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Demand Forecasts

Learn about the three principles of forecasting techniques, the truth about forecasts, and how to select and use the right technique for accurate predictions. Explore judgmental, market research, time series, and causal methods, along with error measures and data collection tips.

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Demand Forecasts

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  1. Demand Forecasts • The three principles of all forecasting techniques: • Forecasting is always wrong • Every forecast should include an estimate of error • The longer the forecast horizon the worst is the forecast • Aggregate forecasts are more accurate

  2. Two comments frequently made by managers • We’ve got to have better forecasts • I don’t trust these forecasts or understand where they came from • These comments suggest that forecasts are held in disrepute by many managers

  3. The truth about forecasts • They are always wrong • Sophisticated forecasting techniques do not mean better forecasts • Forecasting is still an art rather than an esoteric science • Avoid single number forecasting • Single number substitutes for the decision

  4. Selecting a forecasting technique • What is the purpose of the forecast? • How is it to be used? • What are the dynamics of the system for which forecast will be made? • How important is the past in estimating the forecast?

  5. Forecasting Techniques • Judgmental methods • Market research methods • Time series methods • Casual methods Qualitative Quantitative

  6. Judgmental methods • Sales-force composite • Panels of experts • Delphi method

  7. Market research method • Markey testing • Market survey

  8. Time Series methods • Moving average • Exponential smoothing • Trend analysis • Seasonality • Use de-seasonalized data for forecast • Forecast de-seasonalized demand • Develop seasonal forecast by applying seasonal index to base forecast

  9. Components of an observation Observed demand (O) = Systematic component (S) + Random component (R) Level (current deseasonalized demand) Trend (growth or decline in demand) Seasonality (predictable seasonal fluctuation)

  10. Causal methods • Single Regression analysis • Multiple Regression analysis

  11. Error measures • MAD • Mean Squared Error (MSE) • Mean Absolute Percentage Error (MAPE) • Bias • Tracking Signal

  12. Collection and preparation of data • Record data in the same terms as needed for forecast • Demand vs. shipment • Time interval should be the same • Record circumstances related to data • Record demand separately for different customer groups

  13. Time Series Forecasting Forecast demand for the next four quarters.

  14. Time Series Forecasting

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