供应链需求预测的方法

demand forecast n Understand and identify customer segments n Determine the appropriate forecasting
technique n Establish performance and error measures
供应链需求预测的方法
2020年4月25日星期六
大綱
n 預測在供應鏈的角色 n 預測的特性 n 主要企業預測項目 n 預測的方法與組成 n 時間序列預測 n 預測誤差的衡量指標 n 執行預測的建議 n CPFR
預測在供應鏈的角色
n The basis for all strategic and planning decisions in a supply chain
Level and Trend因子的估計
n Before estimating level and trend, demand data must be deseasonalized
n Deseasonalized demand = demand that would have been observed in the absence of seasonal fluctuations
D4 = {D2 + D6 + Sum(i=3 to 5) [2Di]}/8 = {13000+18000+[(2)(23000)+(2)(34000)+(2)(10000)]/8
= 20625
去季節因子的需求資料
Then include trend
Dt = L + tT where Dt = deseasonalized demand in period t L = level (deseasonalized demand at period 0)
n Static methods n Adaptive forecasting
靜態法
n Assume a mixed model: Systematic component = (level + trend)(seasonal factor)
Ft+l = [L + (t + l)T]St+l = forecast in period t for demand in period t + l
n Long-term forecasts are less accurate than short-term forecasts (forecast horizon is important)
n Aggregate forecasts are more accurate than disaggregate forecasts
n Examples:
q Production: scheduling, inventory, aggregate planning q Marketing: sales force allocation, promotions, new
production introduction q Finance: plant/equipment investment, budgetary
?longtermforecastsarelessaccuratethanshorttermforecastsforecasthorizonisimportant?aggregateforecastsaremoreaccuratethandisaggregateforecasts5主要企業預測項目?市場需求量?母體數預測?單位需求量預測?驅動變數預測?市場佔有率預測?企業銷售量預測?單價預測?生命週期預測6預測的方法?主觀法subjectivemethods預測人員依個人主觀的判斷進行預測常應用在缺乏歷史資料時透過專家進行主觀預測?草根法grassroots?bottomup?市調法marketresearch?longrange?newproductsales?歷史類推法historicalanalogy?類似的產品經驗類推?delphimethod?以問卷方式蒐集專家意見以進行預測?經由問卷溝通專家間無直接互動以避免主控性?以統計量收斂為停止指標7預測的方法?客觀法objectivemethods以歷史資料為基礎進行預測?timeseries外插法?假設過去之需求資料是未來需求良好指標下使用歷史資料進行預測適合當需求環境穩定無劇烈變動時進行?causal因果關係法?假設需求與環境中某些因素是高度相關藉由發現需求與環境因素的相關性去估計未來的需求?transferfunctionmodel轉換函數模式?結合timeseries與causal兩者經由解釋變數與應變數之歷史資料產生轉換函數再將解釋變數之預測值代入轉換函數產生應變數之預測值?arimatsarimat8需求資料的組成observeddemandosystematiccomponentsrandomcomponentrlevelcurrentdeseasonalizeddemandtrendgrowthordeclineindemandseasonalitypredictableseasonalfluctuation?systematiccomponent
Dt =
[Dt-(p/2) + Dt+(p/2) + S 2Di] / 2p
(sum is from i = t+1-(p/2) to t+1+(p/2))
for p even
S Di / p
for p odd
(sum is from i = t-(p/2) to t+(p/2)), p/2 truncated to lower integer
n Static n Adaptive
q Moving average q Simple exponential smoothing q Holt’s model (with trend) q Winter’s model (with trend and seasonality)
預測的流程
n Understand the objectives of forecasting n Integrate demand planning and forecasting n Identify major factors that influence the
Systematic component (S) + Random component (R)
Level (current deseasonalized demand)
Trend (growth or decline in demand)
Seasonality (predictable seasonal fluctuation)
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)
預測未來需求
Using the original equation, we can forecast the next four periods of demand:
for the forecast
時間序列預測
n Goal is to predict systematic component of demand
q Multiplicative: (level)(trend)(seasonal factor) q Additive: level + trend + seasonal factor q Mixed: (level + trend)(seasonal factor)
去季節因子的需求資料
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
預測的方法
n 客觀法(objective methods)
以歷史資料為基礎進行預測
q Time Series(外插法)
n 假設過去之需求資料是未來需求良好指標下，使用歷史資料進 行預測，適合當需求環境穩定、無劇烈變動時進行
q Causal (因果關係法)
n 假設需求與環境中某些因素是高度相關，藉由發現需求與環境 因素的相關性去估計未來的需求
靜態法
n Estimating level and trend n Estimating seasonal factors
範例資料分析
n 產品之需求有季節性的現象 n 每年度之第二季為全年度需求最低之時 n 需求皆是從每年度之第二季遞增至下年度之第
一季 n 此需求變化呈現週期現象，每個週期為一年 n 三個週期的需求水準有逐漸上升的趨勢
• Systematic component: Expected value of demand • Random component: The part of the forecast that deviates
from the systematic component • Forecast error: difference between forecast and actual demand
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
需求資料組成的關係類型
n 相乘
q 系統部分＝水準 ×趨勢 ×季節性因素
n 相加
q 系統部分＝水準 ＋ 趨勢 ＋ 季節性因素
n 混合
q 系統部分＝（水準 ＋ 趨勢）× 季節性因素
時間序列預測
Forecast demand for the next four quarters.
時間序列預測
預測的方法
n Bottom up q 市調法Market research
n Long-range n New product sales q 歷史類推法Historical analogy n 類似的產品經驗類推 q Delphi Method n 以問卷方式蒐集專家意見以進行預測 n 經由問卷溝通，專家間無直接互動以避免主控性 n 以統計量收斂為停止指標
估計季節因子(Fig. 7.4)
估計季節因子
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, 1<i<p, the seasonal factor for season i is
主要企業預測項目
n 市場需求量 n 母體數預測 n 單位需求量預測 n 驅動變數預測 n 市場佔有率預測 n 企業銷售量預測 n 單價預測 n 生命週期預測
預測的方法
n 主觀法(subjective methods)
預測人員依個人主觀的判斷進行預測 常應用在缺乏歷史資料時透過專家進行主觀預測 q 草根法Grass roots
n Periodiciห้องสมุดไป่ตู้y (p)
q the number of periods after which the seasonal cycle repeats itself
q for demand at Tahoe Salt (Table 7.1, Figure 7.1) p = 4
去季節因子的需求資料
q Transfer Function Model(轉換函數模式)
n 結合Time Series 與 Causal 兩者，經由解釋變數與應變數之 歷史資料產生轉換函數，再將解釋變數之預測值代入轉換函數 產生應變數之預測值
n ARIMAT 、SARIMAT
需求資料的組成
Observed demand (O) =
Si = [Sum(j=0 to r-1) Sjp+i]/r In the example, there are 3 seasonal cycles in the
data and p=4, so
S1 = (0.42+0.47+0.52)/3 = 0.47 S2 = (0.67+0.83+0.55)/3 = 0.68 S3 = (1.15+1.04+1.32)/3 = 1.17 S4 = (1.66+1.68+1.66)/3 = 1.67
planning q Personnel: workforce planning, hiring, layoffs
n All of these decisions are interrelated
預測的特性
n Forecasts are always wrong. Should include expected value and measure of error.
In the example, L = 18,439 and T = 524
需求的時間序列 (Figure 7.3)
估計季節因子
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 = 0.67 The seasonal factors for the other periods are calculated in the same manner