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Stanford University Graduate School of Business October 23, 2012

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Capacity Management at Littlefield Technologies


In early January, Littlefield Technologies retooled its factory to produce a newly developed model of DSS receiver. High-tech products often have short product life cycles, and Littlefield’s DSS receiver is no exception. After just 268 days of operation, the plant will cease production and retool. All remaining machines and inventory will become obsolete after Day 268, and thus have no residual value.

Littlefield’s marketing department expects demand for this model will grow at a constant rate for five months, stabilize for another month, and then decline at a constant rate until the product becomes obsolete. New orders arrive at random intervals throughout the day, but management trusts average demand will follow the described trend.

Management’s main concern is purchasing capacity that will serve the predicted demand pattern within contracted lead times.


It is now late February, and LT has started to notice lead times are increasing as business picks up. Littlefield’s last two model runs were fraught with capacity errors. In the first, they bought too much capacity and suffered low returns on investment; and in the second, they purchased too little capacity and spent more on rebates than they earned in revenue.

Management has just employed a high-powered operations team (you) to optimize factory performance. They have only one metric of success: total cash in hand on Day 268. For the next 168 simulated days, you must buy or sell machines to maximize their factory’s final cash position.

Littlefield’s factory is currently equipped with one board stuffing machine, one tester, and one tuning machine. Examination of their production process shows some variability in the time required to fill each order. Management has not done any measurements of process time but they do record daily average utilization rates at each station.

You may change the way testing is scheduled at Station 2. Littlefield’s workers have been following a first-in-first-out (fifo) policy, but they will give priority status to either the shorter initial test (pri2) or the longer final test (pri4). You just need to tell them which step is most important.

* Based on an assignment written by Sunil Kumar and Samuel C. Wood, Stanford University Graduate

School of Business. Copyright 2009. No part of this document may be reproduced without permission from Responsive Learning Technologies, Inc. [email protected]

Capacity Management at Littlefield Technologies 2

Management has provided a historic record of their first 50 days operations, representing the period from early January to late February. They will also provide all necessary inventories—you will not have to worry about raw material stockouts. There is a single pricing contract offered for all jobs and you may not split manufacturing lots. The contract has a quoted lead time of 24 hours and a maximum lead time of 72 hours.

Littlefield’s factory simulator will run at the rate of one simulated day per real hour for the next seven real days. On simulated Day 218, you will relinquish control of Littlefield’s factory and the simulator will run the final 50 simulated days at an accelerated rate. Management expects that you will leave all factory parameters set to values that maximize their final cash position on simulated Day 268. After the simulation ends, you may review the final status of your factory and download historic data but the factory will no longer be active.

Stanford University Graduate School of Business rev. Aug 2010

Capacity Management at Littlefield Technologies


In early January, Littlefield Technologies (LT) opened its first and only factory to produce its newly developed Digital Satellite System (DSS) receivers. LT mainly sells to retailers and small manufacturers using the DSS’s in more complex products. LT competes by promising to ship a receiver within 24 hours of receiving the order, or the customer will receive a rebate based on the delay.

In the initial months, demand is expected to grow linearly, stabilizing after about 5 months. After another month, demand should begin to decline linearly. Although orders arrive randomly to LT, management expects that, on average, demand will follow the expected trends.

Management’s main concern is managing the capacity of the factory in response to the complex demand pattern predicted. Delays resulting from insufficient capacity would undermine LT’s promised lead times and ultimately force LT to turn away orders.

Operations Policies at Littlefield

Currently there is one sample preprarer, one tester, and one centrifuge. Jobs at the tester are scheduled First-In-First-Out (FIFO), but you can give priority status either to the short initial tests or the long final tests. You may buy or sell or machines by clicking on the desired station and then the “edit data” button on the resulting menu. Click on the tester station and then “edit data” to change the queue sequencing rule at the tester. The times to perform each step are:

Step 1 Step 2 Step 3 Step 4

Stuffer Tester Tuner Tester

5.3 hours 0.5 hours 1.8 hours 1.4 hours

This note was written by Samuel C. Wood and Sunil Kumar, assistant professors at the Stanford University Graduate School of Business


It is now late February, and LT has started to notice that a few of their receivers have been delivered after their due dates. In response, management has installed a high-powered opera- tions team (you) to manage the factory’s capacity. For the next 168 simulated days you must buy or sell machines to maximize the factory’s overall cash position. Currently there is one board stuffing machine, one tester, and one tuning machine.

When the assignment begins, there will already be 50 days of history available for your review, representing the period from early January to late February. The simulator will run at a rate of 1 simulated day per 1 real hour for the next week. After the assignment window ends, an additional 50 days of simulation will be executed at once. Thus, there will be a total of 268 days of simulation corresponding to a product life time of about 9 months. After this simulation is over, you can check the status of your factory, but the factory will no longer be running. Also any remaining machines will have zero value at the end of game.

Your team should turn in one two-page summary f what actions you took during the week you had access to the factory, why you took those actions, and in retrospect whether you think you did the right thing. Show analysis to justify your conclusions. Your team’s grade will be partially based on your performance, but mainly based on your summary.

As you manage your factory and then write your summary, be sure to consider the following: • How did you forecast demand? • How did you translate your demand forecast to machine requirements? • How did you decide when to purchase the machines you purchased? • Did you sell any machines? If so, how did you decide when and how many to sell? • Did you change the queue sequencing rule? Why?

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