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 judgement, 12 Egress employees have indpendnelty estimated demand for their new design for the upcoming season. their estimates are listed in Table 15.2

14,000, 13,000, 14,000, 14,000, 14,000, 15,500, 10,500, 16,000, 8,000, 5,000, 11,000, 8,000, 15,000

To assist in the decision on the number of units for the production run, mgmt has gathere d the data in table 15.3. Note that S is the priuce 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 incurred regardless of the size of the production run.

15.3

Variable Prod. Cost Per Unit (C): $80, Selling Price Per Unit (S): $100, Salebvage Value Per Unit (V): $30, Fixed prodcution Cost (F): $100,000

Run 1000 simulation runs for the order quantities suggested in Excel file 15_01 Record the expected profit as well as the standard deviation of the profit for each order quantity. 

Ski Jacket Production Simulation
Inputs				Production quantities to try for simulation:
Unit variable cost		80		7800	8500	9000	10000	10500	11000	11500	12000
Unit selling price		100
Unit salvage value		30
Fixed cost		100,000
Mean demand		12000
Stdev of demand		3497
Production quantity (Q)
Simulation
Demand	Revenue	Salvage value	Variable cost	Profit
Summary measures of profit - based on 1000 iterations for each simulation
Simulation	1	2	3	4	5	6	7	8
Order quantity	7800	8500	9000	10000	10500	11000	11500	12000
Minimum
Maximum
Average
Standard deviation