Littlefield Simulation 2 strategy: Littlefield Technologies Simulation Game 2 strategy Share
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Littlefield Simulation 2 strategy
Sunday, July 17, 2011
Littlefield Technologies Simulation Game 2 strategy
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Just went through this last semester. We ended up in first place even though we made a few minor mistakes. First a few links that helped us:
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Here is what we did:
PreGame Activities: The team met the Tuesday before class to examine the data and discuss strategies. It was apparent that both Stations 1 and 3 were operating at full capacity, frequently hitting 100% utilization. Station 3 seemed more strained since it had higher queues (Mean=506, STD=498) than Station 1(Mean=187, STD=175).
Since the average job lead time exceeded 2 days during days 43 through 46, inclusive, we thought it would be unprofitable to attempt to move to the $1,000 contracts. We discussed the options of altering the lot sizes, but decided that the extra setup time would only create more bottlenecks downstream.
▼ 2011 (1)
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▼ July (1)
Stage 1: As a result of our analysis, the team’s initial actions included: 1. Leave the contracts at $750.
2. Change the reorder point to 3000 (possibly risking running out of stock).
3. Change the reorder quantity to 3600 kits.
4. Purchase a second machine for Station 3 as soon as our cash balance reached $137,000 ($100K + 37K).
This strategy proved successful and after the second machine for Station 3 was purchased on Day 56 and the queue cleared, we were able to switch to the $1,000 contracts. We occasionally lost a few dollars for being a little late, but we always made more than we would have under the $750 contracts.
Stage 2: The next goal was to save enough cash to purchase a machine for Station 1 so that we could switch to the $1,250 contracts. During the cash building stage, we made the inventory order quantity as high as we could afford, which was 6,900 kits at a purchase price of $70,000. When the 6,900 kits were delivered, we switched the order quantity back to 3,600 so that we could purchase a Station 1 machine as soon as our cash balance reached $127,000 ($90K + 37K). After 21 factory days, we were able to purchase the fourth machine for Station 1 and immediately moved to the $1,250 contracts.
The average lead time declined to under a half a day during factory days 69 through 76. There was a substantial decline in arriving orders during the same time period. The team noticed the drop in lead time and regrets not having moved to the $1,250 contracts sooner. We lost $22,750 of potential revenue for not moving on the information sooner. On the other hand, orders are random and an early move could have backfired on us.
Stage 3: During our preliminary meeting, the team discussed the possibility of purchasing a fifth machine for Station 1. We decided to wait and see if the loss of potential ea rnings was sufficient to justify a $90 K purchase. We knew that if we were going to buy a fifth machine we should do it as soon as possible to maximize the return on investment. We calculated the loss of potential revenue as ($1,250 – actual average revenues * jobs completed). Our initial estimates showed a potential revenue loss of $266 per day, but within a few factory days the rate of potential loss rose to $419 per day.
There is another consideration in the decision to purchase a fifth machine for Station 1. The title of the Littlefield Technologies game 2 is Customer Responsiveness. The title implies that we should be concerned about the consistency with which we deliver on our service level agreements (SLAs). The potential loss of $419 per day barely covers the $90,000 machine purchase; however we were missing our SLAs 13 out of 15 days and the percent of potential revenues lost due to missing SLAs was 3%. We decided to purchase the fifth machine on Day 94 primarily to improve our customer responsiveness.
This strategy did not perform as well as we had hoped. While our potential revenues lost declined to 1%, we were still missing our SLAs six out of seven days. Stage 4. During Stage 4, we explored job splitting as a solution to the SLA problem. First, we split jobs into two batch of 30 kits each. This strategy worked so well that we wondered why we hadn’t explored job splitting during Stage 2 or 3. We met our SLAs 12 out of 16 days and our percent of potential revenues lost declined to 0.4%. We calculated the setup times as a proportion of a
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