供應鏈需求預測

1、Demand Forecastingin a Supply Chain7-1大綱預測在供應鏈的角色預測的特性主要企業(yè)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(zhí)行預測的建議CPFR2預測在供應鏈的角色The basis for all strategic and planning decisions in a supply chainExamples:Production: scheduling, inventory, aggregate planningMarketing: sales force allocation, promotions, new production in

2、troductionFinance: plant/equipment investment, budgetary planningPersonnel: workforce planning, hiring, layoffsAll of these decisions are interrelated3預測的特性Forecasts are always wrong. Should include expected value and measure of error.Long-term forecasts are less accurate than short-term forecasts (

3、forecast horizon is important)Aggregate forecasts are more accurate than disaggregate forecasts4主要企業(yè)預測項目市場需求量母體數(shù)預測單位需求量預測驅(qū)動變數(shù)預測市場佔有率預測企業(yè)銷售量預測單價預測生命週期預測5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 rootsBottom up市調(diào)法Market researchLong-rangeNew product sales歷史類推法Historical

4、 analogy類似的產(chǎn)品經(jīng)驗類推Delphi Method以問卷方式蒐集專家意見以進行預測經(jīng)由問卷溝通，專家間無直接互動以避免主控性以統(tǒng)計量收斂為停止指標6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(huán)境穩(wěn)定、無劇烈變動時進行Causal (因果關(guān)係法)假設需求與環(huán)境中某些因素是高度相關(guān)，藉由發(fā)現(xiàn)需求與環(huán)境因素的相關(guān)性去估計未來的需求Transfer Function Model(轉(zhuǎn)換函數(shù)模式)結(jié)合Time Series 與 Causal 兩者，經(jīng)由解釋變

5、數(shù)與應變數(shù)之歷史資料產(chǎn)生轉(zhuǎn)換函數(shù)，再將解釋變數(shù)之預測值代入轉(zhuǎn)換函數(shù)產(chǎn)生應變數(shù)之預測值ARIMAT 、SARIMAT7需求資料的組成Observed demand (O) =Systematic component (S) + Random component (R)Level (current deseasonalized demand)Trend (growth or decline in demand)Seasonality (predictable seasonal fluctuation) Systematic component: Expected value of demand R

6、andom component: The part of the forecast that deviates from the systematic component Forecast error: difference between forecast and actual demand8需求資料組成的關(guān)係類型相乘 系統(tǒng)部分水準 趨勢 季節(jié)性因素相加 系統(tǒng)部分水準 趨勢 季節(jié)性因素混合 系統(tǒng)部分（水準 趨勢） 季節(jié)性因素9時間序列預測Forecast demand for thenext four quarters.10時間序列預測11預測的方法Static AdaptiveMoving

7、 averageSimple exponential smoothingHolts model (with trend)Winters model (with trend and seasonality)12預測的流程Understand the objectives of forecastingIntegrate demand planning and forecastingIdentify major factors that influence the demand forecastUnderstand and identify customer segmentsDetermine th

8、e appropriate forecasting techniqueEstablish performance and error measures for the forecast13時間序列預測Goal is to predict systematic component of demandMultiplicative: (level)(trend)(seasonal factor)Additive: level + trend + seasonal factorMixed: (level + trend)(seasonal factor)Static methodsAdaptive f

9、orecasting14靜態(tài)法Assume a mixed model: Systematic component = (level + trend)(seasonal factor)Ft+l = L + (t + l)TSt+l = forecast in period t for demand in period t + lL = estimate of level for period 0T = estimate of trendSt = estimate of seasonal factor for period tDt = actual demand in period tFt =

10、forecast of demand in period t15靜態(tài)法Estimating level and trendEstimating seasonal factors16範例資料分析產(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有逐漸上升的趨勢17Level and Trend因子的估計Before estimating level and trend, demand data must be deseasonalizedDeseasonalized demand = de

11、mand that would have been observed in the absence of seasonal fluctuationsPeriodicity (p) the number of periods after which the seasonal cycle repeats itselffor demand at Tahoe Salt (Table 7.1, Figure 7.1) p = 418去季節(jié)因子的需求資料 Dt-(p/2) + Dt+(p/2) + S 2Di / 2p for p evenDt = (sum is from i = t+1-(p/2) t

12、o 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 integer19去季節(jié)因子的需求資料For the example, p = 4 is evenFor t = 3:D3 = D1 + D5 + Sum(i=2 to 4) 2Di/8= 8000+10000+(2)(13000)+(2)(23000)+(2)(34000)/8= 19750D4 = D2 + D6 + Sum(i=3 to 5) 2Di/8= 13000+18000+(2)(23000)+(2)

13、(34000)+(2)(10000)/8= 2062520去季節(jié)因子的需求資料Then include trendDt = L + tTwhere Dt = deseasonalized demand in period tL = 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 variab

14、le and period as the independent variable (can be done in Excel)In the example, L = 18,439 and T = 52421需求的時間序列 (Figure 7.3)22估計季節(jié)因子Use the previous equation to calculate deseasonalized demand for each periodSt = Dt / Dt = seasonal factor for period tIn the example, D2 = 18439 + (524)(2) = 19487 D2

15、= 13000S2 = 13000/19487 = 0.67The seasonal factors for the other periods are calculated in the same manner23估計季節(jié)因子(Fig. 7.4)24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

16、+i, 1ip, the seasonal 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, soS1 = (0.42+0.47+0.52)/3 = 0.47 S2 = (0.67+0.83+0.55)/3 = 0.68S3 = (1.15+1.04+1.32)/3 = 1.17 S4 = (1.66+1.68+1.66)/3 = 1.6725預測未來需求Using the original equation,

17、we can forecast the next four periods of demand:F13 = (L+13T)S1 = 18439+(13)(524)(0.47) = 11868F14 = (L+14T)S2 = 18439+(14)(524)(0.68) = 17527F15 = (L+15T)S3 = 18439+(15)(524)(1.17) = 30770F16 = (L+16T)S4 = 18439+(16)(524)(1.67) = 4479426動態(tài)預測法The estimates of level, trend, and seasonality are adjust

18、ed after each demand observationGeneral steps in adaptive forecastingMoving averageSimple exponential smoothingTrend-corrected exponential smoothing (Holts model)Trend- and seasonality-corrected exponential smoothing (Winters model)27動態(tài)預測模式的符號說明Ft+1 = (Lt + lT)St+1 = forecast for period t+l in perio

19、d t Lt = 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 deviati

20、on for period t = |Et|MAD = Mean Absolute Deviation = average value of At 28動態(tài)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 equationEstimate error:

21、 Compute 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 forecastRepeat steps 2, 3, and 4 for each subsequent period29移動平均法Used when demand has no observable trend or seasonalitySystematic compone

22、nt of demand = levelThe 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 levelLt = (Dt + Dt-1 + + Dt-N+1) / N Ft+1 = Lt and Ft+n = Lt After observing the demand

23、 for period t+1, revise the estimates as follows:Lt+1 = (Dt+1 + Dt + + Dt-N+2) / N Ft+2 = Lt+1 30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 = 195

24、00F5 = 19500 = F6 = F7 = F8Observe demand in period 5 to be D5 = 10000Forecast error in period 5, E5 = F5-D5 = 19500-10000 = 9500Revise estimate of level in period 5:L5 = (D5+D4+D3+D2)/4 = (10000+34000+23000+13000)/4 = 20000F6 = L5 = 2000031簡單指數(shù)平滑法Used when demand has no observable trend or seasonal

25、itySystematic component of demand = levelInitial estimate of level, L0, assumed to be the average of all historical dataL0 = Sum(i=1 to n)Di/nCurrent 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 D

26、t+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 32簡單指數(shù)平滑法From Tahoe Salt data, forecast demand for period 1 using exponential smoothingL0 = average of all 12 periods of data= Sum(i=1 to 12)Di/12 = 22083 F1 = L0 = 22083Observed demand for period 1 = D1

27、 = 8000Forecast error for period 1, E1, is as follows:E1 = F1 - D1 = 22083 - 8000 = 14083Assuming a = 0.1, revised estimate of level for period 1:L1 = aD1 + (1-a)L0 = (0.1)(8000) + (0.9)(22083) = 20675F2 = L1 = 20675Note that the estimate of level for period 1 is lower than in period 033Holts ModelA

28、ppropriate when the demand is assumed to have a level and trend in the systematic component of demand but no seasonalityObtain initial estimate of level and trend by running a linear regression of the following form:Dt = at + b T0 = a L0 = bIn period t, the forecast for future periods is expressed a

29、s follows:Ft+1 = Lt + Tt Ft+n = Lt + nTt 34Holts ModelAfter 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 levelb = smoothing constant for trendExample: Tahoe Salt demand data.

30、Forecast demand for period 1 using Holts model (trend corrected exponential smoothing)Using linear regression,L0 = 12015 (linear intercept) T0 = 1549 (linear slope)35Holts ModelForecast for period 1:F1 = L0 + T0 = 12015 + 1549 = 13564Observed demand for period 1 = D1 = 8000E1 = F1 - D1 = 13564 - 800

31、0 = 5564Assume a = 0.1, b = 0.2L1 = aD1 + (1-a)(L0+T0) = (0.1)(8000) + (0.9)(13564) = 13008T1 = b(L1 - L0) + (1-b)T0 = (0.2)(13008 - 12015) + (0.8)(1549) = 1438F2 = L1 + T1 = 13008 + 1438 = 14446F5 = L1 + 4T1 = 13008 + (4)(1438) = 1876036Winters ModelAppropriate when the systematic component of dema

32、nd is assumed to have a level, trend, and seasonal factorSystematic component = (level+trend)(seasonal factor)Assume periodicity pObtain initial estimates of level (L0), trend (T0), seasonal factors (S1,Sp) using procedure for static forecastingIn period t, the forecast for future periods is given b

33、y:Ft+1 = (Lt+Tt)(St+1) and Ft+n = (Lt + nTt)St+n 37Winters ModelAfter 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)TtSt+p+1 = g(Dt+1/Lt+1) + (1-g)St+1 a = smoothing constant for levelb

34、 = smoothing constant for trendg = smoothing constant for seasonal factor38Winters ModelExample: 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 forecasting case39 Winters ModelL0 = 18439 T0 = 524

35、S1=0.47, S2=0.68, S3=1.17, S4=1.67F1 = (L0 + T0)S1 = (18439+524)(0.47) = 8913The observed demand for period 1 = D1 = 8000Forecast error for period 1 = E1 = F1-D1 = 8913 - 8000 = 913Assume a = 0.1, b=0.2, g=0.1; revise estimates for level and trend for period 1 and for seasonal factor for period 5L1 = a(D1/S1)+(1-a)(L0+T0) = (0.1)(8000/0.47)+(0.9)(18439+524)=18769T1 = b(L1-L0)+(1-b)T0 = (0.2)(18769-18439)+(0.8)(524) = 485S5 = g(D1/L1)+(1-g)S1 = (0.1)(8000/18769)+(0.9)(0.47) = 0.47F2 = (L1+T1)S2 = (18769 + 485)(0.68) = 1309340預測誤差類型偏差(bias)與隨機誤差(random error)偏