As we discussed at the beginning of the chapter, Starbucks has a large, global supply chain that must efficiently supply over 17,000 stores. Although the stores might appear to be very similar, they are actually very different. Depending on the location of the store, its size, and the profile of the customers served, Starbucks management configures the store offerings to take maximum advantage of the space available and customer preferences.
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 are given below ( week 21 is the week before week 1 in the table, 22 is two weeks before week 1, etc.). Evaluate the fore-casts 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, .2 and .4. Use the same criteria for evaluating the model as in part 1. When using an alpha value of .2, assume that the forecast for week 1 is the past three-week average (the average demand for periods 23, 22, and 21). For the model using an alpha of .4, assume that the forecast for week 1 is the past five-week average.
3. Starbucks is considering simplifying the supply chain for its coffeemaker. Instead of stocking the coffeemaker in all five distribution centers, it is 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 parts 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 oneDC?

  • CreatedApril 09, 2014
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