Expert Panel
It most definitely would. This topic is going to be one of the big differentiating factors of the future. Generally speaking, this overhaul business, or »rebuild«, as we call it, is one of major pillars of services. Our machines are high-end investment goods; some of them cost as much as a house. So we want to offer our customers a maximum degree of investment security – and that comes with digital solutions.
Smart maintenance is no stand-alone solution. It is predicated on a holistic view of things. At the end of the day, databased comprehensive smart maintenance is trailblazing the implementation of innovative business models, the kind of business models that industrial product service systems are calling for. With Pay-Per-X models, the focus is on the user benefits and not solely on selling the product. I need to have a very deep understanding about the condition of my systems in order to provide such benefits.
For one thing, maintenance tends to be a rather down-to-earth sector. The whole point is to keep machines up and running. So having to cope with digitalization can prove difficult. On the whole, I would say that the expectations placed on digitalization have been a little too high. Following the Gartner Hype Cycle for Emerging Technologies, I would say that some of the technologies that could be used for smart maintenance have not yet reached the Slope of Enlightenment. Maintenance is often viewed as inferior, or not as valuable as production, because production brings in the profits while maintenance costs money. Companies need to recognize that maintenance itself makes a significant contribution to value creation.
Strictly speaking, there is nothing earth-shatteringly new about digitalization. The difference is that today, for the first time we are in a position to store and process huge amounts of data within our IoT systems and analyze them at lightning speed. What is holding up digitalization at the moment is the readiness of customers and companies to embrace such networking. We can still sense a great deal of reluctance and have to be very persuasive. People are terrified of data theft, and the issue of data security usually crops up in every second sentence. The concepts are there, the technical possibilities are in place, but we still need a great number of good arguments and in many cases a proof of concept, solid evidence that it really does work. This is why we are totally transparent in our work with our customers and show them which data we are drawing from the system. We are going to need a certain change of mindset here. Perhaps it is also a generation thing – the generation of our children will have quite a different take on it all. For today, there is still a lot of anxiety and misgivings around.
Customers today are mostly unwilling to have their machines continuously online. Our solution is that customers should aggregate their machine data on the company intranet locally on an edge device. Only at the customer's request will this device network with our system via a remote service. Customers can decide for themselves, when they want to open the data tunnel and when they want to close it.
All of the decision makers must wrap their heads around the fact that production can only be efficient when all production systems are functioning perfectly. When talking about Industry 4.0, we are talking about an ecosystem in which all stakeholders must cooperate with one another to make production efficient. As Mr. Plüss just said, that is not a technical hurdle, it is more a matter of organization.
When it comes to digitalization, we like to cater to customer benefits, not to hype. That is why we have plotted the whole of the value creation chain in a »customer journey«, and why we support our customers across the whole of the product lifecycle. The maintenance phase is the longest phase in the life of a machine. Our machines spend several decades in operation, so this phase is actually the most elementary one in terms of the lifecycle. Software solutions, updates and upgrades can make a huge contribution to optimizing performance. Data collection is essential, because it allows us to make conclusions about whether certain processes are working the way they are supposed to. It allows us to avoid unnecessary service operations that could involve high costs for labor and spare parts.
This is where digitalization and acquisition of a great deal of sensor data helps to shed light on the darkness. How does my machine wear correlate with the machine's production load? When we have better findings – findings based on the evidence of the data collected – that will make it much easier to relay to those in charge why certain measures are necessary.
One big problem has been the highly proprietary mindset that is widespread in the industry. After all, the end customer does not depend solely on a single supplier. There is a huge range of components, like sensors and control options, and everybody is jealously guarding their own ecosystem. This makes analysis and data evaluation really tedious. That is why we are working with the VDM (editor's note: Verein Deutscher Werkzeugmaschinenfabriken – German Machine Tool Builders’ Association) on a universal machine tool specification.
It would also be great to have standardized evaluations and analyses, not just standardized interfaces. We have often found that the results of various machine tool providers are not comparable, because they do not reveal their algorithms.
We view the machine overhaul business as one of the services we offer in our one-stop solution package. For instance, if a customer wants to scrap a machine, we offer to take it back and overhaul it. There is a high demand for our »second life machines« on certain markets – for instance, as entry-level machines for customers who cannot afford a new one. When it comes to sustainability, this is a clean solution.
Maintenance per se is sustainable, as it seeks to keep resources useful for as long as possible. It is pretty obvious that a poorly maintained system is going to use more energy than expected. We also use and evaluate the drive currents for condition monitoring. This is where we see a direct connection with progressive wear and tear. And with the acquired data, we can also pinpoint optimization potential for present and future systems and give feedback to designers.
has been with United Grinding for ten years. In April 2019, he was appointed Chief Technology Officer (CTO) of the company.
With an annual turnover of some 700 million euros, UNITED GRINDING Group is one of the world’s leading manufacturers of precision machinery for grinding, eroding, laser cutting, measuring, and combined machining.
is head of the Industrie 4.0 Transfer Center Berlin at Fraunhofer IPK. His research focus is predictive maintenance.
Fraunhofer Institute for Production Systems and Design Technology