# Enhancing Production Efficiency Through Digital Twin Simulation Scheduling
## 6 Conclusions
The accuracy and reliability of the proposed model in real-world operation can be influenced by external destabilizing factors, and addressing these is crucial for sustained performance. In a controlled simulation or pilot environment, we assumed that the input data (from the RTLS and other sensors) were mostly accurate and the primary sources of disturbance were modeled (e.g., variable processing times or minor machine stoppages). However, actual manufacturing environments are often rife with unplanned events that can challenge the model. Machines can break down unexpectedly, operators may be absent or reallocated, urgent rush orders might be inserted into the schedule, or upstream supply delays could deprive a production line of the needed materials. Such events can quickly render an optimized schedule suboptimal if the model does not detect and adapt to them. Our system's ability to handle these disturbances depends on how promptly and accurately the digital twin is informed of the changes. This is where robust sensor validation and error analysis become essential. Each sensor (including the RTLS units) should be continuously monitored for accuracy; faults like a dropped signal, location drift, or incorrect readings must be detected and filtered out. Implementing sensor fusion (cross-verifying data from multiple sources) can help to identify anomalies--for example, if a pallet's RFID tag states that it moved stations but the RTLS shows that it is stationary, an alert can be raised to check for an error. We performed a post-experiment error analysis by comparing the digital twin's predicted performance metrics with the actual observed outcomes on the production floor, which helped to identify any deviations. This type of analysis is useful in pinpointing whether discrepancies are due to model assumptions or noisy data. In fact, other researchers have emphasized frameworks that synchronize the digital twin with the physical shopfloor under uncertainty, underscoring the importance of aligning the virtual model closely with reality [42]. In our case, we found that occasional mismatches between the simulation prediction and actual production output were often traced to sensor errors (such as slight delays in RTLS data transmission, causing outdated position information). To counteract this, we stress the necessity of rigorous sensor validation: calibration routines, the periodic maintenance of devices, and fallback strategies (e.g., using default conservative estimates when data are missing) should be in place. Additionally, the model should incorporate buffers or failsfares for extreme outlier events--for instance, maintaining a small slack in the schedule that can absorb sudden disruptions without cascading failures. Overall,acknowledging and planning for external destabilizing factors ensures that the digital twin scheduling system remains robust and reliable. By investing in error analysis and validation, practitioners can continuously improve the model's accuracy, making it resilient against the inevitable uncertainties of a factory environment. In the current implementation, the cycle times used for production planning are derived from historical RTLS measurements collected in the real manufacturing environment. These values inherently account for performance losses and minor stoppages typical for each workplace and thus reflect the actual equipment effectiveness (OEE) rather than idealized or nominal times. This approach was chosen deliberately to ensure that both the original planning method and the proposed digital twin-based system would operate on identical baseline assumptions. By using the same empirical cycle times, any observed improvements in the scheduling performance can be attributed directly to the applied planning methodology and not to differences in input data quality or assumptions. Regarding supply chain constraints, potential material shortages are addressed at the ERP level, where a material availability check ensures that only feasible production orders enter the planning cycle.