Little Field simulation Strategy and report.docxVIP

B6016 Managing Business Operations Report on Littlefield Technologies Simulation Exercise By Group 4: Anise Tan Qing Ye Aneel Gautam Chu Kar Hwa, Leonard Tan Kok Wei Ranking Reflecting on the simulation exercise, we have made both correct and incorrect decisions. Nevertheless, although we ranked 4th (Exhibit 1: OVERALL TEAM STANDING), we believe we gained a deeper understanding of queuing theory and have obtained invaluable experience from this exercise. Rank Team Cash Balance ($) 1 bigmoney1 1,346,320 2 techwizard 1,312,368 3 makebigmoney 1,141,686 4 beaters123 895,405 5 donothing 588,054 6 mas001 472,296  Exhibit 1 : OVERALL TEAM STANDING Decisions Made A summary of the rationale behind the key decisions made would perhaps best explain the results we achieved. . Decision 1 DayParameterValue54 station 1 machine count 2  When the exercise started, we decided that when the lead time hit 1 day, we would buy one station 1 machine based on our analysis that station 1 takes the longest time which is 0.221 hrs simulation time per batch. (Exhibit 2: Average time per batch of each station). As day 7 and day 8 have 0 job arrivals, we used day 1-6 figures to calculate the average time for each station to process 1 batch of job arrivals. Base on the average time taken to process 1 batch of job arrivals, we were able to figure out how many batches each machine can handle. daynumber of jobs arriving each dayutilization of station 1, averaged over each dayutilization of station 2, averaged over each dayutilization of station 3, averaged over each day120.4330.0790.019220.2720.1580.108310.3980.1060.1654000.0520520.330.0790.06610.3320.1580.013Average process time per batch0.2210.0790.046Maximum batch processed a day4.5212.6621.74Exhibit 2: Average time per batch of each station We did not pay much attention to the station 3 machine at this stage even though there appears t