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And then we applied the knowledge we learned in the . Change the reorder quantity to 3600 kits. In early January 2006, Littlefield Technologies (LT) opened its first and only factory to produce its newly developed Digital Satellite System (DSS) receivers. 0000001293 00000 n Avoid ordering too much of a product or raw material, resulting in overstock. November 4th, 2014 Subjects. Ending Cash Balance: $1,915,226 (6th Place) 749 Words. 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. Revenue Station 2 never required another machine throughout the simulation. Station Utilization: achieve high efficiency operating systems. http://quick.responsive.net/lt/toronto3/entry.html 2. forecasting demand 3. kit inventory management. $400 profit. and 9, After this, demand was said to be declined at a linear rate (remaining 88 days). Write a strategy to communicate your brand story through: Each hour of real time represents 1 day in the simulation. Following, we used regression analysis to forecast demand and machine productivity for the remaining of the simulation. We didnt consider the cost of paying $1000 a purchase versus the lost interest cost on the payment until demand stabilized after day 150 and we had resolved our problem with batch size and setup times. Ranking Available in PDF, EPUB and Kindle. 64 and the safety factor we decided to use was 3. Hewlett packard company Hewlett Packard Company Deskjet Printer Supply Chain, Toyota Motor Manufacturing Inc - Case Study, Silvio Napoli at Schindler India-HBS Case Study, Kristins Cookie Company Production process and analysis case study, Donner Case, Operation Management, HBR case, GE case study two decade transformation Jack Welch's Leadership, GE's Two-Decade Transformation: Jack Welch's Leadership. Littlefield was developed with Sunil Kumar and Samuel Wood while they were on the faculty of Stanfords Graduate School of Business. We nearly bought a machine there, but this would have been a mistake. Demand planning should be a continuous process that's ingrained in your business. Littlefield Strategy = Calculating Economic Order Quantity (EOQ) 9 years ago The Economic Order Quantity (EOQ) minimizes the inventory holding costs and ordering costs. This method relies on the future purchase plans of consumers and their intentions to anticipate demand. 41 The few sections of negative correlation formed the basis for our critical learning points. : The following is an account of our Littlefield Technologies simulation game. A linear regression of the day 50 data resulted in the data shown on Table 1 (attached)below. 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. A huge spike in demand caused a very large queue at station 3 and caused our revenues to drop significantly. We also looked at, the standard deviation of the number of orders per day. We used demand forecast to plan purchase of our, machinery and inventory levels. In Littlefield, total operational costs are comprised of raw material costs, ordering costs and holding costs. 5 PM on February 22 . 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! The students absolutely love this experience. Stage 2 strategy was successful in generating revenue quickly. 129 To generate a demand forecast, go to Master planning > Forecasting > Demand forecasting > Generate statistical baseline forecast. Our team finished the simulation in 3rd place, posting $2,234,639 in cash at the end of the game. Related research topic ideas. So we purchased a machine at station 2 first. Moreover, we also saw that the demand spiked up. ). It is worth mentioning that the EOQ model curve generally has a very flat bottom; and therefore, it is in fairly insensitive to changes in order quantity. Littlefield Simulation Report Essay Sample. This quantity minimizes the holding and ordering costs. Day 50 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. By getting the bottleneck rate we are able to predict which of the . Operations at Littlefield Labs Littlefield Labs uses one kit per blood sample and disposes of the kit after the processing of the sample is completed After matching the sample to a kit, LL then processes the sample on a four step process on three machines as shown in Figure 2. 20 Going into this game our strategy was to keep track of the utilization for each machine and the customer order queue. 2 moving average 10 and 15 day, and also a linear trend for the first 50 days that predicts the 100th day. 233 Assignment options include 2-hour games to be played in class and 7-day games to be played outside class. 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. Cunder = $600/order Cover = $1200 (average revenue) - $600 = $600/order, Qnecessary = 111 days * 13 orders/day * 60 units/order = 86,580 units. 4816 Comments Please sign inor registerto post comments. Upon further analysis, we determined the average demand to date to have been 12. This paper presents a systematic literature review of solar energy studies conducted in Nordic built environments to provide an overview of the current status of the research, identify the most common metrics and parameters at high latitudes, and identify research gaps. Use forecasting to get linear trend regression and smoothing models. Essay on Littlefield Executive Summary Production Planning and Inventory Control CTPT 310 Littlefield Simulation Executive Report Arlene Myers: 260299905 Rubing Mo: 260367907 Brent Devenne: . Raw material costs are fixed, therefore the only way to improve the facilitys financial performance without changing contracts is to reduce ordering and holding costs. 0 (98. Going into this game our strategy was to keep track of the utilization for each machine and the customer order queue. Please discuss whether this is the best strategy given the specific market environment. S: Ordering cost per order ($), and 241 3. 2. We, than forecasted that we would have the mean number of, orders plus 1.19 times the standard deviation in the given, day. 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. Thus should have bought earlier, probably around day 52 when utilization rate hit 1. 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. Leena Alex Thus, at the beginning, we did not take any action till Day 62. We also reorder point (kits) and reorder quantity (kits), giving us a value of 49 and 150. Lastly don't forget to liquidate redundant machines before the simulation ends. 03/05/2016 0000008007 00000 n Using the EOQ model you can determine the optimal order quantity (Q*). A huge spike in Capacity Management at Littlefield Labs By whitelisting SlideShare on your ad-blocker, you are supporting our community of content creators. 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. 0000004484 00000 n To You can read the details below. Even with random orders here and there, demand followed the trends that were given. 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. Instant access to millions of ebooks, audiobooks, magazines, podcasts and more. startxref Get higher grades by finding the best MGT 3900 PLAN REQUIREMENTS FOR MIYAOKA LITTLEFIELD SIMULATION notes available, written by your fellow students at Clemson University. Littlefield Simulation Kamal Gelya. Revenue maximization:Our strategy main for round one was to focus on maximizing revenue. Before purchasing our final two machines, we attempted to drop the batch size from 3x20 to 5x12. When we reached the end of first period, we looked on game, day 99 and noticed that demand was still growing. 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. When do we retire a machine as it March 19, 2021 Cash Balance These data are important for forecasting the demand and for deciding on purchasing machines and strategies realized concerning setting up . We left batch size at 2x30 for the remainder of the simulation. And then we applied the knowledge we learned in the . Regression Analysis: The regression analysis method for demand forecasting measures the relationship between two variables. Rank | Team | Cash Balance ($) | 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"). Our team operated and managed the Littlefield Technologies facility over the span of 1268 simulated days. It can increase profitability and customer satisfaction and lead to efficiency gains. The purpose of this simulation was to effectively manage a job shop that assembles digital satellite system receivers. If so, when do we adjust or maximum cash balance: 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 . 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. H6s k?(. ko"ZE/\hmfaD'>}GV2ule97j|Hm*o]|2U@ O Has anyone done the Littlefield simulation? Thereafter, calculate the production capacity of each machine. The objective was to maximize cash at the end of the product life-cycle (270 days) by optimizing the process design. 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.