This basic analysis indicated that we would immediately need to purchase at least one station 1 machine to handle the current demand. Day by day it was awesome to see that our strategy was working and the factory report emails were crucial to our competitive advantage.
The lines represent the upper and lower bounds for demand. Text 5 years ago As we see in an earlier post about predicting demand for the Littlefield Simulationand its important to remember that the predicted demand and the actual demand will vary greatly.
To assist in our process analysis, we determined the standard deviation from the predicted demand profile. These reports enable factory managers to quickly assess performance and make Littlefield strategy decisions.
Share on Facebook Littlefield Technologies is an online factory management simulator program produced since by Responsive Learning Technologies for college students to use while taking business management courses.
There are strategically designed class assignments accompanying the game that the best planners typically complete to their advantage. Machines and Lot Size The customer management part of the simulation measures inventory and cash management and students need to plan which contracts to take.
Follow Coursework Littlefield Techonologies is offered as a part of college coursework.
In gameplay, the demand steadily rises, then steadies and then declines in three even stages. To predict the demand value when demand stabilizes on daywe performed a linear regression on the first 50 days data.
Transactions on Education, Miyaoka recommends not taking too much stock in reducing lot size because "smaller lot sizes cause queuing problems at stations 1 and 3.
In the capacity management part of the simulation, customer demand is random and student gamers have to use how to forecast orders and build factory capacity around that. We then plotted one standard deviation and two standard deviation bounds on our predicted demand profile.
We were able to see bottlenecks and, in general, we were able to make the right decisions faster than our competitors.
If your demand profile is more complex and has more than one knee in the curve e. Littlefield simulation 1 5 years ago "I am absolutely convinced that the success of my team in Littlefield simulation 1 Littlefield game was due to our ability to make rapid decisions. Teams that are successful will not overbuy production capacity during the peak, as it will leave them with excess capacity when demand dies down.
Text 5 years ago One of the first tasks to undertake when preparing for the Littlefield Technologies Simulation was to estimate demand. Using this equation we calculated the expected demand on day In additional blog posts, we will dig into more process analysis.
The regression analysis minimizes the mean squared error between the actual points and the regression line. This information is useful when considering the process analysis and determining the number of machines to purchase.
Manage Capacity The purpose of the game is to be the management team with the most cash at the end of the day simulation run.
As part of the simulation preparation we were given a expected demand profile that looked like this: Video of the Day "Reactive teams generally do not do as well as proactive teams," assistant professor Julia Miyaoka the wrote in the article "Making Operations Management Fun: While it is important for teams to plan for economic order quantity, cash will be too short in the beginning of the game to implement it, so it is better to wait until the factory is generating money to purchase additional machines.
Those that plan their simulation the best get the best scores. This post is brought to you by Little Dashboarda service to monitor your factory and email you up-to-date results. Littlefield Technologies" in the educational journal Informs:Littlefield Simulation.
Little Field Simulation Going into this game our strategy was to keep track of the utilization for each machine and the customer order queue. 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.
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.
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%(87).
Littlefield Simulation - Appendix 7. Contract Selection Decision cashcow, Blue cohort Anita Lal, Ketaki Gangal, Jaimin Patel, Kamal Gelya Decision Analysis Day Contract 2 Based on job lead times Day Contract 1 Little’s Law: Average Flow Time = Average Inventory (day ) / Average Flow Rate ( kits/day) = Days.
Littlefield Simulation 2 - Executive Summary Decisions Made Reorder point to 63 and reorder quantity to Station 2 - Priority to step 4 Contract number 2 Buy machine 3 Buy machine 2 Contract number 3 Contract number 2. Littlefield Report #1 - Team Money.
Littlefield Simulation Preparation_Sdocx.
Littlefield Technologies Report. Documents Similar To Littlefield Simulation Report. Littlefield Simulation 2. Uploaded by. Adair Gallo Junior. Littlefield Team9. Uploaded by. Manjot. Littlefield,+Version++ Uploaded by.5/5(2). Littlefield Technologies Simulation 1 Summer Team Chelsea Koo, Chris Kim, Hee-Yoon Choi, Quentin Hsu, Taryn McNamee Strategy description Revenue maximization: Our strategy main for round one was to focus on maximizing revenue%().Download