As in the previous Walton Bookstore example, Walton needs to place an order for next year’s calendar. We continue to assume that the calendars sell for $10, and customer demand for the calendars at...

As in the previous Walton Bookstore example, Walton needs to place an order for next year’s calendar. We continue to assume that the calendars sell for $10, and customer demand for the calendars at this price is triangularly distributed with minimum value, most likely value, and maximum value equal to 100, 175, and 300. However, there are now two other sources of uncertainty. First, the maximum number of calendars Walton’s supplier can supply is uncertain and is modeled with a triangular distribution. Its parameters are 125 (minimum), 200 (most likely), and 250 (maximum). When Walton places an order, the supplier charges $7.50 per calendar if he can supply the entire Walton order. Otherwise, he charges only $7.25 per calendar. Second, unsold calendars can no longer be returned to the supplier for a refund. Instead, Walton puts them on sale for $5 apiece after February 1. At this price, Walton believes the demand for leftover calendars is triangularly distributed with parameters 0, 50, and 75. Any calendars still left over, say, after March 1, are thrown away. Walton again wants to use simulation to analyze the resulting profit for various order quantities.

Objective To develop and analyze a simulation model with multiple sources of uncertainty using @RISK, and to introduce @RISK’s sensitivity analysis features.