Cranberry Litlefield Simulation free essay sample

Littlefield Technologies is a low volume, high margin manufacturer and distributor of digital satellite system receivers. Littlefield Technologies seeks to minimize production costs and sell at the highest price the market will allow, with the end result of maximizing profit, or value for shareholders. In order to be successful they needed to maximize utilization of every stage of the process from inventory control to shipping. With the negotiated contracts available in order to maximize profits Littlefield has little room for inefficiencies and must meet all delivery requirements. At the time of change over, Littlefield was not maximizing the capacity of the plant. It was not effectively processing items through station 2 and was exceeding the utilization of all stations in the production process. In addition, it did not have a delivery agreement with suppliers that would maximize raw product inflows at the least cost. Finally it did not have a contract negotiated with customers that maximized the price the market was willing to bear given taking into account the order lead time the customers required. Capacity Management In order to determine the number of machines to purchase for each station we started by pulling all historical data for the plant to analyze the potential bottlenecks. We analyzed the utilization and capacity of each station and determined the current number of machines was inadequate and would not meet demand even if inventory levels were under control. We started with only adding one additional machine to station 1 which had a large backlog, had the highest utilization, and was the starting point for the production process. Soon after we noted the backlog of the equipment in all other stations began to exceed station 1 so one machine was purchased for station 2 and one machine was purchased for station 3. This effectively decreased the queue at each station and materials started moving through the factory more efficiently. It was noted the queue for station 2 was not effectively balancing the flow from stations 1 and 3. We took control over the factory at a point when there was a backlog at all stations. Even assuming the correct capacity at each station, we were still left with a backlog in the system. Therefore, we changed the priority at station 2 to favor station 3. This was done so that the final step of the process (final verification of the products) has priority before the initial verification. We hypothesized that in doing so, we will efficiently decrease the backlog in the system. We noted that through increased capacity (purchased machines for stations 1, 2 and 3) and through switching station to preferentially process the input from station 3, the backlog was reduced quickly which allowed us to decrease the delivery times from approximately 7 days (when we took over the control) to 0.8 days. This, in turn, allowed us to move to the contract which required delivery in maximum 24 hours, which maximized our profits. Our approach was effective. Product began efficiently moving through the plant and order lead time was being met on most orders. If we could do the simulation again we would purchase new machines for all three stations at the onset to allow the backlog to clear quicker and the company to begin realizing profits earlier. Inventory Management Prior to the start of the simulation we calculated the current reorder point and quantity. We determined the amounts were too low to meet customer demand based on the number of stock outs and number of orders completed each day. In response, we updated the reorder point and quantity to a level consistent with the calculated average demand and stock outs ended. We utilized calculations from OM class, namely optimal Q calculation and ROP calculation. After the plant began operating efficiently, we noted inventory levels were staying higher than anticipated. In response the reorder point and quantity were adjusted down slightly to decrease the amount of inventory on hand in an effort to decrease inventory holding costs. Close to the end of the project it was noted the inventory was still high and the reorder points and kits were reduced one last time. Our approach was effective in that the number of stock outs went to zero and the plant was no longer waiting on material to produce product. Our execution of readjusting the reorder point and reorder quantity as the plant became efficient was not effective. We were ordering smaller amounts more frequently and holding more inventory on hand each day than was necessary to meet average demand. If we could play the game over again we would decrease the reorder point to where it would create a more just in time inventory. In addition, we would increase the reorder quantity in an effort to decrease the number of shipments required to maintain the necessary inventory thereby decreasing the amount of shipping fees paid. Priority Management After other inefficiencies were lessened, it was noted the queue for station 2 was not effectively balancing the flow from stations 1 and 3. To resolve we changed the scheduling policy to process items from station 3 with priority. The reasoning was that when we took control of the plant, there were 2 issues. First issue was that the capacity of the plant was too low compared to the demand. The second issue (arising from the first) was that the low capacity vs. demand created a backlog in the factory, and we had to deal with this backlog. There were 2 ways to deal with the backlog: we could have increased the capacity of the factory more, so that the extra kits already in process are processed, or we could have changed the priority to station. We chose to change the priority at station 2, because it was free (compared to purchasing additional machines). We believe the prioritization scheme selected was the best optimization of the unit to ensure finished goods were flowing through the factor. The change decreased the overall queue at station 2 from an average of 341 to 145 and increased the number of completed jobs per day from an average of 4 to an average of 7. Endgame Management Toward the end of the game we began calculating the potential impact of remaining on contract 3 with all levels the same, or switching to a new contract, and selling off equipment. Plan one was to sell off the equipment purchased, drop back down to contract one, and decrease the reorder point and quantities to minimize the inventory cost and penalties for late deliveries. Plan 2 was to decrease the reorder point and quantity, sell equipment and switch to contract 2 in order to maximize the per unit profit. In the end we ran out of time and made no adjustments. As a result we stayed in third place overall after reaching a high of second place. If given the chance to play the game over again we would have changed the reorder point and quantity earlier to decrease inventory shipping and holding costs. Next, we would have sold off one piece of equipment from each station. This would allowed us to still meet production levels while increasing the inflow of cash prior to shut down. Finally, to ensure we met delivery requirements we would have changed to the second contract which would have maximized our profit on the units in the final processing stages while minimizing the penalties for late delivery. Conclusions We learned how to apply some of the basic manufacturing operations concepts through the Littlefield simulation. We learned to first observe the current operating processes of the plant to locate efficiencies and inefficiencies. By calculating the existing reorder point and reorder quantity we were able to arrive at an estimate that provided the necessary raw materials to complete the products in work, fill the estimated customer demand over the short term, and create a small inventory buffer to prevent the plant from being unable to fulfill orders due to a lack of inventory. This inventory buffer was enough to absorb the variability in customer demand. In addition to inventory we were able to decrease the production time, increase the productivity, and increase the overall plant capacity by adding one machine to each station. Adding more than one machine may have allowed us to fill all customer’s orders within the minimum lead time and maximize our profits but given the short duration of the factory it was best to retain the funding in house to create a cash buffer for future projects. Comparing 268 day of data versus 50 days is not an accurate comparison for items such as profit, quantity sold, overall efficiencies, etc. One point that can be compared is the utilization of each station. As you can see from the chart provided below by adding just one additional machine the overall utilization of each station was brought into reasonable levels. Before the additional machines the utilization of each station frequently hit or exceeded 100% which indicated it had no capacity or buffer to take into account the customer’s variable demand. Also we have included below a chart showing the transactions processed by group 3 during the project period.