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Prof. Dr.-Ing. Dipl.-Wirt. Ing. Günther Schuh

Prof. Dr.-Ing. Dipl.-Wirt. Ing. Günther Schuh
has held the Chair in Production Systems at RWTH Aachen since September 2002, and is a member of the Board of Directors of the Laboratory for Machine Tools and Production Engineering (WZL) and the Fraunhofer Institute for Production Technology (IPT) in Aachen. Since October 2004, he has also been a director of the Research Institute for Rationalization (FIR) at RWTH Aachen.

Copyright Photo: WZL Aachen

“Good service needs stable processes.”

Content

March 2017

 

For Aachen-based Professor Günther Schuh, effectively processed information and the knowledge generated thereof are the basis for customer-focused services of the future.

Most companies are still focused on traditional services. Does that surprise you?

Not at all. Our own studies completely confirm this finding. Traditional services will not suddenly become irrelevant in the future. But the fact is that a number of companies exist, who took the next steps some time ago. They can offer smart services, set up platforms, and further develop their business models. They are learning an incredible amount and are rapidly extending their advantage over companies who are only looking at the issue of digital networking from the outside.

How can these basic services benefit from increasing digitalization?

A good service – whether it is a basic, added value, or smart service – needs stable processes. I get an immediate increase in efficiency from the digitalization of these processes. But in essence, digitalization is about the availability of data and information. Examples include the planning and deployment of service engineers, supported by an appropriate IT system, or checking the availability of spare parts. Employees in a typical medium-sized company spend substantial portions of their working hours searching and waiting, very often due to unavailable information. As we understand it, digitalized processes are part of Industry 3.0. Industry 4.0 goes a step further and entails real-time horizontal and vertical networking.

What steps should companies take to tackle the challenges in service provision?

In addition to a range of technological issues, companies are faced with three fundamental problems when offering smart services. Firstly, technical and contractual access to customer data is required. A business case must involve significant economic benefit to the customer. Secondly, smart services cannot generally be produced and then sold off the shelf, so to speak. In reality, each new customer usually requires an implementation service. This implementation expertise has to be built up. Thirdly, viewed as a product or service, smart services tend to be so complex that selling these services overwhelms current sales and particularly purchasing structures. It is normally necessary to convince more than one stakeholder on the customer side, as effective implementation always incorporates several areas, typically production, maintenance, and IT at the least. So you can see that the main way to resolve the new challenges is by engaging with your customers.

Numerous smart services are on the horizon. Which will be the first to establish itself? Will it be predictive maintenance, with its effective early warning system?

First of all, the nature and scope of services that can be implemented depend on the specific branch of industry and the relevant business case. Secondly, individual solutions make no sense. An equipment pool typically consists of machines made by different manufacturers. If I buy a separate predictive maintenance service from each machine manufacturer, I have not really gained anything. It is only through networking – for example with the planning systems in production and maintenance – that these services give me genuine added value. Our own experience shows that the key issue lies not in the development of more powerful early warning systems that enable faults to be detected even sooner, but in the organizational implementation of these systems in service and maintenance processes.

What do you mean by the “digital shadow” term that you coined? Does it affect services?

Digital shadow refers to a sufficiently accurate digital image of processes in production, development, and adjacent areas, including service. This image is the basis for real-time data analysis. Specifically, this includes a description of the necessary data formats, data selection, and the refinement of data classification. The digital shadow always has an economic component too. Recording certain data, for example, for the failure predictions of machines, can make economic sense in one case because it plays a key role in the production process, but may not be so crucial in another case.

How do you get service staff ready for the future?

In future, learning and training will take place on the job with system support, rather than in traditional formal training sessions. This requires trust in the systems and, at the same time, the ability to adopt a critical approach to digital content. On the one hand, we want service staff to have the best possible support for their decisions and to free them from tasks such as searching for information or resolving trivial problems, but on the other hand, people must always be able to evaluate a system’s suggestions or decisions for themselves. Several practical examples from the field of artificial intelligence already demonstrate how people and machines can learn together. This will make experts much faster and more effective.