Q: Please briefly explain what a “digital twin” is.
Grenacher: Simply put, “digital twins” emulate a system in a computer. Ideally, data from the engineering phase—from 3D models to detailed information on poorly constructed components—are included in the operational phase. Sensors deliver live information about operational statuses, and all technical improvements to the systems, like the installation of a replacement part, are also tracked in the “digital twin.” Since it’s always up-to-date, it serves as a detailed “reference book” with all system information.
Q: How important do you think “digital twins” are for service?
Grenacher: They make it possible to execute predictive maintenance: by collecting data you are able to assign measurement data to a specific system status. By means of changing measurement status, it is often incredibly clear in advance that a specific component will malfunction in the near future. This information lets you better coordinate planned system downtimes and adjust repair cycles to accommodate anticipated potential breakdowns.
Q: What system features should a “digital twin” reproduce in order to deliver relevant information for the service sector? Is this already the case with today’s IoT technology?
Grenacher: Imminent component malfunctions can already be predicted really well today with enough sensors within the system and systematic data analysis. However, there are more technical possibilities than we frequently find in plants and factories. Companies are only gradually investing in systems equipped with modern IoT technology because of ongoing write-offs or other economical reasons. Thanks to economies of scale, the costs for this infrastructure will continue to drop rapidly in the years to come. I expect the future will bring the increased spread of sensors that will simultaneously be easier to deploy, more durable, and more affordable.
Q: What is the advantage for customers if the service sector can access the data of a “digital twin”?
Grenacher: By and large, unforeseen system downtimes can be avoided thanks to predictive maintenance. It is also possible to acquire insight into how certain harmful operational conditions can be prevented, for
Q: Please describe how the increased use of “digital twins” will change the service sector in the years to come.
Grenacher: The massive data provided by digital twins offer the potential to create entirely new application areas, both in the service and business intelligence sectors. The use of sensors makes it possible to display machine statuses and achieved product quality in real-time—as well as predictions about problems when they are still in the early stages. In this way, the service sector can intervene before there are expensive machine malfunctions. Maintenance intervals can also be dynamically adjusted to actual demand based on live information.
Q: What will a “digital twin“ look like in ten or fifteen years compared to today? What technological changes will play an important role?
Grenacher: I anticipate that the sensor network in industrial plants and logistics on the whole will become extremely consolidated. At the same time, these sensors will become more efficient and affordable. In the future, all company systems, and also all replacement parts, tools, containers, and products will be represented by their “digital twins
Q: What new business models do you expect in the service sector as a result of the increasing digitalization of factories?
Grenacher: Industry 4.0 cannot function without a digital service concept because service is an essential part of digitalization. Service is already a distinguishing factor in competition. It’s no longer just about the product since it is often similar, even interchangeable. That is why customers primarily choose a partner that offers more and quicker services. And that is why every company’s goal should be to better understand its customers, to create better touchpoints, and to improve service. Here’s where new concepts like “machine-as-a-service“ come into play. This makes it possible for companies to just purchase machine performance from the manufacturer instead of the machine itself – including maintenance to guarantee consistent performance. Service Lifecycle Management plays a key role in this concept. In the future, entire ecosystems consisting of new service providers will arise around these concepts.
Q: Predictive maintenance is an important issue in the service sector. Are there emerging technologies that may soon become similarly important?
Grenacher: Augmented reality will soon allow experienced personnel to perform skilled jobs without detailed knowledge of each machine and with the appropriate on-site digital support. The technician will be supplied with missing details via data glasses, for example. This makes it possible to integrate qualified workers, who are not part of the service company’s workforce, into maintenance and skills management without the need for company-specific training. The full potential of machine learning will also unfold in the years to come: The use of artificial intelligence, for example, facilitates the assessment of a machine’s untapped potential and the extent to which its use in a specific application area or scenario might be beneficial.