## 4 Validation in German Rail Transport  
### Methodological Principles and Application  
The objective of the practical application of the GIS is to validate and correspondingly readjust the developed model where necessary (see Figure 3). To carry out this validation, semi-structured expert interviews were conducted in a single case study-based research [58]. Hereby, DT experts and decision-makers from DB were chosen and secondary corporate data sources from DB were collected. Subsequently, these data were analyzed and evaluated by means of a qualitative content analysis [48] and are being presented hereafter. The overall focus lied on the first four process categories of the GIS (see Section 4.2). Within the German rail transport, DB must act economically and therefore make investment decisions based on the greatest economic benefit. The operational objective is to provide a reliable, attractive, and competitive service for its customers. Therefore, processes and assets must function and be protected, and disturbance variables, e.g.,weather or vandalism, must be avoided and eliminated. Availability can also be ensured by replacement assets, which is financially expensive. Therefore, a solution is required that ensures that these challenges are met, whereby the planned and unplanned maintenance of the assets and their operations have the greatest benefit and need. Hereby, DTs provide the operationally optimal solution for DB, since they enable the provision of information, e.g., condition, history, inferences for the present and future, about assets and allow to map the interaction of different components relatively easily. This can optimize operations as well as planned and unplanned maintenance, what is no longer possible without data. The DT provides necessary data and, above all, condition data, which is indispensable, for example, for complex fault analyses as well as the analysis of the performance of assets. Dependencies of different systems and their mutual influence is very large and complex, so that it is often necessary to diagnose an entire system, e.g., a train, and not just a single component. For this purpose, all data of an asset must be brought together in a central place to carry out various analyses and identify interactions. Due to the practicability and the relevance of maintenance, the use case of providing asset condition data from an existing ICE train asset was chosen, which is intended to optimize maintenance as well as operation. In this context, the DT becomes the hub for all data analysis and visualization tools through intelligent linking, evaluation and use of all condition-relevant data from operation and maintenance. The required data can partly be acquisitioned by the already existing _control and monitoring systems_ (TCMS) of an ICE train, which connects subsystems with each other and enables the control and monitoring of these. Within this TCMS, there are nowadays nested bus systems due to hierarchical control levels, which are very complex, and its data are not fully accessible. This complexity poses a great challenge, as the bus systems are essential for data acquisition and processing. For example, the vehicle control unit has a lot of information and operating records. These data are held by the ICE manufacturers and is currently only partly available for DB. For these reasons, it is essential to consider from the beginning exactly which data are needed for the use case of the DT, because not all data can be acquired for economic reasons. Although it would be possible to obtain the necessary data from the TCMS by reprogramming the DB internally, this would lead to a comprehensive change at all control levels of the bus systems due to its complexity, which would entail a very expensive re-registration of the ICE train. Therefore, the acquisition of this data becomes the central challenge.