# Enhancing Production Efficiency Through Digital Twin Simulation Scheduling
## 6 Conclusions  
While our case study was conducted in a roof window production facility, the methodology and findings are intended to be generalizable to a broad range of manufacturing sectors. The core of the approach is a simulation-based scheduler updated with real-time shopfloor data--a concept that is not specific to any particular product. The roof window assembly line that we examined shares many characteristics with other production systems: multiple workstations, parallel processes, material handling requirements, and the need to sequence tasks efficiently under capacity constraints. These are common features in industries such as automotive, electronics, aerospace, and consumer goods manufacturing. Thus, the digital twin scheduling framework can be adapted to other contexts by inputting the relevant process data and constraints of the new domain. For instance, an automotive assembly plant could integrate real-time vehicle tracking and workstation status data into a digital twin scheduler to optimize its line sequencing; indeed, a recent automotive case study showed a 6% increase in production line efficiency and a dramatic reduction in downtime after implementing digital twin technology [39]. This indicates that the benefits of a digital twin approach translate across different manufacturing environments. In our study, the improvements in throughput and lead time were achieved without any roof window-specific algorithms--the logic can be re-applied to any scenario where tasks, resources, and scheduling rules are defined. Of course, when generalizing, one must account for sector-specific variations (for example, different production rhythms or quality control steps), but these can be incorporated into the twin's model. We argue that the results are broadly applicable and not an artifact of a particular product's process. To further validate its generalizability, future research could deploy the system in other manufacturing settings (such as a machining job shop or an electronics PCB assembly line) and compare the outcomes. We anticipate similar efficiency gains, since the underlying principle of real-time adaptive scheduling is advantageous in any complex production system facing variability. The practical relevance and adaptability of the proposed digital twin-based scheduling system are further supported by two additional industrial case studies. The first example is the implementation of a digital twin system at Marmon Foodservice Technologies, realized in cooperation with Asseco CEIT. This project combined real-time location tracking with a modular digital twin platform, enabling the continuous monitoring of products, operators, and material flows. The solution replaced manual data entry, streamlined logistics, and improved the production visibility. As a result, the company reported a 15% increase in productivity, 2700 ft2 of saved floor space, and the integration of two production lines into one. The fact that the architecture was developed by the same industrial partner involved in our research demonstrates the system's potential for transfer to other production environments [40]. In another case, the integration of a Sewio RTLS into a make-to-order production setting helped to reduce the lead times and improve process transparency. By digitizing work orders and introducing real-time tracking, the company was able to identify inefficiencies more quickly and adapt its production plans dynamically. The benefits included higher throughput, better coordination, and more reliable quality control [41]. These examples illustrate that the architecture and principles described in our study--particularly the use of live data, modularity, and adaptive prioritization--can be effectively applied in various production contexts. Whether in custom manufacturing or high-mix low-volume environments, the system has demonstrated its value in practice, beyond laboratory or purely conceptual implementations.