Forecasting

1. Forecasting OPS 370

2. Forecasting Forecasting - Chapter 4

3. What to Forecast? Demand for Individual Products & Services Short Term (0-3 Months) Demand for Product & ServiceFamilies Medium Term (3 Months – 2 Years) Total Sales, New Offerings Long Term (>2 Years) Forecasting - Chapter 4

4. How to Forecast? • Qualitative Methods • Based On Educated Opinion & Judgment (Subjective) • Particularly Useful When Lacking Numerical Data (Example: Design and Introduction Phases of a Product’s Life Cycle) • Quantitative Methods • Based On Data (Objective) Forecasting - Chapter 4

5. Qualitative Methods • Executive Judgment • Sales Force Composite • Market Research/Survey • Delphi Method Forecasting - Chapter 4

6. Quantitative Methods • Time Series & Regression • Time Series  Popular Forecasting Approach in Operations Management • Assumption: • “Patterns” That Occurred in the Past Will Continue to Occur In the Future • Patterns • Random Variation • Trend • Seasonality • Composite Forecasting - Chapter 4

8. UK Airline Miles Thousandsof Miles

9. Forecasting Steps Data Collection Collect Relevant/Reliable Data Be Aware of “Garbage-In, Garbage Out” Data Analysis Model Selection Monitoring Forecasting - Chapter 4

10. Forecasting Steps Data Collection Plot the Data Identify Patterns Data Analysis Model Selection Monitoring Forecasting - Chapter 4

11. Forecasting Steps Data Collection Choose Model Appropriate for Data Consider Complexity Trade-Offs Perform Forecast(s) Select Model Based on Performance Measure(s) Data Analysis Model Selection Monitoring Forecasting - Chapter 4

12. Forecasting Steps Data Collection Track Forecast Performance (Conditions May and Often Do Change) Data Analysis Model Selection Monitoring Forecasting - Chapter 4

13. Time Series Models • Short Term • Naïve • Simple Moving Average • Weighted Moving Average • Exponential Smoothing Forecasting - Chapter 4

14. Forecasting Example • L&F Bakery has been forecasting by “gut feel.” They would like to use a formal (i.e.,quantitative) forecasting technique. Forecasting - Chapter 4

15. Forecasting Methods • Naïve Forecasting - Chapter 4

16. Forecasting Methods • Naïve (Excel) =C4 =C5 Forecasting - Chapter 4

17. Forecasting Methods • Moving Average

18. Forecasting Methods 30 Day Moving Average of AAPL Price

19. Forecasting Methods • Moving Average (Excel) =AVERAGE(C4:C6) =AVERAGE(C5:C7)

20. Forecasting Methods • Moving Average Example • Assume n = 2

21. Forecasting Methods • Weighted Moving Average

22. Forecasting Methods • Weighted Moving Average =$G$6*C6+$G$5*C5+$G$4*C4 =$G$6*C7+$G$5*C6+$G$4*C5

23. Forecasting Methods • Weighted Moving Average Example • Assume n = 2, W1 = 0.7, W2 = 0.3

24. Forecasting Methods • Exponential Smoothing

25. Forecasting Methods • Exponential Smoothing

26. Forecasting Methods • Exponential Smoothing (Excel) Initial forecast =D4+$G$4*(C4-D4) =D5+$G$4*(C5-D5)

27. Forecasting Methods • Exponential Smoothing Example • Assume a= 0.4

28. Forecasting Methods • How to Select Value of a? • Alpha determine importance of recent forecast results in new forecasts

29. Determining Forecast Quality • How Well Did a Forecast Perform? • Determine Forecast Error Error = Actual Demand – Forecasted Demand Average Error 121.8

30. Determining Forecast Quality • Why is Average Error a Deceiving Measure of Quality? • Better Measures: Mean Absolute Deviation Mean Squared Error Root Mean Squared Error

31. Determining Forecast Quality Measure of Bias: Tracking Signal = Sum of Errors/MAD =731/131.8 = 5.55 *OK if between -4 and +4 MAD MSE

32. Determining Forecast Quality For this MA(2) forecast. What is MAD, MSE, and TS?

33. Linear Regression • <SKIP Section in Textbook on Exponential Smoothing with Linear Trend>

34. Linear Trend Line • Given Data • Y = Values of Response Variable • X = Values of Independent Variable • Parameters to estimate • a = Y-intercept • b = slope • Use “least squares” regression equations to estimate a and b. • Or …

35. Excel for Linear Regression Use SLOPE Function Use INTERCEPT Function