Littlefield 2
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
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