## 4 Designing a Digital Twin for the Factory of the Future
### Enabling Co-Simulation for Holistic FoF Modeling  
As discussed above, the FoF consists of a huge number of connected components covering a huge number of application fields. According to [PERSON] et al. [42], the FoF DTs can be built for manufacturing assets, people, factories and production networks, every component mirrored by DTs. To test the influence of single components on the overall production process and to identify optimization and security gaps for the FoF, it is important to understand the interoperability and dependencies of those components. While building one overall DT for the FoF ex nihilo seems utopic, it appears more realistic to integrate individual DTs according to the hierarchical structure of the FoF. This requires the possibility to connect DTs to mirror the relations between their physical counterparts. Such a composition capability of DTs was presented by [PERSON] et al. [59]. According the authors, DTs can be composed in the following ways:  
* hierarchically such as manufacturing assets building a production environment;
* by association such as producers and consumers in production lines; and
* peer-to-peer, as a network of systems with similar behavior or inputs and outputs. The real power of a DT--and why it could matter so much for Industry 4.0--is that it can provide a near-real-time comprehensive linkage between the physical and digital worlds. DT promise richer models that yield more realistic and holistic measurements of unpredictability. Thanks to cheaper and more powerful computing capabilities, these interactive measurements can be analyzed with modern-day massive processing architectures and advanced algorithms for real-time predictive feedback and offline analysis. These can enable fundamental design and process changes that would almost certainly be unattainable through current methods. The creation of the DT encompasses two main areas of concern:  
* DT process design and information requirements: It is important to link the process flow to the applications, data needs and the types of sensor information required to create the DT. The process design should also be concerned with attributes and features that allows the improvement of cost, time or asset efficiency. These typically form the base line assumptions from which the DT enhancements should begin. * DT conceptual architecture: The architecture should represent a model of a manufacturing process in the physical world and its companion twin in the digital world. The DT serves as a virtual replica of what is actually happening on the factory floor in near-real time. Thousands of sensors distributed throughout the physical manufacturing process collectively capture data along a wide array of dimensions: from behavior characteristics of the productive machinery and works in progress (thickness, color qualities, hardness, torque, speeds, etc.) to environmental conditions within the factory itself. These data should be continuously communicated to and aggregated by the DT application. The DTs in the FoF spread up to different scopes and application fields. The single DTs for components and networks which are involved in production will be developed from different experts and engineers, which lead to various concepts and structures. Composing these DTs together might lead to integration issues. To bring a complex system of various and probably independently developed components together, [PERSON] et al. [130] introduced co-simulation as a suitable approach. Co-simulation \"as the coordinated execution of two or more models that differ in their runtime environments\" [131] differs from other simulation types by the amount underlying models and the amount of solving engines. Using one solver or calculating engine with one model for the overall system is called a \"classic simulation\" (Figure 5); with \"parallel simulation,\" one model is executed with different solvers. Considering the diversity of domains both kinds of simulation do not fit to the FoF because they require a unique model to solve.