第八章預(yù)測供應(yīng)鏈需求ppt課件

1、預(yù)測供應(yīng)鏈需求 CR (2004) Prentice Hall, Inc.Chapter 8I hope youll keep in mind that economic forecasting is far from a perfect science. If recent historys any guide, the experts have some explaining to do about what they told us had to happen but never did.Ronald Reagan, 19841.產(chǎn)品方案三角形Product in the Plannin

2、g TriangleCR (2004) Prentice Hall, Inc.PLANNINGORGANIZINGCONTROLLINGTransport StrategyTransport fundamentalsTransport decisionsCustomer service goalsThe productLogistics serviceOrd. proc. & info. sys.Inventory StrategyForecastingInventory decisionsPurchasing and supply scheduling decisionsStorage fu

3、ndamentalsStorage decisionsLocation StrategyLocation decisionsThe network planning process 方案 組織 控制Transport StrategyTransport fundamentalsTransport decisionsCustomer service goalsThe productLogistics serviceOrd. proc. & info. sys.Inventory StrategyForecastingInventory decisionsPurchasing and supply

4、 scheduling decisionsStorage fundamentalsStorage decisionsLocation StrategyLocation decisionsThe network planning process 庫存戰(zhàn)略 預(yù)測客戶效力目的采購和供應(yīng)時間決策存儲根底知識存儲決策產(chǎn)品物流效力訂單管理和信息系統(tǒng) 庫存決策 運輸戰(zhàn)略 運輸根底知識 運輸決策 選址戰(zhàn)略 選址決策 網(wǎng)絡(luò)規(guī)劃流程2.Forecasting in Inventory StrategyCR (2004) Prentice Hall, Inc.PLANNINGORGANIZINGCONTROLLIN

5、GTransport StrategyTransport fundamentalsTransport decisionsCustomer service goalsThe productLogistics serviceOrd. proc. & info. sys.Inventory StrategyForecastingInventory decisionsPurchasing and supply scheduling decisionsStorage fundamentalsStorage decisionsLocation StrategyLocation decisionsThe n

6、etwork planning processPLANNINGORGANIZINGCONTROLLINGTransport StrategyTransport fundamentalsTransport decisionsCustomer service goalsThe productLogistics serviceOrd. proc. & info. sys.Inventory StrategyForecastingInventory decisionsPurchasing and supply scheduling decisionsStorage fundamentalsStorag

7、e decisionsLocation StrategyLocation decisionsThe network planning process3.供應(yīng)鏈預(yù)測什么Demand, sales or requirements需求，銷售或懇求Purchase prices購買價錢Replenishment and delivery times補給和交貨時間CR (2004) Prentice Hall, Inc.4.8.1需求預(yù)測1.需求的時間和空間特征Spatial versus Temporal Demand2.尖峰需求和規(guī)律性的需求Lumpy versus Regular Demand3.

8、派生需求和獨立需求Derived versus Independent Demand5.CR (2004) Prentice Hall, Inc.典型時間序列方式Typical Time Series Patterns:隨機Random隨機性或程度開展的需求，無趨勢或季節(jié)性要素6.CR (2004) Prentice Hall, Inc.典型時間序列方式Typical Time Series Patterns:隨機有趨勢Random with Trend0501001502002500510152025TimeSalesActual salesAverage sales隨機性需求，上升趨勢，無

9、季節(jié)性要素7.CR (2004) Prentice Hall, Inc.Typical Time Series Patterns:Random with Trend & Seasonal隨機性需求，有趨勢，季節(jié)性要素8.CR (2004) Prentice Hall, Inc.Typical Time Series Patterns:LumpyTimeSales尖峰需求方式9.CR (2004) Prentice Hall, Inc.8.2預(yù)測方法1.定性方法Qualitative 調(diào)查法Surveys 專家系統(tǒng)Expert systems or rule-based2.歷史映射法時間序列分析

10、Historical projection挪動平均Moving average指數(shù)平滑Exponential smoothing3.因果或聯(lián)想法Causal or associative回歸分析Regression analysis4.協(xié)同Collaborative10.8.3 對物流管理者有用的方法8.3.1.挪動平均法Moving AverageBasic formulawherei = time periodt = current time periodn = length of moving average in periods Ai = demand in period iCR (2

11、004) Prentice Hall, Inc.11.Example 3-Month Moving Average ForecastingMonth, iDemand formonth, iTotal demandduring past 3months3-monthmovingaverage.20120.21130360/312022110380/3126.6723140 360/312024110380/3126.672513026?CR (2004) Prentice Hall, Inc.12. 加權(quán)挪動平均Weighted Moving Averageperiod current in

12、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 )( weightsIf1.1133221112211=-+=-+-+-+-+=+=+-=ttttttntnttttniinnFAFFAFMAAAA

13、AAMAwwwhereAwAwAwMAaaaaaaaaaaaa13. I. Level only Ft+1= At + (1-)Ft II. Level and trend St= aAt + (1-a)(St-1 + Tt-1) Tt= (St - St-1) + (1-)Tt-1 Ft+1= St + TtIII. Level, trend, and seasonality St= a(At/It-L) + (1-a)(St-1 + Tt-1) It= g(At/St) + (1-g)It-L Tt= (St - St-1) + (1-)Tt-1 Ft+1= (St + Tt)It-L+1

14、where L is the time period of one full seasonal cycle. IV. Forecast errorMAD=|At-=FNttN|1orS(AF)NFtt2t1N=-=and SF 1.25MAD.8.3.2.指數(shù)平滑公式Exponential Smoothing FormulasCR (2004) Prentice Hall, Inc.14.CR (2004) Prentice Hall, Inc.Example Exponential Smoothing ForecastingTime series data1234Last year12007

15、009001100This year14001000?QuarterGetting startedAssume = 0.2. Average first 4 quarters of data and use for previous forecast, say Fo15.CR (2004) Prentice Hall, Inc.Example (Contd)Begin forecastingFirst quarter of 2nd yearSecond quarter of 2nd year16.CR (2004) Prentice Hall, Inc.Example (Contd)Third

16、 quarter of 2nd yearSummarizing1234Last year12007009001100This year14001000?Fore- cast100010801064Quarter17.CR (2004) Prentice Hall, Inc.Example (Contd)Measuring forecast error as MAD絕對差or RMSE (std. error of forecast) 規(guī)范差18.CR (2004) Prentice Hall, Inc.Example (Contd)Using SF and assuming n=2Note T

17、o compute a reasonable average for SF, n should range over at least one seasonal cycle in most cases.19.SF= 408Example (Contd)Range of the forecastF3=1064RangeIf forecast errors are normally distributed and the forecast is at the mean of the distribution, i.e., ,a forecast confidence band can be com

18、puted. The error distribution for the level-only model results is:Bias should be 0 or close to it in a model of good fitCR (2004) Prentice Hall, Inc.8-1920.CR (2004) Prentice Hall, Inc.Example (Contd)From a normal distribution table, z95%=1.96. The actual time series value Y for quarter 3 is expecte

19、d to range between:or264 Y408(96.11064)(3=FSzFY21.CR (2004) Prentice Hall, Inc.校正趨勢Correcting for Trend in ESThe trend-corrected model is St = At (1 )(St-1 Tt-1) Tt = (St St-1) (1 )Tt-1Ft+1 = St Ttwhere S is the forecast without trend correction.Assuming = 0.2, = 0.3, S-1 = 975, and T-1

20、 = 0 Forecast for quarter 1 of this yearS0 = 0.2(1100) 0.8(975 + 0) = 1000T0 = 0.3(1000 975) 0.7(0) = 8F1 = 1000 8 = 100822.Forecast for quarter 2 of this year S0 T0S1 = 0.2(1400) 0.8(1000 8) = 1086.4T1 = 0.3(1086.4 1000) 0.7(8) = 31.5F2 = 1086.4 31.5 = 1117.9Forecast for quarter 3 of this yearS2 =

21、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, or 1119CR (2004) Prentice Hall, Inc.Correcting for Trend in ES (Contd)23.CR (2004) Prentice Hall, Inc.Correcting for Trend in ES (Contd)Summarizing with trend correction 1234Last year12007009001100T

22、his year14001000?Fore- cast100811181119Quarter24.a01Fore-casterrorCR (2004) Prentice Hall, Inc.Optimizing for ESMinimize averageforecast error8-2425.CR (2004) Prentice Hall, Inc.Controlling Model Fit in ESTracking signal monitors the fit of the model to detect when the model no longer accurately rep

23、resents the datawhere the Mean Squared Error (MSE) isIf tracking signal exceeds a specified value (control limit), revise smoothing constant(s).n is a reasonable numberof past periods dependingon the application8-25經(jīng)典時間序列分解模型Classic Time Series Decomposition ModelBasic formulation F = T S C

24、Rwhere F = 需求預(yù)測forecast T = 趨勢程度trend S = 季節(jié)指數(shù)seasonal index C = 周期指數(shù)cyclical index (usually 1) R = 殘差指數(shù)residual index (usually 1)Some time series data1234Last year12007009001100This year14001000?QuarterCR (2004) Prentice Hall, Inc.27.CR (2004) Prentice Hall, Inc.Classic Time Series Decomposition Mo

25、del (Contd)Trend estimationUse simple regression analysis to find the trend equation of the form T = a bt. Recall the basic formulas:and28.CR (2004) Prentice Hall, Inc.Classic Time Series Decomposition Model (Contd)Redisplaying the data for ease of computation.tYYtt2112001200127001400439002700941100

26、440016514007000256 6 1000600036 t=21Y=6300Yt=22700t2=9129.Classic Time Series Decomposition Model (Contd)Hence,andthenT = 920.01 37.14tForecast for 3rd quarter of this year is:T = 920.01 37.14(7) = 1179.99CR (2004) Prentice Hall, Inc.30.CR (2004) Prentice Hall, Inc.Classic Time Series Decomposition

27、Model (Contd)Compute seasonal indicesThe procedure is to form a ratio of actual demand to the estimated demand for a full seasonal cycle (4 quarters). One way is as follows.tYTSeasonalIndex, St11200957.15*1.25*2700994.290.7039001031.430.87411001068.571.03*T=920.01 37.14(1)=957.15*St=1200/957.15=1.25

28、31.CR (2004) Prentice Hall, Inc.Classic Time Series Decomposition Model (Contd)Compute seasonal indicesSince C and R index values are usually 1, the adjusted seasonal forecast for the 3rd quarter of this year would be:F7 = 1179.99 x 0.87 = 1026.59 32.CR (2004) Prentice Hall, Inc.Classic Time Series

29、Decomposition Model (Contd)Forecast rangeThe standard error of the forecast is:SF預(yù)測的規(guī)范誤差Yt第t期的實踐需求Ft第t期的預(yù)測值N預(yù)測期t的數(shù)量33.CR (2004) Prentice Hall, Inc.Classic Time Series Decomposition Model (Contd)QtrtYtTtStFt111200957.151.2522700994.290.70339001031.430.874411001068.571.031514001105.711.271404.25*26100

30、01142.850.881005.71*371179.991026.59*1105.71x1.27=1404.25*1142.85x0.88=1005.71Tabled computations34.CR (2004) Prentice Hall, Inc.Classic Time Series Decomposition Model (Contd)There is inadequate data to make a meaningful estimate of SF. However, we would proceed as follows:Then,Ft z(SF) Y Ft z(SF)N

31、ormally, a larger sample size would be used giving a positive value for SF35.CR (2004) Prentice Hall, Inc.8.3.4回歸分析Regression Analysis根本式Basic formulationF = o 1X1 2X2 nXn ExampleBobbie Brooks, a manufacturer of teenage womens clothes, was able to forecast seasonal sales from the following relations

32、hipF = constant 1(no. nonvendor accounts) 2(consumer debt ratio)36.CR (2004) Prentice Hall, Inc.Sales period(1)Timeperiod, t(2)Sales (Dt )($000s)(3)Dt t(4)t2(5)Trend value(Tt)(6)=(2)/(5)SeasonalindexForecast($000s)Summer1$9,4589,4581$12,0530.78Trans-season211,54223,084412,5390.92Fall314,48943,467913

33、,0251.11Holiday415,75463,0161613,5121.17Spring517,26986,3452513,9981.23Summer611,51469,0843614,4840.79Trans-season712,62388,3614914,9700.84Fall816,086128,6886415,4561.04Holiday918,098162,8828115,9421.14Spring1021,030210,30010016,4281.28Summer1112,788140,66812116,9150.76Trans-season1216,072192,864144

34、17,4010.92Fall13?17,887*$18,602Holiday14 ? 18,373*20,945Totals78176,7231,218,217650Regression Forecasting Using Bobbie Brooks Sales DataN = 12 Dt t = 1,218,217 t2 = 650 =(,/),.176723121472692 =781265/.Regression equation is: Tt = 11,567.08 + 486.13t *Forecasted values8-35組合模型預(yù)測 Combined Mode

35、l ForecastingCombines the results of several models to improve overall accuracy. Consider the seasonal forecasting problem of Bobbie Brooks. Four models were used. Three of them were two forms of exponential smoothing and a regression model. The fourth was managerial judgement used by a vice preside

36、nt of marketing using experience. Each forecast is then weighted according to its respective error as shown below.Calculation of forecast weightsModeltype(1)Forecasterror(2)Percentof totalerror(3)=1.0/(2)Inverse oferrorproportion(4)=(3)/48.09ModelweightsMJ9.00.4662.150.04R0.70.03627.770.58ES11.20.06

37、315.870.33ES28.40.4352.300.05 Total19.31.00048.091.00CR (2004) Prentice Hall, Inc.38.Combined Model ForecastingCombines the results of several models to improve overall accuracy. Consider the seasonal forecasting problem of Bobbie Brooks. Four models were used. Three of them were two forms of expone

38、ntial smoothing and a regression model. The fourth was managerial judgement used by a vice president of marketing using experience. Each forecast is then weighted according to its respective error as shown below.Calculation of forecast weightsModeltype(1)Forecasterror(2)Percentof totalerror(3)=1.0/(

39、2)Inverse oferrorproportion(4)=(3)/48.09ModelweightsMJ9.00.4662.150.04R0.70.03627.770.58ES11.20.06315.870.33ES28.40.4352.300.05 Total19.31.00048.091.00CR (2004) Prentice Hall, Inc.39.Combined Model Forecasting (Contd)Weighted Average Fall Season Forecast Using Multiple Forecasting TechniquesForecast

40、type(1)Modelforecast(2)Weightingfactor(3)=(1) (2)WeightedproportionRegressionmodel (R)$20,367,0000.58$11,813,000ExponentialSmoothingES120,400,0000.336,732,000Combinedexponentialsmoothing-regressionmodel(ES2)17,660,0000.05883,000Managerialjudgment(MJ)19,500,0000.04 780,000 Weighted average forecast $

41、20,208,000CR (2004) Prentice Hall, Inc.40.CR (2004) Prentice Hall, Inc.Multiple Model Errors8-3841.CR (2004) Prentice Hall, Inc.Actions When Forecasting is Not AppropriateSeek information directly from customersCollaborate with other channel membersApply forecasting methods with caution (may work wh

42、ere forecast accuracy is not critical)Delay supply response until demand becomes clearShift demand to other periods for better supply responseDevelop quick response and flexible supply systems42.CR (2004) Prentice Hall, Inc.8.4 物流管理者遇到的特殊預(yù)測問題1.啟動2.尖峰需求3.地域性預(yù)測4.預(yù)測誤差43.CR (2004) Prentice Hall, Inc.協(xié)同預(yù)

43、測Collaborative Forecasting需求是塊狀或高度不確定Demand is lumpy or highly uncertainInvolves multiple participants each with a unique perspective“two heads are better than one目的是減少預(yù)測誤差Goal is to reduce forecast error預(yù)測過程本質(zhì)上是不穩(wěn)定的The forecasting process is inherently unstable44.CR (2004) Prentice Hall, Inc.Collab

44、orative ForecastingDemand is lumpy or highly uncertainInvolves multiple participants each with a unique perspective“two heads are better than oneGoal is to reduce forecast errorThe forecasting process is inherently unstable45.CR (2004) Prentice Hall, Inc.協(xié)同預(yù)測Collaborative Forecasting: 關(guān)鍵步驟Key Steps建立一個主要過程Establish a process champion確定所需信息和搜集流程Identify the needed Information and collection processes建立多來源信息和分配多權(quán)重的預(yù)測方法建立將預(yù)測轉(zhuǎn)換成各方所需信息的方法Create methods for translating forecast into form needed by each party建立實時預(yù)測和修正的過程Establish process for revising and updating forecast in real time創(chuàng)建預(yù)測方法