Littlefield Simulation II

Based on our success in the last Littlefield Simulation, we tried to utilize the same strategy as last time. Our goals were to minimize lead time by reducing the amount of jobs in queue and ensuring that we had enough machines at each station to handle the capacity. We wanted to keep the lead time between .5 and 1 day in order to get the maximum amount of revenue per job. We utilized data from the first 50 days and put it in an Excel chart to forecast the demand for the jobs. We knew that the demand would follow the same pattern of increasing to a point, leveling off, and then decreasing at the end. Our goal was to keep lead time to a minimum in order to maximize our completed orders and gain maximum profits. In order to keep lead time to a minimum, we attempted to keep all of the queues of the stations below 4 in order to reduce waiting time at each station. To accomplish this, we ordered more machines for each station.

We started by first buying a machine for station 1 on day 52 because it had the highest queue. Next, we ordered a machine for station 2 on day 74 and one for station 3 on day 80. This drastically reduced the number of jobs in queue at each station and kept our lead time below a day. In hindsight, we should have waited more than 6 days to purchase machine 3 until all settled down and we had time to build up capital from the machine 2 purchase. Towards the end of the simulation, we used a similar strategy to the last simulation and sold off one of our machines at each station as demand dropped: at day 199, we sold machine 3, at day 207 we sold machine 2, and at day 210 we sold a machine at station 1. One of the first things that we did was reset reorder point for our kits. Managing the demand for the kits was new for this simulation since in the last simulation it was taken care of for us. We knew lead time for reordering was 4 days so we made sure to set the reorder point at 60 kits.

Based on historical data and our forecast of future demand, on average, 10-15 kits were being used per day, so our reorder point had to be at least 60 units to ensure we had enough safety stock. Since there were no inventory holding costs associated with the kits we switched our reorder quantity to 500 to reduce the amount of times we had to order and the overall order costs. We knew that by day 218 we would no longer have control of the ordering, so to ensure that we did not buy too many kits at the end we adjusted our reorder point again. At day 203 we switched our reorder point to 5 kits and the reorder quantity to 50 to make sure that we would not have an extremely high amount of inventory sitting at the end of the year, which would not generate any revenue for us and would be a sunk cost. Our strategy was to get as close to zero kits at the end as possible without actually getting there and losing a job opportunity. One of the biggest problems that we encountered in this simulation was our contract.

We didn’t realize that we had to manually change the contract we were going for, and thought that the revenue earned was based on the lead time. We realized after day 157 that we were only receiving $750 for each completed job, when our lead time matched up with the contract that paid $1250 per completed job. Had we realized this error earlier and switched our contract to number 3 that paid $1250 we would have made at least an additional $52,000 throughout the process. That additional $52,000 would have moved us up a few places in the standings and made our efforts more successful. In the end, we believe that despite a few crucial mistakes making us fall in the rankings drastically, our overall strategy was appropriate for this simulation. Had we realized the contract price issue at the beginning and not acting so fast on the purchase of machine 3, we would have been a much more competitive business in comparison to the rest of the teams. Historical Data