# Digital Twin-Enabled Decision Support Services in Industrial Ecosystems
## 5 Theoretical and Practical Implications  
This study extended the findings of [1], which integrated the modeling approaches for digital twins and extended these to modeling on the level of service ecosystems. It provided a new concept for structuring decision problems in the operations of industrial equipment into sub-problems that can be modeled by individual digital twins on their respective level in a systematic hierarchy. This procedure resulted in a collection of elements of digital twins as shown in Figure 4. In [35], this collection is referred to as the \"family of twins\", together making up a digital twin concept that is derived from the business decision problems that need to be solved. A top level business decision problem (top of the pyramid) can be broken down into sub-questions on different hierarchical levels of the ecosystem. Each question and sub-question is represented by a digital twin. Moreover, given a specific hierarchical level, there are several sub-questions and thus several twins on the same level. The interrelations between those different twins is semantically modeled by an ontology, which plays a central role in this concept. Additionally, developing individual digital twins in the hierarchical structure and interrelating them through the concept of ontology paves the way for identifying additional digital twins in the system that were not directly derived from the a priori known decision problems but that have the potential to create additional value for specific actors in the system. For instance, when modeling a machine and its components, the ontology can show additional connections to entities or components for which an additional digital twin may improve the overall machine model. Thus, the semantic modeling by ontology substantially supports the modeling of the knowledge in the ecosystem and thus decision making. The study also provided a concept for deriving simulation models given the hierarchical model described by the ontology. A model transformer was applied to transform the ontology model to the simulation model. Thus, the development chain from the top level business question to the simulation model implementation is represented by a consistent modeling chain. By providing an end-to-end implementation of the new concept in a manufacturing SME case study, the theoretical concepts were applied to a specific implementation scenario shedding light on the mixed model approach for the simulation of the concept. This implementation shows how the business decision problems can be broken down into different types of system triggers and how these are modeled by specific elements in a simulation environment in a practical industrial case. This new modeling approach provides a blueprint for companies for developing digital twin based services in their own operations while taking into account the human and technical actors in their ecosystem that are relevant for the decision. Integrating the hierarchical concept with the concepts of ontology modeling enables leveraging the benefit of the multitude of digital twins systematically. Practical cases show that decision support for the operations of industrial equipment is required at specific stages of the lifecycle. In these cases, managers need to decide on managerial actions that have an impact not only on the equipment that is triggering the decision, but also potentially on any other equipment of the installed base. Thus, decision making is a complex task that cannot be supported by a digital twin with a narrow lens on the equipment. However, modeling an entire ecosystem through a comprehensive digital twin would be too complex and too cost intensive. Therefore, the systematic hierarchical modeling process starts with the manager's business question, developing additional digital twins only if required, and achieving their interlinking supported by semantic modeling helps solve the relevant business problems in a cost-effective way. Similarly, three-dimensional visualization of equipment, therefore, is often not part of this concept.