Littlefield Technologies Wednesday, 8 February 2012. (Exhibit 2: Average time per batch of each station). . Our team finished the simulation in 3rd place, posting $2,234,639 in cash at the end of the game. reorder point and reorder quantity will need to be adjusted accordingly. You can find answers to most questions you may have about this game in the game description document. The collective opinion method of data forecasting leverages the knowledge and experience of . Because we didnt want to suffer the cost of purchasing inventory right before the simulation ended we made one final purchase that we thought would last the entire 111 days. 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. 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. Team Contract The only expense we thought of was interest expense, which was only 10% per year. Get started for FREE Continue. Before the simulation started, our team created a trend forecast, using the first 50 days of data, showing us that the bottleneck station was at Station 1. Strategies for the Little field Simulation Game Going into this game our strategy was to keep track of the utilization for each machine and the customer order queue. Using the EOQ model you can determine the optimal order quantity (Q*). 3. For assistance with your order: Please email us at textsales@sagepub.com or connect with your SAGE representative. Littlefield Technologies mainly sells to retailers and small manufacturers using the DSS's in more complex products. The managing of our factory at Littlefield Technologies thought us Production and Operations Management techniques outside the classroom. 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. Responsive Learning Technologies 2010. Next we, calculated what game it would be in 24 hours, and then we, plugged that into the linear regression to get the mean, forecasted number of orders on that day. Manage Order Quantities: 185 In addition, we were placed 17th position in overall team standing. To generate a demand forecast, go to Master planning > Forecasting > Demand forecasting > Generate statistical baseline forecast. H6s k?(. ko"ZE/\hmfaD'>}GV2ule97j|Hm*o]|2U@ O Station 2 never required another machine throughout the simulation. updated on In a typical setting, students are divided into teams, and compete to maximize their cash position through decisions: buying and selling capacity, adjusting lead time quotes, changing lot sizes and inventory ordering parameters, and selecting scheduling rules. 64 and the safety factor we decided to use was 3. Our goal was to buy additional machines whenever a station reached about 80% of capacity. Littlefield Technologies charges a . 0000001482 00000 n reinforces the competitive nature of the game and keeps cash at the forefront of students' minds. 241 Littlefield Simulation #1 Write Up Team: CocoaHuff Members: Nick Freeth, Emanuel Martinez, Sean Hannan, Hsiang-yun Yang, Peihsin Liao 1. . There was no direct, inventory holding cost, however we would not receive money. We believe that it was better to overestimate than to. Lastly don't forget to liquidate redundant machines before the simulation ends. Round 1 of Littlefield Technologies was quite different from round 2. 153 Devotionals; ID Cards; Jobs and Employment . Course Hero is not sponsored or endorsed by any college or university. Specifically we were looking for upward trends in job arrivals and queue sizes along with utilizations consistently hitting 100%. . board The model requires to, things, the order quantity (RO) and reorder point (ROP). stuffing testing 2. Cash Loss From Miscalculations $168,000 Total Loss of $348,000 Overall Standings Littlefield Technologies aims to maximize the revenues received during the product's lifetime. The forecast bucket can be selected at forecast generation time. Avoid ordering too much of a product or raw material, resulting in overstock. Littlefield is an online competitive simulation of a queueing network with an inventory point. Best practice is to do multiple demand forecasts. Part I: How to gather data and what's available. Lab 7 - Grand Theft Auto V is a 2013 action-adventure game developed by Rockstar North This week - An essay guide to help you write better. Our team operated and managed the Littlefield Technologies facility over the span of 1268 simulated days. 0000000649 00000 n Average Daily Demand = 747 Kits Yearly Demand = 272,655 Kits Holding Cost = $10*10% = $1 EOQ = sqrt(2DS/H) = 23,352 Kits Average Daily Demand = 747 Kits Lead Time = 4 Days ROP = d*L = 2,988 99% of Max. Next we calculated what Customer Responsiveness Simulation Write-Up specifically for you for only $16.05 $11/page. PLEASE DO NOT WAIT UNTIL THE FINAL SECONDS TO MAKE YOUR CHANGES. Based on Economy. It also aided me in forecasting demand and calculating the EOQ . Clipping is a handy way to collect important slides you want to go back to later. 2. Daily Demand = 1,260 Kits ROP to satisfy 99% = 5,040 Game 2 Strategy. capacity to those levels, we will cover the Economic Order Quantity (EOQ) and reorder point In particular, we have reversed the previous 50 days of tasks accepted to forecast demand over the next 2- 3 months in the 95% confidence interval. We needed to have sufficient capacity to maintain lead times of less than a day and at most, 1 day and 9 hours. Littlefield Simulation Overview Presentation 15.760 Spring 2004 This presentation is based on: . Throughout the game our strategy was to apply the topic leant in Productions and Operation Management Class to balance our overall operations. where you set up the model and run the simulation. ittlefield Simulation #1: Capacity Management Team: Computronic When the simulation began we quickly determined that there were three primary inputs to focus on: the forecast demand curve (job arrivals) machine utilization and queue size prior to each station. Decision topics include demand forecasting, location, lot sizing, reorder point, and capacity planning, among others. For the short time when the machine count was the same, stations 1 and 3 could process the inventory at a similar rate. 0000002541 00000 n While forecast accuracy is rarely 100%, even in the best of circumstances, proven demand forecasting techniques allow supply chain managers to predict future demand with a high degree of accuracy. Moreover, we also saw that the demand spiked up. Leverage data from your ERP to access analytics and quickly respond to supply chain changes. We also set up financial calculations in a spreadsheet to compare losses on payment sizes due to the interest lost on the payment during the time until the next purchase was required. | Should have bought earlier, probably around day 55 when the utilization hits 1 and the queue spiked up to 5 | ). Pennsylvania State University Using demand data, forecast (i) total demand on Day 100, and (ii) capacity (machine) requirements for Day 100. Anise Tan Qing Ye This will give you a more well-rounded picture of your future sales View the full answer Tags. In this case, all customers (i.e., those wishing to place. the forecast demand curve (job arrivals) machine utilization and queue . Team Purchasing Supplies Using simulation, a firm can combine time-series and causal methods to answer such questions as: What will be the impact of a price pro motion? We, than forecasted that we would have the mean number of, orders plus 1.19 times the standard deviation in the given, day. 0 Question: Annex 3: Digital data and parameters Management of simulation periods Number of simulated days 360 Number of historic days 30 Number of blocked days (final) 30 Financial data Initial cash 160 000 S Annual interest rate 10% Fixed cost in case of loan 10% of loan amount Annual interest rate in case of loan 20% Finished products: orders . In addition, the data clearly showedprovided noted that the demand was going to follow an increasing trend for the initial 150 days at least. They all agreed that it was a very rewarding educational experience and recommend that it be used for future students. The students absolutely love this experience. Related research topic ideas. The second Littlefield simulation game focused on lead time and inventory management in an environment with a changing demand ("but the long-run average demand will not change over the product's 268-day lifetime"). We knew that our output was lower than demand right when Game 2 started. What Contract to work on depending on lead-time? ev Our team finished the simulation in 3rd place, posting $2,234,639 in cash at the end of the game. Cross), Principles of Environmental Science (William P. Cunningham; Mary Ann Cunningham), Psychology (David G. Myers; C. Nathan DeWall), The Methodology of the Social Sciences (Max Weber), Give Me Liberty! 65 Essay. Thus we adopted a relatively simple method for selecting priority at station 2. Identify several of the more common forecasting methods Measure and assess the errors that exist in all forecasts fManagerial Issues Nik Wolford, Dan Moffet, Viktoryia Yahorava, Alexa Leavitt. At s the end of this lifetime, demand will end abruptly and factory operations will be terminated. 1541 Words. Written Assignment: Analysis of Game 2 of Littlefield Technologies Simulation Due March 14, 8:30 am in eDropbox Your group is going to be evaluated in part on your success in the game and in part on how clear, well structured and thorough your write-up is. Your write-up should address the following points: A brief description of what actions you chose and when. At the end of the final day of the simulation we had 50 units of inventory left over Cash Balance: $ 2,242,693 Days 106-121 Day 268 Day 218-268 Day 209 Focus was to find our EOQ and forecast demand for the remaining days, including the final 50 days where we were not in control. That will give you a well-rounded picture of potential opportunities and pitfalls. A new framework for the design of a dynamic non-myopic inventory and delivery network between suppliers and retailers under the assumption of elastic demandone that simultaneously incorporates inventory, routing, and pricingis proposed. the formula given, with one machines on each station, and the average expected utilization rate, we have gotten the answer that the And the station with the fastest process rate is station two. This new feature enables different reading modes for our document viewer.By default we've enabled the "Distraction-Free" mode, but you can change it back to "Regular", using this dropdown. In gameplay, the demand steadily rises, then steadies and then declines in three even stages.