How an algorithm helps BekaertDeslee minimize waste

May 10, 2022

operations
discrete manufacturing
Microsoft
data

Creating the perfect environment for blissful sleep is what Waregem-based company BekaertDeslee is all about. So what could possibly keep the world’s leading manufacturer in mattress fabrics up at night? The answer: the huge amounts of production waste that come standard with the process of weaving and knitting complex textiles. One daring engineer, an ingenious algorithm developed using Azure Machine Learning, and a little help from delaware did away with the bad dreams.

“Producing textile is a tricky business,” begins Rik Holvoet, who has been CIO at BekaertDeslee since 2012. “The quality of the yarn, climatic conditions, humidity… all these factors can impact both the performance of the production machines and the quality of the final product. Add to that the challenging designs our creative people come up with, and it’s not hard to see why fabric manufacturing is notorious for its high waste production.”

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The operators’ intuition

At BekaertDeslee, the quantity of waste produced translates into many soccer field per day. “We knew we had to do something, so we started looking for the factors with the biggest impact,” Rik continues. “Surprisingly, much of that came down to the instincts of our operators. Employees with the most reliable intuition as to when a machine was likely to break down, or who could ‘sense’ that the parameters weren’t exactly right, had the best chance of preventing a bad batch. Sadly, that’s not something you can learn in a few weeks: it comes with years of experience. But maybe artificial intelligence could help us out.”

A goldmine of shop floor data

For over 11 years now, BekaertDeslee has been collecting shop floor data with SAP and VisionBMS software, carefully storing it for the day its insights would be needed. Now, with the waste-reduction project, that day had finally come. “Storing all that production data is costly,” explains Rik. “But we knew it would pay off in the end. This project is proof that we were right.”

Armed with a wealth of information, a daring engineer at BekaertDeslee decided to try his hand at Azure Machine Learning to develop an algorithm that could predict when a machine was likely to break down. “Without any prior knowledge of the platform, he managed to get reliable results and tell operators which machines they should check to prevent mistakes from happening,” says Rik. “That’s when we knew we were onto something.”

Global roll-out

Rik and his team decided to launch a limited pilot project at BekaertDeslee’s plant in Turkey that involved just a few machines. “To ensure that the technology is robust and scalable, we called upon our trusted partner, delaware,” Rik continues. “We’ve been working with them on numerous projects, often within the DEL20 innovation program, so they know our company inside-out. Their expertise was essential in exporting the production data to the cloud, integrating it with our ERP and using it to improve the prediction model over time.”

A waste reduction of 10-20% turned out to be realistic. Today, the algorithm has been implemented in BekaertDeslee’s 20 production sites across the globe. And thanks to the standardized approach, data can be shared easily across production sites. So if an issue occurs in Turkey, the US plant will get notified of the risks when working on a similar production order. “To me, this is a prime example of the long-term value of storing data, and of sharing insights and innovation,” Rik concludes.

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