# Enhancing Production Efficiency Through Digital Twin Simulation Scheduling
## 5 Discussion  
The proposed digital twin simulation scheduling approach offers clear advantages over traditional production scheduling methods, particularly through its integration of real-time RTLS data for dynamic decision-making. Conventional scheduling systems often rely on static schedules or periodic updates, lacking continuous feedback from the shop floor. By contrast, our approach continuously feeds live data (e.g., worker and material locations from the RTLS) into the scheduling algorithm, enabling on-the-fly adjustments to production plans. Prior research has acknowledged that digital twin technology could yield \"step improvements\" in production scheduling, yet only \"modest endeavors\" have been made to apply it in practice [31]. Our work pushes beyond existing methods by not only employing a digital twin for scheduling but also tightly coupling it with an RTLS, a combination that, to our knowledge, is largely novel in the manufacturing domain. In manufacturing settings, the RTLS is typically used to track and optimize material flows and monitor inventory [35]; we extend this capability by using the RTLS data to directly  
Figure 11: Other statistics of the presented schedule. drive scheduling decisions in real time. This yields a more responsive system than those in prior studies, which generally either simulate digital twins without live data input or use real-time data only for monitoring, rather than active scheduling adjustments. The result is a scheduling approach that can immediately react to on-floor changes (such as a delay at one workstation or sudden machine availability) in ways that static or even periodically updated schedules cannot, thus highlighting the practical novelty of our RTLS-integrated digital twin scheduler. We rigorously evaluated the effectiveness of the proposed model using statistical methods to ensure that the observed improvements were significant and not due to random chance. Key performance indicators from the case study (such as the throughput, order lead time, and machine utilization) were compared between the digital twin-driven dynamic scheduling method and the baseline scheduling method. For each KPI, we computed 95% confidence intervals for the mean performance under each scheduling approach. In all cases, the intervals showed no overlap, indicating a clear improvement with the digital twin scheduling method at a high confidence level. We further performed an analysis of variance (ANOVA) to examine whether the differences in the performance metrics were statistically significant across multiple simulation runs and scenarios. The ANOVA results confirmed that the improvements were robust (\(p<0.05\)), i.e., the digital twin scheduling method yielded statistically better outcomes than the traditional scheduling approach in terms of production efficiency. This statistical rigor is in line with the best practices in emerging digital twin research--for example, other scholars have applied ANOVA to analyze the behavior of digital twin-based production scheduling systems [36]. By employing similar methods, we ensured that our claimed improvements were supported by solid evidence. In summary, the observed gains are not only practically meaningful but also statistically significant, strengthening the confidence that the proposed approach truly enhances the production performance, rather than simply tuning the system to known conditions. Despite its benefits, our approach comes with certain limitations, notably in the areas of security and the implementation cost. One key concern is the potential security vulnerabilities introduced by digital twin integration. Because the system continuously exchanges data between physical machines (via IoT sensors and RTLS devices) and the digital model, the attack surface for cyber threats increases. Digital twins often interconnect with IoT devices and cloud services, and each connection point can become an entryway for attackers [37].