Tips for Achieving Competency with Industrie 4.0 and Industrial Analytics

Digital transformation in industrial operations is not just software; it's a mix. You need IT knowledge, and you need to know the processes involved.

Here, we share insights from DigiKompetenz Podcast’s Interview with Thomas Schulz:

• Partner Manager, Central and Eastern Europe, GE Vernova
• Representative for Platform Industrie 4.0
• Editor and contributing author of four books including Analytics in Industry: Key Technology for Digital Transformation

Everything must be integrated in an entire process. How much can you then also start to integrate that with the data analytics in companies that are only at the beginning of digitization?

If I want to do something with data, I need data first. So, integrate it. A lot of people come to us and say: we want to do analytics, we want to build process models, we want to optimize. And then the first thing is the “aha” effect about data collection: I need the data of the past to create my digital twin, to draw conclusions for the future. And that's where it usually starts.

And after data collection?

Then comes the data preparation. I have to process it, I have to put it into certain context, and the data cleansing is very important. After that, we can get into correlations and causations. Pre-processing is quite critical, and that's where tools help today. It's not like you have to sit down with Excel.

Watch the full interview.

What is the right combination of roles to work on analytics?

It is a cross-functional team. Data scientists are scarce and know relatively little about the production processes. They can't say what the relevant variables are, what I want to correlate in the first place. If they don’t know the procedures and processes, then they can’t deliver on industrial analytics to improve operations. Instead, I need to form a team, and the process engineers and other operations subject matter experts are critical.

How is the great wave of retiring workers affecting this?

In the past, the machine operators simply had the experience. They had been there for 20 years, they already knew intuitively why something was wrong, which adjustment screw they had to turn. But now, those workers are leaving or have already left. The question is: can I follow their knowledge so quickly with their successors? However, often the successors are hired only when the previous workers have already retired, and then it is not possible to capture the knowledge.

The knowledge of the people, we can map that, we can pack that in. There are tools to digitize the information and practices, and then analytics are for optimizing.

What are the topics that have the most urgent priority for you in the future?

The most urgent priority is certainly to get this energy crisis under control. Anything that helps is welcome. Second, operations savings. Digitization is going to move forward, and new business models will come. It's going to affect us personally. What we know today will be different in ten years. In the digital realm, the pace is going to accelerate.

What do you recommend for learning?

Ultimately, my recommendation for anyone working in industry now is to have a constant learning process. No matter how old you are, I can give that as a recommendation to everyone. Today it's Python. Before it was C++. Tomorrow it's a different programming language as an example.

And it is a constant process. Build your own knowledge, build up your own competencies. Try where it makes sense. Not everywhere, you don’t have to do everything yourself. Teaming is very important especially as complexity increases.