Executive Summary Our team operated and managed the Littlefield Technologies facility over the span of 1268 simulated days. Our team finished the simulation in 3rd place, posting $2,234,639 in cash at the end of the game. We did intuitive analysis initially and came up the strategy at the beginning of the game. And then we applied the knowledge we learned in the class, did process analysis and modified our strategies according to the performance results dynamically. We have reinforced many of the concepts and lessons learned in class and had a better understanding of the operation of the Littlefield Technologies facility and how certain modifications would affect the throughput and lead time. The Plan - Initial Strategy Our team’s …show more content…
If we change batch size to 1*60, based on day1-50 data, the lead time is always > 0.3, and we could not use contract number 2Contract 2 to increase revenue and still have to use Ccontract number 11 Revenue will be the same as 1 which is $1,596,000 . c. If we buy machine 3 because it's a bottleneck, without changing anything else, utilization for station 3 will become less which will cause less queue, less waiting time, less lead time, no or less penalty, more revenue. Revenue (roughly) = 12 * 1500 * (268-135) = 12*133*1500 = $2,394,000 Machine cost = 100,000 $2,394,000-$100,000 = $2,294,000 > $1,596,000 According to our analysis, So , c) is a betterthe optimal choice whichchoice, which confirmed our aggressive machine buying strategy since Day 135. And on Day 149, and Day 170, we immediately bought machine for station 2 and 1 again when the stationsit becomes bottle neck or when lead time is more than 0.28 which caused revenue decreased to $1,200. 3. Changing lot size : We changed lot size for 3 times. On Day 58, we changed to 2 lots/job in order to take Contract number 2 to make earn more money. On Day 64, we changed to 3 lots/job and hope the lead time would decrease. This was a big mistake we made. After this change, we noticed the queue for station #3 is very high, station #3 became bottleneck and our
When our group first started planning our strategy to win the second Littlefield simulation game, it was evident that the goal of the game was to maximize profits. We were competing against 18 other teams, but we knew with correct system capacity management and correct inventory management we could be the number one team. Even though we made correct decisions overall, we ended up in second place again with a cash balance of $2,660,393 and remaining inventory of 52 kits. Although we were a little disappointed again, we were very satisfied with what we have learned and the important operations management concepts we used such as capacity management and lead-time and inventory management throughout both of the Littlefield simulation games.
c. Based on your analysis, do you believe peak period pricing, by reducing arrival rates during period of heavy demand, might represent and effective means of reducing the costs of over scheduling?
increase, throughput increases exponentially thereby creating an inefficient operation and limiting profitability. Based upon this evaluation, Team A decided that it was prudent to purchase another tuning machine for Station 3 as soon as the cash balance reached $175,440 ($100,000 machine cost, $74,440 raw material cost, $1,000 order cost). Due to Station 1 often hitting 100% utilization, Team A decided to evaluate the need to purchase another board stuffing machine in the future.
Approximate lead time improvements in optimized plan (via lowest M increase for max Lq improvement)
As demonstrated in Exhibit 11, our factory processed 15 orders for materials during the course of the simulation. Had we been able to accurately predict our need for materials at the beginning of the simulation, we could of saved $14,000 through frontloading on inventory and taking advantage of the absence of inventory holding costs. Due to the difficulties of accurately predicting order quantities however, we would have decided to batch our material orders into 2 or 3 batches. Batching material orders would have allowed us to save on fixed order fees and also would have enabled us to respond to order fluctuations with increased/decreased order
This is a stimulation review of a cardiac care unit that is facing working capital shortages. As the lead financial consultant brought into address the financial indicators and evaluate to bring working capital back to in order at the Elijah Heart Center (EHC). The other financial analyst will focused around addressing issues as they relate to this particular cardiac care unit; what funding can be acquired to garner medical equipment; what funds can be used for capital expansion; finally a summation of findings and a conclusion of what the overall stimulation showed, in regards to how through the analyst were.
To achieve a high net income and be effective in fulfilling contracts in the simulation, it was important to have a strategy set in place. This requires a standard of control over how the business was going to operate and fulfill orders effectively. In order to gain a higher reputation to secure higher value contracts within the game, I worked to acquire specific contracts and purchasing materials to produce the items that would be shipped out to customers. I also found it important to have employees who were highly skilled within each area that they were employed in order to maximize the production process. By having higher skilled employees, I found it easier to continually fulfill contracts at a constant rate as and minimize slack between each area of production. I also found it highly important within the simulation to be able to adapt quickly to changing market and contact requirements because each required a different set of machines, number of materials and time to be produced. It was very important to understand the time constants in order to effectively produce and fulfill each contract within the time that was allotted. Having a diversified production process was also important so that the production process wasn’t constrained to producing one type of contract. Through the game, I learned the
* Least expensive of the three strategies due to the lack of excess inventory and employee overtime
Despite the age and immensity of the Universe, we have not been visited nor been contacted by extraterrestrial beings because we are a part of a computer simulation. According to the Simulation Hypothesis, theory provided by Nick Bostrom, humans are unaware of being part of this computer generated simulation. The most compelling piece of evidence that supports this hypothesis is the fact to one can consider a sequence of possible situations which an increasing fraction of all people live in simulations becomes more accurate. (Bostrom 1)
The factory has been running for 50 simulated days, and management has recalled the high-powered operations team (you) to manage the capacity, scheduling, purchasing, lot sizing, and contract quotations to maximize the cash generated by the factory over its lifetime. Management is not providing any operating budget beyond the cash generated by the factory itself. You will have control of the factory from day 50 to day 386. At 1 hour per simulated day, this translates to 14 real days. At day 386, you lose control of the factory, and the simulation will quickly run another 100 days of simulation. When you lose control of the factory, management expects you to leave the factory parameters set to maximize the factory’ cash position when the factory shuts down on day 486. After the simulation s ends on day 486, you can check the status of your factory, but the factory will no longer be running. Team scores and ranking are based “cash balance,” which
Use the Confidential Information with the financial data and valuation tools in the Simulation to
Hence, the bottleneck is due to high variability in order arrival rate and order processing time. Hence, we need to analyse the quarterly utilization level.
to convince him to invest the same amount in convertible debt or preferred stock where he can choose to
The current average utilization rate of the call centre is 30.48% (see appendix XXX). The average arrival rate, rate at which the patients call, is lower than the average service rate, rate at which the patients are serviced. However, both the arrival time and the service time contain moderate variability (see appendix XXX), negatively impacting the flow time during peak hours. There are two arrival rate variability issues: variability amongst the different days the calls are received and variability amongst the hours the calls are received. The problem is bigger than Laura anticipated. As per the ‘Appendix 5’ of the case, the average daily abandoned calls are 338 and not 35. This does not include the patients receiving a busy signal, therefore becoming lost throughputs. Thus given the low utilization rate it is clear that the problem the call centre faces is in managing variability and not capacity.
Having 3 days of delay in electrical can affect the whole project. Therefore, the negotiation of 15 working days would exceed up to 18 days, which could cause the penalty of $300 ($100 per extra day). In order to minimise a project’s critical path, sequencing of tasks should be changed. On the off chance that one can do a project’s tasks in alternate grouping to that initially proposed, one might have the capacity to shorten the critical path. A level of intricacy by striving for shortening the critical path, which is primarily hazardous, is unavoidably acquainted with the task. (Aydar, 2014) With a specific end goal to convey this shorter timescale, the critical path should be controlled