Presentation on theme: "Chapter 7 Demand Forecasting in a Supply Chain Forecasting -5 Adaptive Trend and Seasonality Adjusted Exponential Smoothing Ardavan Asef-Vaziri References:"— Presentation transcript:
Chapter 7 Demand Forecasting in a Supply Chain Forecasting -5 Adaptive Trend and Seasonality Adjusted Exponential Smoothing Ardavan Asef-Vaziri References: Supply Chain Management; Chopra and Meindl USC Marshall School of Business Lecture Notes
Ardavan Asef-Vaziri Monthly US Electric Power Consumption Trend and Seasonality: Adaptive -2
Ardavan Asef-Vaziri Trend and Seasonality Trend and Seasonality: Adaptive -3
Ardavan Asef-Vaziri Trend & Seasonality-Corrected Exponential Smoothing Trend and Seasonality: Adaptive -4 The estimates of level, trend, and seasonality are adjusted after each demand observation. Assume periodicity p F t+1 = ( L t + T t )S t+1 = forecast for period t+1 in period t F t+l = ( L t + lT t )S t+l = forecast for period t+l in period t L t = Estimate of level at the end of period t T t = Estimate of trend at the end of period t S t = Estimate of seasonal factor for period t F t = Forecast of demand for period t (made at period t-1 or earlier) D t = Actual demand observed in period t
Ardavan Asef-Vaziri General Steps in Adaptive Forecasting 0- Initialize: Compute initial estimates of level, L 0, trend,T 0, and seasonal factors, S 1,…,S p. As in static forecasting. 1- Forecast: Forecast demand for period t+1 using the general equation, F t+1 = (L t +T t )×S t+1 2- Estimate error: Compute error E t+1 = F t+1 - D t+1 3- Modify estimates: Modify the estimates of level, L t+1, trend, T t+1, and seasonal factor, S t+p+1, given the error E t+1 in the forecast Repeat steps 1, 2, and 3 for each subsequent period Trend and Seasonality: Adaptive -5
Ardavan Asef-Vaziri 7-2-6 After observing demand for period t+1, revise estimates for level, trend, and seasonal factors as follows: L t+1 = (D t+1 /S t+1 ) + (1- )(L t +T t ) T t+1 = (L t+1 - L t ) + (1- )T t S t+p+1 = (D t+1 /L t+1 ) + (1- )S t+1 = smoothing constant for level = smoothing constant for trend = smoothing constant for seasonal factor Trend & Seasonality-Corrected Exponential Smoothing
Ardavan Asef-Vaziri 7-2-7 Trend & Seasonality-Corrected Exponential Smoothing Example: Tahoe Salt data. Forecast demand for period 1 using Winter’s model. Initial estimates of level, trend, and seasonal factors are obtained as in the static forecasting case L0 = 18439 T0 = 524S1=0.47, S2=0.68, S3=1.17, S4=1.66 F1 = (L0 + T0)S1 = (18439+524)(0.47) = 18963(0.47)= 8913 The observed demand for period 1 = D1 = 8000. Assume = 0.1, =0.2, =0.1
Ardavan Asef-Vaziri 7-2-11 Forecasting in Practice Collaborate in building forecasts The value of data depends on where you are in the supply chain Be sure to distinguish between demand and sales
Ardavan Asef-Vaziri Assignment Trend and Seasonality: Adaptive -16 Each cycle is 4 periods long. Periodicity = 4. There are three cycles. Compute b0, b1, S1, S2, S3, S4 using static method and forecast using trend and seasonality adjusted method for α= β = δ = 0.25
Ardavan Asef-Vaziri Using Static Model We Can Compute Seasonality Trend and Seasonality: Adaptive -17 b0 (Level) and b1 (Trend) are computed exactly the same as static method by applying regression on deseasonalized data. Initial average seasonality indices are also computed in the same way.
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