Customer Story

Learning the tools of a trade

Students at Chalmers University of Technology jump-start their careers in industry with coursework in data methods and Six Sigma

Chalmers University

Chalmers University of Technology

ChallengeGive students experience applying Six Sigma and other industrial data analysis techniques to real-world projects.
SolutionOffer a range of courses aimed at exposing students to the kinds of data challenges they will encounter in their future careers. Empower students to explore and experiment with JMP® software, learning to apply statistical concepts to resolve real challenges and improvement opportunities.
ResultsStudents graduate from Chalmers University of Technology with practical data analysis skills – and experience with an industry-standard software – giving them an advantage as they enter the workforce.

Success often means finding an extra edge, a key talent that adds value in the marketplace. For new college graduates heading to jobs in industry, that may mean refining a skillset like Six Sigma. And though most engineers only begin learning these skills after joining a company, one university is now offering students the chance to get a head start.

At Chalmers University of Technology in Gothenburg, Sweden, engineering and industrial economy students have the opportunity to study Six Sigma and other applied statistical methodologies before they graduate. Professor Peter Hammersberg teaches a variety of courses aiming to equip students with the skills to solve manufacturing problems with analytics. In his classroom, he gets students started early not only thinking about statistical problem-solving, but also using a tool they are likely to encounter on the job.

That tool is JMP® statistical discovery software from SAS. Hammersberg, a Senior Lecturer in Engineering Materials at the Chalmers Department of Industry and Materials Science, says the software, an industry standard, is an excellent exploratory tool that helps students develop a more robust understanding of statistical concepts and their applications. “The JMP tool is something that reinforces a holistic approach,” he says.

From the real world to the classroom

Prior to joining the faculty at Chalmers, Hammersberg himself held a variety of positions in industry, working most recently at SKF Bearings in industrial quality development. A Six Sigma Master Black Belt, he’s supervised more than 100 student-driven research theses in industry, developing material processes, in addition to conducting a dozen of his own research projects. In this, Hammersberg is something of a rarity in the university environment – someone equally at home in industry and in the classroom.

That’s why Hammersberg was a natural choice to lead Chalmers’ course offerings that bridge the academic-industry gap. In a program that pairs students with industry partners, Hammersberg’s students learn how to manage and analyze data in a real-world context. “Students learn how to understand the scope of a project,” Hammersberg says. “They learn how to question a problem statement. And how to reformulate. They learn to ask: ‘How can we shift a project that's scoped in the wrong way with the wrong people?’”

The types of projects Chalmers students encounter in their coursework tend to be relatively broad in scope, and creative thinking is required more often than not. Much like in the real world, there’s no set path to a solution, Hammersberg says. Confronting this kind of challenge early on serves to reinforce in students’ minds the need to first understand the data, see what’s available and design an approach to recognize the difference between signals and noise.

“Not many projects end up being very specific on the definition of control charts and control limits, for example,” says Hammersberg. “But understanding the concept of control and the role of a control chart allows me and the students to talk about noise [versus] something which contains signals. They really understand [these concepts] when they’re forced to dig into the data themselves.”

A tool that encourages exploration

Hammersberg supports students in their project work by providing instruction in data exploration, visualization and more advanced concepts like measurement analysis and Six Sigma. It’s an opportunity for students to take advantage of the drag-and-drop features in JMP to figure out what’s happening inside a data set. By workshopping case studies, students come to understand the difficulties organizations face when using data to make key decisions. As Hammersberg points out, learning how to finesse a project inside an organization can be difficult.

Students often assume they will be given the right data from the outset, he says. But they will soon find they must work with their partners to build new measurement systems. Every industry has its information flow issues, and students troubleshoot unique challenges as they arise.

“Old KPIs (key performance indicators) might not be the best to use for new problems,” Hammersberg said. “So that's a lot of challenge there. But some projects, of course, work directly on existing metrics and existing process data. A typical challenge is to map up such a project to get data on certain points and work on new measurements. There are many projects on that level, which are complex and tricky.

“If my students have a chance to become more used to visualizing data – and I think that's the important first step, getting used to the data – they’ll see that it's simple to create graphs and look for patterns and do exploratory data analysis. JMP has good supporting features. In any course where I teach measurement system analysis and all basic graphical stuff, JMP has modules that make it easy for students to use.”

Bringing best practices from university to industry

Many students instinctively understand the value of using a tool like JMP, Hammersberg says. But some of his colleagues in academia and industry need more convincing; “I'm not sure all the programmers out there understand the importance yet.”

That’s why Hammersberg has made it his mission to convey to his peers in academia the need for students to graduate with a skillset that includes the ability to use the tools of their future trade. “Since I've been in industry, I see that they will need [to know how to use JMP]. Many new hires arrive at their jobs and they're not well-trained. I’m trying to fight that war to get students better prepared with the skillset they need. And I’m gaining ground, slowly, step by step, more and more.”

Manufacturing companies collect a lot of data, and Hammersberg has seen firsthand that decisions are often made after cursory looks at Excel tables and simple graphs that could obfuscate key information. By showing his students the value of a data tool like JMP, he’s hoping they’ll bring better data practices to their workplace and change the way things are done. When you use data to optimize a process or find an efficiency that will speed time to market, Hammersberg says, “That’s where the money comes from, where it is saved and created. That is success.”

This success is evidenced in the published work of a number of graduate students Hammersberg has supervised over the years. “I influence their research with the aid of JMP functionality,” he says, citing a recent publication in the journal Aerospace in which a graduate student developed a virtual method for design of experiments based on custom design in JMP. In it, she joined co-authors in advancing a novel solution for welding optimization in aerospace applications. Their proposed “meta-model” simulates statistically robust experimentation during the early design phase of experiments – a strategy that could significantly reduce manufacturers’ production costs.

“Students have to learn how to handle all the data [that’s collected in a factory],” he continues. “They have to find out what's important without doing a theoretical statistics report. Some engineers may get a little bit frightened off by that, but others will like it a lot. So teach it early. Make JMP a general tool for them, equally with the other software they use for solid mechanics or math. Students should have a good platform for data analysis too.”

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The results illustrated in this article are specific to the particular situations, business models, data input and computing environments described herein. Each SAS customer’s experience is unique, based on business and technical variables, and all statements must be considered nontypical. Actual savings, results and performance characteristics will vary depending on individual customer configurations and conditions. SAS does not guarantee or represent that every customer will achieve similar results. The only warranties for SAS products and services are those that are set forth in the express warranty statements in the written agreement for such products and services. Nothing herein should be construed as constituting an additional warranty. Customers have shared their successes with SAS as part of an agreed-upon contractual exchange or project success summarization following a successful implementation of SAS software.

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