The manager of the I-85 Carpet outlet needs to be able toforecast accurately the demand for Soft Shag carpet (its biggest seller). If the manager does not order enough car-pet from the carpet mill, customers will buy their carpets from one of the outlets many competitors. The managerhas collected the following demand data for the past eightmonths. a. Compute a three-month moving average forecast formonths 4 through 9. b. Compute a weighted three-month moving average fore-cast for months 4 through 9. Assign weights of 0.55, 0.33, and 0.12 to the months in sequence, starting withthe most recent month.c. Compare the two forecasts using MAD. Which forecastappears to be more accurate? Month Demand for Soft Shag Carpet (1000 yd)1 52 103 64 85 146 107 98 12

A manager uses a trend equation plus quarterly relatives to predict demand. Quarter relatives areSR1 = .90, SR2 = .95, SR3 = 1.05, and SR4 = 1.10. The trend equation is: Ft= 10 + 5t. Over thepast nine quarters, demand has been as follows:Period, t 1 2 3 4 5 6 7 8 9Demand 14 20 24 31 31 37 43 48 52Is the forecast performing adequately? Explain.

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 milles driven during the past 5 years are K as follows Year Mileage 3,000 2 3 4,000 3,400 4 3,800 5 3,700 a) Using a 2-year moving average, the forecast for year 6- 3750 miles (round your response to the nearest whole number) b) If a 2-year moving average is used to make the forecast, the MAD based on this miles (round your response to a whole number). (Hint: You will have only 3 years of matched data)

B). Using exponential smoothing, calculate the forecasts for months 2, 3, 4, 5, and 6. The smoothing constant is 0.2, and the old forecast for month I is 245. Month Actual Demand Forecast Demand 1 260 2. 230 3 225 4 245 5 250 6. C). Given the following average demand for each month, calculate the seasonal indices for each month. Month Average Demand Seasonal Index January 30 February 50 March 85 April 110 May 125 June 245 July 255 August 135 September 100 October 90 November 50 December 30 Total 3 D). Using the data in part A and the seasonal indices you have calculated, calculate expected monthly demand if the annual forecast is 2000 units. Month Seasonal Forecast Index January February March April May June July August September October November December