供應(yīng)鏈管理(第5-講)-資料下載頁
2024-10-19 10:57本頁面
【正文】 for period 1 = D1 = 8000 E1 = F1 D1 = 13564 8000 = 5564 Assume a = , b = L1 = aD1 + (1a)(L0+T0) = ()(8000) + ()(13564) = 13008 T1 = b(L1 L0) + (1b)T0 = ()(13008 12021) + ()(1549) = 1438 F2 = L1 + T1 = 13008 + 1438 = 14446 F5 = L1 + 4T1 = 13008 + (4)(1438) = 18760 169。 2021 Pearson Education 738 Trend and SeasonalityCorrected Exponential Smoothing ?Appropriate when the systematic ponent of demand is assumed to have a level, trend, and seasonal factor ?Systematic ponent = (level+trend)(seasonal factor) ?Assume periodicity p ?Obtain initial estimates of level (L0), trend (T0), seasonal factors (S1,…,S p) using procedure for static forecasting ?In period t, the forecast for future periods is given by: Ft+1 = (Lt+Tt)(St+1) and Ft+n = (Lt + nTt)St+n 169。 2021 Pearson Education 739 After observing demand for period t+1, revise estimates for level, trend, and seasonal factors as follows: Lt+1 = a(Dt+1/St+1) + (1a)(Lt+Tt) Tt+1 = b(Lt+1 Lt) + (1b)Tt St+p+1 = g(Dt+1/Lt+1) + (1g)St+1 a = smoothing constant for level b = smoothing constant for trend g = smoothing constant for seasonal factor Example: Tahoe Salt data. Forecast demand for period 1 using Winter’s model. Initial estimates of level, trend, and seasonal factors are obtained as in the static forecasting case 169。 2021 Pearson Education 740 L0 = 18439 T0 = 524 S1=, S2=, S3=, S4= F1 = (L0 + T0)S1 = (18439+524)() = 8913 The observed demand for period 1 = D1 = 8000 Forecast error for period 1 = E1 = F1D1 = 8913 8000 = 913 Assume a = , b=, g=。 revise estimates for level and trend for period 1 and for seasonal factor for period 5 L1 = a(D1/S1)+(1a)(L0+T0) = ()(8000/)+()(18439+524)=18769 T1 = b(L1L0)+(1b)T0 = ()(1876918439)+()(524) = 485 S5 = g(D1/L1)+(1g)S1 = ()(8000/18769)+()() = F2 = (L1+T1)S2 = (18769 + 485)() = 13093 169。 2021 Pearson Education 741 Measures of Forecast Error ?Forecast error = Et = Ft Dt ?Mean squared error (MSE) MSEn = (Sum(t=1 to n)[Et2])/n ?Absolute deviation = At = |Et| ?Mean absolute deviation (MAD) MADn = (Sum(t=1 to n)[At])/n s = 169。 2021 Pearson Education 742 Measures of Forecast Error 169。 2021 Pearson Education 743 Measures of Forecast Error ?Mean absolute percentage error (MAPE) MAPEn = (Sum(t=1 to n)[|Et/ Dt|100])/n ?Bias ?Shows whether the forecast consistently under or overestimates demand。 should fluctuate around 0 biasn = Sum(t=1 to n)[Et] ?Tracking signal ?Should be within the range of +6 ?Otherwise, possibly use a new forecasting method TSt = bias / MADt 169。 2021 Pearson Education 744 Forecasting Demand at 169。 2021 Pearson Education 745 Summary of Learning Objectives 169。 2021 Pearson Education 746 Summary of Learning Objectives ?What are the roles of forecasting for an enterprise and a supply chain? ?What are the ponents of a demand forecast? ?How is demand forecast given historical data using time series methodologies? ?How is a demand forecast analyzed to estimate forecast error?
教學(xué)課件相關(guān)推薦
鄂ICP備17016276號-1