Littlefield Overview free essay sample

When the simulation began, we quickly determined that there were three primary inputs to focus on: the forecast demand curve (job arrivals,) machine utilization, and queue size prior to each station. Specifically we were looking for upward trends in job arrivals and queue sizes along with utilizations consistently hitting 100%. Upon initial analysis of the first fifty days of operations, the team noticed that Station 1 had reached 100% utilization several times between days 40 and 50. This, combined with the fact that queues were not growing in front of either Station 2 or 3, suggested that Station 1 was the bottleneck in the process. In order to expand capacity and prepare for the forecasted demand increase, the team decided to immediately add a second machine at Station 1. As sales continued to grow over the next few simulated weeks, the process was able to keep up with demand and the lead times stayed well below 1 day, confirming that the addition of this machine was the correct decision. We will write a custom essay sample on Littlefield Overview or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page Between days 60 to 70, utilization again hit 100% at Station 1 for a few days but the team decided to delay purchasing a third machine, as lead times remained below one day. At the same time, the queue in front of Station 2 was growing, which was odd as the machine was not completely utilized. This suggested that perhaps the priority of scheduling needed adjustment; so on day 66 the team changed Station 2 priority from FIFO to give preference for Step 4 units. The logic behind this decision was to complete as many units as possible without delay. Once the priority was changed from FIFO to Step 4, the team noticed that both the utilization at Station 2 and the queues began to exhibit high variance from day to day. This suggested that FIFO was a better strategy for Station 2, so the team switched the priority back at day 75. At day 88, the team noticed that utilization at Station 1 was more consistently hitting 100%, and that utilization at station 3 had also hit 100% utilization once. At this time, the team decided to purchase a third machine at Station 1, while holding off on a purchase at Station 3. Although demand was growing and a second machine at Station 3 would ultimately be necessary, the team decided to first isolate and assess the impact of adding a third machine at Station 1. As expected, the purchase of a third machine at Station 1 leveled off the entire process and no further moves were necessary until day 134. At day 134, the team engaged in a debate regarding whether to purchase machines for both Station 2 and 3 or just one for Station 2. Both stations were consistently maxing out at 100% utilization. The decision was made to purchase only a machine for Station 2 and, again, isolate and assess the impact of this decision on the process. The team rationalized the decision to delay to purchase of a second machine at Station 3 because the timeline was approaching the peak of forecasted demand growth. In hindsight, the decision to hold off on purchasing a third machine at Station 3 ended up being our biggest mistake of the simulation†¦darn forecast! Over the next simulated month (specifically days 144 to 169), utilization at Station 3 often hit 100% and lead times spiked to nearly three days, which caused our revenues to drop to below $200. 00 per order. At day 169 the team finally decided to add the second machine at Station 3, but the damage to our revenues was already done. Again, based on our expectation of diminishing demand, the team decided to sell equipment at day 193 (Station 3) and 195. Our expectation of decreased demand was not realized, however, and this decision ended up being very costly. Over the first eighty days of the simulation, daily orders exceeded ten only once. In the last eighty days of the simulation, daily orders exceeded ten, fourteen times. Our now streamlined process proved incapable of keeping up with this demand and our lead times and revenues suffered greatly. Overall, our team’s key mistakes were: (1) misjudging the demand forecast, (2) being too slow to purchase machines, and (3) being too quick to sell machines. Mistakes 2 and 3 stemmed from mistake. Perhaps our most critical mistake, however, was that we began to make decisions based upon our standing in the competition as opposed to being focused solely on what was best for Computronic, Inc. We tried to make some quick cash by selling equipment prematurely to get back to first place, instead of paying attention to the viability of the business through the end of the simulation. This serves as a valuable for business in â€Å"real life;† you need to make the best decisions for your company, regardless of the decisions you think your competitors may be making. Overall, our team’s key mistake was cost too much cash at beginning. Lacking of accurate forecasting and preparing for the increasing demand were the reason also. Even though we waste too much cash at beginning, we still have chance during the process. We did not quick response when station 1 at full capacity. Station 1 meet 100% utilization rate between days 50 and 55, but we purchased additional machine at days 92. And after purchasing, the utilization rate is still 100%. However, we did not have enough cash to purchase the 3rd machine at station 1 until days 142.