Module: Forecasting Operations Management as a Competitive Weapon

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

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

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 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 © 1995 Corel Corp. Exponential Smoothing Example

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

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

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

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

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 Selecting a Forecasting Model

14 Module: Forecasting Forecasting Model Selection

15 Module: Forecasting Forecasting Model Selection

16 Module: Forecasting Forecasting Model Selection

17 Module: Forecasting Forecasting Model Selection

18 Module: Forecasting Forecasting Model Selection

19 Module: Forecasting Forecasting Model Selection

20 Module: Forecasting Forecasting Model Selection

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

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

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

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

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

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

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

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

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