1. Forecasting Demand for Services

2. Learning Objectives l Recommend the appropriate forecasting model for a given situation. l Conduct a Delphi forecasting exercise. l Describe the features of exponential smoothing. l Conduct time series forecasting using exponential smoothing with trend and seasonal adjustments.

3. Forecasting Models l Subjective Models Delphi Methods l Causal Models Regression Models l Time Series Models Moving Averages Exponential Smoothing

4. Delphi Forecasting Question: In what future election will a woman become president of the united states?

5. N Period Moving Average Let : MA T = The N period moving average at the end of period T A T = Actual observation for period T Then: MA T = (A T + A T-1 + A T-2 + …..+ A T-N+1 )/N Characteristics: Need N observations to make a forecast Very inexpensive and easy to understand Gives equal weight to all observations Does not consider observations older than N periods

6. Moving Average Example Saturday Occupancy at a 100-room Hotel Three-period Saturday Period Occupancy Moving Average Forecast Aug. 1 1 79 8 2 84 15 3 83 22 4 81 29 5 98 Sept. 5 6 100 12 7

7. Exponential Smoothing Let : S T = Smoothed value at end of period T A T = Actual observation for period T F T+1 = Forecast for period T+1 Feedback control nature of exponential smoothing New value (S T ) = Old value (S T-1 ) + [ observed error ] or :

8. Exponential Smoothing Hotel Example Saturday Hotel Occupancy ( =0.5) Actual Smoothed Forecast Period Occupancy Value Forecast error Saturday t A t S t F t |A t - F t | Aug. 1 1 79 8 2 84 15 3 83 22 4 81 29 5 98 Sept. 5 6 100 MAD = Mean Absolute Deviation (MAD)

9. Exponential Smoothing Implied Weights Given Past Demand Substitute for If continued:

10. Exponential Smoothing Weight Distribution Relationship Between and N (exponential smoothing constant) : 0.05 0.1 0.2 0.3 0.4 0.5 0.67 N (periods in moving average) : 39 19 9 5.7 4 3 2

11. Saturday Hotel Occupancy Effect of Alpha ( =0.1 vs. =0.5) Actual Forecast

12. Exponential Smoothing With Trend Adjustment Commuter Airline Load Factor Week Actual load factor Smoothed value Smoothed trend Forecast Forecast error t A t S t T t F t | A t - F t | 1 31 31.00 0.00 2 40 31 9 3 43 4 52 47.10 3.74 5 49 49.92 3.47 51 2 6 64 58.69 5.06 53 11 7 58 60.88 4.20 64 6 8 68 66.54 4.63 65 3 MAD 6.7

13. Exponential Smoothing with Seasonal Adjustment Ferry Passengers taken to a Resort Island Actual Smoothed Index Period t A t value S t I t Forecast F t | A t - F t| 1995 January 1 1651 ….. 0.837 ….. February 2 1305 ….. 0.662 ….. March 3 1617 ….. 0.820 ….. April 4 1721 ….. 0.873 ….. May 5 2015 ….. 1.022 ….. June 6 2297 ….. 1.165 ….. July 7 2606 ….. 1.322 ….. August 8 2687 ….. 1.363 ….. September 9 2292 ….. 1.162 ….. October 10 1981 ….. 1.005 ….. November 11 1696 ….. 0.860 ….. December 12 1794 1794.00 0.910 ….. 1996 January 13 1806 ….. February 14 1731 March 15 1733 April 16 1904 May 17 2036

14. Topics for Discussion l What characteristics of service organizations make forecast accuracy important? l For each of the three forecasting methods, what are the developmental costs and associated cost of forecast error? l Suggest independent variables for a regression model to predict the sales volume for a proposed video rental store location. l Suggest how the Delphi method can be incorporated into a cross-impact analysis.