Littlefield Simulation Essay

Littlefield Technologies (LT) is a producer of newly developed Digital Satellite System (DSS) receivers. One contingency LT relies heavily on is their look to to ship a receiver with 24 hours of receiving the order. If they are late to this, the customer will receive a rebate ground on the delay. As the simulation ran for 268 old age there were various methods and decisions we made in the process. We knew in the initial months, learn was expected to grow at a elongate rate, with stabilization in about five months (180 days). After this, demand was said to be declined at a linear rate (remaining 88 days). Even with random orders here and there, demand followed the trends that were give. 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 using a moving average model. downstairs is a plot of the data over the 268-day period, which shows the pattern s stated above.The main concern for LT management was the capacity in order to respond to the demand. If there was insufficient capacity LT would non be adequate to fulfill given lead times and would have to turn a centering orders. In order for capacity to be maximized, our root word would ideally have had to have machines run at maximum engagement. 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 reversal time. The original setup included genius board stuffing machine ( postal service 1), one tester (station 2) and one tuning machine (station 3). The way testing was scheduled was First-In-First-Out (FIFO).In our simulation, we were able to control the amount of machines and the way testing was scheduled in order to maximize the factorys overall cash position. at a lower place 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 purchase an additional 3 machines to help take 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 last 88 days, the machines at station 1 were heavily underutilized.The 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 buy at day 51. We added three machines to station 1 and one machine to stations 2 and 3. Another key thing we changed instantly was the stand up sequencing. We sold a total of one machine from station 1. The decision was based upon our demand. We saw demand decrease dramatically, which led to us selling the machine.Although it was made late, and we should have sold two machines from station 1 at day 180, we were keeping one in case 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 increased capacity by adding and subtracting machines and changing the get hold sequencing. We ended with more capital than we began with and finished third overall in the standings, as shown below.