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

2. 2 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

3. 3 3 Basic Rules of Forecasting 1.All Forecasts are WRONG 2.Short-term forecasts are generally more accurate than long-term forecasts 3.Aggregate forecasts (group of products, stocks, quarterly vs. monthly...etc.) are generally more accurate than individual forecasts 4.Forecasts are self-fulfilling: We never sell more than we make, and we sacrifice price or margin to make them right!

4. 4 4 Consequences 1.All Forecasts are WRONG So, measure and understand the uncertainty inherent in your forecasts. Distinguish systematic bias from noise. 2.Short-term forecasts are generally more accurate than long-term forecasts So, shorten the time you need to forecast

5. 5 5 Consequences 3.Aggregate forecasts (group of products, stocks, quarterly vs. monthly...etc.) are generally more accurate than individual forecasts Many consequences: - Recall our discussion of ports for XYZ - Inventory/Risk Pooling - Delayed Differentiation/Postponement - Make-to-Order vs Make-to-Stock

6. 6 6 Consequences 4.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), …

7. 7 7 Forecasting Methods Judgmental Used when situation is vague and little data exists (i.e. new products, new technologies) Delphi Method Expert Forecasting Game Theory Bootstrapping 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

8. 8 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

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

10. 10 Measuring “Spread” Mean Absolute Error (MAE) Mean Absolute Percentage Error (MAPE) Percent Mean Absolute Deviation (PMAD) Mean squared error (MSE) Root Mean squared error (RMSE)

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

12. 12 Time Series Forecasting Double Exponential Smoothing (Holt’s): Initialization: or… take the Offset and Slope of the linear regression line fitted to the first couple of points. Next period Multiple periods out F t is the forecast O t is the “level” S t is the “slope”

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

14. 14 Diffusion Models Used for estimating Product Life Cycle behavior: 0 0.5 1 1.5 2 2.5 3 147101316192225283134374043464952 Millions Months N(t)

15. 15 Forecasting 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

16. 16 Results Mean:47.55Mean:81.04Mean:1272.98 SE:9.01SE:36.61SE:517.51 CV:0.19CV:0.45CV:0.41 Forecast period: (weeks 53 – 90) Average coefficient of variation = 0.30

17. 17 Forecasting Summary 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