Littlefield Case Study

Before the simulation started, our team created a trend forecast, using the first 50 days of data, showing us that the bottleneck station was at Station 1. On day 50 of the simulation, my team, 1teamsf, decided to buy a second machine to sustain our $1,000 revenue per day and met our quoted lead time for producing and shipping receivers. On day 97, we changed Station 2’s scheduling rule to priority step 2. We noticed that the bottleneck was not just at station 1, but at all stations, and that buying a new machine would not be the right choice to make, but rather, changing the way in which the station’s processing is queued would be the better option. By doing this, we could produce all incoming kits with a priority enabling an even flow of kits to Station 3. At day 97, our team ranked first in the overall standing, and wanted to try to maintain this standing for the rest of the simulation.
On day 131, my team decided to change the Station 2 scheduling rule to priority step 4, because we started to see a 100% utilization for Stations 1 at day 120, and maintained 100% utilization continuously as station 2 had an average capacity of 50.3% as shown in the graph below. At this point, our team should have reevaluated our decisions, and purchased a new machine for Station 1, in order to get production moving faster to Station 2. As our utilization was remaining at a constant 100%, our lead times were also increasing. With full utilization, we were unable to produce enough product to meet our order demands, further increasing the queues at each station and increasing our lead times (as shown). We began to see our revenues drop drastically, as well as our ranking overall in the simulation. We thought that by setting the Station 2 scheduling rule to priority step 4, we would improve the throughput of the kits; we were wrong. We should have noticed this sooner, as its abrupt effect brought in $0 in revenue every day after day 134. On day 149, we decided to