## 1 Introduction
Furthermore, a study by the worldwide largest logistics company _DHL_ comes to the conclusion that within the logistics industry, companies largely have the necessary technologies for DT implementation, but are not yet fully exploiting this potential due to planning and implementation uncertainties [14]. With reference to the listed potential and the expected increase in attractiveness, the question, therefore, arises which aspects make the implementation of DTs more difficult or even prevent it. The application domain of DTs is still dominated by proprietary solutions and prototypes and therefore at an early age. It focuses predominantly on applications in manufacturing and production industries, while DT applications \"lack the necessary generality and openness to be widely used\" [15]. The use of individual DTs for assets amounts to less than one percentage point in a representative survey of CIOs, whereby market maturity is also not expected for three to five years [13]. In the context of barriers to implementation, the transport and logistics industry notes that \"implementing a digital twin is a complex and challenging task\" [14]. A meta-study categorizes possible challenges for companies that consider the implementation of DTs to be useful into technical, commercial, technological, data-driven and other challenges [10]. The analysis of data is a central challenge of DTs, whereby the existence of a sufficient IT infrastructure plays a crucial role [16]. Companies are facing the challenge of designing uniform data structures and management systems as well as merging and analyzing collected data sets in a targeted manner. This raises questions for companies about data security, ownership, and distribution. Hereby the avoidance of data silos is difficult, as there are no uniform legal structures and approaches for the exchange of data. Furthermore, the existence of different IT systems and software complicates the implementation of DTs [10]. Regarding the maturity of DTs, two further challenges are the management of expectations and trust. DTs should only be used where it makes sense and they must be understandable for all stakeholders, regardless of their IT expertise [17]. In addition, there are also challenges regarding the standardization and compatibility of data, interfaces and components [16] as well high costs and interoperability of DTs [14]. Furthermore, \"regarding the implementation of DTs, a critical question is how to build a practically viable DT model. On the one hand, almost every paper [...] acknowledged the importance of DT modeling. On the other hand, no consensus has been reached regarding how to build a DT model in a generic way\" [18]. From the aspects described, it can be deduced that companies in the rail transport sector, which consider the use of DTs regarding operational optimization to be sensible, lack a generic method, which they can use as a guide for the implementation of DTs. In this scientific work, a generic strategy is therefore developed, which serves as a guideline for the implementation of DTs in German rail transport and should thus contribute to overcoming the challenges mentioned. A multi-perspective research question is derived from this, which, following a broad understanding, illuminates technical as well as organizational aspects. The research question is:  
\"How can a generic implementation strategy of digital twins in logistics systems be designed considering German rail transport?\"  
As this research question aims to provide a specific answer that can comprehensively answer the complexity of the research problem, it is complemented by the following research objectives:  
1. To identify and define the essential theory needed for the entirely novel development of a GIS for DTs in German rail transport;
2. To analyze related scientific work and derive requirements for the problem-oriented development of a GIS for DTs;
3. To develop, apply and validate a GIS of DTs in logistics systems of the German rail transport;
4.