I only need help with part $B.$ Please use excel $if$ possible :)

Starbucks has a large, global supply chain that must efficiently supply over $17,000$ stores. Although the stores might appear $tobe$ 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.

Starbucks$$ actual distribution system $is$ much more complex, but for the purpose $of$ our exercise let$s$ focus $on$ a single item that $is$ currently distributed through five distribution centers $in$ the United States. Our item $is$ a $logo-$branded coffeemaker that $is$ sold $at$ some $of$ the larger retail stores. The coffeemaker has been a steady seller over the years due $to$ its reliability and rugged construction. Starbucks does not consider this a seasonal product, but there $is$ some variability $in$ demand. Demand for the product over the past $13$ weeks $is$ shown $in$ the following table. $(week-1is$ the week before week $1in$ the table, $-2is$ two weeks before week $1,$ etc.$).$

Management would like you $to$ experiment with some forecasting models $to$ determine what should $be$ used $in$ a new system $tobe$ implemented. The new system $is$ programmed $to$ use one $of$ two forecasting models: simple moving average $or$ exponential smoothing.

Part $B$: 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. $(Negative$ values should $be$ indicated $by$ a minus sign. Round all answers $to2$ decimal places. Enter "MAPE" answers $as$ a percentage rounded $to2$ decimal places.$)$