## 2 Methodology  
### Methodological Fundamentals  
The methodological fundamentals are systems theory and system dynamics simulation of a digital twin model: systems theory to model the ODAM network as a holistic system without missing any essential element; and system dynamics simulation as it is a high abstraction modelling technique based on stock and flows for the assessment of different policies and measures in a continuous flow. Finally, the obtained data allowed the digital model to provide practical guidance for a similar system under different demand and lead-time conditions.  
Systems theory is a multidisciplinary science and provides different approaches to solve complex problems [22]. First, a system always consists of a combination of several elements. Second, these elements are interdependent and have multiple relationships to one another. A system is the product of these interactions [23]. The third characteristic is the system boundary. It is arbitrarily determined and serves to distinguish it from the environment of the system [24]. The system environment also consists of systems and elements. If relationships exist between the system and other elements in the environment, it is an open system; otherwise, it is a closed system [23].  
Models represent real-world conditions that can be simplified or made more complex to develop and test theoretical solutions to problems. To create a model, the attributes found in a real-world system must be reduced to the relevant aspects [25]. In modeling based on system theory, there is a basic procedure. In the first step, the underlying real system is transferred to a model. A delimitation of the area of investigation and a restriction to essential features of a system are necessary to reduce the complexity [26]. In the second step, the interdependencies are recorded in the model. In the last step, the model is empirically validated regarding its practical applicability and then transferred to a real system [26]. Simulations have been defined as a targeted experiment on a model to gain knowledge for solving a real-world problem [27]. A distinction can be made between two types of simulations, static and dynamic simulation. In static simulations, one point in time is considered; therefore, the time factor is irrelevant. Since time plays an essential role in our study, a dynamic simulation was chosen. A distinction can still be made here between continuous, discrete, and hybrid simulations. Continuous simulations have continuous changes in state that are described by differential equations. In discrete simulations, state changes occur according to a predefined sequence of events. Hybrid simulations use both discrete and continuous elements [28]. Dynamic simulations were defined for this research as follows: \"Simulation is the reproduction of a system with its dynamic processes in an experimental model in order to obtain knowledge that can be transferred to the level of reality\" [29].  
[PERSON] et el. (2017) developed a system dynamics model for the European air transport system with four stakeholders, including their passengers, airlines, airports, and aircraft manufacturers to provide guidance for airport runway and terminal expansions as well as for aircraft acquisition [4]. However, their research provided neither a holistic approach with ODAM options nor any consideration for electric aviation and its characteristics.