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Chapter 7 Demand Forecasting in a Supply Chain

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Chapter 7 Demand Forecasting in a Supply Chain

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    1. Chapter 7 Demand Forecasting in a Supply Chain

    2. Data With Trend Trend and Seasonality: Adaptive -2

    3. Trend Adjusted Exponential Smoothing: Holts Model Appropriate when there is a trend in the systematic component of demand. Trend and Seasonality: Adaptive -3

    4. General Steps in adaptive Forecasting 0- Initialize: Compute initial estimates of level, L0, trend ,T0 using linear regression on the original set of data; L0= b0 , T0 = b1. No need to remove seasonality, because there is no seasonality. 1- Forecast: Forecast demand for period t+1 using the general equation, Ft+1 = Lt+Tt 2- Modify estimates: Modify the estimates of level, Lt+1 and trend, Tt+1. Repeat steps 1, 2, and 3 for each subsequent period Trend and Seasonality: Adaptive -4

    5. Trend-Corrected Exponential Smoothing (Holts Model) In period t, the forecast for future periods is expressed as follows Trend and Seasonality: Adaptive -5

    6. Holts Model Example (continued)

    7. Holts Model Example (continued) Forecast for period 1: F1 = L0 + T0 = 12015 + 1549 = 13564 Observed demand for period 1 = 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 Trend and Seasonality: Adaptive -7

    8. Holts Model Example (continued) Trend and Seasonality: Adaptive -8

    9. Double Exponential Smoothing: a = 0.2 and b = 0.3 Trend and Seasonality: Adaptive -9

    10. Double Exponential Smoothing: a = 0.2 and b = 0.3 Trend and Seasonality: Adaptive -10

    11. Varying Trend Example Trend and Seasonality: Adaptive -11

    12. Varying Trend Example Trend and Seasonality: Adaptive -12

    13. Double Exponential Smoothing Trend and Seasonality: Adaptive -13

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