which represents the virtual copy of the existing physical objects [2], these physical
entities are also called assets. Academicians have abbreviated Digital Twin as DT
[2, 3]. DTs can represent almost any aspect of an associated asset, ranging from
highly aggregated information to a detailed description of its components and
performance; even simulation models of assets can be part of a DT. The final result
depends largely on the use cases, which are addressed with the DTs.
Applying DTs to newly built plants or production lines is challenging and even
more complicated in operating production processes or factories. Heterogeneous
processes have various requirements, which result in different obstacles in
establishing a working Digital Twin Management System (DTMS), used to integrate
different DTs. Plants and their processes often evolve, which leads to complexity.
Inhomogeneous and even incompatible systems result in connectivity problems and
imprecise interrelations. Following Rosen et al. [4], DTs are not just a collection of
virtual objects, but require interrelations, connections, and structure within a DTMS
to leverage the full potential.
Connectivity, modularity, and autonomy are key enablers of DTs [4]. They
improve process development, production planning, process intelligence, production
execution, and the individualization of products and equipment. DTs connect the
virtual networks and systems with the real world. Furthermore, full trackability and
traceability, which are essential for food safety, become feasible. Artificial intelligence (AI)-enabled concepts and technologies lead to state-of-the-art production
concepts and the establishment of production intelligence. Finally, DTs, together
with a DTMS, support companies to prepare for the challenges of I4.0. Kritzinger
et al. [3] categorize a DT into a Digital Model and a Digital Shadow based on the
level of integration. DTs comprise an automatic data flow between physical and
virtual objects. Their functionality depends on the accuracy of the underlying
semantic description, the assignment of relevant information, and a well-designed
structure in the DTMS. Therefore, suitable techniques and standards must be
applied. For example, the Asset Administration Shell (AAS) is a domainindependent standard of the German Platform Industrie 4.0, which specifies how
to construct DTs, namely their data models and their interfaces, that allow efficient
interaction in Industry 4.0 scenarios. It is being developed as a standardized software
interface of any physical assets.
This chapter explains the strategies and tools to implement DTs into operating
value chains or industrial processes and demonstrates its successful implementation
with three case studies by analyzing the value chain, corresponding stakeholders,
and the process as well as the primary infrastructure. From the initial step with an
analysis of the stakeholder and DT goals to the final implementation, the chapter
includes all necessary steps for successful execution. Figure 1 provides a graphical
overview of the general implementation pathway which is outlined in the different
sections of this chapter.
Notably, the status quo associated with the development of the future desired
digitalized structure (DTs and DTMS) is illustrated. A physical model (physical
picture) and a data model (virtual picture) of the considered value chain are also
included showing the interrelations and interfaces among stakeholders. The physical
The Challenge of Implementing Digital Twins in Operating Value Chains
129
entities are also called assets. Academicians have abbreviated Digital Twin as DT
[2, 3]. DTs can represent almost any aspect of an associated asset, ranging from
highly aggregated information to a detailed description of its components and
performance; even simulation models of assets can be part of a DT. The final result
depends largely on the use cases, which are addressed with the DTs.
Applying DTs to newly built plants or production lines is challenging and even
more complicated in operating production processes or factories. Heterogeneous
processes have various requirements, which result in different obstacles in
establishing a working Digital Twin Management System (DTMS), used to integrate
different DTs. Plants and their processes often evolve, which leads to complexity.
Inhomogeneous and even incompatible systems result in connectivity problems and
imprecise interrelations. Following Rosen et al. [4], DTs are not just a collection of
virtual objects, but require interrelations, connections, and structure within a DTMS
to leverage the full potential.
Connectivity, modularity, and autonomy are key enablers of DTs [4]. They
improve process development, production planning, process intelligence, production
execution, and the individualization of products and equipment. DTs connect the
virtual networks and systems with the real world. Furthermore, full trackability and
traceability, which are essential for food safety, become feasible. Artificial intelligence (AI)-enabled concepts and technologies lead to state-of-the-art production
concepts and the establishment of production intelligence. Finally, DTs, together
with a DTMS, support companies to prepare for the challenges of I4.0. Kritzinger
et al. [3] categorize a DT into a Digital Model and a Digital Shadow based on the
level of integration. DTs comprise an automatic data flow between physical and
virtual objects. Their functionality depends on the accuracy of the underlying
semantic description, the assignment of relevant information, and a well-designed
structure in the DTMS. Therefore, suitable techniques and standards must be
applied. For example, the Asset Administration Shell (AAS) is a domainindependent standard of the German Platform Industrie 4.0, which specifies how
to construct DTs, namely their data models and their interfaces, that allow efficient
interaction in Industry 4.0 scenarios. It is being developed as a standardized software
interface of any physical assets.
This chapter explains the strategies and tools to implement DTs into operating
value chains or industrial processes and demonstrates its successful implementation
with three case studies by analyzing the value chain, corresponding stakeholders,
and the process as well as the primary infrastructure. From the initial step with an
analysis of the stakeholder and DT goals to the final implementation, the chapter
includes all necessary steps for successful execution. Figure 1 provides a graphical
overview of the general implementation pathway which is outlined in the different
sections of this chapter.
Notably, the status quo associated with the development of the future desired
digitalized structure (DTs and DTMS) is illustrated. A physical model (physical
picture) and a data model (virtual picture) of the considered value chain are also
included showing the interrelations and interfaces among stakeholders. The physical
The Challenge of Implementing Digital Twins in Operating Value Chains
129