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Forecasting and Demand Modeling ISyE 6203 – Fall 2009

Forecasting and Demand Modeling ISyE 6203 – Fall 2009. Thanks to:. Dr. Anton Kleywegt Dr. Mark Goetschalckx Dr. Evren Ozkaya. Agenda. I. Forecasting What is forecasting? What/Why are we forecasting? Basic Forecasting Rules Forecasting Methods and Accuracy

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Forecasting and Demand Modeling ISyE 6203 – Fall 2009

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  1. Forecasting and Demand Modeling ISyE 6203 – Fall 2009 Thanks to: Dr. Anton KleywegtDr. Mark GoetschalckxDr. Evren Ozkaya 1

  2. Agenda • I. Forecasting • What is forecasting? What/Why are we forecasting? • Basic Forecasting Rules • Forecasting Methods and Accuracy • Forecasting Examples: Winter’s Method, Multiple Regression • II. Demand Modeling • Stationary vs. Non-stationary • Demand distributions • Unconstraining the Demand Data • Demand Modeling Tools 2

  3. Basic Rules of Forecasting • All Forecasts are WRONG • Short-term forecasts are generally more accurate than long-term forecasts • Aggregate forecasts (group of products, stocks, quarterly vs. monthly...etc.) are generally more accurate than individual forecasts • Forecasts are self-fulfilling: We never sell more than we make, and we sacrifice price or margin to make them right! 3

  4. All Forecasts are WRONGSo, measure and understand the uncertainty inherent in your forecasts. Distinguish systematic bias from noise. Short-term forecasts are generally more accurate than long-term forecastsSo, shorten the time you need to forecast Consequences 4

  5. Aggregate forecasts (group of products, stocks, quarterly vs. monthly...etc.) are generally more accurate than individual forecastsMany consequences:- Recall our discussion of ports for XYZ- Inventory/Risk Pooling- Delayed Differentiation/Postponement- Make-to-Order vs Make-to-Stock Consequences 5

  6. Forecasts are self-fulfilling: We never sell more than we make, and we sacrifice price or margin to make them right! So,be careful to balance the inherent risks when working with forecasts (Sport Obermeyer), buffer appropriately (safety stock), manage demand with pricing (revenue management), … Consequences 6

  7. Forecasting Methods • Statistical • Used when situation is stable and historical data exists (i.e. mature products) • Time Series Models • - Moving Average, • - Exponential Smoothing • - ARIMA • Econometric • - Single Regression • - Multiple Regression • Discrete Choice Models • - Logit, Probit • Judgmental • Used when situation is vague and little data exists (i.e. new products, new technologies) • Delphi Method • Expert Forecasting • Game Theory • Bootstrapping 7

  8. Statistical Forecasting Methods “Using the past to 'see' the future is like driving a car by looking into the rear view mirror. As long as the road is straight or curving in wide arcs, the driver can stay on the road by looking backward. However, if a sharp turn occurs or a bridge is out, the driver will crash." Allen R. Beck Allen R. Beck, "Forecasting: Fiction and Utility in Jail Construction Planning", Correctional Building News, August 1998 8

  9. Accuracy: Average Error  0 No systematic bias in the forecasts Precision: “Spread” of the Errors should be small Forecast Error Actual Two Goals 9

  10. Measuring “Spread” 10

  11. Forecast Error Actual Time Series Forecasting Moving Average (m-period): Simple Exponential Smoothing: Smoothing constant α is [0,1] Most recent observation gets α weight. 11

  12. Time Series Forecasting Double Exponential Smoothing (Holt’s): Multiple periods out Next period Initialization: Ft is the forecast Ot is the “level” St is the “slope” or… take the Offset and Slope of the linear regression line fitted to the first couple of points. 12

  13. Time Series Forecasting Triple Exponential Smoothing (Additive Winter’s): Next period Multiple periods out Initialization: with 2 cycles 13

  14. 3 Millions N(t) 2.5 2 1.5 1 0.5 0 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 Months Diffusion Models Used for estimating Product Life Cycle behavior: 14

  15. Causal Forecasting For XYZ Regression model considering the following factors: Index (autocorrelation) Monthly temperature (historical average) “Peak” & “Decline” periods European energy prices European construction index European consumer confidence index Offset for forecast period Forecasting 15

  16. Results Forecast period: (weeks 53 – 90) Average coefficient of variation = 0.30 16

  17. Many techniques Remember: keep it simple! Priority #1: Accuracy Priority #2: Precision Quantify the forecast errors into a distribution – that’s your measure of risk in decision making…to come Design the system to accommodate forecast error Next – Review for Exam, Exam… Managing Inventory Forecasting Summary 17

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