Little field

8 August 2016

In early January 2006, Littlefield Technologies (LT) opened its first and only factory to produce its newly developed Digital Satellite System (DSS) receivers. Littlefield Technologies mainly sells to retailers and small manufacturers using the DSS’s in more complex products. Littlefield Technologies charges a premium and 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. The product lifetime of many high-tech electronic products is short, and the DSS receiver is no exception.

LT managers have decided that, after 268 days of operation, the plant will cease producing the DSS receiver, retool the factory, and sell any remaining inventories. As this is a short life-cycle product, managers expect that demand during the 268 day period will grow as customers discover the product, eventually level out, and then decline. In the initial months, demand is expected to grow at a roughly linear rate. Demand is then expected to stabilize. Eventually, demand should begin to decline at a roughly linear rate. Although orders arrive randomly to LT, management expects that, on average, demand will follow the trends outlined above.

Little field Essay Example

Management’s main concern is managing the capacity of the factory in response to the complex demand pattern. Delays resulting from insufficient capacity undermine LT’s promised lead times and ultimately force LT to turn away orders. In particular, if an order’s lead time exceeds the quoted lead time, then the revenue for that order decreases linearly, from $1000 for the quoted lead time (24 hours) to $0 for the maximum lead time (72 hours). Assignment It is now mid-February 2006, 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 operations team (your team) to manage the factory’s capacity. For the next 168 simulated days (one week in real-time), your team must buy and/or sell machines to maximize the factory’s overall cash position after the 268 days. Currently, the factory has one board stuffing machine (at Station 1), one tester machine (at Station 2), and one tuning machine (at Station 3). The number of machines can be changed at each station by clicking on a station, and then clicking on “Edit Data” in the menu that pops up.

You may also change the way testing is scheduled. Currently, jobs at the tester are scheduled First-In-First-Out (FIFO), but you can give priority status either to the short initial tests (Step 2) or to the long final tests (Step 4). When the assignment begins, there will already be 50 days of history available for your review, representing the period from early January through mid February. The factory 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. Full control of the Littlefield Technologies factory will be turned over to your capable hands at 2pm on Thursday, February 9, 2006. For three days prior to that time (from Monday, February 6 through the start time on Thursday), you will be allowed to access the Littlefield factory in order to observe, download and analyze data from the first 50 days of operations. After this simulation is over, you can check the status of your factory, but the factory will no longer be running.

You can (and should) use your team’s knowledge of forecasting and capacity management, including the concepts and tools from your textbook, to help LT managers maximize their profit. Your data analyses do not have to be super-sophisticated – in fact, simpler is often better … sometimes even a graph will do – you just need to demonstrate that your data analysis supports the actions you take within the factory. Case Questions to Answer in Your Deliverable As part of their contract with you, Littlefield managers want your team to perform data analyses supporting the actions you take in their factory. Managers want you to answer the following:

1Using data from the period prior to Day 50, estimate the capacity of the machine at Station 1. Can you do the same for the machines in Station 2 and Station 3? (Hint: What seems to be the maximum number of jobs processed by a machine in one day during this period? ) 2. Using demand data, forecast (i) total demand on Day 100, and (ii) capacity (machine) requirements for Day 100. (Hint: Assume demand was very close to zero on Day 1. Also, see Appendix 1 for some helpful hints on forecasting using Excel. ) 3. Around Day 100, create forecasts for (i) total demand on Days 130, 150, and 170, and (ii) related capacity (machine) requirements.

Choose your own data periods. List any assumptions you made to create your forecasts. ) How well did you do with these forecasts? 4. Predict on which days (i) demand levels out, and (ii) demand starts to decrease? (Hint: Use a moving average (MA) to track the demand over time. See Appendix 2 for helpful hints. ) Team Deliverable Your team should turn in a 2-3 page summary of what actions you took during the week you had access to the factory, why you took those actions, and in retrospect whether you think your team did the right thing. Show analysis to justify your conclusions.

The top-performing team in each class will be invited to present their capacity management approach during a subsequent class session. Report Guidelines Your report should be single-spaced, and should be written as a memo to the managers of Littlefield Technologies. Your summary should be about 2-3 pages in length. A structure for a good report will include the following sections: Introduction Problem Statement Recommendations/Actions Taken This section may include data analysis, equations, etc. Resulting Outcomes Evaluation of Outcomes Appendix (optional, but highly recommended)*

Up to 2 pages of data analysis, equations, etc. *You are allowed to include an appendix (up to two additional pages) that presents regressions, forecast equations, or any other data analysis you perform during your control of the factory. Grading Your team’s grade will be based on the content of your report. The top performing team in each class will receive 10 points extra credit added to their case score, which includes credit for their class presentation. The second and third best performing teams in each class will receive 4 and 2 points extra credit added to their case score, respectively.

Grading on your written report will be at the instructor’s discretion. Note that insightful, in-depth analyses of why your team did or did not do well can bring up your grade, regardless of your actual simulation performance. Helpful Resources – Statistical Analysis/Forecasting/Capacity Analysis If you are unsure about how to perform a regression analysis or other data analysis using the data from the Littlefield Technologies system, stop by during office hours and ask your MD021 instructor. He/she can show your team how to perform useful data analysis using Excel or SPSS.

Helpful Resources – Littlefield System MBA student Kevin Phillips will hold office hours in Fulton 413 on Monday, February 6 and Wednesday, February 8, 12-4pm, and on Friday, February 10, 9am-1pm. Teams and/or team leaders will be able to ask Kevin questions about using the Littlefield Technologies online website, how to log into it, how to buy/sell machines, how to download data to Excel, and any other question related to the Littlefield Technologies website. Kevin will not answer any questions about how to solve the case – he doesn’t know the answer. Appendix 1 – Forecasting With Excel

Since you have been told that demand is increasing linearly on average, and demand will be increasing during the forecast period, you will need to construct a trend forecast (see Stevenson, p. 77). Thus, your forecast equation will be: Yt = ? + ? t In order to construct this forecast, we need to estimate the slope (? ) and the intercept (? ). Since demand is 0 on Day 0 and still very close to 0 on Day 1, assume that ? = 0. Then, you only need to calculate the slope (? ). In Excel, you can calculate the slope for a trend forecast using the regression SLOPE(known_y’s, known_x’s) function.

Create a column of integers from 1 to X (representing Days 1 to X) – these will be your known_x values. Download the observed customer demand data (i. e. , customer job arrivals) from the Littlefield simulation for these same days. Place them in a column next to your known_x values. You should now have two columns of the same length. The column of demand data is your known_y values. Put the ranges into the function, and viola, it will calculate an estimate of the slope (? ) for you. You can then plug into the above forecasting equation. Remember that the estimated value of ?should become more accurate as you collect more data during the simulation (at least while demand is still moving in the same direction), so you may want to update your forecasts as you go along. For more information on using these in Excel, search for the terms “slope” or “regression” in the Excel Help window. Appendix 2 – Tracking Data Using Moving Averages Sometimes operations analysts want to track historical data sets, and smooth out the data. Smoothing data helps to remove the randomness, and thus, provides a better idea of the underlying direction of the data on individual days.

Moving average indexes can help one do this, by averaging out the randomness on an individual day. A common index to use is a centered moving average. For example, a 3-period centered moving average of demand for Tuesday would be calculated as: Moving_Average_DemandTuesday = (DemandMonday + DemandTuesday + DemandWednesday)/3 In the Littlefield simulation, where demand is known to be increasing and/or decreasing throughout certain long periods during the simulation, such an index helps to average out the increases/decreases before and after acertain day, leaving the analyst with a better estimate of average demand on each day. In your analysis, you may want to try out a few different centered moving averages – I’ve found that longer centered moving averages (e. g. 9 period or 11 period centered moving averages, or even longer) tend to work better on the Littlefield data. Once you calculate such moving averages, you can then simply examine the average values by eye to determine where the demand data seems to level off and where it starts to decrease.

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