# Digital Twin-Enabled Decision Support Services in Industrial Ecosystems
## 4 Results
### Multi-Agent Simulation Supporting a Process Twin Construction for SMEs
#### 4.5.2 Mixed Model Approach  
To meet the expectations of the business case Section 4.5.1 we used a mixed model approach to create a digital twin of the service processes. The mixed model approach structure allows a logical representation of the different hierarchical levels of the digital twins. In our mixed model approach, we combine system dynamic, agent-based, and discrete event models. This combination allows us to approximate the complex real service ecosystem. Here, the goal is to display only as much information as is necessary to answer the question. This simplification reduces the time needed to create the models and thus saves costs. Thanks to the use of agent-based structures in our model, we can represent the individual actors from the ecosystem and their behavior. Within the actors, discrete-event models allow us to represent business processes and thus control the behavior of the actors. Furthermore, by using system-dynamic models, we can simulate the physical behavior of actors, for example, in machines Figure 10.  
For some actors in the ecosystem, agents were created, which represent them in the model. These agents are partly directly nested in each other or interact with each other via the main agent. The following agents are used in the model:  
1. Main (simulation environment)
2. MillerMills (representing the company as a whole)
3. Customer
4. Machine
5. Component
6. Service Technician
7. Vehicle  
Figure 11 depicts the model structure and the nesting of the different agents. Furthermore, the different simulation methods used in the different actors are indicated. This structure links the hierarchical concept shown in Figure 4 and the mixed model approach, since some elements of the simulation would also work on their own. By integrating these elements, more complex questions can be answered. The various actors are embedded in the main agent and simulated nested in a GIS (geographic information system) environment. This GIS environment allows the user (e.g., the decision maker) to directly simulate the interaction of the service technicians, the vehicles and their routes to the customers in a reality-based environment. This allows conclusions to be drawn about travel times and possible optimization at the service technicians' starting locations. Each customer is registered as an independent agent in the GIS environment and contains the machines in operation. Each machine comprises four different components, which are simulated directly nested in the machine. MillerMills is the headquarters of the SME where the warehouse of spare parts and their production is located. The various  
Figure 11: Structure and Types of Modeling Approaches used in Agents. Figure 10: Combining System Dynamics, Agent Base,\(d\) and Discrete Event into a Mixed Model Approach. agents act independently of each other and communicate their needs or inventory and availability when requested. The service technicians, for example, receive orders such as standard maintenance from the headquarters, which they have to carry out. The working hours, as well as the travel times to the customers, are simulated so that a realistic basic behavior can be created in the model. This process twin simulation model is triggered by one of following three possible triggers [46]:  
1. Operator Triggers: where an operator in the system starts the process twin simulation. This triggering could be due to a machine malfunction or any other unplanned event that needs to be handled by the service department. 2. Period Triggers: recurring triggers which need to be planned. For example, yearly maintenance visits. 3. Anomaly Triggers: due to machine data and digital twins detected possible machine failures. This trigger opens the possibility to plan a suitable reaction. Based on the simulation results, the model suggests different alternative actions. Based on this decision support, the decision maker can now plan the response in the real world in combination with its wealth of experience.