1. Module: Forecasting Operations Management as a Competitive Weapon

2. 2 Module: Forecasting Learning Objectives At the end of this module, each student will be able to: 1.Describe forecasting 2.Describe time series 3.Explain forecast selection and monitoring

3. 3 Module: Forecasting 1. What is Forecasting? Process of predicting a future event Underlying basis of all business decisions Company needs Operations needs Sales will be $200 Million!

4. 4 Module: Forecasting Major Demand Components  Average demand for the period  Trend  Cyclical  Seasonal  Random

5. 5 Module: Forecasting Realities of Forecasting  Forecasts are seldom perfect  Most forecasting methods assume that there is some underlying stability in the system  Both product family and aggregated product forecasts are more accurate than individual product forecasts

6. 6 Module: Forecasting  Forecast based only on past values Assumes that factors influencing past, present, & future will continue  Example: Year:199920002001200220032004 Sales:78.763.589.793.292.1? 2. Time Series

7. 7 Module: Forecasting  Form of weighted moving average Weights decline exponentially Most recent data weighted most  Requires smoothing constant (  ) Ranges from 0 to 1 Subjectively chosen  Involves little record keeping of past data Exponential Smoothing Method

8. 8 Module: Forecasting You’re organizing a Kwanza meeting. You want to forecast attendance for 2004 using exponential smoothing (  =.10). The 2003 forecast was 175, actual was 190.. © 1995 Corel Corp. Exponential Smoothing Example

9. 9 Module: Forecasting Exponential Smoothing Solution F t = F t-1 + · (A t-1 - F t-1 ) F 2004 = F 2003 + · (A 2003 – F 2003 ) = 175 + 0.10 (180 – 175) = 175 + 0.10 (5) = 175.5

10. 10 Module: Forecasting  You want to achieve: Low forecast error pattern Low forecast error size 3. Forecasting Selection Guidelines

11. 11 Module: Forecasting Desired Pattern Time (Years) Error 0 Time (Years) Error 0 Trend Not Fully Accounted for Pattern of Forecast Error

12. 12 Module: Forecasting Forecast Error Equations Mean Squared Error Mean Absolute Deviation

13. 13 Module: Forecasting You’re a marketing analyst for Hasbro Toys. You’ve forecast sales with two models. Which model should you use? ActualModel 1Model 2 YearSalesForecastForecast 110610 2101310 3202019 4202720 5403438 Selecting a Forecasting Model

14. 14 Module: Forecasting Forecasting Model Selection

15. 15 Module: Forecasting Forecasting Model Selection

16. 16 Module: Forecasting Forecasting Model Selection

17. 17 Module: Forecasting Forecasting Model Selection

18. 18 Module: Forecasting Forecasting Model Selection

19. 19 Module: Forecasting Forecasting Model Selection

20. 20 Module: Forecasting Forecasting Model Selection

21. 21 Module: Forecasting  Measures how well forecast is predicting actual values  Is my forecast tool out of control? Tracking Signal

22. 22 Module: Forecasting Tracking Signal Computation TS Month = RSFE / MAD Month MAD Month = RSAE / Month RSAE =  (| Error|) RSFE =  ( Error)

23. 23 Module: Forecasting Tracking Signal Computation Error = Actual-Forecast

24. 24 Module: Forecasting Tracking Signal Computation RSFE =  ( Error)

25. 25 Module: Forecasting Tracking Signal Computation |Error| = ABS(Error)

26. 26 Module: Forecasting Tracking Signal Computation RSAE =  (| Error|)

27. 27 Module: Forecasting Tracking Signal Computation MAD Month = RSAE / Month

28. 28 Module: Forecasting Tracking Signal Computation TS Month = RSFE / MAD Month

29. 29 Module: Forecasting Tracking Signal Computation Out of control, > 3