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There is no general or uniform definition of the term “digital twin.” According to the authors in [1], however, the term describes thedigital 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 ofthe 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].
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:
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 bysystem 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.
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. Allpropertiesandbehaviors 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 thatcritical scenarios and situationscan 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].
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 canintentionally simulate faultsin 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 laterdetectand classifyfaults in the plantin 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 capableof detecting faults as they occur, but alsoof reliably predicting futurepotential malfunctions and thus determining optimal maintenance times.
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 areactive 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 atpredefined intervals(for example, an inspection every 3 months; a filter change every 2 years, etc.) [11]. Manufacturers therefore aim to minimize the likelihood of downtimethrough 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.
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:
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.
Manydigitalization 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 incomplex systems[22].
Mr. Hartmaier of IBM Watson IoT advises adopting the approachof 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 advisableto startby 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]:
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1 Rosen, R. et al. (2020): Simulation and Digital Twins 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: The Cornerstone of an Integrated Overall Concept. ZWF: Journal of Industrial Management 115(Special): 94–96.
4 Mitache, R. (2018): The Digital Twin Organization: Can Enterprise Architecture Help? BiZZdesign. Retrieved online on October 7, 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 October 5, 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 October 6, 2020.https://ite-si.de/
virtuelle-inbetriebnahme/.
7 KUKA (N.A.): Engineering. Experts for your automated production. Accessed online on October 4, 2020.https://www.kuka.com/
de-de/produkte-leistungen/produktionsanlagen/
technologie-consulting/engineering.
8 ISG virtuos (N. A.): Virtual commissioning (definition). Retrieved online on October 5, 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 October 6, 2020.https://www.maschinenmarkt.
vogel.de/der-digitale-zwilling-als-baustein-von-industrie-40-a-904923/?p=2.
10van Dijk, N. (2017): From Reactive to Proactive Maintenance: How Maintenance Schedules Are Becoming Obsolete. PLANON. Accessed online on October 6, 2020.https://planonsoftware.com/
de/resources/blogs/from-reactive-to-proactive-maintenance-how-maintenance-schedules-become-obsolete/.
11 PLANON (N. A.): Scheduled preventive maintenance. PLANON. Accessed online on October 7, 2020.https://planonsoftware.com/
de/glossar/geplante-praventive-wartung.
12 Günther, J. (2020): Maintenance 4.0: How to Make Proactive Service Work in Maintenance. Instandhaltung. Accessed online on October 5, 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 Fertigung (2020): How Does Predictive Maintenance Work? NC Fertigung. Accessed online on October 6, 2020.https://www.nc-
fertigung.de/wie-funktioniert-predictive-maintenance.
15 Miller, S. (2019): Predictive Maintenance Using a Digital Twin. MathWorks. Retrieved online on October 5, 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 Certainty, Securing Investments. Miebach Consulting White Paper. Miebach Consulting, Frankfurt am Main.
17 Scheibe, H.-G. (N. A.): Step by Step to a 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 Organizational Boundaries: A Contribution to the Design of Digital Twins with Cross-Organizational Applications in the Product Life Cycle. ZWF: Journal of Industrial Management 115: 84–89.
19 Lambertz, B. (2019): Digital Twin. Maintcare. Retrieved online on October 5, 2020.https://maint-care.de/knowhow/digital-twin/.
20 DiConneX (N.A.): Digital Twin – Where Do I Start? DiConneX GmbH. Accessed online on October 5, 2020.https://diconnex.com/
blog/2019/08/19/digital-twin-where-do-i-start.
21 Device Insight (2020): What a Digital Twin Can and Cannot Do. Retrieved online on October 6, 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 October 4, 2020.https://www.elunic.com/
de/digitaler-zwilling/.
23 Hartmaier, S. (2018): The Digital Twin. Starting with Small Steps. IT & Production ONLINE. Retrieved online on October 6, 2020.https://www.it-production.com/produktentwicklung/
digitaler-zwilling-small-steps/.
1 Rosen, R. et al. (2020): Simulation and Digital Twins 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: The Cornerstone of an Integrated Overall Concept. ZWF: Journal of Industrial Management 115(Special): 94–96.
4 Mitache, R. (2018): The Digital Twin Organization: Can Enterprise Architecture Help? BiZZdesign. Retrieved online on October 7, 2020.https://bizzdesign.com/blog/the-digital-twin-organization-can-enterprise-architecture-help/.
5 Freyer, B. (2019): Why Virtual Commissioning Is Essential Today. Machineering. Retrieved online on October 5, 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 October 6, 2020.https://ite-si.de/virtuelle-inbetriebnahme/.
7 KUKA (N.A.): Engineering. Experts in automated production. Accessed online on October 4, 2020.https://www.kuka.com/de-de/produkte-leistungen/produktionsanlagen/technologie-consulting/engineering.
8 ISG virtuos (N. A.): Virtual commissioning (definition). Retrieved online on October 5, 2020.https://www.isg-stuttgart.de/de/isg-virtuos/virtuelle-inbetriebnahme.html.
9 Wallner, P. (n.d.): The Digital Twin as a Building Block of Industry 4.0. Retrieved online on October 6, 2020.https://www.maschinenmarkt.vogel.de/der-digitale-zwilling-als-baustein-von-industrie-40-a-904923/?p=2.
10van Dijk, N. (2017): From Reactive to Proactive Maintenance: How Maintenance Schedules Are Becoming Obsolete. PLANON. Accessed online on October 6, 2020.https://planonsoftware.com/de/ressourcen/blogs/von-der-reaktiven-zur-proaktiven-instandhaltung-wie-wartungsplane-uberflussig-werden/.
11 PLANON (N. A.): Scheduled preventive maintenance. PLANON. Accessed online on October 7, 2020.https://planonsoftware.com/de/glossar/geplante-praventive-wartung.
12 Günther, J. (2020): Maintenance 4.0: How to Make Proactive Service Work in Maintenance. Instandhaltung. Accessed online on October 5, 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 Fertigung (2020): How Does Predictive Maintenance Work? NC Fertigung. Accessed online on October 6, 2020.https://www.nc-fertigung.de/wie-funktioniert-predictive-maintenance.
15 Miller, S. (2019): Predictive Maintenance Using a Digital Twin. MathWorks. Retrieved online on October 5, 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 Certainty, Securing Investments. Miebach Consulting White Paper. Miebach Consulting, Frankfurt am Main.
17 Scheibe, H.-G. (N. A.): Step by Step to a 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 Organizational Boundaries: A Contribution to the Design of Digital Twins with Cross-Organizational Applications in the Product Life Cycle. ZWF: Journal of Industrial Management 115: 84–89.
19 Lambertz, B. (2019): Digital Twin. Maintcare. Retrieved online on October 5, 2020.https://maint-care.de/knowhow/digital-twin/.
20 DiConneX (N.A.): Digital Twin – Where Do I Start? DiConneX GmbH. Accessed online on October 5, 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 October 6, 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 October 4, 2020.https://www.elunic.com/de/digitaler-zwilling/.
23 Hartmaier, S. (2018): The Digital Twin. Starting with Small Steps. IT & Production ONLINE. Retrieved online on October 6, 2020.https://www.it-production.com/produktentwicklung/digitaler-zwilling-small-steps/.
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