## 1 Introduction  
In the course of digitalization, the possibility of interaction between the real and virtual world promises innovations, new business models and considerable optimization potential in industry and logistics within the framework of Industry 4.0 [1]. To realize this potential, digital networking of production and logistics using the _Internet of things_ (IoT) and cloud technology as well as other digital technologies, such as artificial intelligence, is necessary. With respect to the energy transition and the predicted growth in freight and passenger transport, German rail transport is becoming increasingly important due to its climate-friendly CO2 balance compared to other means of transport [2]. In response, the federally owned railway company _Deutsche Bahn AG_ (DB), which is the largest railway operator and infrastructure owner in Europe, launched its umbrella strategy _Strong Rail_ and decided on the largest modernization program to date, amounting to 87 billion euros [3, 4, 5]. It is a comprehensive approach for expansion of the rail network, passenger and freight transport, which should serve the top maxim of securing the system capacity as it plays an important role in the provision of overall transport services [3, 6]. With the need to modernize the rail network and infrastructure comes the need for targeted operational monitoring and predictive maintenance of relevant assets to ensure the operational reliability and availability of German rail transport. To achieve this, digital technologies promise the necessary innovation potential to solve challenges that have not been satisfactorily solved so far, such as the punctuality of German rail vehicles, which result from rail network congestion and the partially inadequate technical condition [7,8]. The transfer and representation of real objects in a digital and virtual environment, the so-called creation of a DT [9], enables operational and visual monitoring in real time as well as early detection of unscheduled deviations in operation. The early prediction of future events, based on data-driven evaluations as well as simulations and forecasts, can optimize operations and maintenance, enable preventive measures, and generate potential savings. In addition, this holistic data collection and visualization in a downstream instance provides the opportunity, for example, to network the value creation network of maintenance in a cloud-based manner and to profitably exchange and analyze data-driven information among manufacturers, operators and maintenance staff [10]. In an EU research project called _EU OPTIMSED_ it was shown that a DT implementation in rail transport can be profitable. Through the DT, the management of train maintenance work was hereby simplified and efficiency as well as availability of the train fleet realized [11]. Looking to the future, the gradual introduction of DTs in rail transport thus forms the basis for a holistically networked and intelligent system that can monitor and control operations in a targeted and efficient manner [12]. This so-called _Supra-system_ connects self-contained DTs on an open and comprehensively networked platform and thus avoids data silos. Due to the openness of the platform, additional services can be integrated and considerable added value can be created for all stakeholders [10]. A study by the _information technology_ (IT) market research company Gartner reveals that more than 74 percent of companies in a wide range of industries are planning to implement DTs in their processes in the next five year [13]. Furthermore, business scenarios for DT implementations are expected to become more attractive and the technologies used more reliable and cost-effective. Nevertheless, only 13 percent of companies in a wide range of industries that have already implemented IoT technology and solutions, are making use of the promising DTs [13].