Digitalisierung

What is a digital twin?

As part of Industry 4.0, the digital twin is becoming increasingly important. But what opportunities does the use of the digital twin offer?

The digital twin

There is no general or uniform definition of the term “digital twin.” According to the authors in [1], however, the term describes the digital representation of real-world objects. A “digital representation of a specific product” [pp. 1, 2] does not necessarily have to refer to a physically existing object, as intangible goods and services can also be represented using digital twins. According to the Fraunhofer Institute for Manufacturing Engineering and Design IPK, it is a realistic, tangible model [2].

It is important to emphasize that the digital twin consists of a fusion of the digital model and the digital shadow of the product. A digital shadow is created by generating state values, process data, and other operationally relevant parameters. Through a digital twin—such as a product, a production facility, or an entire factory—the user gains a realistic representation that simulates the geometric attributes and behavior of its real-world counterpart. This virtual replica enables predictions to be made and optimizations to be carried out across the various stages of the lifecycle[3].

In the context of Industry 4.0 and against the backdrop of the growing availability, collection, and use of data, the importance of the digital twin is increasing [1]. The potential areas of application are diverse, but digital twins hold great promise particularly in industrial production, warehousing, and logistics [3]. The so-called “Digital Twin of the Organization” involves digitally mapping an entire company, including its business model, processes, and strategies. The goal is to eliminate inefficient processes, improve organizational performance, and better manage change processes [4].

Applications and specific examples

The following two practical examples will illustrate where and for what purpose digital twins can be used in industry. Let’s start with the first example:

Virtual commissioning of production facilities

Starting point: conventional commissioning

There will still be no way around physical commissioning in the future. After all, even in Industry 4.0, goods will continue to be produced in manufacturing facilities and will require the appropriate hardware for their manufacture. However, the nature of commissioning could change significantly with the adoption of digital twins. Traditional commissioning of systems (e.g., mechatronic systems) entails a number of inherent disadvantages and risks.

For example, retrofitting systems is expensive and can be very time-consuming [5]. Physical systems can be damaged, and there is often no time left for systematic testing of multiple scenarios and components [6]. In many cases, there is little room for optimization and improvement of the systems. Errors are either not detected at all or are detected too late, which can result in high follow-up costs due to maintenance and downtime costs caused by system outages.

Furthermore, failure to comply with technical requirements and specifications during commissioning can, in the worst-case scenario, result in contractual penalties or, at the very least, dissatisfied customers. From a business perspective, these drawbacks must definitely be avoided.

Solution: Virtual commissioning using digital twins

Virtual commissioning can drastically reduce development times. It also offers the possibility of avoiding production downtime and operational disruptions [7]. In virtual commissioning, digital models of production facilities or machines are created and rendered in three dimensions. All properties and behaviors of the systems (e.g., electrical, mechanical, thermal, and dynamic behaviors) are simulated using models and algorithms. This holistic approach ensures the best possible approximation of a system’s behavior [8]. Using common simulation tools (e.g., FEM or CFD tools), the components of a complex system and their interaction under various conditions are tested.

This simulation provides early information and insights into potential sources of error during subsequent operation, such as malfunctions, anomalies, or problems with the system. By making adjustments to the 3D model, the interaction between the electrical, software, and mechanical systems is improved even before actual commissioning. One advantage is that critical scenarios and situations can be tested and evaluated virtually—rather than on the actual plant. This reduces risk during actual operation, lowers costs, and leads to fewer production downtimes in the future. The crucial final step is to connect the optimized simulation software to the plant’s actual control system (PLC) [8].

Predictive Maintenance for optimized maintenance.

Solution: Using the digital twin for predictive maintenance

The good news is: Even in this case, the digital twin can deliver significant value and take a company’s maintenance operations to the next level. We are talking about what is known as predictive maintenance, which represents the next evolutionary step beyond preventive maintenance strategies. Mr. Feldmann of Roland Berger calls predictive maintenance “one of the key innovations of Industry 4.0” [13].

First, the plant’s operating and process parameters must be measured, transmitted, and stored using sensors [14]. By creating a digital twin of the plant, engineers can intentionally simulate faults in a risk-free environment and thus identify specific fault conditions in real-world plants [15]. Various degrees and manifestations of potential machine malfunctions are combined and evaluated. A predictive maintenance algorithm is then trained using this simulated dataset so that it can later detect and classify faults in the plant in real-world scenarios [15].

In this DT example as well, the digital twin is used to simulate the consequences of changes to an object and to increase planning reliability for companies [16]. AI-based predictive maintenance algorithms are not only capable of detecting faults as they occur, but also of reliably predicting future potential malfunctions and thus determining optimal maintenance times.

Initial Situation: Inflexible maintenance and waste of resources

As mentioned in the first example, breakdowns of industrial machinery result in very high costs, which often far exceed the cost of the component being replaced. In this context, it becomes clear that companies should strive to reduce downtime and associated losses through more systematic and proactive maintenance [9]. However, the reality on the factory floor is usually quite different:

Many companies still rely on a reactive maintenance strategy. This means that equipment is only serviced once a fault or problem has already occurred [10]. This type of maintenance is reactive because it cannot be planned and occurs unexpectedly. At the same time, companies risk prolonged and costly downtime required for the repair or replacement of components.

It is also common practice to maintain technical equipment or buildings at predefined intervals (for example, an inspection every 3 months; a filter change every 2 years, etc.) [11]. Manufacturers therefore aim to minimize the likelihood of downtime through preventive measures [12]. The problem here is that some machine components are serviced based on the specified maintenance interval even though they are still in good condition and fully functional.

Added value through the use of digital twins

As you have seen in the two examples—digital twins for virtual commissioning and digital twins for predictive maintenance—the use of digital twins can generate a wide range of benefits. Some of these benefits are listed below:

Increase in turnaround times
Improving overall operational efficiency [17]
Reduction in machine downtime
Increased productivity
Faster and more targeted identification of malfunctions, bottlenecks, and error-prone processes
Data generation during use: New insights and context through real-time integration with physical objects; development of new business models through a deeper understanding of customers and processes
Greater transparency and better information
Process Optimization and Control [18]
Minimizing risks and errors [19]
Reducing reliance on prototyping in product development
Monitoring current operating conditions, as well as forecasting and predicting future conditions and events → e.g., preventive quality assurance (e.g., through predictive maintenance based on generated data and AI algorithms)
More reliable overall planning

Recommendations for Successful Implementation

Digital twins can deliver significant value to your business. However, this requires a clear commitment to digital transformation and data-driven processes. It is crucial that the company’s digitalization strategy aligns with its ambitions in the area of digital twins. In particular, the company’s leadership must prioritize this initiative and allocate the necessary budget for pilot projects, upgrading sensor technology, commercial simulation tools, and AI-based services.

Many digitalization projects fail due to poor planning and communication. The complexity of the project is also often underestimated [20]. Initiating, developing, and sustaining digital twin projects is usually more efficient when experts in cloud computing, software development, and IT service providers are brought in [21]. Despite all the justified enthusiasm about the many potential applications of digital twins, the digital twin truly demonstrates its strengths in complex systems[22].

Mr. Hartmaier of IBM Watson IoT advises adopting the approach of first considering meaningful use cases rather than acting hastily [23]. It is essential to analyze where gaps exist and in which areas the company can benefit in the long term by developing a digital twin.

Decision-makers must first clarify what they hope to achieve by implementing digital transformation. Do they want to make their operational processes more efficient, or, for example, offer data-driven services to customers? It is advisable to start by implementing small processes, gaining experience, and getting the workforce on board. Then, larger processes can be implemented step by step, and the use of DT can be scaled in phases [20]. DiConneX recommends answering the following questions regarding the company’s objectives and data literacy [20]:

What does my company hope to achieve with the digital twin?
In which areas should the DT be used?
What data is required, and what quality is needed?
How does my company collect data and information, and how do I then evaluate and analyze it?
Do I have an efficient data management system?

Your journey with OHB Digital Services

Leverage space technology for your business. OHB Digital Services GmbH has been a trusted partner for secure and innovative IT solutions for many years. We are part of one of Europe’s most successful space and technology companies. With our products and services, we can help you digitize your business processes across the value chain and address all security-related issues. Feel free to contact us.

References

Expand Bibliography

1 Rosen, R. et al. (2020): Simulation und digitaler Zwilling im Anlagenlebenszyklus. Virtual Commissioning of Automation Systems, VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik

2 Stark, R. (N. A.): Smarte Fabrik 4.0 – Digitaler Zwilling. Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK, Berlin

3 Ramm, S., Wache, H., Dinter, B., Schmidt, S. (2020): Der Kollaborative Digitale Zwilling: Herzstück eines integrierten Gesamtkonzepts. ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb 115(Special): 94 – 96.

4 Mitache, R. (2018): The Digital Twin Organization: Can Enterprise Architecture Help? BiZZdesign. Online abgerufen am 07.10.2020. https://bizzdesign.com/
blog/the-digital-twin-organization-can-enterprise-architecture-help/.

5 Freyer, B. (2019): Warum ohne die Virtuelle Inbetriebnahme heute nichts geht. Machineering. Online abgerufen am 05.10.2020. https://www.machineering.
de/blog/wissen/article/warum-ohne-die-virtuelle-inbetriebnahme-heute-nichts-geht/.

6 iT ENGINEERING (2020): Virtuelle Inbetriebnahme (VIBN). Der digitale Zwilling bei der SPS-Programmierung. iT ENGINEERING SOFTWARE INNOVATIONS. Online abgerufen am 06.10.2020. https://ite-si.de/
virtuelle-inbetriebnahme/.

7 KUKA (N. A.): Engineering. Experten für Ihre automatisierte Produktion. Online abgerufen am 04.10.2020. https://www.kuka.com/
de-de/produkte-leistungen/produktionsanlagen/
technologie-consulting/engineering.

8 ISG virtuos (N. A.): Virtuelle Inbetriebnahme (Definition). Online abgerufen am 05.10.2020. https://www.isg-stuttgart.de/
de/isg-virtuos/virtuelle-inbetriebnahme.html.

9 Wallner, P. (N. A.): Der Digitale Zwilling als Baustein von Industrie 4.0. Online abgerufen am 06.10.2020. https://www.maschinenmarkt.
vogel.de/der-digitale-zwilling-als-baustein-von-industrie-40-a-904923/?p=2.

10 van Dijk, N. (2017): Von der reaktiven zur proaktiven Instandhaltung: wie Wartungspläne überflüssig werden. PLANON. Online abgerufen am 06.10.2020. https://planonsoftware.com/
de/ressourcen/blogs/von-der-reaktiven-zur-proaktiven-instandhaltung-wie-wartungsplane-uberflussig-werden/.

11 PLANON (N. A.): Geplante präventive Wartung. PLANON. Online abgerufen am 07.10.2020. https://planonsoftware.com/
de/glossar/geplante-praventive-wartung.

12 Günther, J. (2020): Instandhaltung 4.0. So klappt es mit proaktivem Service in der Instandhaltung. Instandhaltung. Online abgerufen am 05.10.2020. https://www.instandhalt
ung.de/instandhaltung-4-0/so-klappt-es-mit-proaktivem-service-in-der-instandhaltung-316.html.

13 Feldmann, S. (2017): Predictive Maintenance. Roland Berger GmbH, München.

14 NC Fertigung (2020): Wie funktioniert Predictive Maintenance? NC Fertigung. Online abgerufen am 06.10.2020. https://www.nc-
fertigung.de/wie-funktioniert-predictive-maintenance.

15 Miller, S. (2019): Predictive Maintenance emit einem digitalen Zwilling. MathWorks. Online abgerufen am 05.10.2020. https://de.mathworks.
com/company/newsletters/
articles/predictive-maintenance-using-a-digital-twin.html.

16 Klibi, K. (2020): Der digitale Zwilling in der Intralogistik: Planungssicherheit erhöhen, Investitionen absichern. Miebach Consulting Whitepaper. Miebach Consulting, Frankfurt am Main.

17 Scheibe, H.-G. (N. A.): Schritt für Schritt zum virtuellen Prozesszwilling. Neue Chancen für die präzisere Analyse und Gestaltung von Wertschöpfungsnetzwerken. ROI Management Consulting AG.

18 Uhlenkamp, J-F., Hribernik, K. A., Thoben, K.-D. (2020): Wie Digitale Zwillinge Unternehmensgrenzen überwinden: Ein Beitrag zur Gestaltung von Digitalen Zwillingen mit unternehmensübergreifenden Anwendungen im Produktlebenszyklus. ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb 115: 84-89.

19 Lambertz, B. (2019): Digital Twin. Maintcare. Online abgerufen am 05.10.2020. https://maint-care.de/knowhow/digital-twin/.

20 DiConneX (N. A.): Digitaler Zwilling – Womit fange ich an? DiConneX GmbH. Online abgerufen am 05.10.2020. https://diconnex.com/
blog/2019/08/19/digitaler-zwilling-womit-fange-ich-an.

21 Device Insight (2020): Was ein Digital Twin leisten kann und was nicht. Online abgerufen am 06.10.2020. https://www.device-insight.com/was-ein-digital-twin-leisten-kann-und-was-nicht/.

22 elunic (2020): Was ist ein Digitaler Zwilling? elunic AG. Online abgerufen am 04.10.2020. https://www.elunic.com/
de/digitaler-zwilling/.

23 Hartmaier, S. (2018): Der digitale Zwilling. Mit kleinen Schritten angefangen. IT & Production ONLINE. Online abgerufen am 06.10.2020. https://www.it-production.com/produktentwicklung/
digitaler-zwilling-small-steps/.

1 Rosen, R. et al. (2020): Simulation und digitaler Zwilling im Anlagenlebenszyklus. Virtual Commissioning of Automation Systems, VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik

2 Stark, R. (N. A.): Smarte Fabrik 4.0 – Digitaler Zwilling. Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK, Berlin

4 Mitache, R. (2018): The Digital Twin Organization: Can Enterprise Architecture Help? BiZZdesign. Online abgerufen am 07.10.2020. https://bizzdesign.com/blog/the-digital-twin-organization-can-enterprise-architecture-help/.

5 Freyer, B. (2019): Warum ohne die Virtuelle Inbetriebnahme heute nichts geht. Machineering. Online abgerufen am 05.10.2020. https://www.machineering.de/blog/wissen/article/warum-ohne-die-virtuelle-inbetriebnahme-heute-nichts-geht/.

7 KUKA (N. A.): Engineering. Experten für Ihre automatisierte Produktion. Online abgerufen am 04.10.2020. https://www.kuka.com/de-de/produkte-leistungen/produktionsanlagen/technologie-consulting/engineering.

8 ISG virtuos (N. A.): Virtuelle Inbetriebnahme (Definition). Online abgerufen am 05.10.2020. https://www.isg-stuttgart.de/de/isg-virtuos/virtuelle-inbetriebnahme.html.

9 Wallner, P. (N. A.): Der Digitale Zwilling als Baustein von Industrie 4.0. Online abgerufen am 06.10.2020. https://www.maschinenmarkt.vogel.de/der-digitale-zwilling-als-baustein-von-industrie-40-a-904923/?p=2.

10 van Dijk, N. (2017): Von der reaktiven zur proaktiven Instandhaltung: wie Wartungspläne überflüssig werden. PLANON. Online abgerufen am 06.10.2020. https://planonsoftware.com/de/ressourcen/blogs/von-der-reaktiven-zur-proaktiven-instandhaltung-wie-wartungsplane-uberflussig-werden/.

11 PLANON (N. A.): Geplante präventive Wartung. PLANON. Online abgerufen am 07.10.2020. https://planonsoftware.com/de/glossar/geplante-praventive-wartung.

12 Günther, J. (2020): Instandhaltung 4.0. So klappt es mit proaktivem Service in der Instandhaltung. Instandhaltung. Online abgerufen am 05.10.2020. https://www.instandhaltung.de/instandhaltung-4-0/so-klappt-es-mit-proaktivem-service-in-der-instandhaltung-316.html.

13 Feldmann, S. (2017): Predictive Maintenance. Roland Berger GmbH, München.

14 NC Fertigung (2020): Wie funktioniert Predictive Maintenance? NC Fertigung. Online abgerufen am 06.10.2020. https://www.nc-fertigung.de/wie-funktioniert-predictive-maintenance.

15 Miller, S. (2019): Predictive Maintenance emit einem digitalen Zwilling. MathWorks. Online abgerufen am 05.10.2020. https://de.mathworks.com/company/newsletters/articles/predictive-maintenance-using-a-digital-twin.html.

16 Klibi, K. (2020): Der digitale Zwilling in der Intralogistik: Planungssicherheit erhöhen, Investitionen absichern. Miebach Consulting Whitepaper. Miebach Consulting, Frankfurt am Main.

17 Scheibe, H.-G. (N. A.): Schritt für Schritt zum virtuellen Prozesszwilling. Neue Chancen für die präzisere Analyse und Gestaltung von Wertschöpfungsnetzwerken. ROI Management Consulting AG.

19 Lambertz, B. (2019): Digital Twin. Maintcare. Online abgerufen am 05.10.2020. https://maint-care.de/knowhow/digital-twin/.

20 DiConneX (N. A.): Digitaler Zwilling – Womit fange ich an? DiConneX GmbH. Online abgerufen am 05.10.2020. https://diconnex.com/blog/2019/08/19/digitaler-zwilling-womit-fange-ich-an.

21 Device Insight (2020): Was ein Digital Twin leisten kann und was nicht. Online abgerufen am 06.10.2020. https://www.device-insight.com/was-ein-digital-twin-leisten-kann-und-was-nicht/.

22 elunic (2020): Was ist ein Digitaler Zwilling? elunic AG. Online abgerufen am 04.10.2020. https://www.elunic.com/de/digitaler-zwilling/.