1. Chapter Two Forecasting

2. Planning Forecast Customer Production Process Finished Goods Inputs

6. Forecasting Techniques Judgmental Models Time Series Methods Causal Methods Forecasting Technique Delphi Method Moving Average Exponential Smoothing Regression Analysis Seasonality Models

15. Forecasting Stationary Series

17. Time Series Patterns Fig. 2-2

22. Forecast Errors Over Time Fig. 2-3

25. Moving Average

26. 90 Oct 110 Sep 130 Aug 75 Jul 50 Jun 110 May 75 Apr 100 Mar 90 Feb 120 Jan Deliveries Month

27. 94 110 92 90 83 91 MA(6) 105 85 78 78 95 88 103 MA(3) 90 Oct 110 Sep 130 Aug 75 Jul 50 Jun 110 May 75 Apr 100 Mar 90 Feb 120 Jan Deliveries Month

29. 17 20 24 12 19 22 15 18 22 11 13 16 20 10 11 14 18 9 9 12 16 8 7 10 14 7 8 12 6 6 10 5 4 8 4 6 3 4 2 2 1 MA(6) MA(3) Deliveries Month

30. Moving-Average Forecasts Lag Behind a Trend Fig. 2-4

32. Exponential Smoothing F t = F t-1 – (fraction of the observed forecast error in t-1) If we forecast high in period t-1  error is positive  adjustment to decrease current forecast If we forecast low in period t-1  error is negative  adjustment to increase current forecast

34. Example 220 190 8 211 305 7 203 285 6 201 225 5 202 186 4 205 175 3 200 250 2 200 200 1 Forecast Failures Quarter

36. Weights in Exponential Smoothing Fig. 2-5

37. Exponential Smoothing for Different Values of Alpha Fig. 2-6

38. Smaller values of α produce more stable forecasts, whereas larger values of α will produce forecasts which react more quickly to changes in the demand pattern.

39. Comparison