'Littlefield Simulation Essay\r'

'Littlefield Technologies (LT) is a producer of pertly developed Digital Satellite System (DSS) receivers. angiotensin converting enzyme contingency LT relies to a great extent on is their promise to embark a receiver with 24 hours of receiving the shape. If they are slow to this, the customer will receive a rebate based on the delay. As the air ran for 268 long time at that place were various methods and decisions we made in the process. We knew in the initial months, accept was expected to grow at a one-dimensional score, with stabilization in approximately five months (~180 long time). After this, engage was tell to be declined at a linear rate (re principal(prenominal)ing 88 days). Even with random orders here and there, beseech followed the trends that were given. Future demand for forecast was based on the information given. We looked at the first 50 days of raw data and made a linear regression with assumed values. Those values were calculated apply a movin g average model. Below is a plot of the data everywhere the 268-day period, which shows the patterns stated above.\r\nThe main concern for LT management was the capacity in order to respond to the demand. If there was insufficient capacity LT would non be open to fulfill given mince times and would discombobulate to turn away orders. In order for capacity to be maximized, our group would ideally have had to have machines run at upper limit utilization. Looking at the first 50 days of data we were able to see where more machines were needed in order to produce that 24-hour turnaround time. The true setup included one board bandaging machine ( broadcast 1), one tester ( air 2) and one tune machine (station 3). The way examen was scheduled was First-In-First-Out (FIFO).\r\nIn our simulation, we were able to control the amount of machines and the way testing was scheduled in order to maximize the grinder’s general cash position. Below is a graph showing the utilization of the machines at station 1. Based on graph we were instantly able to see that at station 1 there was a massive bottleneck because utilization was over 100%. This made us decide to leveraging an additional 3 machines to help reduce that. As shown, utilization was brought down and become helpful during the five-month demand hike. The mistake our group made was not selling off the machines when we noticed that the demand dropped. It is evident that during the get 88 days, the machines at station 1 were heavily underutilized.\r\nThe purchasing decision was based off assumptions. We knew that demand would rise for another 130 days (since the simulation already ran for 50 days), so we decided to misdirect at day 51. We added three machines to station 1 and one machine to stations 2 and 3. some other key thing we changed instantly was the queue sequencing. We change a total of one machine from station 1. The decision was based upon our demand. We saw demand lessen dramatically, w hich led to us selling the machine.\r\nAlthough it was made late, and we should have sold two machines from station 1 at day 180, we were keeping one in fibre demand suddenly changed. With these changes and decisions, our team (team 8) was able to be very successful. We presented growth within our company and change magnitude capacity by adding and subtracting machines and changing the queue sequencing. We finish with more capital than we began with and finished third overall in the standings, as shown below.\r\n'