Determining the Demand for T-Shirts
Analysis
From a spreadsheet simulation using @Risk, I was able to determine a demand of 5,439 T-shirts that Lucinda would be able to sell. The simulation took into consideration all the different variables that would affect the demand for North Star T-shirts.
The main variables that would affect the demand for t-shirts that were accounted for in my simulation were the number of people attending the concert, the percent of attendees who would buy one of her shirts and the risk that North Star would cancel the show. For the number of people attending the concert, I created a triangular distribution of min 25,000, base (most likely) 50,000 and max 90,000 (as stated in the case). For the percent of attendees who would buy one of her shirts, I created a triangular distribution of min 5%, base (most likely) 10% and max 18% (as stated in the case). For the cancellation risk, I created a discrete distribution where 0 was defined as a 10% of cancellation and 1 was defined as a 90% no cancellation. The 10% cancellation was calculated as the number of times North Star cancelled a show historically divided by the total number of scheduled shows (5/45 = 10%). In order to create a simulation, I defined the output distribution, “Output Shirts”, by multiplying the “Stadium attendance” by “Shirt purchased” by “Cancellation of concert”. I then ran the simulation with 5,000 iterations to determine a mean demand of 5,439 t-shirts (see Appendix 1 for simulation graph).
After determining the demand for t-shirts, I needed to determine how many t-shirts Lucinda should actually order. From the case, Lucida could only order batches of 2,000 shirts with specific costs associated with each order size. Lucinda could order either 4,000, 6,000 or 8,000 t-shirts. She would sell each t-shirt at the concert for $12.00 (144/12) and any shirts that could not be sold at the concert, would be sold for $2.75 at a discount clothing chain.