1. Irwin/McGraw-Hill  The McGraw-Hill Companies, Inc. 2004 1 Forecasting Chapter 11

2. Irwin/McGraw-Hill  The McGraw-Hill Companies, Inc. 2004 2 Outline A Forecasting Framework Qualitative Forecasting Methods Time-Series Forecasting Moving Average Exponential Smoothing Forecast Errors Advanced Time-Series Forecasting Causal Forecasting Methods Selecting a Forecasting Method

3. Irwin/McGraw-Hill  The McGraw-Hill Companies, Inc. 2004 3 A Forecasting Framework Focus of the chapter Difference between forecasting and planning Forecasting application in various decision areas of operations (capacity planning, inventory management, others) Forecasting uses and methods (See Table 11.1)

4. Irwin/McGraw-Hill  The McGraw-Hill Companies, Inc. 2004 4 Use of Forecasting (Table 11.1) Operations Decisions

5. Irwin/McGraw-Hill  The McGraw-Hill Companies, Inc. 2004 5 Use of Forecasting (Table 11.1) Marketing, Finance, HRM

6. Irwin/McGraw-Hill  The McGraw-Hill Companies, Inc. 2004 6 Qualitative Forecasting Methods Major methods: –Delphi Technique –Market Surveys –Life-cycles Analogy –Informed Judgement Characteristics of the methods (see Table 11.2)

7. Irwin/McGraw-Hill  The McGraw-Hill Companies, Inc. 2004 7 Time-Series Forecasting Common components in time-series (see Figure 11.1): –Average –Seasonality –Cycle –Trend –Error (random component) “Decomposition” of time-series

8. Irwin/McGraw-Hill  The McGraw-Hill Companies, Inc. 2004 8 Simple Moving Average: Weighted Moving Average: Moving Average

9. Irwin/McGraw-Hill  The McGraw-Hill Companies, Inc. 2004 9 Simple Exponential Smoothing: Smoothing Coefficient (alpha) determination Determination of the initial forecast Exponential Smoothing

10. Irwin/McGraw-Hill  The McGraw-Hill Companies, Inc. 2004 10 Time-Series Data Plot (Figure 11.2)

11. Irwin/McGraw-Hill  The McGraw-Hill Companies, Inc. 2004 11 Exponential Smoothing Basic logic: The forecast

12. Irwin/McGraw-Hill  The McGraw-Hill Companies, Inc. 2004 12 Forecast Errors Cumulative Sum of Forecast Error (CFE) Mean Square Error (MSE) Mean Absolute Deviation (MAD) Mean Absolute Percentage Error (MAPE) Tracking Signal (TS)

13. Irwin/McGraw-Hill  The McGraw-Hill Companies, Inc. 2004 13 Forecast Errors: Formulas Cumulative sum of Forecast Errors Mean Square Error Mean Absolute Deviation Mean Absolute Percentage Error Tracking Signal Mean Error

14. Irwin/McGraw-Hill  The McGraw-Hill Companies, Inc. 2004 14 Advanced Time-Series Forecasting Adaptive exponential smoothing Comparison of time-series forecasting methods (see Table 11.5) Box-Jenkins method

15. Irwin/McGraw-Hill  The McGraw-Hill Companies, Inc. 2004 15 Causal Forecasting Models The general model: Other forms of causal model (see Table 11.7): –Econometric –Input-output –Simulation models –Others

16. Irwin/McGraw-Hill  The McGraw-Hill Companies, Inc. 2004 16 Example of Causal Method Y t = a + b(t) F 7 = 117.87 + 1.66 (7) = 129.47

17. Irwin/McGraw-Hill  The McGraw-Hill Companies, Inc. 2004 17 Selecting a Forecasting Method User and system sophistication Time and resource available Use or decision characteristics Data availability Data pattern

18. Irwin/McGraw-Hill  The McGraw-Hill Companies, Inc. 2004 18 Graphical Comparison Moving average method with various n ME

19. Irwin/McGraw-Hill  The McGraw-Hill Companies, Inc. 2004 19 Graphical Comparison Moving average method with various n MAD

20. Irwin/McGraw-Hill  The McGraw-Hill Companies, Inc. 2004 20 Graphical Comparison Moving average method with various n MSE

21. Irwin/McGraw-Hill  The McGraw-Hill Companies, Inc. 2004 21 Graphical Comparison Moving average method with various n MAPE