供應(yīng)鏈需求預(yù)測(cè)實(shí)務(wù)(完整版)
【正文】 /4 = 19500 F5 = 19500 = F6 = F7 = F8 Observe demand in period 5 to be D5 = 10000 Forecast error in period 5, E5 = F5D5 = 1950010000 = 9500 Revise estimate of level in period 5: L5 = (D5+D4+D3+D2)/4 = (10000+34000+23000+13000)/4 = 20230 F6 = L5 = 20230 32 簡(jiǎn)單指數(shù)平滑法 ? Used when demand has no observable trend or seasonality ? Systematic ponent of demand = level ? Initial estimate of level, L0, assumed to be the average of all historical data L0 = [Sum(i=1 to n)Di]/n Current forecast for all future periods is equal to the current estimate of the level and is given as follows: Ft+1 = Lt and Ft+n = Lt After observing demand Dt+1, revise the estimate of the level: Lt+1 = aDt+1 + (1a)Lt Lt+1 = Sum(n=0 to t+1)[a(1a)nDt+1n ] 33 簡(jiǎn)單指數(shù)平滑法 From Tahoe Salt data, forecast demand for period 1 using exponential smoothing L0 = average of all 12 periods of data = Sum(i=1 to 12)[Di]/12 = 22083 F1 = L0 = 22083 Observed demand for period 1 = D1 = 8000 Forecast error for period 1, E1, is as follows: E1 = F1 D1 = 22083 8000 = 14083 Assuming a = , revised estimate of level for period 1: L1 = aD1 + (1a)L0 = ()(8000) + ()(22083) = 20675 F2 = L1 = 20675 Note that the estimate of level for period 1 is lower than in period 0 34 Holt’s Model ? Appropriate when the demand is assumed to have a level and trend in the systematic ponent of demand but no seasonality ? Obtain initial estimate of level and trend by running a linear regression of the following form: Dt = at + b T0 = a L0 = b In period t, the forecast for future periods is expressed as follows: Ft+1 = Lt + Tt Ft+n = Lt + nTt 35 Holt’s Model After observing demand for period t, revise the estimates for level and trend as follows: Lt+1 = aDt+1 + (1a)(Lt + Tt) Tt+1 = b(Lt+1 Lt) + (1b)Tt a = smoothing constant for level b = smoothing constant for trend Example: Tahoe Salt demand data. Forecast demand for period 1 using Holt’s model (trend corrected exponential smoothing) Using linear regression, L0 = 12023 (linear intercept) T0 = 1549 (linear slope) 36 Holt’s Model Forecast for period 1: F1 = L0 + T0 = 12023 + 1549 = 13564 Observed demand 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 12023) + ()(1549) = 1438 F2 = L1 + T1 = 13008 + 1438 = 14446 F5 = L1 + 4T1 = 13008 + (4)(1438) = 18760 37 Winter’s Model ? 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
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