Digitization

What is a digital twin?

In the course of Industry 4.0, the digital twin is becoming increasingly important. But what possibilities does the use of the digital twin offer?

The digital twin

There is no general or uniform definition for the term "digital twin". However, according to the authors in [1], the term describes the digital representation of things from the real world. A "digital image of a specific product" [p. 1, 2] does not necessarily have to refer to a real existing object, as intangible goods and services can also be represented with digital twins. According to the Fraunhofer Institute for Production Systems and Design Technology IPK, it is a realistic model that can be touched [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 status values, process data and other operationally relevant characteristic values. By means of a digital twin, e.g. of a product, a production plant or an entire factory, the user receives a realistic instance that simulates the geometric attributes and behavior of the real counterpart. The virtual mirror image can be used to make predictions and carry out optimizations across the various stages of the life cycle [3].

In the context of Industry 4.0 and against the backdrop of the increasing availability, acquisition and use of data, the importance of the digital twin is growing [1]. The possible areas of application are diverse, but digital twins promise great potential, especially in industrial production, warehousing and logistics [3]. The so-called "digital twin of the organization" is about digitally mapping an entire company, including its business model, processes and strategies. The aim is to eliminate inefficient processes, improve the organization and better implement change processes [4].

Applications and concrete examples

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

The virtual commissioning of production systems

Initial situation: conventional commissioning

There will be no way around physical commissioning in the future. After all, even in Industry 4.0, goods will continue to be produced in production halls and require the corresponding hardware for production. However, with the establishment of digital twins, the nature of commissioning may change significantly. The traditional commissioning of systems (e.g. mechatronic systems) harbors a number of inherent disadvantages and risks.

For example, subsequent adjustments to systems are expensive and sometimes very time-consuming [5]. Real systems can be damaged and there is often no time for systematic testing of multiple scenarios and components [6]. In many cases, there is little scope for optimizing and improving the systems. Faults are not detected or are detected too late, which can result in high follow-up costs due to maintenance and downtime costs due to system downtime.

In addition, non-compliance with technical requirements and specifications during commissioning can lead to contractual penalties or at least unhappy customers in the worst-case scenario. These disadvantages should definitely be avoided from a business perspective.

Solution: Virtual commissioning through the use of digital twins

Virtual commissioning can drastically reduce development times. It also offers the possibility of avoiding production downtimes and malfunctions during operation [7]. During virtual commissioning, digital models of the production systems or machines are created and mapped in three dimensions. All properties and the behavior of the systems (e.g. electrical, mechanical, thermal, dynamic behavior) are simulated using models and algorithms. This holistic approach ensures the best possible approximation of the system behavior of a plant [8]. Common simulation tools (e.g. FEM or CFD tools) are used to test the components of a complex system and their interaction under different conditions.

This simulation provides early information and insights into possible sources of error during subsequent operation, e.g. faults, anomalies or problems with the system. Corrections to the 3D model improve the interaction of the electrical, software and mechanical systems even before real commissioning. One advantage is that critical scenarios and situations can be tested and evaluated virtually - and not on the real system. This reduces the risk in real operation, lowers costs and leads to a reduction in production downtime in the future. The decisive final step is to connect the optimized simulation software with the real control system of the plant (PLC) [8].

Predictive maintenance for optimized maintenance.

Solution: Application of the digital twin for predictive maintenance

The good news is that the digital twin can also deliver great added value in this case and take maintenance in the company to a higher level. We are talking about predictive maintenance, which is the next evolutionary step after the preventive maintenance strategy. Mr. Feldmann from Roland Berger calls predictive maintenance "one of the key innovations of Industry 4.0" [13].

First of all, operating parameters and process parameters of the system must be measured, transmitted and stored using sensors [14]. By creating a digital twin of the system, engineers can intentionally simulate faults in a risk-free environment and thus record certain fault conditions of the real systems [15]. Different degrees and characteristics of possible machine faults are combined and evaluated. A predictive maintenance algorithm is then trained with this simulated data set in order to be able to recognize and classify faults on the system later in the real case [15].

In this DT example, the digital twin is also used to simulate the consequences of changes to an object and increase planning reliability for companies [16]. AI-based predictive maintenance algorithms are not only able to detect faults when they occur, but also reliably predict future potential faults and thus forecast optimal maintenance times.

Initial situation: Too inflexible maintenance and waste of resources

As already mentioned in the first example, machine failures on industrial machines cause very high costs, which often far exceed the cost of the component to be replaced. In this context, it is clear that companies should strive to reduce downtimes and the associated losses through more systematic and predictive maintenance [9]. However, the reality on the store floor is usually different:

Many companies still rely on a reactive maintenance strategy. This means that the affected equipment is only serviced once a fault or problem has already occurred [10]. This form of maintenance is reactive because it is unplannable and occurs unexpectedly. At the same time, companies risk a longer and more expensive downtime, which is necessary to repair or replace the components.

Maintenance of technical systems or buildings at predefined intervals (e.g. an inspection every 3 months; a filter change every 2 years ...) is also common [11]. Manufacturers therefore aim to preventively minimize the probability of downtimes [12]. The problem here is that machine components are sometimes serviced on the basis of 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 - DT for virtual commissioning and DT for predictive maintenance - a number of benefits can be generated through the use of digital twins. Some added values are listed below:

Increase in throughput times
Increase overall operational efficiency [17]
Reduction of machine downtime
Increase in productivity
Faster and more targeted identification of faults, bottlenecks and error-prone processes
Data generation during use: New insights and backgrounds through real-time coupling with real objects; derivation of new business models through deeper customer and process understanding
Better transparency and information
Optimization and control of processes [18]
Minimization of risks and errors [19]
Reduction of dependency on prototype construction in product development
Recording of current operating states, but also forecasting and prediction of future states 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 bring considerable added value to your company. However, this requires a clear commitment to digital transformation and data-based processes. It is crucial that the digitalization strategy in the company is in line with the ambitions in the area of the digital twin. The company's management team in particular must prioritize the topic and provide the necessary financial budget for pilot projects, upgrading sensor technology, commercial simulation tools and AI-based services.

Many digitalization projects fail due to inadequate planning and communication. The complexity of the project is also often underestimated [20]. Driving forward, setting up and continuing DT projects is usually more efficient when experts in the cloud, software development and IT service providers are involved [21]. Despite all the justified enthusiasm about the many possible applications of digital twins: The digital twin shows its strengths above all in complex systems [22].

Mr. Hartmaier from IBM Watson IoT advises to first think about meaningful use cases instead of acting hastily [23]. It is important 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 be clear about what goal they want to achieve with the use of DT. Do they want to make their operational processes more efficient or, for example, offer customers data-based services? Initially, it is advisable to implement small processes, gain experience and get the workforce on board. Larger processes can then be implemented step by step and the use of DT can be scaled in stages [20] DiConneX recommends answering the following questions regarding the company's objectives and data competence [20]:

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

Your journey with OHB Digital Services

Use the knowledge from space travel for your business. OHB Digital Services GmbH has been a reliable partner for secure & innovative IT solutions for many years. We are part of one of the most successful space and technology companies in Europe. With our products and services, we support you in the digitalization of your business processes along the value chain and in all security-related issues. Please feel free to contact us.

Literature sources

Fold out literature sources

1 Rosen, R. et al. (2020): Simulation and digital twin in the plant life cycle. Virtual Commissioning of Automation Systems, VDI/VDE Society for Measurement and Automation Technology

2 Stark, R. (N. A.): Smart Factory 4.0 - Digital Twin. Fraunhofer Institute for Production Systems and Design Technology IPK, Berlin

3 Ramm, S., Wache, H., Dinter, B., Schmidt, S. (2020): The Collaborative Digital Twin: Centerpiece of an integrated overall concept. ZWF Journal for Economic Factory Operation 115(Special): 94 - 96.

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

5 Freyer, B. (2019): Why nothing works today without virtual commissioning. Machineering. Retrieved online on 05.10.2020. https://www.machineering.
de/blog/wissen/article/warum-ohne-die-virtuelle-inbetriebnahme-heute-nichts-geht/.

6 iT ENGINEERING (2020): Virtual commissioning (VIBN). The digital twin in PLC programming. iT ENGINEERING SOFTWARE INNOVATIONS. Retrieved online on 06.10.2020. https://ite-si.de/
virtuelle-inbetriebnahme/.

7 KUKA (N. A.): Engineering. Experts for your automated production. Accessed online on 04.10.2020. https://www.kuka.com/
en/products-services/production-systems/
technology-consulting/engineering.

8 ISG virtuoso (N. A.): Virtual commissioning (definition). Retrieved online on 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. Retrieved online on 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): From reactive to proactive maintenance: how maintenance plans become superfluous. PLANON. Retrieved online on 06.10.2020. https://planonsoftware.com/
de/ressourcen/blogs/von-der-reaktiven-zur-proaktiven-instandhaltung-wie-wartungsplane-uberflussig-werden/.

11 PLANON (N. A.): Planned preventive maintenance. PLANON. Retrieved online on 07.10.2020. https://planonsoftware.com/
en/glossary/planned-preventive-maintenance.

12 Günther, J. (2020): Maintenance 4.0. How proactive service in maintenance works. Maintenance. Retrieved online on 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, Munich.

14 NC Manufacturing (2020): How does predictive maintenance work? NC Fertigung. Retrieved online on 06.10.2020. https://www.nc-
fertigung.de/wie-funktioniert-predictive-maintenance.

15 Miller, S. (2019): Predictive maintenance with a digital twin. MathWorks. Retrieved online on 05.10.2020. https://de.mathworks.
com/company/newsletters/
articles/predictive-maintenance-using-a-digital-twin.html.

16 Klibi, K. (2020): The digital twin in intralogistics: increasing planning reliability, safeguarding investments. Miebach Consulting white paper. Miebach Consulting, Frankfurt am Main.

17 Scheibe, H.-G. (N. A.): Step by step to the virtual process twin. New opportunities for more precise analysis and design of value creation networks. ROI Management Consulting AG.

18 Uhlenkamp, J-F., Hribernik, K. A., Thoben, K.-D. (2020): How digital twins overcome company boundaries: A contribution to the design of digital twins with cross-company applications in the product life cycle. ZWF Journal for Economic Factory Operation 115: 84-89.

19 Lambertz, B. (2019): Digital Twin. Maintcare. Retrieved online on 05.10.2020. https://maint-care.de/knowhow/digital-twin/.

20 DiConneX (N. A.): Digital twin - Where do I start? DiConneX GmbH. Retrieved online on 05.10.2020. https://diconnex.com/
blog/2019/08/19/digital-twin-with-what-do-I-start-with.

21 Device Insight (2020): What a digital twin can and cannot do. Retrieved online on 06.10.2020. https://www.device-insight.com/was-ein-digital-twin-leisten-kann-und-was-nicht/.

22 elunic (2020): What is a digital twin? elunic AG. Retrieved online on 04.10.2020. https://www.elunic.com/
en/digitaler-zwilling/.

23 Hartmaier, S. (2018): The digital twin. Starting with small steps. IT & Production ONLINE. Retrieved online on 06.10.2020. https://www.it-production.com/produktentwicklung/
digitaler-zwilling-small-steps/.

1 Rosen, R. et al. (2020): Simulation and digital twin in the plant life cycle. Virtual Commissioning of Automation Systems, VDI/VDE Society for Measurement and Automation Technology

2 Stark, R. (N. A.): Smart Factory 4.0 - Digital Twin. Fraunhofer Institute for Production Systems and Design Technology IPK, Berlin

3 Ramm, S., Wache, H., Dinter, B., Schmidt, S. (2020): The Collaborative Digital Twin: Centerpiece of an integrated overall concept. ZWF Journal for Economic Factory Operation 115(Special): 94 - 96.

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

5 Freyer, B. (2019): Why nothing works today without virtual commissioning. Machineering. Retrieved online on 05.10.2020. https://www.machineering.de/blog/wissen/article/warum-ohne-die-virtuelle-inbetriebnahme-heute-nichts-geht/.

7 KUKA (N. A.): Engineering. Experts for your automated production. Accessed online on 04.10.2020. https://www.kuka.com/de-de/produkte-leistungen/produktionsanlagen/technologie-consulting/engineering.

8 ISG virtuosic (N. A.): Virtual commissioning (definition). Retrieved online on 05.10.2020. https://www.isg-stuttgart.de/de/isg-virtuos/virtuelle-inbetriebnahme.html.

9 Wallner, P. (N. A.): The digital twin as a building block of Industry 4.0. Retrieved online on 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): From reactive to proactive maintenance: how maintenance plans become superfluous. PLANON. Retrieved online on 06.10.2020. https://planonsoftware.com/de/ressourcen/blogs/von-der-reaktiven-zur-proaktiven-instandhaltung-wie-wartungsplane-uberflussig-werden/.

11 PLANON (N. A.): Planned preventive maintenance. PLANON. Retrieved online on 07.10.2020. https://planonsoftware.com/de/glossar/geplante-praventive-wartung.

12 Günther, J. (2020): Maintenance 4.0. How proactive service in maintenance works. Maintenance. Retrieved online on 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, Munich.

14 NC Manufacturing (2020): How does predictive maintenance work? NC Manufacturing. Retrieved online on 06.10.2020. https://www.nc-fertigung.de/wie-funktioniert-predictive-maintenance.

15 Miller, S. (2019): Predictive maintenance with a digital twin. MathWorks. Retrieved online on 05.10.2020. https://de.mathworks.com/company/newsletters/articles/predictive-maintenance-using-a-digital-twin.html.

16 Klibi, K. (2020): The digital twin in intralogistics: increasing planning reliability, safeguarding investments. Miebach Consulting white paper. Miebach Consulting, Frankfurt am Main.

17 Scheibe, H.-G. (N. A.): Step by step to the virtual process twin. New opportunities for more precise analysis and design of value creation networks. ROI Management Consulting AG.

19 Lambertz, B. (2019): Digital Twin. Maintcare. Retrieved online on 05.10.2020. https://maint-care.de/knowhow/digital-twin/.

20 DiConneX (N. A.): Digital twin - Where do I start? DiConneX GmbH. Accessed online on 05.10.2020. https://diconnex.com/blog/2019/08/19/digitaler-zwilling-womit-fange-ich-an.

21 Device Insight (2020): What a digital twin can and cannot do. Retrieved online on 06.10.2020. https://www.device-insight.com/was-ein-digital-twin-leisten-kann-und-was-nicht/.

22 elunic (2020): What is a digital twin? elunic AG. Retrieved online on 04.10.2020. https://www.elunic.com/de/digitaler-zwilling/.

Current magazine articles on the topic of digitization

IT security

Why should vulnerability analysis also be an issue for SMEs?

More than half of all SMEs in Germany have already been victims of a cyberattack, with financial losses running into the millions depending on the extent of the attack.

Digitization

ExANT

Consortium from Germany uses AI to convert technical text into a form that computers can understand.

Digitization

What do CIOs and astronauts have in common?

Not much at first glance, but both have a unique view of their cosmos.

Does that sound interesting for you and your company?

Then get in touch with us.