...Background In early January 2006, Littlefield Technologies (LT) opened its first and only factory to produce its newly developed Digital Satellite System (DSS) receivers. Littlefield Technologies mainly sells to retailers and small manufacturers using the DSS’s in more complex products. Littlefield Technologies charges a premium and competes by promising to ship a receiver within 24 hours of receiving the order, or the customer will receive a rebate based on the delay. The product lifetime of many high-tech electronic products is short, and the DSS receiver is no exception. LT managers have decided that, after 268 days of operation, the plant will cease producing the DSS receiver, retool the factory, and sell any remaining inventories. As this is a short life-cycle product, managers expect that demand during the 268 day period will grow as customers discover the product, eventually level out, and then decline. In the initial months, demand is expected to grow at a roughly linear rate. Demand is then expected to stabilize. Eventually, demand should begin to decline at a roughly linear rate. Although orders arrive randomly to LT, management expects that, on average, demand will follow the trends outlined above. Management’s main concern is managing the capacity of the factory in response to the complex demand pattern. Delays resulting from insufficient capacity undermine LT’s promised lead times and ultimately force LT to turn away orders. In particular, if an order’s...
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...LittleField In the LittleField Game 2, our team had to plan how to manage the capacity, scheduling, purchasing, and contract quotations to maximize the cash generated by the lab over its lifetime. We knew that our output was lower than demand right when Game 2 started. 1st stage, we knew there will be bottleneck at station 1 and 3 so additional machines must be purchased. We decided to purchase an additional machine for station 1 because it was $10,000 cheaper, utilization was higher here, and this is where all the orders started. We also reorder point (kits) and reorder quantity (kits), giving us a value of 49 and 150. 2nd stage, we have to reorder quantity (kits) again giving us a value of 70. In two days, we spend a lot of money on kits so we realize we only needed two machines at station 2 and 3. We then reorder point (kits) to a value of 55 and reorder quantity (kits) to 104. We are making money now at station 2 and station 3. However, we realize that we are not making money quick enough so we change our station 2 priority to 4 and use the money we generate to purchase additional machine at station 1. We now have a total of five machines at station 1 to clear the bottlenecks and making money quickly. Stage 2 strategy was successful in generating revenue quickly. 3rd stage, while the focus of the first two stages was making the most money, we will now turn our strategy in keeping our lead against other teams. We bought more reorder point (kits) and sold it for contract...
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...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...
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...Strategy description Revenue maximization:Our strategy main for round one was to focus on maximizing revenue. We did not want the revenue to ever drop from $1000, so we took action based on the utilization rates of the machines. We needed to have sufficient capacity to maintain lead times of less than a day and at most, 1 day and 9 hours. Based on the linear decrease in revenue after a lead time of one day, it takes 9 hours for the revenue to drop to $600 and our profits to be $0. In terms of when to purchase machines, we decided that buying machines as early as possible would be ideal as there was no operating costs after the initial investment in the machine. Having more machines seemed like a win-win situation since it does not increase our expenses of running the business, yet decreases our risk of having lead times of over a day. The only expense we thought of was interest expense, which was only 10% per year. Therefore, we took aproactive approach to buying machines and purchased a machine whenever utilization rates rose dangerously high or caused long queues. As we will see later, this was a slight mistake since the interest rate did have a profound impact on our earnings compared to other groups. Machine configuration: Our final machine configuration (which was set on Day 67) was 3 machine 1's, 2 machine 2's, and2 machine 3's. This lasted us through the whole simulation with only a slight dip in revenue during maximum demand. In terms of choosing a priority for...
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...Responsiveness at Littlefield Technologies Background Littlefield Technologies (LT) has developed another DSS product. The new product is manufactured using the same process as the product in the assignment “Capacity Management at Littlefield Technologies” — neither the process sequence nor the process time distributions at each tool have changed. The LT factory began production by investing most of its cash into capacity and inventory. Specifically, on day 0, the factory began operations with three stuffers, two testers, and one tuner, and a raw materials inventory of 9600 kits. This left the factory with zero cash on hand. Customer demand continues to be random, but the long-run average demand will not change over the product’ 486-day lifetime. At s the end of this lifetime, demand will end abruptly and factory operations will be terminated. At this point, all capacity and remaining inventory will be useless, and thus have no value. Management is currently quoting 7-day lead times, but management would like to charge the higher prices that customers would pay for dramatically shorter lead times. In addition, because the factory is essentially bootstrapping itself financially, management is worried about the possibility of bankruptcy. To minimize this threat, management policy dictates that new equipment cannot be purchased if the remaining cash balance would be insufficient to purchase at least one order quantity’ worth of raw materials. s Operations Policies at Littlefield LT uses...
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...Background In early January 2006, Littlefield Technologies (LT) opened its first and only factory to produce its newly developed Digital Satellite System (DSS) receivers. Littlefield Technologies mainly sells to retailers and small manufacturers using the DSS’s in more complex products. Littlefield Technologies charges a premium and competes by promising to ship a receiver within 24 hours of receiving the order, or the customer will receive a rebate based on the delay. The product lifetime of many high-tech electronic products is short, and the DSS receiver is no exception. LT managers have decided that, after 268 days of operation, the plant will cease producing the DSS receiver, retool the factory, and sell any remaining inventories. As this is a short life-cycle product, managers expect that demand during the 268 day period will grow as customers discover the product, eventually level out, and then decline. In the initial months, demand is expected to grow at a roughly linear rate. Demand is then expected to stabilize. Eventually, demand should begin to decline at a roughly linear rate. Although orders arrive randomly to LT, management expects that, on average, demand will follow the trends outlined above. Management’s main concern is managing the capacity of the factory in response to the complex demand pattern. Delays resulting from insufficient capacity undermine LT’s promised lead times and ultimately force LT to turn away orders. In particular, if an order’s...
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...June 9, 2013 Section 2, Team 9 Managing Capacity and Lead Time at Littlefield Technologies – Team 9’s Summary The purpose of this simulation was to effectively manage a job shop that assembles digital satellite system receivers. The objective was to maximize cash at the end of the product life-cycle (270 days) by optimizing the process design. REVENUE 25000 20000 15000 10000 5000 1 9 17 25 33 41 49 57 65 73 81 89 97 105 113 121 129 137 145 153 161 169 177 185 193 201 209 217 225 233 241 249 257 265 Total Revenue Demand DAYS 0 Figure 1 : Revenue and demand DEMAND 25 20 15 10 5 0 As shown by the figure above, total revenues generally followed the same trend as demand. The few sections of negative correlation formed the basis for our critical learning points. Although the process took a while to completely understand during the initial months of the simulation, the team managed to adjust, learn quickly and finish in 7th place with a cash balance of $1,501,794. For the purpose of this report, we have divided the simulation into seven stages after day 50, explicating the major areas of strategically significant decisions that were made and their resulting effects...
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...B6016 Managing Business Operations Report on Littlefield Technologies Simulation Exercise By Group 4: Anise Tan Qing Ye Aneel Gautam Chu Kar Hwa, Leonard Tan Kok Wei Ranking Reflecting on the simulation exercise, we have made both correct and incorrect decisions. Nevertheless, although we ranked 4th (Exhibit 1: OVERALL TEAM STANDING), we believe we gained a deeper understanding of queuing theory and have obtained invaluable experience from this exercise. Rank | Team | Cash Balance ($) | 1 | bigmoney1 | 1,346,320 | 2 | techwizard | 1,312,368 | 3 | makebigmoney | 1,141,686 | 4 | beaters123 | 895,405 | 5 | donothing | 588,054 | 6 | mas001 | 472,296 | Exhibit 1 : OVERALL TEAM STANDING Decisions Made A summary of the rationale behind the key decisions made would perhaps best explain the results we achieved. . Decision 1 Day | Parameter | Value | 54 | station 1 machine count | 2 | When the exercise started, we decided that when the lead time hit 1 day, we would buy one station 1 machine based on our analysis that station 1 takes the longest time which is 0.221 hrs simulation time per batch. (Exhibit 2: Average time per batch of each station). As day 7 and day 8 have 0 job arrivals, we used day 1-6 figures to calculate the average time for each station to process 1 batch of job arrivals. Base on the average time taken to process 1 batch of job arrivals, we were able to figure out how many...
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...ev ev Littlefield Simulation Report: Team A Ending Cash Balance: $1,915,226 (6th Place) Return On Investment: 549% ROI=Final Cash-Day 50 Cash-PP&E ExpenditurePP&E Expenditure→ 1,915,226-97,649-280,000280,000=549% Analysis of the First 50 Days The Littlefield Technologies management group hired Team A consulting firm to help analyze and improve the operational efficiency of their Digital Satellite Systems receivers manufacturing facility. Upon the preliminary meeting with Littlefield management, Team A were presented with all pertinent data from the first 50 days of operations within the facility in order for the firm to analyze and develop an operational strategy to increase Littlefield’s throughput and ultimately profits. Figure 1: Day 1-50 Demand and Linear Regression Model Figure 1: Day 1-50 Demand and Linear Regression Model With little time to waste, Team A began by analyzing demand over the first 50 days of operations in order to create a linear regression model to predict demand into the future in order to make critical operational decisions; refer to Figure 1. In Littlefield, total operational costs are comprised of raw material costs, ordering costs and holding costs. Raw material costs are fixed, therefore the only way to improve the facility’s financial performance without changing contracts is to reduce ordering and holding costs. As such, the first decision to be made involved inventory management and raw material ordering. Inventory management...
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...INTRODUCTION The managing of our factory at Littlefield Technologies thought us Production and Operations Management techniques outside the classroom. We experienced live examples of forecasting and capacity management as we moved along the game. Throughout the game our strategy was to apply the topic leant in Productions and Operation Management Class to balance our overall operations. The game started off by us exploring our factory and ascertaining what were the do’s and don’ts. The initial goal of the goal was to correlate the Re Order Point with the Customer Order Queue. To forecast Demand we used Regression analysis. We looked and analyzed the Capacity of each station and the Utilization of same. The team ascertained our job completion and our Lead Time. Our strategy throughout the stimulation was to balance our work station and reduce the bottleneck. PRIOR TO THE GAME The team consulted and decided on the name of the team that would best suit the team. With much anticipation we reviewed all the literate that was provided subsequently to assist us in decision making at Littlefield Technologies. DAY 1 (8 OCTOBER 3013) Data was extracted from “plot job arrival “and analyzed. The information was used to calculate the forecast demand using the regression analysis. The next step was to calculate the Economic Order Point (EOP) and Re Order Point (ROP) was also calculated. A discussion ensued and we decided to monitor our revenue on this day. The strategy yield a...
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...Thundercats Team Contract November 4th, 2014 Mission The mission of our team is to complete all aspects of the team assignment on time and to the full requirements set forth by Professor McNickle. Project We will work to the best of our abilities on the Littlefield simulation and will work as a team to make agreed upon manufacturing changes as often as is deemed needed. In addition, we will research and tour Darigold Inc. to evaluate their operations, providing analysis and recommended changes where we deem applicable. Our goal is to function as a reciprocal interdependent team, using each members’ varied skills and time to complete tasks both well and on time. To ensure we are focused and accomplish these set goals, the following guidelines...
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...Running head: Capacity Management Capacity Management at Littlefield Technologies Anteaus Rezba Leena Alex Marcio de Godoy Pennsylvania State University Initial Strategy Definition Our strategy was to keep track of each machine’s capacity and the order queue. Initially, we tried not to spend much money right away with adding new machines because we were earning interest on cash stock. However, once the initial 50 days data became available, we used forecasting analyses to predict demand and machine capacity. Our goal was to buy additional machines whenever a station reached about 80% of capacity. Day 50 Once the initial first 50 days of data became available, we plotted the data against different forecasting methods: Moving average, weighted moving average, exponential smoothing, exponential smoothing with trend, and exponential smoothing with trend and season. Out of these five options, exponential smoothing with trend displayed the best values of MSE (2.3), MAD (1.17), and MAPE (48%). In addition, the data clearly showedprovided noted that the demand was going to follow an increasing trend for the initial 150 days at least. Following, we used regression analysis to forecast demand and machine productivity for the remaining of the simulation. As explained on in chapter 124, we used the following formula: y = a + b*x. A linear regression of the day 50 data resulted in the data shown on Table 1 (attached)below. | |Station ...
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...LITTLEFIELD CAPACITY GAME REPORT When we started to play game, we waited a long time to play game because there are several stations for buying machines and these machines have different processes. Thus, we did not know which machine is suitable for us; therefore, we waited 95 days to buy a new machine. First of all, we purchased a second machine from Station 1; however, we could not think Station 1 would be a bottleneck process. Hence, we wasted our cash and our revenue decreased from $1,000,000 to $120,339, which was a bad result for us. Moreover, we bought two machines from Station 2 because; it would be better idea to increase our revenue more than Station 1. Furthermore, we thought that buying machines from Station 3 was unnecessary because of the utilization’ in that station. In addition to this factor, we thought that buying several machines from different stations would decrease our revenue in the following days. Before buying machines from two main stations, we were in good position among our competitors. After we purchased machines from Station 1 and Station 2, our revenue and cash balance started to decrease due to the variable costs of buying kits. In addition, we were placed 17th position in overall team standing. Within the framework of all these, our cash balance was $120,339 at the end of the game, since we could not sell those machines and our result was not quite good as our competitors’ positions. We could have used different strategies for the Littlefield...
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...Littlefield Initial Strategy When the simulation first started we made a couple of adjustments and monitored the performance of the factory for the first few days. Initially we set the lot size to 3x20, attempting to take advantage of what we had learned from the goal about reducing the lead-time and WIP. We also changed the priority of station 2 from FIFO to step 4. After viewing the queues and the capacity utilization at each station and finding all measures to be relatively low, we decided that we could easily move to contract 3 immediately. Except for one night early on in the simulation where we reduced it to contract 2 because we wouldn’t be able to monitor the factory for demand spikes, we operated on contract 3 almost the entire time. This proved to be the most beneficial contract as long as we made sure that we had the machines necessary to accommodate the increasing demand through day 150. Machine Purchases The first time our revenues dropped at all, we found that the capacity utilization at station 2 was much higher than at any of the other stations. So we purchased a machine at station 2 first. Station 2 never required another machine throughout the simulation. After all of our other purchases, utilization capacity and queuing at station 2 were still very manageable. As demand began to rise we saw that capacity utilization was now highest at station 1. We nearly bought a machine there, but this would have been a mistake. A huge spike in demand...
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...Capacity Management at Littlefield Labs I. Introduction There are 3 stations in the game called sample preparing, testing, and centrifuging, while there are 4 steps to process the jobs. Before the game started, we tried to familiarize with the process of the laboratories and calculating the costs (both fixed and variable costs) based on the information on the sheet given. We did not intend to buy any machines too early, as we wanted to see the demand fluctuation and the trend first. Thus, at the beginning, we did not take any action till Day 62. II. Summary of actions | Actions | Reasons | What should have been done | 62 | Buy Machine 1 | The revenue dropped and the utilizations of Machine 1 were constantly 1 or near 1 on the previous 5 days. | We should have bought both Machine 1 and 3 based on our calculation on the utilization rate (looking at the past 50 days data) during the first 7 days. Thus should have bought earlier, probably around day 52 when utilization rate hit 1. | 66 | Buy Machine 3 | Both Machine 1 and 3 reached the bottleneck rate as the utilizations at day 62 to day 66 were around 1. Moreover, we also saw that the demand spiked up. | Should have bought earlier, probably around day 55 when the utilization hits 1 and the queue spiked up to 5 | 98 | Buy Machine 1 | The utilization of Machine 1 on day 88 to day 90 was around 1. | Should have bought it earlier, perhaps around day 90 | 133 | Buy Machine 3 | We bought Machine 3 in view of the decrease...
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