## 2 Context and Research Objectives
### FoF Modeling and Simulation Challenge  
Among other technological and organizational developments, DTs have made their entry as digital replicas of the physical manufacturing assets to support remote monitoring and other value-added functions which would otherwise require direct observation or intervention on machines [37]. Most DTs today on the market essentially replicate single equipment, enabling use-cases such as process control and optimization or predictive maintenance. They appear to be of mutual interest to machine manufacturers and uses, as they are meant to remain connected to the physical asset in operation, enabling each party to retrieve informative data on machine performance, maintenance needs and tracks for design improvement or process optimization. It makes it possible to simulate the results by changing some parameters, enabling to confirm the relevant requirements in relation to the  
Figure 1: Key capacities of CyberFactory#1. manufacturing and production capacity during product development [38]. Moreover, this entire process can be considered and managed in real time. A limitation, however, to the value of such digital models from the user's point of view is their inability to comprehensively inform decision not at individual machine level, but rather at SoS level, understand here SoS as the complete production ecosystem. Few attempts have been made to extend the range of application of DTs beyond the limits of individual physical assets [39]. In the context of Industry 4.0, this manufacturing SoS involves a substantial share of physical assets, an increasing share of information systems, but also humans. We consider here a vision of Industry 4.0 where the human is not replaced by the machine, but collaborates with it and remains ultimately accountable as the law provides it [40]. While we accept that information systems are quite extensively addressed by state-of-the-art virtualization technology, humans will remain an essential element of the FoF. Their behavior is known to be less predictable than that of machines or networks [41]. For a manufacturing professional, it is not easy to take the full benefit of DTs as they do not properly model all key elements of the production process, and, thus, they may not provide support to rational decision from a holistic point of view [42]. Therefore, we propose in this paper to address two specific gaps in state-of-the-art DT technology:  
* the lack of simulation capacity to properly model the behavior of humans in the FoF [43]; and
* the lack of a co-simulation capacity to support holistic modeling of distinct physical, digital and human manufacturing assets involved in a common production process [42]. The two gaps are addressed in this paper with proposed innovations to fill them. As our ambition is to set up a complete manufacturing SoS modeling and simulation capacity, we make new uses of the DT for the FoF possible. First, the realistic modeling of interactions among machines, networks and humans enables going beyond mere monitoring of the production and really supporting decisions, if not also the autonomous reconfiguration of the manufacturing SoS for optimization purposes. This property can be considered for static architecture selection at factory design stage, as well as a dynamic optimization capacity in operation [44]. In the first case, it would enable radically new concepts of factories optimized for production in the digital age and freed from the architectural bias of the legacy. In the second case, it would enable continuous improvement of either legacy or new factories in operation, considering all environmental, physical, economical and psychological factors which a single human could not possibly handle in decision making [45]. This framework also enables optimum production planning in distributed manufacturing topologies such as networks of factories or fab labs.