With our second littlefield simulation complete, we have reinforced many of the concepts and lessons learned in class. We had a better understanding of the operation of the littlefield facility and how certain modifications would affect the throughput and lead time. Though we are pleased with our final results compared to the rest of the class, we see there is still room for improvement. We made many mistakes, but most importantly we have learned from. Here is a discussion of the pros and cons regarding the decisions we made.

In this simulation we decided to take the message of The Goal and apply it as fast as we could. The goal of our company was to make money, so we needed to upgrade to contract 3 as quickly as possible. We bought additional machines at stations with high utilization rates in an attempt to relieve those bottlenecks. As soon as we noticed our lead times drop sufficiently enough for a new contract, we upgraded immediately. This same approach was used until our lead times dropped enough to consistently fulfill contact 3. After contract 3 was reached, our simulation flowed very well with the maximum amount of profit for almost the full remainder of the simulation.

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. 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. We were afraid to go to the 5 by 12 because of the large setup time at stations one. We ended up with a total of 6 machines at station one, which allowed two orders to be simultaneously worked on with a batch of 3 x 20. One focus of ours during this simulation was minimizing the cost of inventory orders and stock outs. Given the average demand and an order lead time of 4 days we were able to calculate an approximate reorder point. To...

...LittlefieldTechnologies Game 2 Strategy – Group 28
1. CUSTOMER ORDERS AND ORDERS WAITING FOR MATERIAL: When considering
the demand level and changes, we would configure a time series of that data using
short range forecasting. Time series are important because they are often the
drivers of decision models. Trend projection and regression analysis models will be
used to forecast the future demand as the growth of the demand increases at a
lower level, increases to a higher level, and then decreases over the course of the
project. The following equation applies to this analysis:
Regression Analysis
ŷ = a + bx
After using the first 50 days to determine the demand for the remainder of the
project, we can then determine the average daily demand for raw material kits by
dividing the total of the forecasted demand by the life of the project (365 days). This
calculation will give us the average number of daily orders and thus the average
number of raw material kits needed, assuming the ratio of 1:1 between receivers
and raw material kits remains constant. The calculation can be expressed as:
Average daily demand for raw material kits =
∑demand
n
As the demand for orders increases, the reorder point and reorder quantity will also
need to be increased. As the demand for orders decreases, the reorder point and
reorder quantity will need to be adjusted accordingly. To estimate the standard
deviation of demand, the following formula...

...LittlefieldSimulation Write-up
December 7, 2011
Operations Management 502
Team 9
Littlefield Lab
We began our analysis by searching for bottlenecks that existed in the current system. It was easily identified that major issues existed in the ordering process. Without calculations, you could tell the reorder point was too low since the historical plots showed inventory levels at zero for two or more days at a time. The number of jobs in customer orders showed correlating spikes at the same time of the inventory outages. We reviewed the utilization and queues of the other stations in the system but were hesitant to make in immediate changes since we were not entirely certain the effects of correcting the inventory policy.
To correct the inventory policy, we want to find the optimal ordering quantity based on the calculation EOQ=. Demand was calculated by taking the average number of customer orders per day over the first 50 days. We came up with a figure near 12.24 orders per day which we multiplied by 268 days for the entire simulation. Setup cost was provided for us at $1,000 per order and holding cost was generated by multiplying the cost per unit by the interest rate which gave us a yield of 60. Based on this information, EOQ was 331 units after rounding. From the history, this was the second change we made to the inventory policy, up from 299. This was a result of a discussion in demand, where we had...

...Stanford University Graduate School of Business
rev. August 2004
Managing Customer Responsiveness at LittlefieldTechnologies
Background
LittlefieldTechnologies (LT) has developed another DSS product. The new product is manufactured using the same process as the product in the assignment “Capacity Management at LittlefieldTechnologies” — neither the process sequence nor the process time distributions at each tool have changed. On day 0, the factory began operations with three stuffers, one tester, and one tuner, and a raw materials inventory of 9600 kits. This left the factory with $1,000,000 in reserves. Customer demand continues to be random, but the long-run average demand will not change over the product’ 268-day lifetime. At the end s of this lifetime, demand will end abruptly and factory operations will be terminated. At this point, all capacity and remaining inventory will be useless, and thus have no value. Management would like to charge the higher prices that customers would pay for dramatically shorter lead times. However, historic lead times often extend into several days, so management has been unwilling to quote the shorter lead times.
Operations Policies at Littlefield
LT uses a Reorder Point / Order Quantity raw material purchase policy. That is, raw kits are purchased as soon as the following three criteria are all met: (1) the inventory of raw...

...Executive Summary
Our team operated and managed the LittlefieldTechnologies 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 LittlefieldTechnologies facility and how certain modifications would affect the throughput and lead time.
The Plan - Initial Strategy
Our team’s objective was to maximize the cash generated by the factory over the product lifetime. To achieve this goal, our team did the initial planning by using 50 days of historical data. On the one hand, we ran the regression analysis for demand and anticipated the increasing of the demand in the next 150 days. Based on 50 day’s data, the regression analysis gave us around 8 jobs/day in day 150. We also calculated mean with 2.5 jobs/day, standard deviation with 1.78 and variance with 3.15. We noticed the demand fluctuated a lot. CV is 0.71 (standard deviation/mean).
Table 1
On the other hand, we reviewed the utilization, queue size for each machine,...

...LittlefieldSimulationLittlefieldTechnologies (LT) is a producer of newly developed Digital Satellite System (DSS) receivers. One contingency LT relies heavily on is their promise to ship a receiver with 24 hours of receiving the order. If they are late to this, the customer will receive a rebate based on the delay. As the simulation ran for 268 days there were various methods and decisions we made in the process. We knew in the initial months, demand was expected to grow at a linear rate, with stabilization in about five months (~180 days). After this, demand was said to be declined at a linear rate (remaining 88 days). Even with random orders here and there, demand followed the trends that were given. Future demand for forecast was based on the information given. We looked at the first 50 days of raw data and made a linear regression with assumed values. Those values were calculated using a moving average model. Below is a plot of the data over the 268-day period, which shows the patterns stated above.
The main concern for LT management was the capacity in order to respond to the demand. If there was insufficient capacity LT would not be able to fulfill given lead times and would have to turn away orders. In order for capacity to be maximized, our group would ideally have had to have machines run at maximum utilization. Looking at the first 50 days of data we were able to see where more machines...

...Stanford University Graduate School of Business
September 2007
LittlefieldTechnologies: Overview
Introduction
LittlefieldTechnologies is a job shop which assembles Digital Satellite System receivers.
These receivers are assembled from kits of electronic components procured from a single
supplier. The assembly process consists of four steps carried out at 3 stations called board
stuffing, testing and tuning. The first step consists of mounting the components onto PC
Boards and soldering them. This is done at the board stuffing station. The digital components are then briefly tested at the testing station in step 2. In the third step, key components are tuned at the tuning station. Finally, the boards are exhaustively “final tested” in
step 4 at the testing station before delivery to the customer. Every receiver passes final
test.
All the stations consist of automated machines which perform the operations. You may
purchase additional machines during the assignment. Board Stuffing machines cost
$90,000, testers cost $80,000, and tuning machines cost $100,000. You can also sell any
machine at a retirement price of $10,000, provided there is at least one other machine left
at that station. The operators are paid a fixed salary, and increasing the number of
machines at a station does not require any increase in the number of operators.
Written by Sunil Kumar and Samuel C. Wood, both Assistant...

...continue to hit close to 100% for the next few days. We had thought that we could tide through it like before without the need of an additional machine. However, this time round, a bottleneck formed at station 1 and the revenue started to drop quite severely. Therefore, we decided that an additional machine is needed at station 1 to prevent further drop in the revenue.
However, the purchase of an additional machine did not salvage our situation as the queue size at station 1 was too large. During this period, a lot of income was lost due to our production not being able to meet the 3 days of lead time. Our revenue only stabilized on day 130 for 2 days before dipping again. This time, the bottleneck transferred to station 3 and the queue has risen drastically to about 600 jobs. Therefore, we made the decision to purchase another machine for station 3. With this, our revenue finally stabilised at day 139.
In the following days, we continued the strategy of monitoring the revenue, as well as the stations’ utilization and queue size, before deciding whether to purchase additional machines. Following this strategy, we acquired a total of 4 Machine 1s, 2 Machine 2s and 2 Machine 3s. As the demand fell towards the end of the game, we decided to sell off machines at the under-utilized stations so that we could increase our revenue from the sales of the machines, as well as gain more interest, and increase our ranking before the game ends. Therefore,...

...April 8, 2013
Group Report 1: Capacity Management
The following is an account of our LittlefieldTechnologiessimulation game. The account includes the decisions we made, the actions we took, and their impact on production and the bottom line.
Day 53
Our first decision was to buy a 2nd machine at Station 1. We did not have any analysis or strategy at this point. Nonetheless, this turned out to be a wise investment, since Station 1 was in danger of becoming a bottleneck in production.
Station 1 Utilization
One of our team members conducted a full operations analysis. Using the analysis, demand for the 268 days of production was forecasted, and our strategy set accordingly.
Day 71
On Day 71 Station 3 suddenly spiked to full capacity. The team made a rash decision to buy a 2nd machine at station 3 to avoid a bottleneck. The increase on Day 71 turned out to be a random spike; almost immediately, utilization subsided. Station 3 utilization stayed below 50% on average for the remainder of production, so the 2nd machine may have been an unnecessary 100K investment.
Station 3 Utilization
Day 77
Station 1 reached full capacity, so to avoid a bottleneck we bought 3rd machine. This brought utilization back down to an average of 50-60%. Utilization would remain steady until Day 121 when Station 1 started to trend back toward full capacity.
Station 1 Utilization Day 77-121
Day 78
At Station 2, there was a...