The sales manager at Grossmieller Importers in New York City needs to determine a monthly forecast for the number of men’s golf shirts that will be sold so that he can order an appropriate number of packing boxes.

Grossmieller ships shirts to retail stores throughout the United States and Canada. Shirts are packed six to a box. Data for the past 16 months are in the data file called Grossmieller.

a. Plot the sales data using a time-series plot. Based on the graph, what time-series components are present?
Discuss.

b. (1) Use a single exponential smoothing model with a = 0.30 to forecast sales for month 17. Assume that the initial forecast for period 1 is 36,000. (2) Compute the MAD for this model. (3) Graph the smoothingmodel-
fitted values on the time-series plot.

c. (1) Referring to part

b, try different alpha levels to determine which smoothing constant value you would recommend. (2) Indicate why you have selected this value and then develop the forecast for month 17. (3) Compare this to the forecast you got using a = 0.30 in part b.
16-45. Refer back to Exercise 16-44, in which the sales manager for Grossmieller Imports of New York City needs to forecast monthly sales.

a. Discuss why a double exponential smoothing model might be preferred over a single exponential smoothing model.

b. (1) Develop a double exponential smoothing model using a = 0.20 and b = 0.30 as smoothing constants. To obtain the starting values, use the regression trend line approach discussed in this section. (2) Determine the forecast for month 17. (3) Also compute the MAD for this model.
(4) Graph the fitted values on the time-series graph.

c. Compare the results for this double exponential smoothing model with the “best” single exponential smoothing model developed in part c of Exercise