Littlefield Simulation Report

Topics: Safety stock, Economics, Bottleneck Pages: 6 (1533 words) Published: September 24, 2013
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 time, make money as much as possible for factory. As we have more data being provided, we ranredo regression analysis constantly to forecast the orders . The regression report showed that demand level and it became higher compared with initial forecast. We did the following parameter changes when operating factory: 1. Changing Contract Terms

Ts: the game begins with Contract 1 which makes $1,000/job. In order to make more money, on Day 54, we changed ontract makes $1,500/jobConsidering because the average lead time is less than 0.5 day/job on Day 54. Contract 2 can make extra $500/job, we changed to Contract 2, which makes $1,500/job, to boost profit. . When we could not communicate with each other, for 2 times we also changed contract number to 1 Howeverwhen we noticed that wewe’ve been penalized got penalty andand revenue wais dropped to less than $1000/job in the next few days . Reactively, we changed back to Contract 1 to make sure that we make money at least $1000/job. After an analysis on marginal benefit and marginal cost, But we immediately decided to changed thechoose Ccontract number to 2 as theits speed to make money is much faster. 2. Purchasing Machines

: Since our goal is to maximize the cash position and it is costly to buy a new machine, we decided to invest in a conservative way—we only buy machines when it becomes bottleneck. Be more specific, we use lead time to decide whether capacity is constraint or not. And then, we identify the bottleneck based on utilization. For example, In the beginning, we are conservative to buy machines and wewe only buy machine s when it has becomes bottleneck: more than 90% utilization ,and higher queue, which caused longer lead time and triggered penalty with revenue less than $1000/job. Those reactive e machine buying strategies y happened before Day 135. On Day 135, we changed our conservative machine buying stratifies into aggressive strategies based on did some revenue data analysis: a. If we don't...
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