The game was held over a week and enabled us to increase our understanding of inventory management at the production stage in the supply chain. In this game, the objective was to accomplish production and delivery of the production within 3 days of lead time. Over the course of the game, we were to monitor the production numbers and make adjustments through the number of machines which we owned. Machines affected the revenues through the purchases and the goal was to obtain as much revenue as possible. Through the game, we learnt how to analyse our production numbers and plot strategies to tackle the demand that was constantly changing.
Our strategy was to obtain a stable utilization rate throughout all machines to prevent incurring any late penalty. For example at Day 50, before the game started, we observed that machine 1 was running at 100% utilization for a few days before, and we predicted a bottleneck situation would occur here. Therefore we decided to purchase an additional machine immediately as the game started so as to allow continuous processing of lots to meet the demand and prevent losses in revenue. After purchasing additional machine 1, we continued observing the utilization of the machine. We were careful not to buy additional machines unnecessarily so that we can earn more revenue from the interest. On day 88, machine 1 hit 100% utilization for 4 consecutive days. However, the average revenue earned was still $1000 despite the high utilization.
Therefore we decided not to purchase additional machines at station 1. This proved to our favour as the revenue earned did not drop during this period of time even though station 1 utilization continue to be close to 1. Furthermore, we continued to earn a higher interest than other teams who bought additional machines. Due to this plan, we were amongst the top few teams at this period of time. At day 120, a situation similar to day 88 began to occur; machine 1 hit 100% utilization continuously for 6 days and continue to hit close to 100% for the next few days. We had thought that we could tide through it like before without the need of an additional machine. However, this time round, a bottleneck formed at station 1 and the revenue started to drop quite severely. Therefore, we decided that an additional machine is needed at station 1 to prevent further drop in the revenue. However, the purchase of an additional machine did not salvage our situation as the queue size at station 1 was too large.
During this period, a lot of income was lost due to our production not being able to meet the 3 days of lead time. Our revenue only stabilized on day 130 for 2 days before dipping again. This time, the bottleneck transferred to station 3 and the queue has risen drastically to about 600 jobs. Therefore, we made the decision to purchase another machine for station 3. With this, our revenue finally stabilised at day 139. In the following days, we continued the strategy of monitoring the revenue, as well as the stations’ utilization and queue size, before deciding whether to purchase additional machines. Following this strategy, we acquired a total of 4 Machine 1s, 2 Machine 2s and 2 Machine 3s. As the demand fell towards the end of the game, we decided to sell off machines at the under-utilized stations so that we could increase our revenue from the sales of the machines, as well as gain more interest, and increase our ranking before the game ends.
Therefore, at the end, we were left with 3 Machine 1s, 1 Machine 2 and 2 Machine 3s. In the case of station 2, which executed steps 2 and 4 of the production, we were able to toggle between three policies: giving priority to First In First Out (FIFO), step 2 or step 4. In the early stages of the game, the utilization at station 2 maintained at a relatively safe level. However, at about day 120, the average demand began to increase and the utilization began to hit 100%. It remained at this range until we made the decision to purchase an additional machine for the station at day 150. As the performance of station 2 affects the jobs arriving at station 3, the delays in production snowballed and this drastically affected our revenue.
In hindsight, it is unfortunate that we were not able to maintain our advantage as the leading few teams. We suffered a major setback during the day 120 period due to unexpected demand spikes and poor decision making. However, we did manage to salvage our situation in the end and obtained a respectable rank of 7th place. We have learnt that we cannot assume the best of any situation and that we have to be prepared for sudden influx of demand and also that there is no one size fits all policy. The game simulates a real assembly scenario. While the strategy helped us gain revenue, the scenario does not comply with certain real life supply chain conditions such as taxes. All fixed overhead which we have no control, such as salaries, rent, utilities, etc. are ignored. These factors will introduce more complexities into making decisions.