# Digital Twin-Enabled Decision Support Services in Industrial Ecosystems
## 6 Conclusions and Further Research  
Business processes and, in particular, service processes in industrial ecosystems extend over the lifecycle and across several entities of the ecosystem. Managerial decisions in operational processes, therefore, are complex and require taking into account a multitude of perspectives. Digital twins lend themselves to support decision making in such complex systems. However, in practical cases, the development of digital twins for complex systems may either encounter the problem of being too isolated, i.e., focusing on a too small subset of the problem space (e.g., individual assets), thus having not enough relevance for decisions impacting larger fractions of the ecosystem. Alternatively, if a more comprehensive modeling of the ecosystem is considered, the digital twin may become overly complex, resulting in too high modeling costs and potential feasibility hurdles (e.g., not enough data available across the ecosystem). Therefore, the development of a digital twin for decision support in complex industrial ecosystems requires an approach that takes into account the entities that are relevant for supporting the decision and that leverages the interrelationships between these. The hierarchical modeling approach presented in this paper achieves this by deriving sub-questions from the top level business question, defining individual digital twins per sub-question, and systematically interlinking these twins using semantic modeling based on the concept of ontologies. Through model-based system engineering, digital twins are described by KARMA language which can generate ontology models automatically. Such ontology models can be used as middleware to automatically transform them to simulation models in digital twins. Additionally, the hierarchical concept allows for a systematic perspective on the business ecosystem and for the identification of additional potential for value creation by digital twins. The new conceptual model was developed on the basis of real cases of manufacturing companies and condensed into an application case that covers the different aspects of the ten case studies. This application shows that using the new conceptual model, different operational states in the ecosystem can be simulated, thus supporting the decision-making process for the optimal response to events. In this application, comprehensively modeling the entire ecosystem system is not required. Instead, it is sufficient to focus on the elementsin the system that are relevant to the business decision questions, which reduces complexity and saves time and costs. Therefore, referring to the research question formulated in the introduction, we can summarize that the paper provided an answer on how to combine and model multiple types of digital twins in complex industrial service ecosystems in order to create value for decision support. Specifically, the paper described how decision support problems can be broken down to a set of hierarchical questions, which can be translated into a hierarchical architecture of digital twins. The design and the interlinking of these twins are enabled by semantic models based on the concept of the ontology, thus promoting data interoperability in complex industrial service ecosystems. The combination of these twins is specifically targeted at supporting the decision maker's questions. The approach is effective in the sense that this specific focus on the problem means that it contains only the technical elements necessary for it. There are a couple of limitations to this work. On one hand, the generic applicability of the framework should be validated with further cases. There is the hypothesis that resulting from this, typical patterns of combinations of digital twins will emerge. Another extension of this work may take into consideration the different phases of the equipment lifecycle.