Reference no: EM13330487

Monte Carlo Simulation

Ski Jacket Production
Egress, Inc. is a small company that designs, produces, and sells ski jackets and other coats. The creative design team has labored for weeks over its new design for the coming winter season. It is now time to decide how many ski jackets to produce in this production run. Because of the lead times involved, no other production runs will be possible during the season.
Predicting ski jacket sales months in advance of the selling season can be quite tricky. Egress has been in operation for only three years, and its ski jacket designs were quite successful in two of those years. Based on realized sales from the last three years, current economic conditions, and professional judgment, twelve Egress employees have independently estimated demand for their new design for the upcoming season. Their estimates are shown in Table 1.

Table 1: Estimated Demands
14,000 16,000
13,000 8,000
14,000 5,000
14,000 11,000
15,500 8,000
10,500 15,000

To assist in the decision on the number of units for the production run, management has gathered the data in Table 2. Note that S is the price Egress charges retailers. Any ski jackets that do not sell during the season can be sold by Egress to discounters for V per jacket. The fixed cost of plant and equipment is F. This cost is incurred irrespective of the size of the production run.

Table 2: Monetary Values
Variable production cost per unit (C): $80
Selling price per unit (S): $100
Salvage value per unit (V): $30
Fixed production cost (F): $100,000

Questions
1. Egress management believes that a normal distribution is a reasonable model for the unknown demand in the coming year. What mean and standard deviation should Egress use for the demand distribution?
2. Use a spreadsheet model to simulate 1000 possible outcomes for demand in the coming year. Based on these scenarios, what is the expected profit if Egress produces Q = 7,800 ski jackets? What is the expected profit if Egress produces Q = 12,000 ski jackets? What is the standard deviation of profit in these two cases?
3. Based on the same 1000 scenarios, how many ski jackets should Egress produce to maximize expected profit? Call this quantity Q*
4. Should Q* equal estimated mean demand or not? Explain.
5. Create a histogram of profit at the production level Q*. Create a histogram of profit when the production level Q equals mean demand. What is the probability of a loss greater than $100,000 in each case?