From the start of this simulation, we understood that we needed to analyze the limited data that was given to us. More specifically, we looked at the mean and standard deviation of demand for the first 50 days of the simulation. We did this by exporting the data from Littlefield into an Excel document and utilizing the AVG and STDEV functions within it. These functions gave us an average demand of 14.52 jobs per day, and a standard deviation of 4.17 jobs per day for the first 50 days. By comparing these numbers we realized that the standard deviation was nearly 30% of the average daily demand. This told us that although demand would be relatively predictable and overall would show no trend, we could expect to see some dramatic spikes, both
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We learned that when demand is relatively constant within a range, stabilizing the machine utilization around 50% to 70% by purchasing new machines can help us ensure the continuous flow of the process. Because we bought a new machine for each station at the beginning and tried to maintain a relatively constant level of the utilization rate for each station, the throughput of the process was pretty smooth during the rest of the simulation, which ultimately led us to much greater profit accessibility. Additionally, we learned firsthand that each process is intimately linked to the surrounding processes. After we make one change in a certain section, we need to alter other processes accordingly. For instance, after we purchased new machines, we needed to switch the order contract because sticking with Contract 1 meant our machines would sit idle sometimes and we could not operate efficiently. Finally, we drew a lesson from the calculation on EOQ. At first, we struggled with finding out the new EOQ given that each members of our team generated different numbers. But after we compared each computing process, we realized that the reason we had different numbers was because some members did not keep the unit consistent. Once we corrected the mistake we had made, we were able to easily calculate the EOQ in the later
We learned that when demand is relatively constant within a range, stabilizing the machine utilization around 50% to 70% by purchasing new machines can help us ensure the continuous flow of the process. Because we bought a new machine for each station at the beginning and tried to maintain a relatively constant level of the utilization rate for each station, the throughput of the process was pretty smooth during the rest of the simulation, which ultimately led us to much greater profit accessibility. Additionally, we learned firsthand that each process is intimately linked to the surrounding processes. After we make one change in a certain section, we need to alter other processes accordingly. For instance, after we purchased new machines, we needed to switch the order contract because sticking with Contract 1 meant our machines would sit idle sometimes and we could not operate efficiently. Finally, we drew a lesson from the calculation on EOQ. At first, we struggled with finding out the new EOQ given that each members of our team generated different numbers. But after we compared each computing process, we realized that the reason we had different numbers was because some members did not keep the unit consistent. Once we corrected the mistake we had made, we were able to easily calculate the EOQ in the later