IT Experts has received a consulting engagement from Lakeshore Service Station to design a system (simulation) that would help them predict gasoline demand, order quantity, and profits. Lakeshore is a service station that sells gasoline to boat owners. The demand for gasoline depends on weather conditions and fluctuates according to the following distribution.

Weekly Demand	Probability
1000	.09
2000	.12
3000	.23
4000	.38
5000	.18

Shipments arrive once a week. Since Lakeshore is in a remote place, it must order and accept a fixed quantity of gasoline every week for 10 weeks before the order quantity can be changed. Joe, the owner, faces the following problem: If he orders too small a quantity, he will lose, in terms of lost business and goodwill, 15 cents per gallon demanded and not provided. If he orders too large a quantity, he will have to pay 10 cents per gallon shipped back due to lack of storage. For each gallon sold he makes 95 cents profit. Now, Joe receives 3,600 gallons at the beginning of each week before he opens for business. He feels that he should receive more, maybe 3,700 or even 3,800 gallons. The tanks current capacity is 4,000 gallons. The problem is to find the best order quantity (you must answer this question at a minimum). Assume, Joe starts the first week off with 1000 gallons in beginning inventory.

EOQ can not be used due to the unpredictability of the weather. This problem can be solved by trial and error over time. That is, the service station must order each quantity for approximately 10 weeks, then compare the results. However, a simulation can give an answer in a few minutes (this is a Monte Carlo simulation and requires random numbers). Furthermore, the results of the simulation will be much more accurate, since years of operations can be simulated rather than only 10 weeks. Also, the losses are not real, they are only on paper (modified from Turban, MIS/Database texts).

THE ENGAGEMENT:

(1) Lakeshore wants IT Experts to design a simulation that would help them predict demand. This simulation should be flexible enough so if conditions change (for example, probabilities or if their tank size would increase) that Lakeshores staff can modify the simulation based on new information. Include comments (written) within the cells and do not hardcode numbers within formulas (see the Hints document).

(2) They would also, like to see a graphical representation (vertical bar graph) of the simulated demand vs. the units sold AND a line graph of the weekly profit.

(3) Also they are looking at purchasing a new business, see separate sheet labeled Lakeshores 12 Month Trend. The companys total costs and units of sales over the last 12 months are provided and they want you to run a regression analysis, so they can better understand the costs (numbers do not correlate with the above situationhowever, I want you to explore regression within Excelpost questions in the discussion board). Include as a separate worksheet (tab) within your file.

Lakeshores 12 Month Trend (to be used for regression)

Month	units sold	total cost
Jan	2,100	19,800
Feb	3,000	27,000
Mar	3,700	32,600
Apr	3,800	33,400
May	4,200	36,600
Jun	5,000	43,000
Jul	4,800	41,400
Aug	4,500	39,000
Sep	3,800	33,400
Oct	3,500	31,000
Nov	3,200	28,600
Dec	2,700	24,600

(4) They require a training manual (documentation) in MSWord (1-2 pages in length). The manual (instructions) you would provide your client should describe how to use the file, modify the file, and interpret the data. Also, in your first paragraph you should tell your client what this system, which you just designed, will do for themits purpose.

(5) The last thing they are requesting is a NPV analysis of a new tankif a new tank will cost $20,000 and increase net cashflow by $6,000 in the first year, and $5,200 for the next 4 years (years 2-5), should they invest in the tank if their discount rate is 10%? 15%? What is the internal rate of return?