A job-shop manufacturer that specializes in replacement parts has no forecasting system in place and manufactures products based on last month’s sales. Twenty-four months of sales data are available and

A job-shop manufacturer that specializes in replacement parts has no forecasting system in place and manufactures products based on last month’s sales. Twenty-four months of sales data are available and are given in Table P-16.
a. Plot the sales data as a time series. Are the data seasonal?
Hint: For monthly data, the seasonal period is s = 12. Is there a pattern (e.g., summer sales relatively low, fall sales relatively high) that tends to repeat itself every 12 months?
TABLE P-16
b. Use a naive model to generate monthly sales forecasts (e.g., the February 2005 forecast is given by the January 2005 value, and so forth). Compute the MAPE.
c. Use simple exponential smoothing with a smoothing constant of.5 and an initial smoothed value of 430 to generate sales forecasts for each month. Compute the MAPE.
d. Do you think either of the models in parts b and c is likely to generate accurate forecasts for future monthly sales? Explain.
e. Use Minitab and Winters’ multiplicative smoothing method with smoothing constants ? = ? = ? =.5 to generate a forecast for January 2007. Save the residuals.
f. Refer to part e. Compare the MAPE for Winters’ method from the computer printout with the MAPE s in parts b and c. Which of the three forecasting procedures do you prefer?
g. Refer to part e. Compute the autocorrelations (for six lags) for the residuals from Winters’ multiplicative procedure. Do the residual autocorrelations suggest that Winters’ procedure works well for these data? Explain.

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