Littlefield Overview
ittlefield Simulation #1: Capacity Management
Team: Computronic
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.
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
Team: Computronic
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.
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