供应链需求预测--rickyblcu.ppt

1、Demand Forecastingin a Supply Chain,7-1,大綱,預測在供應鏈的角色 預測的特性 主要企業預測項目 預測的方法與組成 時間序列預測 預測誤差的衡量指標 執行預測的建議 CPFR,預測在供應鏈的角色,The basis for all strategic and planning decisions in a supply chain Examples: Production: scheduling, inventory, aggregate planning Marketing: sales force allocation, promotions, new

2、 production introduction Finance: plant/equipment investment, budgetary planning Personnel: workforce planning, hiring, layoffs All of these decisions are interrelated,預測的特性,Forecasts are always wrong. Should include expected value and measure of error. Long-term forecasts are less accurate than sho

3、rt-term forecasts (forecast horizon is important) Aggregate forecasts are more accurate than disaggregate forecasts,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,預測的方法,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 草根法Grass roots Bottom up 市調法Market research Long-ra

4、nge New product sales 歷史類推法Historical analogy 類似的產品經驗類推 Delphi Method 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,預測的方法,客觀法(objective methods) 以歷史資料為基礎進行預測 Time Series(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 Transfer Func

5、tion Model(轉換函數模式) 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 ARIMAT 、SARIMAT,需求資料的組成,Observed demand (O) = Systematic component (S) + Random component (R),Systematic component: Expected value of demand Random component: The part of the forecast that deviates from the

6、systematic component Forecast error: difference between forecast and actual demand,需求資料組成的關係類型,相乘 系統部分水準 趨勢 季節性因素 相加 系統部分水準 趨勢 季節性因素 混合 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,Static Adaptive Moving average Simple exponential smoothing Holts model (with tren

7、d) Winters model (with trend and seasonality),預測的流程,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 perform

8、ance and error measures for the forecast,時間序列預測,Goal is to predict systematic component of demand Multiplicative: (level)(trend)(seasonal factor) Additive: level + trend + seasonal factor Mixed: (level + trend)(seasonal factor) Static methods Adaptive forecasting,靜態法,Assume a mixed model: Systematic

9、 component = (level + trend)(seasonal factor) Ft+l = L + (t + l)TSt+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,靜態法,Estima

10、ting level and trend Estimating seasonal factors,範例資料分析,產品之需求有季節性的現象 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 此需求變化呈現週期現象，每個週期為一年 三個週期的需求水準有逐漸上升的趨勢,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

11、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 7.1, Figure 7.1) p = 4,去季節因子的需求資料,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 (s

12、um is from i = t-(p/2) to t+(p/2), p/2 truncated to lower integer,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 D4 = D2 + D6 + Sum(i=3 to 5) 2Di/8 = 13000+18000+(2)(23000)+(2)(34000)+(2)(10000)/8 = 2062

13、5,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) 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

14、independent variable (can be done in Excel) 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/

15、19487 = 0.67 The seasonal factors for the other periods are calculated in the same manner,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, 1ip, the seas

16、onal factor for season i is 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,預測未來需求,Using the original equation, we can forecast

17、 the next four periods of demand: F13 = (L+13T)S1 = 18439+(13)(524)(0.47) = 11868 F14 = (L+14T)S2 = 18439+(14)(524)(0.68) = 17527 F15 = (L+15T)S3 = 18439+(15)(524)(1.17) = 30770 F16 = (L+16T)S4 = 18439+(16)(524)(1.67) = 44794,動態預測法,The estimates of level, trend, and seasonality are adjusted after ea

18、ch demand observation General steps in adaptive forecasting Moving average Simple exponential smoothing Trend-corrected exponential smoothing (Holts model) Trend- and seasonality-corrected exponential smoothing (Winters model),動態預測模式的符號說明,Ft+1 = (Lt + lT)St+1 = forecast for period t+l in period t Lt

19、 = Estimate of level at the end of period t Tt = Estimate of trend at the end of period t St = Estimate of seasonal factor for period t Ft = Forecast of demand for period t (made period t-1 or earlier) Dt = Actual demand observed in period t Et = Forecast error in period t At = Absolute deviation fo

20、r period t = |Et| MAD = Mean Absolute Deviation = average value of At,動態預測的基本步驟,Initialize: Compute initial estimates of level (L0), trend (T0), and seasonal factors (S1,Sp). This is done as in static forecasting. Forecast: Forecast demand for period t+1 using the general equation Estimate error: Co

21、mpute error Et+1 = Ft+1- Dt+1 Modify estimates: Modify the estimates of level (Lt+1), trend (Tt+1), and seasonal factor (St+p+1), given the error Et+1 in the forecast Repeat steps 2, 3, and 4 for each subsequent period,移動平均法,Used when demand has no observable trend or seasonality Systematic componen

22、t of demand = level The level in period t is the average demand over the last N periods (the N-period moving average) Current forecast for all future periods is the same and is based on the current estimate of the level Lt = (Dt + Dt-1 + + Dt-N+1) / N Ft+1 = Lt and Ft+n = Lt After observing the dema

23、nd for period t+1, revise the estimates as follows: Lt+1 = (Dt+1 + Dt + + Dt-N+2) / N Ft+2 = Lt+1,移動平均法,From Tahoe Salt example (Table 7.1) At the end of period 4, what is the forecast demand for periods 5 through 8 using a 4-period moving average? L4 = (D4+D3+D2+D1)/4 = (34000+23000+13000+8000)/4 =

24、 19500 F5 = 19500 = F6 = F7 = F8 Observe demand in period 5 to be D5 = 10000 Forecast error in period 5, E5 = F5-D5 = 19500-10000 = 9500 Revise estimate of level in period 5: L5 = (D5+D4+D3+D2)/4 = (10000+34000+23000+13000)/4 = 20000 F6 = L5 = 20000,簡單指數平滑法,Used when demand has no observable trend o

25、r seasonality Systematic component 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 obs

26、erving demand Dt+1, revise the estimate of the level: Lt+1 = aDt+1 + (1-a)Lt Lt+1 = Sum(n=0 to t+1)a(1-a)nDt+1-n ,簡單指數平滑法,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 deman

27、d for period 1 = D1 = 8000 Forecast error for period 1, E1, is as follows: E1 = F1 - D1 = 22083 - 8000 = 14083 Assuming a = 0.1, revised estimate of level for period 1: L1 = aD1 + (1-a)L0 = (0.1)(8000) + (0.9)(22083) = 20675 F2 = L1 = 20675 Note that the estimate of level for period 1 is lower than

28、in period 0,Holts Model,Appropriate when the demand is assumed to have a level and trend in the systematic component 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 f

29、uture periods is expressed as follows: Ft+1 = Lt + Tt Ft+n = Lt + nTt,Holts Model,After observing demand for period t, revise the estimates for level and trend as follows: Lt+1 = aDt+1 + (1-a)(Lt + Tt) Tt+1 = b(Lt+1 - Lt) + (1-b)Tt a = smoothing constant for level b = smoothing constant for trend Ex

30、ample: Tahoe Salt demand data. Forecast demand for period 1 using Holts model (trend corrected exponential smoothing) Using linear regression, L0 = 12015 (linear intercept) T0 = 1549 (linear slope),Holts Model,Forecast for period 1: F1 = L0 + T0 = 12015 + 1549 = 13564 Observed demand for period 1 =

31、D1 = 8000 E1 = F1 - D1 = 13564 - 8000 = 5564 Assume a = 0.1, b = 0.2 L1 = aD1 + (1-a)(L0+T0) = (0.1)(8000) + (0.9)(13564) = 13008 T1 = b(L1 - L0) + (1-b)T0 = (0.2)(13008 - 12015) + (0.8)(1549) = 1438 F2 = L1 + T1 = 13008 + 1438 = 14446 F5 = L1 + 4T1 = 13008 + (4)(1438) = 18760,Winters Model,Appropri

32、ate when the systematic component of demand is assumed to have a level, trend, and seasonal factor Systematic component = (level+trend)(seasonal factor) Assume periodicity p Obtain initial estimates of level (L0), trend (T0), seasonal factors (S1,Sp) using procedure for static forecasting In period

33、t, the forecast for future periods is given by: Ft+1 = (Lt+Tt)(St+1) and Ft+n = (Lt + nTt)St+n,Winters Model,After observing demand for period t+1, revise estimates for level, trend, and seasonal factors as follows: Lt+1 = a(Dt+1/St+1) + (1-a)(Lt+Tt) Tt+1 = b(Lt+1 - Lt) + (1-b)Tt St+p+1 = g(Dt+1/Lt+

34、1) + (1-g)St+1 a = smoothing constant for level b = smoothing constant for trend g = smoothing constant for seasonal factor,Winters Model,Example: Tahoe Salt data. Forecast demand for period 1 using Winters model. Initial estimates of level, trend, and seasonal factors are obtained as in the static

35、forecasting case,Winters Model,L0 = 18439 T0 = 524S1=0.47, S2=0.68, S3=1.17, S4=1.67 F1 = (L0 + T0)S1 = (18439+524)(0.47) = 8913 The observed demand for period 1 = D1 = 8000 Forecast error for period 1 = E1 = F1-D1 = 8913 - 8000 = 913 Assume a = 0.1, b=0.2, g=0.1; revise estimates for level and tren

36、d for period 1 and for seasonal factor for period 5 L1 = a(D1/S1)+(1-a)(L0+T0) = (0.1)(8000/0.47)+(0.9)(18439+524)=18769 T1 = b(L1-L0)+(1-b)T0 = (0.2)(18769-18439)+(0.8)(524) = 485 S5 = g(D1/L1)+(1-g)S1 = (0.1)(8000/18769)+(0.9)(0.47) = 0.47 F2 = (L1+T1)S2 = (18769 + 485)(0.68) = 13093,預測誤差,類型 偏差(bias)與隨機誤差(random error) 偏差的原因 未涵蓋正確之變數 錯誤的變數關係