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Big 10 Sweater Case Study

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Inventory Management at Big10Sweaters.com case study

1. Unit Unit Sales Sale Price Cost Revenue Cost Margin
Ohio 2,300 $120 73.88 $276,000 $169,924 $106,076
Michigan 1,468 $120 73.88 $176,160 $108,456 $67,704
Purdue 890 $120 73.88 $106,800 $65,753 $41,047 eBay 342 $50 60.88 $17,100 $20,821 ($3,721)
Totals 5,000 $576,060 $364,954 $211,106 Overhead: $120,000 Net Profit: $91,106

After paying 25% to the venture capital firm, $22,776.50, the net profit before taxes would be $68,329.50. Then after paying taxes of $34,164.75 they would be left with $34,165 of an increase in cash.

2. Aggregate demand forecasting is used by the company because the business is centered around the custom printing of the respective school logos. This works best for the company so that they can ensure that they enough overall sweaters on hand. Since the sweaters are customized in house they do not need to be concerned with keeping inventory of specific schools. They should continue to use the aggregate demand forecasting because to order by school the forecasting would not meet the suppliers minimum order quantity of 5000 and it would take too long to get the sweaters based on the 20 week lead time as well. The current system gives them flexibility in the order delivery as they can order large quantities and they can customize them according to the orders.

3. The result of underestimating demand is the loss of profit but when overestimating demand the result is the company will lose money if they have to sell at a discount. In this case it is the difference between the supplier cost of $60.88 and the eBay price of $50 which would be $10.88. In this instance the single period model is appropriate as the sweaters would be sold at $120 and would cost $73.88, resulting in a return of $46.12 per sweater. The critical probability then is Cu/(Co+Cu) =

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