The owner of a local car dealership has just received a call from a regional distributor stating that a

$3,000 bonus will be awarded if the owner’s dealership sells anywhere between 7 to 10 new cars next

Saturday and a $5000 bonus if it sells more than 10 new cars next Saturday. The owner is fairly certain

that the number of customers who show up on a given Saturday would not be less than 20, but also

thinks it would be unrealistic to expect more than 100 (which is the largest number of customers ever

to show up in one day). The owner believes that all values in between 20 and 100 are equally likely,

thus, it is reasonable to assume that the number of customers is uniformly distributed, with minimum

20, and maximum 100 (keeping of course in mind that the number of customers must be a whole

number). The owner determined that there is a 0.09 probability that any customer that shows up will

buy a new car.

Using Crystal Ball:

a. Develop a simulation model and determine the expected number of cars the dealership will

sell next Saturday. [14pts] (Attach the frequency chart with statistics output embedded in it.

Please see point 2 (b) of instructions)

b. What is the probability that the dealership will earn the $3,000 bonus? [3pts] (Attach the

frequency chart which displays the probability value. There is no need to display statistics

output here)

c. What is the probability the dealer will earn the $5000 bonus? [3pts] (Keep in mind that the

dealership will earn only one of the two bonus amounts. Attach the frequency chart which

displays the probability value. There is no need to display statistics output here)

- Michael Abrams runs a specialty clothing store that sells collegiate sports apparel. One of his primary

business opportunities involves selling custom screen printed sweatshirts for college football bowl

games. He is trying to determine how many sweatshirts to produce for the upcoming Tangerine Bowl

game. During the month before the game, Michael plans to sell his sweatshirts for $30 each. At this

price, he believes the demand for sweatshirts will be normally distributed with mean 7,000 and

standard deviation 1,500 (keeping of course in mind that the number of sweaters must be a whole

number). During the month after the game, Michael plans to sell any remaining sweatshirts for $12

each. At this price, he believes the demand for sweatshirts will be triangularly distributed with a

minimum demand of 300, maximum demand of 1,000, and a most likely demand of 500. Two months

after the game, Michael plans to sell any remaining sweatshirts to a surplus store that has agreed to

buy up to 1,500 sweatshirts for a price of $3 per shirt. Michael can order custom screen printed

sweatshirts for $10 per sweatshirt in lot sizes of 200.

Using Crystal Ball:

a. Determine the average profit that Michael would earn if he orders 10,000 sweatshirts. [20pts]

(Attach the frequency chart with statistics output embedded in it. Please see point 2 (b) of

instructions)

b. Find the probability that Michael’s profit would be less than or equal to $100,000 if he orders

10,000 sweatshirts. [3pts] (Attach the frequency chart which displays the probability value.

There is no need to display statistics output here)

c. How many sweatshirts would you recommend Michael order? Hint: Explore values for the

order size in the range of 7,000-13,000 sweatshirts keeping in mind that Michael can only

order in lot sizes of 200. [7pts] (Attach the Decision Table and Forecast chart for the number

of sweatshirts you recommend)