A sweatshirt supplier is trying to decide how many sweatshirts to print for the upcoming WNBA basketball championships. The final four teams have emerged from the quarterfinal round, and there is now a week left until the semifinals, which are then followed in a couple of days by the finals. Each sweatshirt costs $10 to produce and sells for $25. However, in three weeks, any leftover sweatshirts will be put on sale for half price, $12.50. The supplier assumes that the demand for his sweatshirts during the next three weeks (when the interest in the tournament is at its highest) has the following distribution (see below table). The residual demand, after the sweatshirts have been put on sale, has the distribution also shown in the below table. The supplier, being a profit maximizer, realizes that every sweatshirt sold, even at the sale price, yields a profit. However, he also realizes that any sweatshirts produced but not sold (even at the sale price) must be thrown away, resulting in a $10 loss per sweatshirt.

Use @RISK to analyze

1. Try this for the discrete distributions given in the problem. Present summary statistics and risk profile.
2. Try this for normal distributions. For the normal case, assume that the regular demand is normally distributed with mean 9800 and standard deviation 1300 and that the demand at reduced price is normally distributed with mean 3800 and standard deviation 1400. Present summary statistics and risk profile.

\begin{tabular}{|l|l|} \hline \multicolumn{2}{|l|}{Demanddistributionatregularprice} \\ \hline Demand\# & Probability \\ \hline 7000 & 0.05 \\ \hline 8000 & 0.10 \\ \hline 9000 & 0.25 \\ \hline 10000 & 0.30 \\ \hline 11000 & 0.20 \\ \hline 12000 & 0.10 \\ \hline \end{tabular} \begin{tabular}{|l|l|} \hline \multicolumn{2}{|l|}{Demanddistributionatreducedprice} \\ \hline Demand\# & Probability \\ \hline 2000 & 0.20 \\ \hline 3000 & 0.30 \\ \hline 4000 & 0.20 \\ \hline 5000 & 0.15 \\ \hline 6000 & 0.10 \\ \hline 7000 & 0.05 \\ \hline \end{tabular}