Littlefield Simulation Report
From day 50 to day 268 of round 2, we controlled Team 5’s factory’s capacity, purchasing, and lot size as an operations team to optimize utilization and cash produced.
At the conclusion of the Littlefield Technologies Simulation, Team 5 came in seventh position. We began with a cash balance of $2,000,000 on day 0 and finished with $1,003,51 on day 268. The simulation’s benchmark was team do-nothing, and we were below it. The gross revenue for the simulation was $1,003,51, with interest of $107,126 and machine retirement of $10,000. Machine purchases totaled $450,000, and inventory accumulated $1,620,000.
History of Decisions Made
|Day||Action Taken||Cash Balance||Total Machine Count|
|50||Purchased a machine at Station 1||-90,000||S1: 2 Machines|
|57||Purchased a machine at Station 1||-90,000||S1: 3 Machines|
|102||Purchased a machine at Station 2||-80,000||S2: 2 Machines|
|133||Purchased a machine at Station 3||-100,000||S3: 2 Machines|
|147||Purchased a machine at Station 1||-90,000||S1: 4 Machines|
|173||Sold a Machine at Station 2||+10,000||S2: 1 Machine|
Day 50: The first day we got access to the simulation, it has been running for 50 days. On Day 50: Station 1 machine count was 2. During this round, we purchased an additional machine at station 1 in response to the high utilization rates that it was experiencing. We knew we had to increase capacity in order to avoid the bottleneck at station 1. Therefore, it made sense to purchase a machine at the beginning of the simulation.
Day 57: Station 1 machine count was now at 3. Our team noticed that utilization rates were still at maximum capacity, so we cohesively decided to buy a third machine.
Day 102: Our next action taken was purchasing a machine at station 2. To increase the capacity, it was necessary to add an additional machine since there was a clear spike around Day 90.
Day 133: On day 133, we found a pattern of Station 3 being at its peak of use for several days in a row, so we purchased another machine because it was becoming a bottleneck in our system.
Day 147: By day 147, we added another machine to Station 1, bringing the total number of machines at Station 1 to four. Since the number of jobs accepted was increasing, we needed to expand our capacity in order to reduce the utilization rate.
Day 173: Based on the practice round, we recalled that at station 2, demand begins to decrease in the middle of the simulation. As a result, we assumed that selling a machine at station 2 was the best option rather than waiting a few more days. We received $10,000 when this machine was retired.
The main objective of our actions throughout the experiment was to obtain as much revenue as possible. The best way to achieve revenue is by decreasing lead times below 1 day as dictated by the job contract. To keep lead times low, capacity would be increased by buying additional machines. Station 1 is an example of a station that acquired new machines throughout the simulation. A machine was bought first on day 50, second on day 57, and third on day 133 totaling four machines at station 1. Station 1 remained a primary bottleneck throughout the simulation with consistent high utilization until arrivals declined. Machines would also be sold if utilization were deemed too low, this occurred once for station 2. A machine for station 2 was bought when utilization reached 100 percent and a queue was beginning to form. It appeared as if station 2 only needed one machine to reach satisfactory utilization, so one of sold. This was a mistake as utilization quickly reached 100 percent and a queue formed.
Day 90: We should have added two additional machines instead of just one machine on Day 133. Utilization remained extremely high even with four machines total was not enough capacity to keep up with arrivals.
Day 173: We should have kept two machines at station 2. One station was not enough to keep up in-flow from station 1 and station 3.
Due to being near last place in ranking for the final round of the Littlefield Simulation there were a lot of mistakes that we made during the simulation. One mistake that needs to be recognized is the lack of action during the practice round that would have prepared us for the actual round by enabling us to experiment and learn from mistakes. The next mistake was a general lack of decisiveness, decision making was slower than it should have been. Our decision-making process, each team member was assigned a specific day to review the simulation on a regular basis and report back to the group through Discord on the status of utilizations and lead time. Following that, members will decide whether to buy or sell a machine based on our forecasting. The group’s decision will then be carried out on the simulation by the members assigned that day. This method of decision-making process allowed other team members to participate and ensure a unanimous decision to a change in the simulation. However, the time spent waiting for a response was also time with an inefficient process in the simulation. Another mistake during the simulation was a lack of a long-term strategy. While, we had a general strategy for maintaining station capacity by trying to keep station utilization below 100%. Prioritization would be changed first and if utilization remained at 100% a machine would generally be acquired. There was a reluctance to acquire new machines due to their high price, which was a short-term concern. This reluctance benefited us in the short-term as for a short period of time we were in first place amongst the rankings. However, in the long run we lost substantial funds due to increased lead times due to being unable to keep up to increasing releases. Overall, we would have greatly benefited from more in-depth planning.
Actions not taken which should have been taken:
The three main actions we should have taken were 1). Buying a machine at station 3 as soon as the queue began to grow, instead of waiting, 2). Buying a machine at station 1 around the same time and selling a machine at stations 1 and 3 on the last day before the simulation entered autonomous mode.
When the queue started to rise at station 3 on about day 120, we spent almost two real-time days deliberating whether we should invest in another machine, waiting to see new data, and generally not acting decisively. Given that we still had not reached the halfway point, and demand was expected to continue to rise, we should have bought the machine basically as soon as we saw the queue begin to grow. This missed opportunity really hurt us long term because while we were waiting to buy the machine, the queue continued to grow, so when we finally did buy it, our capacity was being used to fill orders that were already late enough that we wouldn’t make revenue from them. It was also out of the question to purchase an extra machine to deal with the backlog since they were very expensive and sold for very little. To make matters worse, while the new capacity was filling “stale”, non-revenue-generating orders, the all the orders coming in get “stale” and we lost out on that revenue as well for many days.
While we were focusing on this, basically the exact same thing was going on at station 1. We were distracted by the intense situation at station 3, and it took us another day to realize, although thankfully we didn’t deliberate as much on this purchase. Ideally, we would have bought this machine around the same time as the machine at station 3, around day 120. These delays combined to cause a long stretch of no revenue that didn’t end until demand dried up around day 220.
Our final missed opportunity was much less impactful to our score, but still worth mentioning. During the autonomous period at the end of the simulation, station one was rarely over 75% utilization, and station 3 was rarely over 66% utilization, which means that both of those stations had a machine in that time frame that was almost never used, and we should have sold those machines before the end of the simulation. The amount of money we would have attained by doing this is not a lot, but it would have been better than the zero dollars we got for holding those machines until they were obsolete.
Conclusions and Reflections
Appendix- Round 2 Historical Charts
Stations 1-3 Queue
Stations 1-3 Utilization
Average Lead Time
Average Revenue per Job