Matthias, your team and you are dealing with digitalization at REHAU Automotive. Where do you start and where do you stop?

We live in a world that has become extremely complex. If we digitize processes or set them up digitally in a better way, we can make many things easier or save costs. But yes, there are of course many possibilities. That's why we've come up with application examples, so-called use cases. We are now going to digitize these in a very concrete way.

You are also working with machine learning, i.e. artificial intelligence that can learn from large volumes of data. Do you have the necessary knowledge on board?

When dealing with such challenging topics, it's good to have a partner with good expertise. That's why we're cooperating with Renumics, a startup company that specializes in artificial intelligence in the field of technology and manufacturing. This is very exciting because completely different worlds are coming together. We're just getting to experience a lot of project management and see that things work in a completely different way.

I've seen that you divide your use cases into T-shirt sizes, that's a bit unconventional. 

It is, but it's also striking, transparent and makes decisions easier. I can also present a 300-question catalog to justify my decision on a project. Or I say: this is an M-size project, we have a manageable risk with a result that is quite decent. Other projects are XL projects: Big risk, high cost – but also drastic results if everything works out. But I can also slip into a shirt like that if a team has the confidence to do so and the general conditions fit.

We're already talking specifically about your use cases, can you describe one?

We want to automate wall thickness analysis, for example. In the design process, it is important that the components follow certain design rules. These include certain wall thicknesses. Currently, the evaluation of wall thickness is still the responsibility of the designer and the injection-molding expert. This is a laborious, manual process. Our goal is: With the help of machine learning methods, these design rules are automatically checked and the result is presented in a kind of engineering cockpit. The algorithm learns and evaluates the result independently.

That means the work that a human does today will be done by a computer tomorrow? 

That's a fear that resonates with some people. And it's the job of managers to alleviate these fears. At the same time, you have to see: Through digitization, we manage to automate less appealing tasks. What remains are the exciting fields that people continue to plow. That doesn't necessarily make jobs superfluous; if anything, it makes them more interesting.

You work together in an international team, from Germany to the Czech Republic to India. How does that work?

It's great! The way you benefit from each other culturally impresses and fulfils me. Many colleagues from India, for example, have a very different attitude to digitization and automation – and it's a pleasure to be able to tap into that. On the other hand, the Germans, for example, have many years of experience in project management and know how to bring an innovation to the road. That is enriching throughout. New approaches are also emerging from the collaboration with Renumics – we are learning how to work in an agile manner. Instead of pure teaching, we carve out the elements that fit REHAU.

Developers talk to colleagues from production, Indians talk to Czechs – how do you ensure that there are no misunderstandings? 

This is only possible through a lot of communication with each other. That also has a lot to do with a contemporary understanding of leadership: As a leader, I feel like an enabler. I want to get everything out of the way for my team that could slow them down. Good communication is indispensable. I need to keep my ear close to my people, know pain points and what motivates my people.

Thank you for sharing a few special REHAU moments and insights with us, dear Matthias!