Littlefield Technologies and Its Simulation

Littlefield Technologies (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.

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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 were needed in order to produce that 24-hour turnaround time.

The original setup included one board stuffing machine (station 1), one tester (station 2) and one tuning machine (station 3). The way testing was scheduled was First-In-First-Out (FIFO).

In our simulation, we were able to control the amount of machines and the way testing was scheduled in order to maximize the factory’s overall cash position. Below is a graph showing the utilization of the machines at station 1. Based on graph we were instantly able to see that at station 1 there was a massive bottleneck because utilization was over 100%. This made us decide to purchase an additional 3 machines to help reduce that. As shown, utilization was brought down and become helpful during the five-month demand hike. The mistake our group made was not selling off the machines when we noticed that the demand dropped. It is evident that during the last 88 days, the machines at station 1 were heavily underutilized.

The purchasing decision was based off assumptions. We knew that demand would rise for another 130 days (since the simulation already ran for 50 days), so we decided to buy at day 51. We added three machines to station 1 and one machine to stations 2 and 3. Another key thing we changed instantly was the queue sequencing. We sold a total of one machine from station 1. The decision was based upon our demand. We saw demand decrease dramatically, which led to us selling the machine.

Although it was made late, and we should have sold two machines from station 1 at day 180, we were keeping one in case demand suddenly changed. With these changes and decisions, our team (team 8) was able to be very successful. We presented growth within our company and increased capacity by adding and subtracting machines and changing the queue sequencing. We ended with more capital than we began with and finished third overall in the standings, as shown below.