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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 $-1$ is the week before week $+$ 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.

Simple Moving Avg Model

$\backslash$table$[[$WEEK$,-5,-4,-3,-2,-1],[$Atlanta$,45,38,30,58,37],[$Boston$,62,18,48,40,35],[$Chicago$,62,22,72,44,48],[$Dallas$,42,35,40,64,43],[$LA$,43,40,54,46,35],[$Total$,254,153,244,252,198]]$

Next, consider using a simple exponential smoothing model. In your analysis, test two alpha values, programmed to use one of two forecasting models: simple moving average or exponential smoothing. $2$ and $\mathrm{.}4.$ Use the same criteria for evaluating the model as in part $1.$ When using an alpha value of $\mathrm{.}2,$ assume that the forecast for week $1$ is the past three$-$week average $($the average demand for periods $-3,-2,$ and $-1).$ For the model using an alpha of$,4,$ assume that the forecast for week $1$ is the past five$-$week average.

Starbucks is considering simplifying the supply chain for its coffeemaker. Instead of stocking the coffeemaker in all five distribution centers, Starbucks 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.

What are some advantages and disadvantages of aggregating demand from a forecasting view? What else should be considered when going from multiple Distribution Centers $($DC$)$ to one DC$?$

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