Transcribed image text: Questions 1. Consider using a simple moving average model. Experiment with models using five weeks' and three weeks' past data. The past data in each region follow (week -1 is the week before weck 1 in the table, -2 is two weeks before week 1, etc.). Evaluate the forecasts that would have been made over the 13 weeks using the overall (at the end of the 13 weeks) mean absolute deviation, mean absolute percent error, and tracking signal as criteria. 2. Next, consider using a simple exponential smoothing model. In your analysis, test two alpha values, 0.2 and 0.4. Use the same criteria for evaluating the model as in question 1. When using an alpha value of 0.2 , assume that the forecast for week 1 is the past threeweek average (the average demand for periods 3,2, and -1 ). For the model using an alpha of 0.4 , assume that the forecast for week 1 is the past five-week average. 3. Starbucks is considering simplifying the supply chain for their coffeemaker. Instead of stocking the coffeemaker in all five distribution centers, they are considering only supplying it from a single location. Evaluate this option by analyzing how accurate the forecast would be based on the demand aggregated across all regions. Use the model that you think is best from your analysis of questions 1 and 2. Evaluate your new forecast using mean absolute deviation, mean absolute percent error, and the tracking signal. 4. What are the advantages and disadvantages of aggregating demand from a forecasting view? Are there other things that should be considered when going from multiple DCs to a DC