Littlefield Simulation Report
Executive Summary
Our team operated and managed the Littlefield Technologies facility over the span of 1268 simulated days. Our team finished the simulation in 3rd place, posting $2,234,639 in cash at the end of the game. We did intuitive analysis initially and came up the strategy at the beginning of the game. And then we applied the knowledge we learned in the class, did process analysis and modified our strategies according to the performance results dynamically. We have reinforced many of the concepts and lessons learned in class and had a better understanding of the operation of the Littlefield Technologies facility and how certain modifications would affect the throughput and lead time.
The Plan - Initial Strategy
Our team’s objective was to maximize the cash generated by the factory over the product lifetime. To achieve this goal, our team did the initial planning by using 50 days of historical data. On the one hand, we ran the regression analysis for demand and anticipated the increasing of the demand in the next 150 days. Based on 50 day’s data, the regression analysis gave us around 8 jobs/day in day 150. We also calculated mean with 2.5 jobs/day, standard deviation with 1.78 and variance with 3.15. We noticed the demand fluctuated a lot. CV is 0.71 (standard deviation/mean).
Table 1 On the other hand, we reviewed the utilization, queue size for each machine, checked the revenue, completed jobs and lead time data. We noticed that on Day 40, Day42, and Day 44, machine in station 1 has the utilization more than 90% which means station 1 is a bottleneck. After identifying the bottleneck, We decided to purchase machine in station 1 first on Day 51 to see how this modification action would affect the factory operation.
Decision analysis—Actions & Analysis
Overall, we watched on our factory diligently, every few hours we would monitor it to check the health of the factory. We tried to adjust various parameters not to lose money, at the same
Our team operated and managed the Littlefield Technologies facility over the span of 1268 simulated days. Our team finished the simulation in 3rd place, posting $2,234,639 in cash at the end of the game. We did intuitive analysis initially and came up the strategy at the beginning of the game. And then we applied the knowledge we learned in the class, did process analysis and modified our strategies according to the performance results dynamically. We have reinforced many of the concepts and lessons learned in class and had a better understanding of the operation of the Littlefield Technologies facility and how certain modifications would affect the throughput and lead time.
The Plan - Initial Strategy
Our team’s objective was to maximize the cash generated by the factory over the product lifetime. To achieve this goal, our team did the initial planning by using 50 days of historical data. On the one hand, we ran the regression analysis for demand and anticipated the increasing of the demand in the next 150 days. Based on 50 day’s data, the regression analysis gave us around 8 jobs/day in day 150. We also calculated mean with 2.5 jobs/day, standard deviation with 1.78 and variance with 3.15. We noticed the demand fluctuated a lot. CV is 0.71 (standard deviation/mean).
Table 1 On the other hand, we reviewed the utilization, queue size for each machine, checked the revenue, completed jobs and lead time data. We noticed that on Day 40, Day42, and Day 44, machine in station 1 has the utilization more than 90% which means station 1 is a bottleneck. After identifying the bottleneck, We decided to purchase machine in station 1 first on Day 51 to see how this modification action would affect the factory operation.
Decision analysis—Actions & Analysis
Overall, we watched on our factory diligently, every few hours we would monitor it to check the health of the factory. We tried to adjust various parameters not to lose money, at the same