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SHORT-TERM FORECASTING TECHNIQUES

OPS 463 - Short Term Fcsting. 2. BASE-LEVEL FORECASTING MODELS. Assume absence of trend and seasonalitySeparate base-level from randomnessFt -- Forecast for period tAt -- Actual sales for period tBt -- Base level component for period tet -- Random element for period t. OPS 463 - Short

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SHORT-TERM FORECASTING TECHNIQUES

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    1. OPS 463 - Short Term Fcsting 1 SHORT-TERM FORECASTING TECHNIQUES Base-level Forecasting Models Moving Averages Exponential Smoothing Exponential Smoothing With Trend and Seasonality Components Selecting a Technique: Do the Most With the Least

    2. OPS 463 - Short Term Fcsting 2 BASE-LEVEL FORECASTING MODELS Assume absence of trend and seasonality Separate base-level from randomness Ft -- Forecast for period t At -- Actual sales for period t Bt -- Base level component for period t et -- Random element for period t

    3. OPS 463 - Short Term Fcsting 3 BASE-LEVEL FORECASTING MODELS Example: tricycle sales at Bikes-R-Us Actual July Sales: 105 “True” Base level for July: 100 Random spike for July: 105-100 = 5 Assumption: At = Bt + et ; [105 = 100 + 5] Practical implications for forecasting: Step 1: Smooth randomness out of At to estimate Bt Step 2: Set Ft+1 = Bt

    4. OPS 463 - Short Term Fcsting 4 NAĎVE METHOD No smoothing of data Step 1: Bt = At Step 2: Ft+1 = Bt

    5. OPS 463 - Short Term Fcsting 5 NAĎVE METHOD No smoothing of data Step 1: Bt = At Step 2: Ft+1 = Bt

    6. OPS 463 - Short Term Fcsting 6 NAĎVE METHOD No smoothing of data Step 1: Bt = At Step 2: Ft+1 = Bt

    7. OPS 463 - Short Term Fcsting 7 NAĎVE METHOD No smoothing of data Step 1: Bt = At Step 2: Ft+1 = Bt

    8. OPS 463 - Short Term Fcsting 8 SIMPLE MOVING AVERAGE Smoothes out randomness by averaging positive and negative random elements over several periods n -- number of periods Step 1: Step 2: Ft+1 = Bt

    9. OPS 463 - Short Term Fcsting 9 SIMPLE MOVING AVERAGE Smoothes out randomness by averaging positive and negative random elements over several periods n -- number of periods Step 1: Step 2: Ft+1 = Bt

    10. OPS 463 - Short Term Fcsting 10 SIMPLE MOVING AVERAGE Smoothes out randomness by averaging positive and negative random elements over several periods n -- number of periods Step 1: Step 2: Ft+1 = Bt

    11. OPS 463 - Short Term Fcsting 11 SIMPLE MOVING AVERAGE Smoothes out randomness by averaging positive and negative random elements over several periods n -- number of periods Step 1: Step 2: Ft+1 = Bt

    12. OPS 463 - Short Term Fcsting 12 WEIGHTED MOVING AVERAGE Same idea as SMA, but less smoothing: more weight on recent sales data n -- number of periods ai – weight applied to period t-i+1 Step 1: Step 2: Ft+1 = Bt

    13. OPS 463 - Short Term Fcsting 13 WEIGHTED MOVING AVERAGE Same idea as SMA, but less smoothing: more weight on recent sales data n -- number of periods ai – weight applied to period t-i+1 Step 1: Step 2: Ft+1 = Bt

    14. OPS 463 - Short Term Fcsting 14 WEIGHTED MOVING AVERAGE Same idea as SMA, but less smoothing: more weight on recent sales data n -- number of periods ai – weight applied to period t-i+1 Step 1: Step 2: Ft+1 = Bt

    15. OPS 463 - Short Term Fcsting 15 WEIGHTED MOVING AVERAGE Same idea as SMA, but less smoothing: more weight on recent sales data n -- number of periods ai – weight applied to period t-i+1 Step 1: Step 2: Ft+1 = Bt

    16. OPS 463 - Short Term Fcsting 16 EXPONENTIAL SMOOTHING (I) Simpler equation, equivalent to WMA a – exponential smoothing parameter (0< a<1) Step 1: Step 2: Ft+1 = Bt

    17. OPS 463 - Short Term Fcsting 17 EXPONENTIAL SMOOTHING (I) Simpler equation, equivalent to WMA a – exponential smoothing parameter (0< a<1) Step 1: Step 2: Ft+1 = Bt

    18. OPS 463 - Short Term Fcsting 18 EXPONENTIAL SMOOTHING (I) Simpler equation, equivalent to WMA a – exponential smoothing parameter (0< a<1) Step 1: Step 2: Ft+1 = Bt

    19. OPS 463 - Short Term Fcsting 19 EXPONENTIAL SMOOTHING (I) Simpler equation, equivalent to WMA a – exponential smoothing parameter (0< a<1) Step 1: Step 2: Ft+1 = Bt

    20. OPS 463 - Short Term Fcsting 20 EXPONENTIAL SMOOTHING (I) Simpler equation, equivalent to WMA a – exponential smoothing parameter (0< a<1) Step 1: Step 2: Ft+1 = Bt

    21. OPS 463 - Short Term Fcsting 21 EXPONENTIAL SMOOTHING (II) A higher smoothing parameter means less smoothing and a more reactive forecast

    22. OPS 463 - Short Term Fcsting 22 E.S. WITH TREND Assumes existence of Trend and Base Level Tt – Trend component in period t a – Base-level smoothing parameter (0< a<1) b – Trend smoothing parameter (0< b<1) Step 1: Step 2: Ft+1 = Bt + Tt

    23. OPS 463 - Short Term Fcsting 23 E.S. WITH TREND Assumes existence of Trend and Base Level Tt – Trend component in period t a – Base-level smoothing parameter (0< a<1) b – Trend smoothing parameter (0< b<1) Step 1: Step 2: Ft+1 = Bt + Tt

    24. OPS 463 - Short Term Fcsting 24 E.S. WITH TREND Assumes existence of Trend and Base Level Tt – Trend component in period t a – Base-level smoothing parameter (0< a<1) b – Trend smoothing parameter (0< b<1) Step 1: Step 2: Ft+1 = Bt + Tt

    25. OPS 463 - Short Term Fcsting 25 E.S. WITH TREND Assumes existence of Trend and Base Level Tt – Trend component in period t a – Base-level smoothing parameter (0< a<1) b – Trend smoothing parameter (0< b<1) Step 1: Step 2: Ft+1 = Bt + Tt

    26. OPS 463 - Short Term Fcsting 26 E.S. WITH TREND Assumes existence of Trend and Base Level Tt – Trend component in period t a – Base-level smoothing parameter (0< a<1) b – Trend smoothing parameter (0< b<1) Step 1: Step 2: Ft+1 = Bt + Tt

    27. OPS 463 - Short Term Fcsting 27 E.S. WITH TREND Assumes existence of Trend and Base Level Tt – Trend component in period t a – Base-level smoothing parameter (0< a<1) b – Trend smoothing parameter (0< b<1) Step 1: Step 2: Ft+1 = Bt + Tt

    28. OPS 463 - Short Term Fcsting 28 E.S. WITH TREND & SEASONS St – Seasonality component in period t L – Number of seasons in a year g – Seasonality smoothing parameter (0< g<1) Step 1: Step 2: Ft+1 = (Bt +Tt )St-L+1

    29. OPS 463 - Short Term Fcsting 29 E.S. WITH TREND & SEASONS St – Seasonality component in period t L – Number of seasons in a year g – Seasonality smoothing parameter (0< g<1) Step 1: Step 2: Ft+1 = (Bt +Tt )St-L+1

    30. OPS 463 - Short Term Fcsting 30 E.S. WITH TREND & SEASONS St – Seasonality component in period t L – Number of seasons in a year g – Seasonality smoothing parameter (0< g<1) Step 1: Step 2: Ft+1 = (Bt +Tt )St-L+1

    31. OPS 463 - Short Term Fcsting 31 E.S. WITH TREND & SEASONS St – Seasonality component in period t L – Number of seasons in a year g – Seasonality smoothing parameter (0< g<1) Step 1: Step 2: Ft+1 = (Bt +Tt )St-L+1

    32. OPS 463 - Short Term Fcsting 32 E.S. WITH TREND & SEASONS St – Seasonality component in period t L – Number of seasons in a year g – Seasonality smoothing parameter (0< g<1) Step 1: Step 2: Ft+1 = (Bt +Tt )St-L+1

    33. OPS 463 - Short Term Fcsting 33 E.S. WITH TREND & SEASONS St – Seasonality component in period t L – Number of seasons in a year g – Seasonality smoothing parameter (0< g<1) Step 1: Step 2: Ft+1 = (Bt +Tt )St-L+1

    34. OPS 463 - Short Term Fcsting 34 E.S. WITH TREND & SEASONS St – Seasonality component in period t L – Number of seasons in a year g – Seasonality smoothing parameter (0< g<1) Step 1: Step 2: Ft+1 = (Bt +Tt )St-L+1

    35. OPS 463 - Short Term Fcsting 35 E.S. WITH TREND & SEASONS St – Seasonality component in period t L – Number of seasons in a year g – Seasonality smoothing parameter (0< g<1) Step 1: Step 2: Ft+1 = (Bt +Tt )St-L+1

    36. OPS 463 - Short Term Fcsting 36 E.S. WITH TREND & SEASONS St – Seasonality component in period t L – Number of seasons in a year g – Seasonality smoothing parameter (0< g<1) Step 1: Step 2: Ft+1 = (Bt +Tt )St-L+1

    37. OPS 463 - Short Term Fcsting 37 E.S. WITH TREND & SEASONS St – Seasonality component in period t L – Number of seasons in a year g – Seasonality smoothing parameter (0< g<1) Step 1: Step 2: Ft+1 = (Bt +Tt )St-L+1

    38. OPS 463 - Short Term Fcsting 38 E.S. WITH TREND & SEASONS St – Seasonality component in period t L – Number of seasons in a year g – Seasonality smoothing parameter (0< g<1) Step 1: Step 2: Ft+1 = (Bt +Tt )St-L+1

    39. OPS 463 - Short Term Fcsting 39 FORECASTING MORE THAN ONE PERIOD AHEAD m – # periods ahead to be forecast Base level forecasts: Ft+m = Bt Forecasts with trend: Ft+m = Bt +mTt Forecasts with seasonality: Ft+m = (Bt +mTt )St-L+m

    40. OPS 463 - Short Term Fcsting 40 SELECTING A TECHNIQUE Ockham's razor -- use the simplest possible model or theory (William of Ockham, 1300-1349, England) 1) Determine type of technique which is appropriate (i.E., Base-level, trend, etc.) 2) Select a group of competing techniques which satisfy condition (1) 3) Select a set of data as a test set 4) Simulate forecasts for this set of data using all techniques from (2) 5) Pick the technique with the best combination of MAD/MAPE and Bias

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