Financial Analysis of an Arco Gas Station

Financial Analysis of an ARCO Gas Station

Gasoline prices have risen to an extremely high price throughout the nation, while California is left with some of the highest prices. Meanwhile, oil companies are making record profits. We will be discussing where the money is going and what types of profits a local gas station can expect to see by using a local ARCO as a case study. There are more than 1,300 ARCO-branded sites that currently operate in five Western States: California, Nevada, Oregon, Washington and Arizona (ARCO, 2012). They are known among major gasoline brands as the "high-volume, low-cost retailer." This particular business model makes it one of the largest gasoline retailers in the U.S. BP, the parent company of ARCO, is one of the largest oil and gas producers in the world. BP markets more than 15 billion gallons of gasoline every year to U.S. consumers through 11,000 retail outlets (BP, 2012).

The process of obtaining retail gasoline prices is based on a complex model based on crude oil prices, state and federal taxes, refining costs, and individual station mark ups. Although these factors exist, we will concentrate on the local gas station’s performance and determine whether it is a worthwhile venture for a potential investor.

Our analysis is based on an ARCO gas station located in Southern California that operates a standard single-channel waiting line steady state model. This particular gas station sells 300,000 gallons of gasoline per month, with a $0.10 per gallon profit (Chen, 2011). There are 8 pumps that handle an average of 25,000 cars per month, 5,774 cars per week, 825 cars per day, 52 cars per hour. Based on this information, we determined that there is a .4274 probability that there will be zero arrivals during a one minute period, a .3633 probability that there will be 1 customer, a .1544 probability that there will be 2 customers and a .0437 that there will be 3 or more customers. (Anderson, Sweeney, Williams, Camm, Martin, 2010) .

Using formulas based on waiting line models, with Poisson distribution of arrivals, and exponential service times, we were able to compute that there is a 39% probability that there are no units in the system. At any given time during operating hours, there