Predictive Maintenance for Industry 4.0: A Q&A with Christof Siebert, Head of Technology & Innovation at Trumpf
Industry 4.0 describes our increasingly connected and automated world as it relates to manufacturing technologies. With technology rapidly advancing to enable more complex data analysis, the manufacturing industry is recognizing the need to adapt and capitalize on new data-driven opportunities.
Trumpf, a leading global provider of machine tools and electronics, is deeply entrenched in Industry 4.0 technologies with teams dedicated to scouting and cooperating with new startups. Trumpf’s digital business platform, Axoom, provides services ranging from resource management to production monitoring and allows smart technology companies to run their apps in the Axoom environment with direct access to valuable customers.
Christof Siebert, Head of Technology and Innovation Management at Trumpf, spoke with JOIN Capital about the predictive maintenance technologies driving manufacturing and the opportunities for success within this space.
What is predictive maintenance and why is it important?
Predictive maintenance and analytics are extremely important for the industry. Machines, tools, and lasers are able to collect data on what they do, but the big question is how we can use this data to bring advantages to customers. Our customers need high uptime to be able to predict when maintenance should happen; in order to do that, they need to be able to interpret the data, draw conclusions, devise models and then predict when they would have to change something or optimize a process.
What is Trumpf’s strategy in this space?
We have two directions in digitalization. One is horizontal, which refers to the order-to-cash process and helping customers to digitalize there. But in predictive maintenance, we’re talking about the vertical process — going from the sensor data to aggregated data in a dashboard, then to automatic data analysis to predict when maintenance cases will occur.
Why is predictive maintenance able to be done now? What are the underlying tech trends enabling it?
One aspect is that the industry is learning more about how to aggregate data and analyze it in a way that they can draw conclusions from it. Collecting data is relatively easy, but deriving models — interpretations and linking the data to new cases — is something that the industry is still learning. In terms of technical enablers, stable and low-cost sensor technology resulting in huge amounts of data from products as well as from other systems in the ecosystem is available now. These products and systems can be connected to share data. Data analytics algorithms have reached a state in which they can process the available data and derive actions.
Which startups are well-positioned to be successful in the space?
There are many right now. Without naming specifics, I will say that having the ability to program in the predictive maintenance space is not enough. I would instead emphasize the need to cooperate with the people who know their systems, tools, and machines. Success will come only if data scientists work closely with manufacturers of tools and machines. You have to generate a closed loop between the data and real-world applications.
