供應(yīng)鏈需求預(yù)測--rickyblcu(文件)
【正文】 Tahoe Salt (Table , Figure ) p = 4 18 去季節(jié)因子的需求資料 [Dt(p/2) + Dt+(p/2) + S 2Di] / 2p for p even Dt = (sum is from i = t+1(p/2) to t+1+(p/2)) S Di / p for p odd (sum is from i = t(p/2) to t+(p/2)), p/2 truncated to lower integer 19 去季節(jié)因子的需求資料 For the example, p = 4 is even For t = 3: D3 = {D1 + D5 + Sum(i=2 to 4) [2Di]}/8 = {8000+10000+[(2)(13000)+(2)(23000)+(2)(34000)]}/8 = 19750 D4 = {D2 + D6 + Sum(i=3 to 5) [2Di]}/8 = {13000+18000+[(2)(23000)+(2)(34000)+(2)(10000)]/8 = 20625 20 去季節(jié)因子的需求資料 Then include trend Dt = L + tT where Dt = deseasonalized demand in period t L = level (deseasonalized demand at period 0) T = trend (rate of growth of deseasonalized demand) Trend is determined by linear regression using deseasonalized demand as the dependent variable and period as the independent variable (can be done in Excel) In the example, L = 18,439 and T = 524 21 需求的時間序列 (Figure ) 010000202303000040000500001 2 3 4 5 6 7 8 9 10 11 12P e r i o dDemandDtD t b a r22 估計季節(jié)因子 Use the previous equation to calculate deseasonalized demand for each period St = Dt / Dt = seasonal factor for period t In the example, D2 = 18439 + (524)(2) = 19487 D2 = 13000 S2 = 13000/19487 = The seasonal factors for the other periods are calculated in the same manner 23 估計季節(jié)因子 (Fig. ) t Dt D tb ar S b ar1 8000 18963 = 8000/ 189632 13000 19487 = 13000/ 194873 23000 20231 = 23000/ 202314 34000 20535 = 34000/ 205355 10000 21059 = 10000/ 210596 18000 21583 = 18000/ 215837 23000 22107 = 23000/ 221078 38000 22631 = 38000/ 226319 12023 23155 = 12023/ 2315510 13000 23679 = 13000/ 2367911 32023 24203 = 32023/ 2420312 41000 24727 = 41000/ 2472724 估計季節(jié)因子 The overall seasonal factor for a “season” is then obtained by averaging all of the factors for a “season” If there are r seasonal cycles, for all periods of the form pt+i, 1ip, the seasonal factor for season i is Si = [Sum(j=0 to r1) Sjp+i]/r In the example, there are 3 seasonal cycles in the data and p=4, so S1 = (++)/3 = S2 = (++)/3 = S3 = (++)/3 = S4 = (++)/3 = 25 預(yù)測未來需求 Using the original equation, we can forecast the next four periods of demand: F13 = (L+13T)S1 = [18439+(13)(524)]() = 11868 F14 = (L+14T)S2 = [18439+(14)(524)]() = 17527 F15 = (L+15T)S3 = [18439+(15)(524)]() = 30770 F16 = (L+16T)S4 = [18439+(16)(524)]() = 44794 26 動態(tài)預(yù)測法 ? The estimates of level, trend, and seasonality are adjusted after each demand observation ? General steps in adaptive forecasting ? Moving average ? Simple exponential smoothing ? Trendcorrected exponential smoothing (Holt’s model) ? Trend and seasonalitycorrected exponential smoothing (Winter’s model) 27 動態(tài)預(yù)測模
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