Littlefield Essay

1735 Words Apr 28th, 2016 7 Pages
Strategy description

Revenue maximization:Our strategy main for round one was to focus on maximizing revenue. We did not want the revenue to ever drop from $1000, so we took action based on the utilization rates of the machines. We needed to have sufficient capacity to maintain lead times of less than a day and at most, 1 day and 9 hours. Based on the linear decrease in revenue after a lead time of one day, it takes 9 hours for the revenue to drop to $600 and our profits to be $0. In terms of when to purchase machines, we decided that buying machines as early as possible would be ideal as there was no operating costs after the initial investment in the machine. Having more machines seemed like a win-win situation since it does not
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In addition, we were able to inverse the slope after day 183 and predict the demand for the last 50 days. This demand was also similar to the first 50 days, after day 50 and so we decided to configure our machines for the last 50 days as 2 machine 1's, 1 machine 2, and 1 machine 3 in order to get back $30,000, the equivalent of 30 jobs.

Analysis tools

In order to visualize what was going on, we created one central excel document to gather all our data from the simulation. We downloaded all the information about utilization and queues for each machine and the information for job arrivals and lead times. We plotted charts comparing utilization with job queue, and also forecasted demand using linear regression and moving average on graphs of the job arrivals. In addition, we designed charts comparing utilization with job queue. Since we could gather information to get the average jobs completed and the average utilization, we solved for the average capacity (breaking it down by time periods i.e. day 1-50) and then experimented with our capacity calculations based off of completed jobs.


As our original strategy involved long-term projections and expected returns, our results for the first round were very effective, a reflection of our final result of placing rank 2nd. Because our originally strategy was oriented towards the long term, we made much of our initial investments towards the beginning of the simulation.

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