Little Field 2 Report
Team Bombshell

Samantha Chan
Robert Der
Nicholas Sanchilli
Chelsea Smith
Richard Wei

Summary of decisions:
Our first course of action at the start of the game was to increase our inventory and buy more machines. We first increased our inventory reorder point to 10,800 units and changed the order quantity amount to 1800 units. In order to process this increase in units, we bought 2 machines for station 1, 3 machines for station 2, and 2 machine for station 3. Seeing that the machines could process a lot more inventory faster than we expected, we decided to change our reorder points and order quantities, to 6000 units and 24,000 units, respectively. After letting our system adjust to the changes, we moved to contract 2 when the lead time was decreased to about 1 day. From there we let the simulation run for another six days before lead times went down to less than 1, at which time we switched to contract 3. We made no further changes after switching to contract 3.

Part 1: Reasoning for Decisions
Our decisions were somewhat limited to our EOQ model’s completion and our risk adversity. In the last simulation we relied much more heavily on our EOQ model and planned out purchases of machinery with the raise in demand. However, it was because we did not create a safety margin for production which came from our over estimating our carrying costs. We knew that the initial status quo was limited by the inventory quantity. But we did not know if it was the reason for the full utilization of the machinery. Thus we decided to change the most pressing variable, inventory, and see where it went from there. But we knew that this time we needed to act faster than before to acquire new machinery. However, our risk adversity limited us again when it came to switching to contract 3 right away. We knew that with contract 3, the time window for profits was only 12 hours. So we overestimated the impact that a spike in demand would create and waited for the change in our second variable which was the increase in machinery with a change to contract 2 (as opposed to 3).

Part 2: Assessment of Effectiveness of Decisions
It is have to say that our decisions were ineffective since we finished in second place. The first place team had been there for most of the days but we would have surpassed them if there was more time. We were at the highest paying contract and filling most orders. Our goal was to get to contract 3 as soon as possible by increasing our reorder point and increasing the number of machines. It was possible to switch earlier from contract 2 to contract 3 but it was less than a day to satisfy our goal. A lot of the teams did not have the high initial costs we did and stayed near the top for most of the game. Our group did not take WIP into consideration which may have been able to push us into first. Also, we were not too sure about the EOQ and made it low to be on the safe side. Along with that, we did not end with zero inventory. Overall, our decisions were very effective; but a combination of EOQ and ending inventory may have put us in first.

Part 3: Analysis and Justification of Conclusions
We conducted a 4 part analysis to determine the actions we needed to take at Littlefield. The first step was to conduct a modified discounted cash flow analysis to determine the feasibility of capital investment and the return we could expect. We determined that the incremental profit from switching to contract 3 was $1.8 million and the incremental profit to switching to contract 2 was $900,000. At this point we set the maximum amount of capital investment to $900,000 to reduce the risk of loss in the event that contract 3 was unsustainable.

At this point we focused on the question of how many machines we needed to buy to ensure smooth operations at contract 3. We created a queuing model that estimated total lead time with different machines at various stations. We ultimately determined that 4, 6, 4 machines on station 1, 2, 3 was the optimal amount with an expected average lead time of 0.25 days. Other large assumptions included the estimated failure rate of 47% from station 2 to 3, which was derived from linear program that optimized the relation of the unknown failure rate with the known average time in the system. Important caveats were the inclusion of inventory related delays which was not factored out.

With the capital expenditure schedule complete the need to optimize inventory and reorder points was calculated. We utilized an EOQ model where lamda was 6205 unit/year, ordering cost were $1,000, holding cost were $60/unit-year given the 10% interest. The model yielded an optimal inventory of 450 units. From there we decided to determine the re-order point based on max daily expected demand of 26 units a day and 4 day lead time which lead us to use 100 as our reorder point. It is important to note that 400 was used as our Q in the simulation to guard against inventory overstocking and low cash balances at first.

The fourth analysis was a predictive model where we estimated our final cash balance in respect of our competition. This would also allow us to determine if we were meeting out incremental revenue projections determined through our modified discount cash flow analysis.

Part 4: Recommendations
In looking at the project retrospectively, we have identified recommendations on how the company can improve operations, as well as how our group could have more effectively executed our decisions. In the simulation, we only had control over setting parameters such as the reorder points, number of machines to buy, inventory purchases, choice of contract and timing of decisions. Changes our team would make based on parameters we had control over, we would have, first, changed to contract 3 immediately in order to take advantage of the revenue gaining opportunities of having all purchases processed under that contract. Additionally, although we learned from the previous simulation to purchase more machines early on, we could have integrated our knowledge of the demand of Littlefield’s industry and been less conservative by ordering more machines earlier on. This would have allowed us to process the orders more readily while maintaining a proper level of utilization. Finally, we would have ensured calculating our EOQ model prior to the start of the simulation so as to ensure we entered the simulation with the ability to make informed decisions from day 1.

In terms of recommendations for the company, we recommend the company find a new supplier for their machines for station 2. In light of the observed 47% failure rate at machine 2 on this simulation, it would be cost effective for the company to invest in either a higher quality or more specialized machine with a significantly lower failure rate. By doing so, the number of machines necessary to be purchased for this station could be decreased as less items would require reworking. Additionally, the company should find a new supplier for raw materials that has a shorter lead time. Having a shorter lead time to receive raw materials will enable the company to lower their reorder point and respond more quickly to orders.