumpyorhighlyuncertainInvolvesmultipleparticipantseachwithauniqueperspective twoheadsarebetterthanone GoalistoreduceforecasterrorTheforecastingprocessisinherentlyunstable CR 2004 PrenticeHall Inc 协同预测CollaborativeForecasting 关键步骤KeySteps 建立一个主要过程Establishaprocesschampion确定所需信息和收集流程IdentifytheneededInformationandcollectionprocesses建立多来源信息和分配多权重的预测方法建立将预测转换成各方所需信息的方法Createmethodsfortranslatingforecastintoformneededbyeachparty建立实时预测和修正的过程Establishprocessforrevisingandupdatingforecastinrealtime创建预测方法Createmethodsforappraisingtheforecast协同预测带给各方的益处应该是明确而真实的Showthatthebenefitsofcollaborativeforecastingareobviousandreal CR 2004 PrenticeHall Inc CollaborativeForecasting KeySteps Establ