## 3 Results
### Systematic Evaluation of the Identified Literature
#### 3.4.4 Enterprise Information Systems  
A total of 21.43% of the papers were assigned to the cluster \"enterprise information systems\". To successfully implement a DT, a bidirectional connection to established enterprise information systems is necessary. In general, a digital thread through enterprise information systems is an important part of enabling the use of DT technologies throughout the product lifecycle. Therefore, product lifecycle management (PLM) systems, enterprise resource planning (ERP) systems, manufacturing execution systems (MES), and customer relationship management (CRM) systems should be considered and integrated to cover operational tasks such as production planning, control actions for production and logistics systems, as well as monitoring and dispatching of production processes and equipment [19]. In more detail, [PERSON] et al. describe the required data and their sources for machine-building enterprises. Portfolio information, production program data, as well as important performance indicators can be obtained from advanced planning systems (APS), MES, supervisory control and data acquisition (SCADA) systems, multi database container (MDC), and machine data acquisition (MDA) systems. product data management (PDM), CAD and computer-aided manufacturing (CAM) systems contain information on design and process parameters. These systems can also be accessed for the description of the tech nological process and the required resources, as well as information on maintenance and repair processes. The availability, reliability, and utilization of technological resources and information of WIP are available through ERP, SCADA, MDC, and/or MDA systems [37]. Other authors propose an MES-centric approach for DT, whereby the MES is connected to the physical system and contains all information the DT needs for its purpose. In addition, the DT is connected to the MES and receives all historical and real-time data required for the mirroring of the operational tasks. A use case for the optimization of the material supply scheduling process within a frame shop production is presented. Thereby, an analytical model is used to predict the impact of different decisions, and the LINGO software is used for optimization. This has improved the line balance rate of the production by 45% compared to the previous planning process as well as significantly reducing the reaction time to errors [41]. Likewise, [PERSON] et al. present an MES-centric approach, whereby a DES is used as a VM. The model represents a workshop of an automotive supplier. A sequence diagram shows how the communication between MES, optimization algorithm, and DES could proceed in time. In this case, dispatching rules and production schedules should be optimized by using a genetic algorithm. Compared to the previously calculated schedule, the simulation results show potential regarding the number of tardy jobs, throughput time, and monthly working time usage. However, the conclusions are derived from offline simulations only [36]. Nevertheless, to address dynamics on different levels of a production logistics system, the connection with other enterprise information systems is necessary. To dissolve dynamics on various levels, [PERSON] et al. suggest different computer paradigms. The first level of dynamics described occurs at the shop-floor and concerns individual units. These dynamics can be eliminated by rescheduling tasks and resources using edge computing if no other operations are affected. If the production system also suffers from dynamics, it should be solved by fog computing, if no other decision-making subsystem is concerned. When dynamics occur on an enterprise level, it is suggested to solve them by using cloud computing. Thus, the dynamics can be solved by rescheduling resources, adding new resources, and changing customer demands. A synchronization method is suggested to pass the solutions through the different levels of observation.