1. What characteristics of service organizations make forecast accuracy important? 2. For each of the three forecasting methods (i.e., time series, causal, and subjective), what costs are associated with the development and use of the forecast model? What costs are associated with forecast error? 3. The number of customers at a bank likely will vary by the hour of the day and by the day of the month. What are the implications of this for choosing a forecasting model? 4. Suggest a number of independent variables for a regression model to predict the potential sales volume of a given location for a retail store (e.g., a video rental store). 5. Why is the N-period moving-average model still in common use if the simple exponential smoothing model has superior qualities? 6. What changes in ,, and would you recommend to improve the performance of the trendline seasonal adjustment forecast shown in Figure 17.4? 1. What characteristics of service organizations make forecast accuracy important? 2. For each of the three forecasting methods (i.e., time series, causal, and subjective), what costs are associated with the development and use of the forecast model? What costs are associated with forecast error? 3. The number of customers at a bank likely will vary by the hour of the day and by the day of the month. What are the implications of this for choosing a forecasting model? 4. Suggest a number of independent variables for a regression model to predict the potential sales volume of a given location for a retail store (e.g., a video rental store). 5. Why is the N-period moving-average model still in common use if the simple exponential smoothing model has superior qualities? 6. What changes in ,, and would you recommend to improve the performance of the trendline seasonal adjustment forecast shown in Figure 17.4