## 3 Results
### Simulation Validation  
To validate the realism and predictive accuracy of the simulation model, a focused comparison was conducted between virtual factory simulation outputs and real production data collected through the DIMUSA system. The validation focused on availability metrics and aimed to identify discrepancies between simulated assumptions and real-world behavior across multiple workstations [29]. Figure 9 presents a direct visual comparison of workstation availability across one selected production day. The upper chart displays the availability values used in the simulation model, derived from baseline process assumptions. Availability represents the share of actual working time relative to a full 8-h shift (480 min), where 100% means uninterrupted operation throughout the shift. The lower chart reflects actual availability as measured by DIMUSA sensors during the same operational window. The contrast between the two layers highlights differences in timing patterns, utilization rates, and workstation coordination. This side-by-side view revealed that simulated data tended to assume more uniform availability across workstations, while real-world data showed greater fluctuation, particularly during shift transitions and material handling events. These findings informed subsequent updates to the simulation model, ensuring more accurate modeling of downtime and micro-delays. Figure 8: Visual representation of the micro-batch production sequence. To further contextualize these observations, the plotter and press workstations were analyzed in detail for two consecutive days-October 16 th and 17 th. On these days, production was structured around micro-batches, and both sensor data and operator-reported task logs were available. Tables 3 and 4 summarize this cross-validation, comparing timestamps and durations across the simulation, DIMUSA monitoring, and manual reporting systems [30]. The analysis confirmed that while the simulation provided a solid approximation of expected process flows, it occasionally underestimated idle periods and overstated continuity. In contrast, DIMUSA sensor logs revealed nuanced interruptions, particularly in the press workstation, where material readiness and operator interactions had a greater impact than initially modeled. This three-way validation-spanning simulation, sensor feedback, and operator input proved instrumental in refining the digital twin's predictive capacity [31]. By closing the loop between planning and execution, the simulation framework became better aligned with real production rhythms, supporting more effective forecasting and targeted optimization strategies. The methodology demonstrated here is scalable to additional workstations and process types, underscoring the importance of empirical feedback in refining digital twins. Table 3 presents task-level data from the plotter workstation on 16 October 2024, comparing three sources: manually logged task start and end times by operators, corresponding activity durations from the simulation model, and sensor-based records collected through the DIMUSA system. While the overall timing was similar across data sources, slight deviations were observed in transition gaps between micro-batches. These gaps were better captured by DIMUSA sensors, which identified short but recurring idle periods not reflected in simulation assumptions or manual logs. This highlighted the usefulness of sensor-level granularity in exposing brief disruptions that accumulate into meaningful inefficiencies. Figure 9: Comparison of workstation availability between (**a**) simulation model assumptions and (**b**) actual DIMUSA measurements for a single production day (16 October 2024).