What is meant by industrial management

The Digital Twin Theory - A new take on a buzzword

The digital twin is seen as an essential means of increasing productivity in the age of industrial digitization. Therefore, numerous publications deal with this term. This article first shows the origins of the term and deals with selected definitions. However, these provide little support in the practical implementation of digital twins, as the definitions sometimes differ greatly. As an alternative to a classic definition, a theoretical model is therefore proposed that contains assumptions about the digital twin. This novel approach is intended to help improve the management of digital twins in practice.

A digitized industry offers great economic potential: In mechanical engineering alone, Industry 4.0 can expect a cumulative increase in productivity of 30% by 2025 [1]. This increase is essentially based on the seamless networking of all players and systems, both horizontally and vertically.

Various research projects are already devoted to improving the vertical networking of the hierarchical levels in accordance with IEC 62264 and the processes and services based on it. One example is work on the topic of “predictive maintenance” based on sensor data [2]. Solutions for the unfavorable situation in vertical information integration caused by heterogeneous fieldbuses and IT protocols are also being worked on. For example, comprehensive Industry 4.0 communication based on standardized information models is currently being developed [3].

Need for digital twins

In the future, machines and systems can be integrated into higher-level systems via "Plug & Monitor" without the high level of integration effort previously required. However, vertical networking alone is not enough to achieve the desired increases in productivity.

The next necessary step is the stronger horizontal networking of the value chains. This horizontal networking is described in the reference architecture model Industry 4.0 (RAMI 4.0) of IEC 62890 in the Life Cycle & Value Stream axis [4]. Along this axis, which depicts the product life cycle creation, production, use, etc., the practitioner today has to deal with proprietary interfaces and information models [5]. Structural information and models from design and engineering tools could, for example, facilitate the diagnosis of production machines in the event of a fault, but have so far been incompatible with the systems used there. Consequently, the greatest increase in productivity in this area is expected in cost-intensive engineering [6]. The digital twin plays a special role in this. According to Gartner, as an important element of product lifecycle management, it has the potential to save billions of euros [7].

In addition to the Life Cycle & Value Stream axis, the digital twin also works in the Layer axis and in the Hierarchy Levels axis of RAMI 4.0. Figure 1 symbolically shows the RAMI 4.0 room in which the digital twin “floats”. However, such images do not help practitioners to clarify how the hoped-for increases in productivity can be achieved. Therefore, we will first take a closer look at the current definition of the digital twin.


Figure 1: Digital twin in the context of RAMI 4.0.

Heterogeneous definition

In the technical domain, a twin has been known by NASA since the late 1960s. This meant the identical replica of a spacecraft that remained on Earth to analyze the effects of control commands before sending them to the distant spacecraft. It was again NASA that added the attribute “digital” to a technical twin for the first time in 2010. She meant a simulation model that depicts the behavior of a physical spacecraft [8].

At about the same time, the term was introduced in the industrial domain. This meant the virtual image of a physical product in PLM systems (PLM - Product Lifecycle Management) [9]. However, the term only became popular with the emergence of the idea of ​​Industry 4.0 and when companies began to use the digital twin for their own marketing, as for example in [10]. Since then, numerous definitions have emerged, as the following selection shows:

The digital twin is a digital representation of things from the real world [11]; a concept by which data and information are mapped from atoms to bits [12]; a computerized model of a tangible or intangible object [13]; a comprehensive physical and functional representation of a product that contains all the information needed to process it [14]; a digitized (3D) image of a product to be created [15]; a synonym for the Industry 4.0 administration shell [16].

As a systematic mapping study carried out as part of a seminar paper according to the procedure described in [17] shows, the list of definitions could be extended. The study, which so far only includes English-language articles in the ACM Digital Library, Science Direct and IEEEXplore databases, lists 51 relevant publications in which the digital twin is defined. In addition, other similar terms are circulating such as digital shadow, digital master, digital type and digital instance.

The following interim conclusion can therefore be drawn: There are a large number of definitions that differ in scope, level of detail and technical focus. The digital twin is most likely to be understood as a form-based simulation model, but this is not generally valid or accepted. In principle, different definitions for a scientific subject do not prevent this subject from being implemented. In the specific case, however, numerous challenges for the management of digital twins are known, such as the identification and data management of the product along the product life cycle, the creation of simulation models in different IT systems and the control of the huge amounts of data [18]. Formulating a precise definition of the term and working towards its general recognition would be a possible approach to help address these challenges. However, we are putting an alternative approach up for discussion, as it seems unrealistic to us that the many stakeholders from science and industry can agree on a definition.


Figure 2: Model of information enrichment for digital twins [14].

The digital twin theory

This approach is a theoretical model built on hypotheses. The starting point for the hypotheses was, on the one hand, the work in [14], according to which the information describing a digital twin is enriched in every phase of the product life cycle (Fig. 2). On the other hand, the idea of ​​the “Digital Twin Theory” matured during a chance contact with quantum physics and the subject of electrons: From the point of view of quantum physics, electrons are in several places at the same time. Their state is unknown until they are put into an observation state. It seemed exciting to examine whether these properties can also be assumed for digital twins.

After the initial formulation, the hypotheses were discussed with representatives from the industry, including: at a specialist forum of OWL Maschinenbau e.V. in June 2018 [19] and at the PLM Europe conference in October 2018 [20]. They were then revised and reformulated. The hypotheses of the Digital Twin Theory are:
1. A digital twin is a digital representation of an asset.
2. A digital twin is in several places at the same time.
3. A digital twin has a variety of states.
4. In an interaction situation, the digital twin has a context-specific state.
5. The information model for digital twins is infinitely large, it is a real information model.
6. The real information model can be approximated finitely for a specific application scenario and thus becomes a rational information model.
7. The rational information model cannot be stored in one place.
8. The rational information model is never fully visible.

Figure 3 illustrates these hypotheses. An asset is an object of value. What an asset is specifically for a specific application scenario depends on the application scenario. Whether this object is tangible or intangible, a product or a production system, a type or an instance, is irrelevant. The digital twin becomes visible at several locations at the same time along the product life cycle and interacts with an actuator (human, machine, etc.) at these locations. As a result, the digital twin has a variety of states. However, the digital twin is put into a context-specific state in a concrete interaction situation. An example of an interaction situation is the creation of the CAD model of a product type (context) by a designer (actuator). In this case, a CAD model has a state (in process or similar). Another example of a concrete interaction with the digital representation of the same asset, i.e. in the case of the product type, is the software design (context) by a software architect (actor).

The information describing a digital twin is consequently very different in nature and depends on the asset. It is therefore not possible to define a complete information model for digital twins. The information model is infinitely large and should be understood as a real information model. The attribute “real” is based on mathematics, in which the range of real numbers includes rational and irrational numbers. However, in order to be able to interact with a digital twin in a specific application scenario, an approximated information model must exist. We refer to this, again based on mathematics, as the rational information model. As can be seen in Figure 3, the data of the rational information model is distributed along the product life cycle. They are not stored in one place, for example in a central database. In order to feed the data required for a specific interaction situation to a specific actuator, this data must be transported via a suitable interface infrastructure. Consequently, all data of the rational information model are never completely visible.


Image 3: Possible infrastructure for digital twins.

What should be concluded from this

Productivity increases can be achieved by digitizing products and production. The vertical integration of factory and IT systems is making great strides. The horizontal integration over the product life cycle with the help of digital twins, however, offers at least as great potential for productivity increases, especially in engineering.

However, digital twins are not clearly defined today and this makes their management difficult in practice. This article proposes a theoretical model in order to break away from the attempt to create a clear definition and to be able to concentrate on concrete mechanisms and added values ​​of the abstract concept.

Since a scientific theory can only be refuted and not proven, a discussion of the hypotheses presented above is necessary. So far, this has not been done, as the aim of this article is to introduce the idea of ​​the Digital Twin Theory and to present it for discussion as an alternative to a classic definition. In order to make the Digital Twin Theory more precise, further research requires an active examination of the hypotheses it contains. For this purpose, inter alia. The research project "Technical infrastructure for digital twins" initiated by Fraunhofer IOSB-INA and the Ostwestfalen-Lippe University of Applied Sciences as part of the it’s OWL top-class cluster [21].

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Keywords:

Digital twin, Industry 4.0, asset administration shell, interoperability

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