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 the Table (and attached as an Excel file too). 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? b. Use a naive model to generate monthly sales forecasts (e.g., the February 2021 forecast is given by the January 2021 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 a software and build Winters' multiplicative smoothing method with smoothing constants ===0.5 to generate a forecast for January 2023. Save the residuals. f. Refer to part e. Compare the MAPE for Winters' method with the MAPEs 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. 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 the Table (and attached as an Excel file too). 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? b. Use a naive model to generate monthly sales forecasts (e.g., the February 2021 forecast is given by the January 2021 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 a software and build Winters' multiplicative smoothing method with smoothing constants ===0.5 to generate a forecast for January 2023. Save the residuals. f. Refer to part e. Compare the MAPE for Winters' method with the MAPEs 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