The all cotton, long-wearing T-shirt needs to be on store shelves by Nov 11 to take advantage of the upcoming Holiday Shopping Season. Therefore, you have a hard deadline of November 2 to get this report done. Any delay will result in a 10% reduction in your fee (grade) for this project. No exceptions.

As with other products, Bazinga faces the decision of how many T-shirts should be ordered for the upcoming season. As any good CEO would, Mr. Cooper has decided to get the input of his management team. Each of them has recommended the following quantities be ordered.

Manager | Suggestion |

Amy | 500,000 |

Bernadette | 450,000 |

Howard | 400,000 |

Leonard | 350,000 |

Penny | 300,000 |

Raj | 250,000 |

As you can see, this wide range of suggestions indicates considerable disagreement between the management team as to how many should be ordered. They just cannot agree on the market potential for these T-Shirts. Of course, this is to be expected with this group.

The thing is, the production manager, Leslie Winkle, really has no idea how many T-Shirts to make. After all, she is a designer by training, not an analyst. What she really wants to avoid is extremes. She does not want to be left holding excess merchandise. This means she will have to sell these at a loss. Also, she does not want a stock out where demand exceeds supply and she leaves money on the table, either.

Leslie reckons that she can use the previous sales as a starting point for the sales of the T-shirt. Last year, a similar T-shirt with a Soft Kitty theme on them sold quite well. Leslie wants you to use the average sales and standard deviation to figure out the probability of a stock out for each of the managers’ recommendations.

The Sales Manager, Stuart Bloom, has figured out that he can sell the T-Shirts for $25 each. He also knows that if he does not sell them retail, he can sell them wholesale for $10 each. Finally, he tells you that the cost of each shirt is $15.

So, what should you do with all of this data? Well, you need to make some sense of it all. Here’s what I would do…

- Use the managers’ predictions to figure out what the expected value and standard deviation would be if these were the actual sales figures.

- Use that expected value and standard deviation to develop a normal distribution graph (curve) that you can use to analyze the data.

- Using the SWAG method, the CEO believes that the best-case scenario is selling 560,000 units. The most likely scenario is to sell 390,000 units. The worst-case scenario is to sell on 228,000 units. Develop the probability of a stock out for each of these three cases along with the profit projections associated with each one. That is, how much profit will we make if we actually sell all of these T-Shirts?

- Then, using the worst-case, most likely case, and the best-case scenarios develop the profit projections for each of the managers’ recommendations. For instance, Raj thinks we should make 250,000 T-shirts. If we make 250,000 and only sell 228,000, we will have to sell 22,000 at a loss. So you will need to take this into account.

- The CIO, Cooper Hoefsteder, has decided that the potential for this product is so great that he would like to make sure that they have only a 25% chance of a stock out and a 75% chance of meeting demand. How many would we need to make under these conditions? What would be the profit projection if we made this number under the worst-case, most-likely, and best-case scenarios?

- Ms. Winkle needs to know how many T-Shirts to make. Therefore, it is up to you to make a decision. You must decide how many T-Shirts to make. You must back this number up with hard and fast analysis. Bottom line: come up with a number. Do not hedge your bets. This must be a single number that the production line can use to make the T Shirts.

In you managerial report…

Page 1: Cover Sheet

Page 2: Executive summary of your findings. Tell me what our expected profits will be under the different scenarios, etc. and all of the findings you have. Do not just copy the questions and answer them. Charts and graphs are okay, but explain them as though the person reading it knows nothing about what you are doing.

Page 3+: Appendices with the graphs and tables showing your numbers.

**CASE STUDY#2**

Prof. Snow

Lynn University

April 15^{th}, 2016

The main idea of this executive summary is to analyze the manager’s forecasts of the T-shirt sales based on three promising outcomes: the best possible, the most likely and the worst scenario. The information and evaluation will be obtained from the suggested sales information from the managers. All of these data will be useful for the upcoming season of sales.

For the first question, the mean (average of sold t-shirts) was calculated in order to obtain the expected value. This will give us the amounts of t-shirts that should be should to obtain a good amount of profits without any losses. The resulted mean was of 375,000 with a standard deviation of 93,541.43. With the SWAG method, we were able to get the best, the worst and the most likely case scenario with the probabilities that one of these scenarios will occur. It is clear that there is going to be a $10 profit per t-shirt deriving it from the cost of each shirt is $15 and the selling price of $25. The resulting probabilities that each case occurs are the following: the best case scenario has a probability is 2.397%; the occurrence of the most likely scenario is 43.62%; and the worst case scenario has a probability of 94.19%.

In conclusion, the study showed that the total amount needed to be produced for Ms. Winkle is of 438,000 taking in account the risks of losing money. This is the single number that will be made in the production line to obtain the most profits.

**APPENDIX:**

A)

B)

**Number 3**

Best

Most likely

Worst

560000

390000

228000

P(X>560000)

P(x>390000)

P(x>228000)

0.023978888

0.436299737

0.941967118

C)

Revenue

25

Cost

15

Profit

10

Profitability of best case

5600000

Profitability of most liklet case

3900000

Profitability of worst case

2280000

D)