Littlefield technology game capacity dissertation

The overall game was held over a week and enabled all of us to increase our understanding of inventory management on the production stage in the supply chain. From this game, the objective was to attain production and delivery of the production within 3 days of lead period. Over the course of the game, we were to monitor the availability numbers and make modifications through the range of machines which in turn we owned or operated. Machines afflicted the revenues through the acquisitions and the aim was to attain as much earnings as possible.

Through the game, we all learnt tips on how to analyse the production numbers and plan strategies to handle the demand that was regularly changing.

Approach

Our approach was to get a stable use rate during all devices to prevent occuring any late penalty. One example is at Day time 50, prior to the game started, we seen that equipment 1 was running at 100% use for a few days and nights before, and we predicted a bottleneck scenario would occur here. For that reason we chosen to purchase an extra machine right away as the game started to be able to allow constant processing of lots to satisfy the demand preventing losses in revenue.

After purchasing additional machine 1, we continued observing the utilization in the machine. I was careful to never buy added machines without cause so that we can earn more revenue from the interest. In day 88, machine one particular hit completely utilization for 4 progressive, gradual days. Nevertheless , the average income earned would still be $1000 despite the high use.

Therefore all of us decided not to purchase additional machines at stop 1 . This kind of proved to the favour as the income earned would not drop during this period of time despite the fact that station 1 utilization continue to be close to 1 ) Furthermore, all of us continued to earn an increased interest than any other teams who bought further machines. Due to this plan, i was amongst the leading few teams at this time frame. At working day 120, a scenario similar to time 88 started to occur; machine 1 strike 100% use continuously intended for 6 days and nights and continue to hit near to 100% for the next few days. There were thought that we could tide through it just like before without an additional machine. However , now round, a bottleneck produced at train station 1 and the revenue began to drop quite severely. Therefore , we determined that an added machine should be used at stop 1 to stop further drop in the income. However , the purchase of yet another machine did not salvageour scenario as the queue size at station 1 was too large.

During this time period, a lot of income was lost because of our production not being able to fulfill the 3 days of lead time. Our income only stabilized on working day 130 pertaining to 2 days and nights before sinking again. This time, the logjam transferred to stop 3 plus the queue features risen drastically to about 600 jobs. Therefore , we all made the decision to purchase another machine for place 3. With this, the revenue finally stabilised for day 139. In the following days, all of us continued the strategy of monitoring the revenue, in addition to the stations’ utilization and for a size, before deciding whether to purchase further machines. Following this strategy, we acquired a total of four Machine 1s, 2 Equipment 2s and 2 Machine 3s. As the demand dropped towards the end of the video game, we chose to sell away machines on the under-utilized areas so that we could increase each of our revenue from your sales from the machines, and also gain even more interest, and increase our ranking prior to game ends.

Therefore , at the end, we were left with 3 Equipment 1s, you Machine 2 and a couple of Machine 3s. In the case of train station 2, which will executed measures 2 and 4 from the production, we were able to toggle between three policies: providing priority to First In First Out (FIFO), 2 or step four. In the early stages with the game, the utilization at place 2 taken care of at a relatively safe level. However , at about day one hundred twenty, the average demand began to maximize and the use began to strike 100%. This remained with this range right up until we made the decision to purchase an additional machine intended for the station at day 150. Since the efficiency of place 2 affects the jobs arriving at station three or more, the holdups hindrances impediments in production snowballed and this drastically affected our earnings.

Conclusion

In hindsight, it is unfortunate that individuals were not capable to maintain our advantage because the leading handful of teams. We all suffered a significant setback throughout the day 120 period due to unforeseen demand spikes and poor decision making. Nevertheless , we did manage to salvage our situation in the end and obtained a respectable rank of 7th place. We have discovered that we simply cannot assume the very best of any situation which we have to be equipped for sudden influx of require and also there is no one size fits every policy. The game simulates an actual assembly situation. While the technique helped all of us gain income, the situation does not abide by certain real world supply cycle conditions such as taxes. Every fixed over head which we now have no control, such as wages, rent, programs, etc . are ignored. These factors is going to introduce even more complexities in making decisions.

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