{"id":17890,"date":"2024-11-08T11:22:34","date_gmt":"2024-11-08T10:22:34","guid":{"rendered":"https:\/\/www.ohb-ds.de\/wpml\/magazin\/was-ist-eigentlich-ein-digital-twin"},"modified":"2026-09-17T12:22:05","modified_gmt":"2026-09-17T10:22:05","slug":"what-is-a-digital-twin","status":"publish","type":"magazin","link":"https:\/\/www.ohb-ds.de\/en\/magazine\/what-is-a-digital-twin","title":{"rendered":"What is a digital twin?"},"content":{"rendered":"\t\t
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\n\t\t\t\t\tDigitalisierung<\/span>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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What is a digital twin?<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tAs part of Industry 4.0, the digital twin is becoming increasingly important. But what opportunities does the use of the digital twin offer?\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tSchedule an initial consultation now<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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The digital twin<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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There is no general or uniform definition of the term \u201cdigital twin.\u201d According to the authors in [1], however, the term describes the digital representation of real-world objects<\/em>. A \u201cdigital representation of a specific product\u201d [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].<\/p>

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\u2014such as a product, a production facility, or an entire factory\u2014the 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<\/strong>[3].<\/p>

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 \u201cDigital Twin of the Organization\u201d <\/strong>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].<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Applications and specific examples<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The following two practical examples will illustrate where and for what purpose digital twins can be used in industry. Let\u2019s start with the first example:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Virtual commissioning of production facilities<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Starting point: conventional commissioning<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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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.\u00a0<\/p>

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<\/strong>.<\/p>

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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Solution: Virtual commissioning using digital twins<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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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 <\/strong>and behaviors of the systems <\/strong>(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\u2019s 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.<\/p>

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 <\/strong>can be tested and evaluated virtually\u2014rather 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\u2019s actual control system (PLC) [8].<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Predictive Maintenance for optimized maintenance.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Solution: Using the digital twin for predictive maintenance<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The good news is: Even in this case, the digital twin can deliver significant value and take a company\u2019s 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 \u201cone of the key innovations of Industry 4.0\u201d [13].<\/p>

First, the plant\u2019s 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 <\/strong>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 <\/strong>and classify faults in the plant <\/strong>in real-world scenarios [15].\u00a0<\/p>

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<\/strong> detecting faults as they occur, but also\u00a0of reliably predicting<\/strong> future\u00a0potential malfunctions and thus determining optimal maintenance times<\/strong>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Initial Situation: Inflexible maintenance and waste of resources<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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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:<\/p>

Many companies still rely on a reactive maintenance strategy<\/strong>. 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.<\/p>

It is also common practice to maintain technical equipment or buildings at predefined intervals <\/strong>(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 <\/strong>[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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Added value through the use of digital twins<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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As you have seen in the two examples\u2014digital twins for virtual commissioning and digital twins for predictive maintenance\u2014the use of digital twins can generate a wide range of benefits. Some of these benefits are listed below:<\/p>