Analytics Capstone Projects Every fall, students in the Analytics master’s program put their skills to the test in a realworld setting by completing an exciting capstone project. These projects are proposed by businesses and the results carry the potential to have both local and global ramifications. In small teams of two or three, students interact closely with their industry sponsor and a faculty advisor as they use data-driven methodologies to build actionable models and, at the end of the semester, communicate their results and recommendations. What follows are two of the standout projects from the fall 2020 semester.
Discovering Outliers in Aerospace Manufacturing At the Collins Aerospace manufacturing facility in Burnsville, assembly line workers construct thousands of parts used to assemble airplanes of all speeds and sizes. “One of the challenges with our particular business is that we have so many different products,” says David Potasek, an engineer at Collins Aerospace. “We are an aerospace company, so every engine or plane we support will typically need a custom solution. We have to manage multiple assembly lines to accommodate all of our products.” This low-volume, high-mix operational structure can lead to labor differences, such as more time spent producing a specific airplane sensor as compared to similar sensors on other planes. For their project, Potasek challenged students Tony Roberts, Luke Riveness, and Vijay Varadarajan with analyzing Collins Aerospace’s assembly line data and identifying variations, or outliers. Collins Aerospace could then take
those standout parts or procedures and determine if a new assembly process or more training was needed. Very quickly Potasek was impressed by the group’s ability to make sense of the data—despite never getting the chance to visit the company’s Burnsville site due to COVID-19 restrictions. “The students actually identi-
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This is real-world data; it’s not perfect. And they embraced the imperfections of that and turned it on its head. —David Potasek, project sponsor
fied some metrics that I never would have thought about,” he says. “One of them was ‘missing labor entries,’ which are typically ignored. If you see greater clusters of the omissions of data associated with different product lines or people, that’s also a clue that there could be a potential issue there. “This is real-world data; it’s not perfect. And they embraced the imperfections of that and turned it on its head. I was most impressed with that: turning that negative into a positive.” The group presented their findings, along with a prototype to identify future outliers, to managers and product line groups at Collins Aerospace. “Some very high-level people were at those presentations and they did an excellent job,” says Potasek. “It’s a challenge to identify where to put your resources when you work in this sort of environment. But the students gave us more insight into our operations.”