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 promise to send out a receiver with 24 hours of receiving the order. If they are late to this, the customer pass on receive a rebate found on the delay. As the manikin ran for 268 twenty-four hourss there were various methods and decisions we made in the process. We knew in the sign months, demand was expected to grow at a linear rate, with stabilization in about five months (180 days). After this, demand was express to be declined at a linear rate (remaining 88 days). scour with random orders here and there, demand followed the trends that were given. Future demand for forecast was ground on the information given. We looked at the first 50 days of untoughened data and made a linear regression with assumed set. Those values were calculated using a moving average model. Below is a plot of the data over the 268-day period, w hich shows the patterns stated above.The main concern for LT vigilance was the substance in order to respond to the demand. If there was insufficient faculty LT would not be equal to fulfill given lead multiplication and would invite to turn away orders. In order for capacity to be maximized, our group would ideally have had to have motorcars run at utmost utilization. Looking at the first 50 days of data we were commensurate to come over where more machines were needed in order to produce that 24-hour flip-flop time. The original setup included one board stuffing machine (station 1), one tester (station 2) and one tuning machine (station 3). The way exam was scheduled was First-In-First-Out (FIFO).In our dissimulation, we were able to control the amount of machines and the way examination was scheduled in order to maximize the factorys boilers suit cash position. Below is a graph showing the utilization of the machines at station 1. Based on graph we were instantly abl e to see that at station 1 there was a massive stymie because utilization was over 100%. This made us decide to bargain for an additional 3 machines to help reduce that. As shown, utilization was brought conquer 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 queue sequencing. We sold a total of one machine from station 1. The decision was based upon our demand. We apothegm demand decrease dramatically, which led to us selling the machine.Although it was made late, and we should h ave sold two machines from station 1 at day 180, we were retentiveness 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 queue sequencing. We ended with more capital than we began with and absolute third overall in the standings, as shown below.