第六章供应链中的需求预测

1、1-1Copyright 2013 Pearson Education.Copyright 2013 Pearson Education.Copyright 2013 Pearson Education.1-1Copyright 2013 Pearson Education.1-1Copyright 2013 Pearson Education.7-1Copyright 2013 Pearson Education.第七章Demand Forecastingin a Supply Chain7-2Copyright 2013 Pearson Education.Learning Objecti

2、ves1. Understand the role of forecasting for both an enterprise and a supply chain.2. Identify the components of a demand forecast.3. Forecast demand in a supply chain given historical demand data using time-series methodologies.4. Analyze demand forecasts to estimate forecast error.7-3Copyright 201

3、3 Pearson Education.Role of Forecasting in a Supply ChainThe basis for all planning decisions in a supply chainUsed for both push and pull processes Production scheduling, inventory, aggregate planning Sales force allocation, promotions, new production introduction Plant/equipment investment, budget

4、ary planning Workforce planning, hiring, layoffsAll of these decisions are interrelated7-4Copyright 2013 Pearson Education.Characteristics of Forecasts1. Forecasts are always inaccurate and should thus include both the expected value of the forecast and a measure of forecast error2. Long-term foreca

5、sts are usually less accurate than short-term forecasts3. Aggregate forecasts are usually more accurate than disaggregate forecasts4. In general, the farther up the supply chain a company is, the greater is the distortion of information it receives7-5Copyright 2013 Pearson Education.Components and M

6、ethodsCompanies must identify the factors that influence future demand and then ascertain the relationship between these factors and future demand Past demand Lead time of product replenishment Planned advertising or marketing efforts Planned price discounts State of the economy Actions that competi

7、tors have taken7-6Copyright 2013 Pearson Education.Components and Methods1. Qualitative Primarily subjective Rely on judgment2. Time Series Use historical demand only Best with stable demand3. Causal Relationship between demand and some other factor4. Simulation Imitate consumer choices that give ri

8、se to demand7-7Copyright 2013 Pearson Education.Components of an ObservationObserved demand (O) = systematic component (S) + random component (R)Systematic component expected value of demand Level (current deseasonalized demand) Trend (growth or decline in demand) Seasonality (predictable seasonal f

9、luctuation)Random component part of forecast that deviates from systematic componentForecast error difference between forecast and actual demand7-8Copyright 2013 Pearson Education.Basic Approach1. Understand the objective of forecasting.2. Integrate demand planning and forecasting throughout the sup

10、ply chain.3. Identify the major factors that influence the demand forecast.4. Forecast at the appropriate level of aggregation.5. Establish performance and error measures for the forecast.7-9Copyright 2013 Pearson Education.Time-Series Forecasting MethodsThree ways to calculate the systematic compon

11、ent MultiplicativeS = level x trend x seasonal factor AdditiveS = level + trend + seasonal factor MixedS = (level + trend) x seasonal factor7-10Copyright 2013 Pearson Education.Static MethodsSystematic component =(level+trend)seasonal factorFt+l=L+(t+l)TSt+lwhereL= estimate of level at t = 0 T= esti

12、mate of trendSt= estimate of seasonal factor for Period tDt= actual demand observed in Period tFt= forecast of demand for Period t7-11Copyright 2013 Pearson Education.Tahoe SaltYearQuarterPeriod, tDemand, Dt1218,00013213,00014323,00021434,00022510,00023618,00024723,00031838,00032912,000331013,000341

13、132,000411241,000Table 7-17-12Copyright 2013 Pearson Education.Tahoe SaltFigure 7-17-13Copyright 2013 Pearson Education.Estimate Level and TrendPeriodicity p = 4, t = 3Dt=Dt(p/2)+Dt+(p/2)+2Dii=t+1(p/2)t1+(p/2)/(2p) for p evenDi/p for p oddi=t(p1)/2t+(p1)/2Dt=Dt(p/2)+Dt+(p/2)+2Dii=t+1(p/2)t1+(p/2)/(2

14、p)=D1+D5+2Dii=24/ 87-14Copyright 2013 Pearson Education.Tahoe SaltFigure 7-27-15Copyright 2013 Pearson Education.Tahoe SaltFigure 7-3A linear relationship exists between the deseasonalized demand and time based on the change in demand over timeDt=L+Tt7-16Copyright 2013 Pearson Education.Estimating S

15、easonal FactorstttDDS =Figure 7-47-17Copyright 2013 Pearson Education.Estimating Seasonal FactorsSi=Sjp+1j=0r1rS1=(S1+S5+S9) / 3 =(0.42+0.47+0.52) / 3 = 0.47S2=(S2+S6+S10) / 3 =(0.67+0.83+0.55) / 3 = 0.68S3=(S3+S7+S11) / 3 =(1.15+1.04+1.32) / 3 =1.17S4=(S4+S8+S12) / 3 =(1.66+1.68+1.66) / 3 =1.67F13=

16、(L+13T)S13=(18,439+13524)0.47 =11,868F14=(L+14T)S14=(18,439+14524)0.68 =17,527F15=(L+15T)S15=(18,439+15524)1.17 = 30,770F16=(L+16T)S16=(18,439+16524)1.67 = 44,7947-18Copyright 2013 Pearson Education.Adaptive ForecastingThe estimates of level, trend, and seasonality are adjusted after each demand obs

17、ervationEstimates incorporate all new data that are observed7-19Copyright 2013 Pearson Education.Adaptive ForecastingFt+1=(Lt+lTt)St+1whereLt=estimate of level at the end of Period t Tt=estimate of trend at the end of Period t St=estimate of seasonal factor for Period t Ft=forecast of demand for Per

18、iod t (made Period t 1 or earlier)Dt=actual demand observed in Period t Et=Ft Dt = forecast error in Period t7-20Copyright 2013 Pearson Education.Steps in Adaptive ForecastingInitialize Compute initial estimates of level (L0), trend (T0), and seasonal factors (S1,Sp)Forecast Forecast demand for peri

19、od t + 1 Estimate error Compute error Et+1 = Ft+1 Dt+1 Modify estimates Modify the estimates of level (Lt+1), trend (Tt+1), and seasonal factor (St+p+1), given the error Et+17-21Copyright 2013 Pearson Education.Moving AverageUsed when demand has no observable trend or seasonalitySystematic component

20、 of demand = levelThe level in period t is the average demand over the last N periods Lt = (Dt + Dt-1 + + DtN+1) / NFt+1 = Lt and Ft+n = Lt After observing the demand for period t + 1, revise the estimatesLt+1 = (Dt+1 + Dt + + Dt-N+2) / N, Ft+2 = Lt+17-22Copyright 2013 Pearson Education.Moving Avera

21、ge ExampleA supermarket has experienced weekly demand of milk of D1 = 120, D2 = 127, D3 = 114, and D4 = 122 gallons over the past four weeks Forecast demand for Period 5 using a four-period moving average What is the forecast error if demand in Period 5 turns out to be 125 gallons?7-23Copyright 2013

22、 Pearson Education.Moving Average ExampleL4= (D4 + D3 + D2 + D1)/4 = (122 + 114 + 127 + 120)/4 = 120.75Forecast demand for Period 5F5 = L4 = 120.75 gallonsError if demand in Period 5 = 125 gallonsE5 = F5 D5 = 125 120.75 = 4.25Revised demandL5 = (D5 + D4 + D3 + D2)/4= (125 + 122 + 114 + 127)/4 = 1227

23、-24Copyright 2013 Pearson Education.Simple Exponential SmoothingUsed when demand has no observable trend or seasonalitySystematic component of demand = levelInitial estimate of level, L0, assumed to be the average of all historical data7-25Copyright 2013 Pearson Education.Simple Exponential Smoothin

24、gLt+1=aDt+1+(1a)LtRevised forecast using smoothing constant 0 a 1L0=1nDii=1nGiven data for Periods 1 to nFt+1=Lt and Ft+n=LtCurrent forecastLt+1=a(1a)nDt+1n+(1a)tD1n=0t1Thus7-26Copyright 2013 Pearson Education.Simple Exponential SmoothingSupermarket dataL0=Dii=14/ 4 =120.75F1=L0=120.75E1 = F1 D1 = 1

25、20.75 120 = 0.75L1=aD1+(1a)L0= 0.1120+0.9120.75 =120.687-27Copyright 2013 Pearson Education.Trend-Corrected Exponential Smoothing (Holts Model)Appropriate when the demand is assumed to have a level and trend in the systematic component of demand but no seasonalitySystematic component of demand = lev

26、el + trend7-28Copyright 2013 Pearson Education.Trend-Corrected Exponential Smoothing (Holts Model)Obtain initial estimate of level and trend by running a linear regressionDt = at + bT0 = a, L0 = bIn Period t, the forecast for future periods isFt+1 = Lt + Tt and Ft+n = Lt + nTt Revised estimates for

27、Period tLt+1 = aDt+1 + (1 a)(Lt + Tt)Tt+1 = b(Lt+1 Lt) + (1 b)Tt7-29Copyright 2013 Pearson Education.Trend-Corrected Exponential Smoothing (Holts Model)MP3 player demandD1 = 8,415, D2 = 8,732, D3 = 9,014, D4 = 9,808, D5 = 10,413, D6 = 11,961a = 0.1, b = 0.2Using regression analysisL0 = 7,367 and T0

28、= 673Forecast for Period 1F1 = L0 + T0 = 7,367 + 673 = 8,0407-30Copyright 2013 Pearson Education.Trend-Corrected Exponential Smoothing (Holts Model)Revised estimateL1= aD1 + (1 a)(L0 + T0)= 0.1 x 8,415 + 0.9 x 8,040 = 8,078T1= b(L1 L0) + (1 b)T0 = 0.2 x (8,078 7,367) + 0.8 x 673 = 681With new L1F2 =

29、 L1 + T1 = 8,078 + 681 = 8,759ContinuingF7 = L6 + T6 = 11,399 + 673 = 12,0727-31Copyright 2013 Pearson Education.Trend- and Seasonality-Corrected Exponential SmoothingAppropriate when the systematic component of demand is assumed to have a level, trend, and seasonal factorSystematic component = (lev

30、el + trend) x seasonal factorFt+1 = (Lt + Tt)St+1 and Ft+l = (Lt + lTt)St+l7-32Copyright 2013 Pearson Education.Trend- and Seasonality-Corrected Exponential SmoothingAfter observing demand for period t + 1, revise estimates for level, trend, and seasonal factorsLt+1 = a(Dt+1/St+1) + (1 a)(Lt + Tt)Tt

31、+1 = b(Lt+1 Lt) + (1 b)TtSt+p+1 = g(Dt+1/Lt+1) + (1 g)St+1 a = smoothing constant for levelb = smoothing constant for trendg = smoothing constant for seasonal factor7-33Copyright 2013 Pearson Education.Winters ModelL0 = 18,439 T0 = 524S1= 0.47, S2 = 0.68, S3 = 1.17, S4 = 1.67F1 = (L0 + T0)S1 = (18,4

32、39 + 524)(0.47) = 8,913The observed demand for Period 1 = D1 = 8,000Forecast error for Period 1 = E1 = F1 D1 = 8,913 8,000 = 9137-34Copyright 2013 Pearson Education.Winters ModelAssume a = 0.1, b = 0.2, g = 0.1; revise estimates for level and trend for period 1 and for seasonal factor for Period 5L1

33、= a(D1/S1) + (1 a)(L0 + T0) = 0.1 x (8,000/0.47) + 0.9 x (18,439 + 524) = 18,769T1= b(L1 L0) + (1 b)T0 = 0.2 x (18,769 18,439) + 0.8 x 524 = 485S5= g(D1/L1) + (1 g)S1 = 0.1 x (8,000/18,769) + 0.9 x 0.47 = 0.47F2 = (L1 + T1)S2 = (18,769 + 485)0.68 = 13,0937-35Copyright 2013 Pearson Education.Time Ser

34、ies ModelsForecasting MethodApplicabilityMoving averageNo trend or seasonalitySimple exponential smoothingNo trend or seasonalityHolts modelTrend but no seasonalityWinters modelTrend and seasonality7-36Copyright 2013 Pearson Education.Measures of Forecast ErrorEt=FtDtMSEn=1nEt2t=1nAt=Et MADn=1nAtt=1

35、ns=1.25MADMAPEn=EtDt100t=1nnbiasn=Ett=1nTStbiastMADtat=at1r+at1=1r1rtDeclining alpha7-37Copyright 2013 Pearson Education.Selecting the Best Smoothing ConstantFigure 7-57-38Copyright 2013 Pearson Education.Selecting the Best Smoothing ConstantFigure 7-67-39Copyright 2013 Pearson Education.Forecasting

36、 Demand at Tahoe SaltMoving averageSimple exponential smoothingTrend-corrected exponential smoothingTrend- and seasonality-corrected exponential smoothing7-40Copyright 2013 Pearson Education.Forecasting Demand at Tahoe SaltFigure 7-77-41Copyright 2013 Pearson Education.Forecasting Demand at Tahoe Sa

37、ltMoving averageL12 = 24,500F13 = F14 = F15 = F16 = L12 = 24,500s = 1.25 x 9,719 = 12,1487-42Copyright 2013 Pearson Education.Forecasting Demand at Tahoe SaltFigure 7-87-43Copyright 2013 Pearson Education.Forecasting Demand at Tahoe SaltSingle exponential smoothingL0 = 22,083L12 = 23,490F13 = F14 =

38、F15 = F16 = L12 = 23,490s = 1.25 x 10,208 = 12,7617-44Copyright 2013 Pearson Education.Forecasting Demand at Tahoe SaltFigure 7-97-45Copyright 2013 Pearson Education.Forecasting Demand at Tahoe SaltTrend-Corrected Exponential SmoothingL0 = 12,015 and T0 = 1,549L12 = 30,443 and T12 = 1,541F13 = L12 +

39、 T12 = 30,443 + 1,541 = 31,984F14 = L12 + 2T12 = 30,443 + 2 x 1,541 = 33,525F15 = L12 + 3T12 = 30,443 + 3 x 1,541 = 35,066F16 = L12 + 4T12 = 30,443 + 4 x 1,541 = 36,607s = 1.25 x 8,836 = 11,0457-46Copyright 2013 Pearson Education.Forecasting Demand at Tahoe SaltFigure 7-107-47Copyright 2013 Pearson

40、Education.Forecasting Demand at Tahoe SaltTrend- and Seasonality-CorrectedL0 = 18,439 T0 =524S1 = 0.47 S2 = 0.68 S3 = 1.17 S4 = 1.67L12 = 24,791 T12 = 532F13 = (L12 + T12)S13 = (24,791 + 532)0.47 = 11,940F14 = (L12 + 2T12)S13 = (24,791 + 2 x 532)0.68 = 17,579F15 = (L12 + 3T12)S13 = (24,791 + 3 x 532

41、)1.17 = 30,930F16 = (L12 + 4T12)S13 = (24,791 + 4 x 532)1.67 = 44,928s = 1.25 x 1,469 = 1,8367-48Copyright 2013 Pearson Education.Forecasting Demand at Tahoe SaltForecasting MethodMADMAPE (%)TS RangeFour-period moving average9,719491.52 to 2.21Simple exponential smoothing10,208591.38 to 2.15Holts model8,836522.15 to 2.00Winters model1,46982.74 to 4.00Table 7-27-49Copyright 2013 Pearson Education.The Role of IT in ForecastingForecasting module is core supply chain softwareCan b