Predictive analytics gives Owens Corning an edge in turbine blade development

With help from the manufacturer's analytics center of excellence, the Owens Corning innovation group has reduced the time it takes to test composite materials from weeks to hours.

Predictive analytics gives Owens Corning an edge in turbine blade development
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At manufacturer Owens Corning, data scientists have had the rare opportunity to work hand-in-hand with scientists to meld predictive analytics with scientific research to streamline the company's innovation efforts.

"We don't typically deal with very in-depth scientific, technical problems," says Malavika Melkote, director of global information services and the analytics center of excellence at Owens Corning. "We work with a lot of problem sets that are in marketing, customer supply chain, manufacturing. This was the first time we were doing a project with pure science in the innovation group."

That initiative resulted from the Fortune 500 company's need to streamline its process for developing and testing materials used in the creation of wind turbine blades. Addressing that challenge required a close collaboration between IT, the analytics CoE, and the company's innovation group in the form of a team led by Senior Scientific Advisor Eric Carlier.

To grow its wind energy business and meet customers' requirements, Owens Corning must continuously improve composite material properties through optimized glass composition, higher modulus for stiffer, longer blades and superior fatigue performance for longevity, Carlier says.

"We have a special group within our innovation organization that is focused on developing new materials," Carlier says. "When we talk about new materials, it's at many levels."

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