Week 09B – Forecasting (Chapter 4)

1. Week 09B – Forecasting(Chapter 4) Demand behavior, approaches to forecasting, measures of forecast error SJSU Bus 140 - David Bentley

2. Why Forecast? • You’re wrong more than you’re right • Often ignored or used as scapegoat • Thankless job! • Examples of the downside of forecasting SJSU Bus 140 - David Bentley

3. Why Forecast (cont’d)? • Example # 1 (from San Jose Mercury News 02/11/02) • “____________________________________________________________________________________________________________________________________________________________________________________________.” SJSU Bus 140 - David Bentley

4. Why Forecast (cont’d)? • Example #2 (from San Jose Mercury News 03/01/05) • “________________________________________________________________________________________________________________________________________________________________________________.” • “____________________________________________________________________________________________________.” SJSU Bus 140 - David Bentley

5. Why Forecast – (the answer) We need to ___________in advance! SJSU Bus 140 - David Bentley

6. Forecast accuracy • Which would tend to be more accurate? • Level of aggregation • Rather forecast sales of allFord automobiles or forecast a specificmodel and configuration? • Time • Rather forecast Ford sales for 2015or for 2019? SJSU Bus 140 - David Bentley

7. Forecast accuracy • Aggregation • Forecast sales of all Ford automobiles or a specific model and configuration? ___ • Forecasts tend to be more accurate for groups of items than for individual items in the group • Time • Forecast Ford sales for 2014 or for 2019? ____ • Forecasts tend to be more accurate for the near future than for the distant future SJSU Bus 140 - David Bentley

8. Common Features of Forecasts • Forecasts often (but not always) assume that what happened in the past will continue in the future • Forecasts are rarely perfect • “You are either lucky or lousy” • Forecasts tend to be more accurate for groups of items than for individual items • Forecasts tend to be more accurate for the short range than for the long range SJSU Bus 140 - David Bentley

9. Demand Components • Components or Elements or Behavior • Trend – long-term linear movement up or down • Seasonal – short term recurring variations • Cyclical – long-term recurring variations • Random & Irregular – doesn’t fit other three components SJSU Bus 140 - David Bentley

10. Trend • Long-term linear movement up or down SJSU Bus 140 - David Bentley

11. Seasonal • Short term recurring variations SJSU Bus 140 - David Bentley

12. Cyclical • Long-term recurring variations SJSU Bus 140 - David Bentley

13. Random & Irregular • Doesn’t follow any pattern (trend, seasonal, or cyclical) SJSU Bus 140 - David Bentley

14. Forecasting Approaches • Qualitative (“subjective”) • JudgmentandOpinion • Quantitative (“objective”) • Associative • External sources of data • Historical • Internal sources of data used SJSU Bus 140 - David Bentley

15. Judgment and Opinion - 1 • Sources • Executives • Marketing & Sales Projections • Customers • Potential customers • “Experts” • Delphi method SJSU Bus 140 - David Bentley

16. Judgment and Opinion - 2 • Appropriate Use • Irregular or random demand • New products • Absence of historical data • Techniques • Surveys, questionnaires, interviews, focus groups, observation • Delphi method SJSU Bus 140 - David Bentley

17. Associative • Sources • External industry data • Demographic and econometric data • Appropriate use • Cyclical demand • Technique • Leading indicator, and • Linear regression, in conjunction with • Correlation SJSU Bus 140 - David Bentley

18. Historical • Sources • Historical (“time series”) data • Appropriate use • Varies (see later slides) • Technique types • Multi-period pattern projection • Single period patternless projection SJSU Bus 140 - David Bentley

19. Multi-period Pattern Projection Techniques - Trend • Appropriate use • Clear trend pattern over time • Techniques • Best fit (“eyeball”) • Linear trend equation or least squares • Yt = a + bt • b = n (ty) – (t)(y) n t2 – (t)2 a = y - b t n SJSU Bus 140 - David Bentley

20. Multi-period Pattern Projection Techniques - Seasonal • Appropriate use • Seasonal demand • Related to weather, holidays, sports, school calendar, day of the week, etc. • Techniques • Seasonal indexes or relatives • Seasonally adjusted trend • Separate trend from seasonality SJSU Bus 140 - David Bentley

21. Single Period Patternless Projection - 1 • Appropriate use • Lack of clear data pattern • Limited historical data • Techniques • Moving Average (older method) • Ft = A n • Weighted moving average • Ft = a(At-1) + b(At-2) + … + x(At-n) SJSU Bus 140 - David Bentley

22. Single Period Patternless Projection - 2 • Techniques (continued) • Exponential Smoothing (newer method) • Ft = Ft-1 + ( At-1 – Ft-1 ) • Naïve Forecast • Simple (stable series) • = last period’s actual (often used with seasonality) • Ft = At-1 • Advanced (some trend) • Ft = At-1+(At-1 - At-2) SJSU Bus 140 - David Bentley

23. Single Period Patternless Projection - 3 • Techniques (continued) • Double exponential smoothing • aka second order exponential smoothing • Special case • Incorporates some trend • Uses exponential smoothing formula plus second formula with additional smoothing constant SJSU Bus 140 - David Bentley

24. Multiperiod Pattern Projection SJSU Bus 140 - David Bentley

25. Single Period Patternless Projection SJSU Bus 140 - David Bentley

26. Other Forecasting Methods SJSU Bus 140 - David Bentley

27. Outliers • Exceptional demand in a time period and not expected to recur • Needs to be adjusted for forecast • Can’t ignore it • Can’t include it without adjusting or it will distort forecast • Approximate normal demand • Techniques discussed in lecture SJSU Bus 140 - David Bentley

28. Measures of Forecast Error - 1 • Forecast Error (e, E, or FE) • Et = At - Ft • Average Error (AE) • AE = E n • Mean Absolute Deviation (MAD) • MAD = |E| n SJSU Bus 140 - David Bentley

29. Measures of Forecast Error - 2 • Mean Squared Error (MSE) • MSE = E2 n-1 • Standard Deviation (SD) • SD = square root ofE2 n-1 • Mean Absolute Percent Error (MAPE) • MAPE = (|E|/A) (100) n SJSU Bus 140 - David Bentley

30. Controlling the forecast - 1 • Control charts • Upper and lower control limits (remember SPC?) – See Example 12.9 • Formulas: ___ • Upper limit: 0 + z √MSE • Lower limit: 0 – z √MSE where z = number of standard deviations from the mean SJSU Bus 140 - David Bentley

31. Controlling the forecast - 2 • Tracking Signal (TS) • Reflects “bias” in the forecast • TS = (A – F) MAD • Look forvalueswithin ± 4 SJSU Bus 140 - David Bentley

32. Choosing and … • Choosing a forecasting technique • Nature of data (plot data: pattern?) • Forecast horizon • Preparation time • Experience (may want to try several) • Choosing a measure of forecast error • Ease of use • Control tool to be used SJSU Bus 140 - David Bentley

33. … Using • Using forecast information • Proactive vs. reactive • Look at reasonability • Assure everyone works off same data • “What – if” SJSU Bus 140 - David Bentley