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| J L M N O P Forecasting 250 Y 200 150 175/ Value A B D E F G H 1 CHAPTER 4: Example 4 (alpha = 0.5) 2 Enter alpha (between 0 and 1), enter the past demands in the shaded column then enter a 3 starting forecast. If the starting forecast is not in the first period then delete the error analysis for all rows above the starting forecast. 4 5 6 7 Alpha 0.5 8 Data Forecasts and Error Analysis 9 Quarter Demand Forecast Error Absolute Squared Abs Pct Err 10 1 180 5 5 25 02.78% 11 2 2 168 177.5 -9.5 9.5 90.25 05.65% 12 3 159 172.75 -13.75 13.75 189.06 08.65% 13 41 175 165.88 9.13 9.13 83.27 05.21% 14 5 190 170.44 19.56 19.56 382.69 10.30% 15 6 205 180.22 24.78 24.78 614.11 12.09% 16 7 180 192.61 -12.61 12.61 159.00 07.01% 17 8 182 186.30 -4.30 4.30 18.53 0.023652 18 Total 18.30 98.63 1561.91 54.05% 19 Average 2.29 12.33 195.24 06.76% 20 Bias MAD MSE MAPE 21 SE 16.13 22 Next period 184.152344 23 24 25 100 50 0 1 2 3 4 5 6 7 Time 180 175 O 4.5 The Carbondale Hospital is considering the purchase of a new ambulance. The decision will rest partly on the anticipated mileage to be driven next year. The miles driven during the past 5 years are as follows: YEAR MILEAGE 1 3,000 2 4,000 3 3,400 4 3,800 5 3,700 d. Compute the forecast for year 6 using exponential smoothing, an initial forecast for year 1 of 3,000 miles, and a = .5. PX