Littlefield Technologies Simulation Solution Essays On Friendship

Littlefield Technologies Simulation 1 Summer 2012 Team 300: Chelsea Koo, Chris Kim, Hee-Yoon Choi, Quentin Hsu, Taryn McNamee    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 increase our expenses of running the business, yet decreases our risk of having lead times of over a day. The only expense we thought of was interest expense, which was only 10% per year. Therefore, we took a proactive approach to buying machines and purchased a machine whenever utilization rates rose dangerously high or caused long queues. As we will see later, this was a slight mistake since the interest rate did have a profound impact on our earnings compared to other groups. Machine configuration: Our final machine configuration (which was set on Day 67) was 3 machine 1's, 2 machine 2's, and 2 machine 3's. This lasted us through the whole simulation with only a slight dip in revenue during maximum demand. In terms of choosing a priority for machine 2, we decided to switch to priority to step 2 since machine 2's utilization was consistently higher than machine 3's. We wanted machine 3 to never be idle and thus, kept the priority at 2. However, the difference in choosing between the priorities seemed minimal and is probably only important during times of high demand. Forecasting: We set up a spreadsheet to forecast demand throughout the simulation. After collecting a substantial amount of data (around day 120), we were able to forecast what maximum demand would be. 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


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