1 / 33

W12B – Forecasting ( Chapter 12)

W12B – Forecasting ( Chapter 12). Demand behavior, approaches to forecasting, measures of forecast error. Why Forecast?. You’re wrong more than you’re right Often ignored or used as scapegoat Thankless job! Examples of the downside of forecasting. Why Forecast (cont’d) ?.

vinnie
Download Presentation

W12B – Forecasting ( Chapter 12)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. W12B – Forecasting (Chapter 12) 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 planresources in advance! SJSU Bus 140 - David Bentley

  6. Forecast accuracy • Which would tend to be more accurate? • Aggregation • Rather forecast sales of all Ford automobiles or forecast a specific model? • Time • Rather forecast Ford sales for 2013or for 2018? SJSU Bus 140 - David Bentley

  7. Forecast accuracy • Aggregation • Rather forecast sales of all Ford automobiles or forecast a specific model? • Forecasts tend to be more accurate for groups of items than for individual items in the group • Time • Rather forecast Ford sales for 2013or for 2018? • 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 • Cost 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

More Related