Forecasting

1. Forecasting • Anticipating service requirements over the short term • Operational purposes • Planning and scheduling resources • Examples: • Manufacturing and distributing printers at HP • Staffing levels (NS) • etc.

2. Objectives • What is forecasting • What are the issues • What are the tools

3. Forecasting • Developing predictions or estimates of future values • Demand volume • Price levels • Lead times • Resource availability • ...

4. Taco Bell Feed the dog • Labor is 30% of revenue • Make to order environment • Significant “seasonality” • 52% of days sales during lunch • 25% of days sales during busiest hour • Balance staff with demand

5. Value Meals • Drove demand • Forecasting system in each store • forecasts arrivals within 15 minute intervals • Simulation system • “predicts” congestion and lost sales • Optimization system • Finds the minimum cost allocation of workers

6. Forecasting System • Customer arrivals by 15-minute interval of day (e.g., 11:15-11:30 am Friday) • Fed by in-store computer system • 6-week moving average • Estimated savings: Over $40 Million in 3 years.

7. Independent vs Dependent • Independent • Exogenously controlled • Subject to random or unpredictable changes • What we forecast • Dependent or Derived • Calculated or derived from other sources • Bill of Materials • Related activity like packaging

8. Phenomena To Capture • Randomness • Trend • Linear • Exponential • Seasonality

9. Randomness

10. Linear Trend

11. Exponential Trend

12. Seasonality

13. Seasonality

14. Forecasting Methods • Qualitative or Judgemental • Ask people who ought to know • Historical Projection or Extrapolation • Moving Averages • Exponential Smoothing • Regression based methods • Neural Networks • Econometric or Causal • Regression • Simulation

15. Moving Averages • Simple, ubiquitous • Reduce random noise • One Extreme • Predict next period = This period • Another Extreme • Predict next period = Long run average • Intermediate View: K period moving avg. • Predict next period = Average of last K periods

16. The Dow

17. Questions • What’s the forecast for 6 months out? • Will a shorter span always be better? • Is moving average a good method here? • Which will handle this better?

18. Which is better? • Average Error • 3-month 1490% • 6-month 619%

19. Exponential Smoothing • Moving Averages • Equal weight to older observations • Exponential Smoothing • More weight to more recent observations • Forecast for next period is a weighted average of • Observation for this period • Forecast for this period • Alpha*Observation + (1-Alpha)*Past Forecast

20. Initial Values • First Observation • Average of previous observations • etc.

21. Which is which? Alpha = .01 or Alpha = .2

22. Modeling Trend • Holt’s Method • Forecast is weighted combination of • Current Observation • Current Forecast for Next Period • Forecast Trend as weighted combination of • Current Trend in Forecasts • (our estimate of trend) • Current Forecast for Trend • (differences in successive forecasts) • Why this way?

23. With and Without Trend

24. Intel’s Trend

25. Exponential Growth • Forecasted Sales = et • Natural Log of Forecasted Sales • Ln + t • That’s linear growth • Take Natural Log of observations • Forecast Natural Log of Sales • Convert back to Forecast of Sales

26. Seasonality • Deseasonalize the data • Forecast • Seasonalize the forecast • Seasonal Factors • Ratio of Actual to “Average” • Updated with Exp. Smooth. • Weighted combination of • Actual/Deseasonalized Forecast • Current Forecast of seasonal factor

27. Seasonality • Deseasonalized Forecast • Alpha*(Actual/Seasonal Factor)+ • (1-Alpha)*(Past Deseasonalized Forecast) • Seasonalized Forecast • Deseasonalized Forecast * Seasonal Factor • Updating the seasonal factors • Gamma * (Actual/Deseasonalized Forecast) + • (1- Gamma) * Previous estimate of seasonal factor

28. Initialization and Factors • Level • Trend • Seasonality

33. Regression-Based Models • Time Series • Find best fit of proposed model to past data • Project that fit forward • Econometric • Find exogenous factors driving value • Weather • Economic factors • Rainfall • Develop formula for (future) values based on these factors

34. Example • Locating a new retail store • Build a model of sales volume (profitability) based on existing stores • Population • Wealth • Competitors • Access • … • Predict sales for new store with this model

35. Forecast Error • Building a Forecast • Fit to historical data • Project future data • Forecast Error • How well does model fit historical data • Do we need to tune or refine the model • Can we offer confidence intervals about our predictions

36. Measuring Forecast Error • MAD or MAE • average of the absolute errors • RMSE (root mean square error) • Square root of average squared error • Sample std deviation • Differs by 1 degree of freedom (N-1) • MAPE (mean absolute percentage error) • Average absolute ratio of error to actual

37. Issues • Forecasting is a necessary evil, try to reduce the need for it. • Complexity costs money, does it provide better forecasts? • Aggregation provides accuracy, but precludes local information • Forecast the right thing

38. What about HP? • Why are forecasts bad in Europe?