供應(yīng)鏈需求預(yù)測--rickyblcu-文庫吧資料
2025-01-23 03:51本頁面
【正文】 23000 I, 2023 34000 II, 2023 10000 III, 2023 18000 IV, 2023 23000 I, 20 0 5 38000 II, 20 0 5 12023 III , 200 5 13000 IV, 200 5 32023 I, 20 0 6 41000 Forecast demand for the next four quarters. 10 時間序列預(yù)測 01 0 , 0 0 02 0 , 0 0 03 0 , 0 0 04 0 , 0 0 05 0 , 0 0 00 3 , 2 0 3 , 3 0 3 , 4 0 4 , 1 0 4 , 2 0 4 , 3 0 4 , 4 0 5 , 1 0 5 , 2 0 5 , 3 0 5 , 4 0 6 , 111 預(yù)測的方法 ? Static ? Adaptive ? Moving average ? Simple exponential smoothing ? Holt’s model (with trend) ? Winter’s model (with trend and seasonality) 12 預(yù)測的流程 ? Understand the objectives of forecasting ? Integrate demand planning and forecasting ? Identify major factors that influence the demand forecast ? Understand and identify customer segments ? Determine the appropriate forecasting technique ? Establish performance and error measures for the forecast 13 時間序列預(yù)測 ? Goal is to predict systematic ponent of demand ? Multiplicative: (level)(trend)(seasonal factor) ? Additive: level + trend + seasonal factor ? Mixed: (level + trend)(seasonal factor) ? Static methods ? Adaptive forecasting 14 靜態(tài)法 ? Assume a mixed model: Systematic ponent = (level + trend)(seasonal factor) Ft+l = [L + (t + l)T]St+l = forecast in period t for demand in period t + l L = estimate of level for period 0 T = estimate of trend St = estimate of seasonal factor for period t Dt = actual demand in period t Ft = forecast of demand in period t 15 靜態(tài)法 ? Estimating level and trend ? Estimating seasonal factors 16 範(fàn)例資料分析 ? 產(chǎn)品之需求有季節(jié)性的現(xiàn)象 ? 每年度之第二季為全年度需求最低之時 ? 需求皆是從每年度之第二季遞增至下年度之第一季 ? 此需求變化呈現(xiàn)週期現(xiàn)象，每個週期為一年 ? 三個週期的需求水準(zhǔn)有逐漸上升的趨勢 17 Level and Trend因子的估計 ? Before estimating level and trend, demand data must be deseasonalized ? Deseasonalized demand = demand that would have been observed in the absence of seasonal fluctuations ? Periodicity (p) ? the number of periods after which the seasonal cycle repeats itself ? for demand at 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 depende
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