The Millers Ice Cream store in Bar Harbor, Maine, is one of the most popular tourist destinations in the downtown area. Bar Harbor is the definition of a seasonal attraction, with most of its crowds and foot traffic occurring during the busy summer months, from Memorial Day to Labor Day. During this time, Bar Harbor receives thousands of visitors each week and business at the ice cream store is brisk. On the other hand, once the summer season ends, foot traffic quickly drops off and the shop settles in for the slower fall and winter seasons. In past years, Millers has not done a good job estimating demand for its products and as a result have often ended the season with a large inventory of ice cream that will not keep well for the next summer. Millers even has a problem with estimating demand during the hot summer months. If too much ice cream is ordered each time, Millers must rent extra storage freezers. Ordering too little ice cream means lost sales or high shipping rates to rush extra supplies to the store.

This summer, the owner€™s nephew, Pat, has returned from his university€™s business school with better knowledge of simulation and has set about organizing the ordering system to make it more efficient. After going through the past year€™s sales records, Pat determined that the majority of sales came from ice cream cones and decided to use simulation to estimate the demand for the upcoming summer. Because of the short summer, he chose to analyze sales each two-week period (the typical time between reordering ice cream). His analysis found the following:

Based on this information:

1. Calculate the probability of demand for ice cream across each increment.

2. Using random numbers, simulate demand values across the 14-day period.

3. Calculate the average daily simulated demand for ice cream.

Optional:

4. Use Excel to calculate average daily simulated demand for ice cream.

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Frequency of Occurrence (in days) Demand for Ice Cream Cones 100 150 3 200 250 1. 300 2.