Littlefield

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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 particular, station 1 had a dangerously high utilization rate, maxing at 0.91 at day 17 and averaging 0.6 for the rest of the first 50 days. Therefore, we immediately purchased a machine 1 when the game started. After monitoring for a day, we saw that the utilization of machines 2 and 3 were quite high at around 80%. We knew that we were at the start of the game and would have increasing demand, so we decided to purchase a machine 3 and change the priority of machine 2 to station 2. Because utilization of station 1 was still high on Day 53 and we anticipated an increasing arrival of a high number of jobs, we made the purchase of an extra machine at Station 1 to accommodate any other spikes in job arrivals, leaving us with 3 machine 1's. On Day 64, there was yet another spike in the number of job arrivals which we were able to anticipate by purchasing the extra machines prior. We considered purchasing a machine 2, but put it off as we thought we should collect more data. On Day 67, we decided that since demand would still increase, we might as well purchase another machine 2. Although these purchases took huge cuts into our initial cash balance thus limiting the amount of interest we received, our early poor rankings saw dramatic improvements as the demand of products began to increase and other teams began losing revenue. By making the initial investments and watching the data, we were able to maintain low lead times and maintain $1000

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