littlefield simulation 1 strategy littlefield simulation 1 strategy

However, once the initial 50 days data became available, we used forecasting analyses to predict demand and machine capacity. The simulation provided five options for cost cutting at the hospital with only two of the options available to select from, in hopes of the best result. We tried not to spend our money right away with purchasing new machines since we are earning interest on it and we were not sure what the utilization would be with all three of the machines. Customer demand continues to be random, but the expected daily demand will not change during the labs life span. 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. 1.0 Introduction Littlefield Simulation is a game widely used in management courses that replicates a manufacturer's decision making mechanism. Littlefield Simulation is about running a factory for 360 days with the goal to maximize the cash position at end of this duration. While focusing on immediate goals keeping long term goals in mind is also important. We decided in favor of the second option. Accessing your factory The simulation ends on day-309. submit it as your own as it will be considered plagiarism. In addition, because the factory is essentially bootstrapping itself financially, management is worried about the possibility of bankruptcy. 5. Consequently, we lost revenues when the demand neared its peak. By accepting all cookies, you agree to our use of cookies to deliver and maintain our services and site, improve the quality of Reddit, personalize Reddit content and advertising, and measure the effectiveness of advertising. Tap here to review the details. 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. One focus of ours during this simulation was minimizing the cost of inventory orders and stock outs. 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. Our team finished the simulation in 3rd place, posting $2,234,639 in cash at the end of the game. By doing so, the labor costs are significantly reduced and the unit demand will be covered. This weeks key learning areas have been eye opening and worthwhile. Forecasting: Our cash position got weaker and we then slipped to position 7 from position 2. Very useful for students who will do the simulation for the first time. Initially, we tried not to spend much money right away with adding new machines because we were earning interest on cash stock. 2 | techwizard | 1,312,368 | In order to expand capacity and prepare for the forecasted demand increase, the team decided to immediately add a second machine at Station 1. We then determined our best course of action would be to look at our average daily revenue per job (Exhibit 7) and see if we could identify any days when that was less than the maximum of $1,000/job, so we could attempt to investigate what days to check on for other issues. 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. 5000 max revenue for unit in Simulation 1. The few sections of negative correlation formed the basis for our critical learning points. Leena Alex We had split the roles. writing your own paper, but remember to One of success parameters were profits, though we did manage to make significant profits over the last two years, we did not focus on it early in the game. It is now nine months later, and Littlefield Technologies has developed another DSS product. Raw material costs are fixed, therefore the only way to improve the facilitys financial performance without changing contracts is to reduce ordering and holding costs. We found our calculations to be performing reasonable well during the initial phases of the simulation. The final result was amazing, and I highly recommend www.HelpWriting.net to anyone in the same mindset as me. 241 on 54th day. Littlefield Simulation Analysis 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. We did many things right to win this simulation. Our revenue per day improved to 200 $/day. We nearly bought a machine there, but this would have been a mistake. Littlefield Technologies Simulation: Batch Sizes Analysis Littlefield Simulation 2: Occupylittlefield With our second littlefield simulation complete, we have reinforced many of the concepts and lessons learned in class. Learn more in our Cookie Policy. Management requires a 10% rate of return on its investments. This was determined by looking at the rate of utilization of the three machines and the number of jobs in the queue waiting for these machines. Correct writing styles (it is advised to use correct citations) In the Littlefield Simulation it would have been better on Day 51 to switch to the order quantity as recommended by the EOQ framework in order to minimize costs. Operations Policies at Littlefield All rights reserved. Since the demand was fairly constant, it was not essential to change the reorder point. What new decisions will you make regarding production levels and pricing for your Widget facility? You may use it as a guide or sample for The account includes the decisions we made, the actions we took, and their impact on production and the bottom line. This study aims to contribute to the ongoing debate on behavioral operational research (BOR), specifically discussing the potential of system dynamics (SD) models to analyze decision-making, 5th International Conference on Higher Education Advances (HEAd'19), Game-based learning refers to the use of game thinking and mechanics to engage and motivate students in the learning process. and ROI=Final Cash-Day 50 Cash-PP&E ExpenditurePP&E Expenditure 1,915,226-97,649-280,000280,000=549% Ranking 1. The Israeli-Palestinian conflict has been one of the most important issues that the United Nations has focused on since its founding in 1945. Not a full list of every action, but the getting second place on the first Littlefield simulation game we knew what we needed to do to win the second simulation game. Out of these five options, exponential smoothing with trend displayed the best values of MSE (2.3), MAD (1.17), and MAPE (48%). Start decision making early. SOMETIMES THEY TAKE A FEW MINUTES TO BE PROCESSED. The only expense we thought of was interest expense, which was only 10% per year. 24 hours. Starting at 5 PM on Wednesday, February 27, the simulation will begin The game will end at 9 PM on Sunday, March 3. I will explain as to why I choose what I did in this paper., Comparing the difference between the production volume variance of the first and second half of the year, we noticed that during the second term, it is more favorable than the first term. Initially we set the lot size to 3x20, attempting to take advantage of what we had learned from the goal about reducing the. The company had excess space in the existing facility that could be used for the new machinery. Because: Expert Answer 100% (1 rating) True In order to rectify the inventory policy, the EOQ framework was to be utiliz View the full answer We did less messing around with the lot size and priority since these were definitely less important to the overall success of your factory than the number of machines you had. One solution was that we should let the inventory run out and not reorder anything. 5 PM on February 22 . We did intuitive analysis initially and came up the strategy at the beginning of the game. Once the priority was changed from FIFO to Step 4, the team noticed that both the utilization at Station 2 and the queues began to exhibit high variance from day to day. The decision making for the machines is typically based on the utilization of machines. 1. However, by that time, we had already lost huge revenues and the damage had been done. At the end of day 350, the factory will shut down and your final cash position will be determined. We noticed that around day 31, revenues dipped slightly, despite the fact that the simulation was still nowhere near peak demand, suggesting that something was amiss in our process. This may have helped us improve our simulation results further. We did switch the lot size to 3 by 20 early in the simulation since we know that smaller batch sizes can speed up production. Activate your 30 day free trialto unlock unlimited reading. Here are our learnings. 5 . Demand Following, we used regression analysis to forecast demand and machine productivity for the remaining of the simulation. Do not sell or share my personal information, 1. 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, Except for one night early on in the simulation where we reduced it to contract 2, because we wouldnt be able to monitor the factory for demand spikes, we operated, on contract 3 almost the entire time. Press J to jump to the feed. In this simulation we decided to take the message of The Goal and apply it as fast as we could. In the game, teams are challenged to optimize the system and maximize cash flow for Littlefield Technologies, a factory that assembles Digital Satellite System Receivers from electronic kits. These key areas will be discussed throughout the journal to express my understanding of the experience. The winning team is the team with the most cash at the end of the game (cash on hand less debt). Second, we controlled the inventory level with finding right QOPT (Optimal Order Quantity) and reorder point according to continuous review system method. 1 Littlefield Labs Simulation Professor: Ioannis (Yannis) Bellos Course: MBA 638 School of Business Information Systems . We knew that the initial status quo was limited by the inventory quantity. As explained on in chapter 124, we used the following formula: y = a + b*x. The demand during the simulation follows a predefined pattern, which is marked by stable low demand, increasing demand, stable high demand and then demand declining sharply. The write-up only covers the second round, played from February 27 through March 3. Our goal was to buy additional machines whenever a station reached about 80% of capacity. 2, As such, the first decision to be made involved inventory management and raw material ordering. $600. Purpose. Eventually, demand should begin to decline at a roughly linear rate. Do a proactive capacity management : Machines. Can you please suggest a winning strategy. Later however, as the demand increased, it became increasingly complex and difficult for me to predict the annual demands needed for correct EOQ and ROP calculations. Retrieved from https://graduateway.com/littlefield-technologies-simulation-batch-sizes/, The Family Tradition of Making a Huge Batch of Ravioli as a Cultural Identity, Differentiating Between Market Structures Simulation. We did not take any corrective measure to increase our profit margins early in the game. As a result, we continued to struggle with overproduction and avoiding stock outs, but made improvements resulting in less drastic inventory swings in the later. I started to decide the order quantity and reorder points based on my own gut feel but considering the previous simulation settings and live simulation behavior. The sales revenue decreased from 9 million to 6 million in 12 years and also they incurred operating losses. Click here to review the details. report, Littlefield Technologies Simulation: Batch Sizes Analysis. Now customize the name of a clipboard to store your clips. However, the majority of business. Check out my presentation for Reorder. We did many things right to win this simulation. Initially we set the lot size to 3x20, attempting to take advantage of w . Day 53 Our first decision was to buy a 2nd machine at Station 1. s ; What are the lowest percentage mark-up items? Initial Strategy Definition 100% (5) 100% found this document useful (5 votes) 13K views. When the machine-count at station-1 reached seven, we were hesitant to add further machines despite heavy utilization. Revenue Littlefield Simulation BLUEs: Anita Lal Jaimin Patel Kamal Gelya Ketaki Gangal 2. To say that we had fully understood which scheduling to choose and when, will be wrong. 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. 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.