## 3 Results
### Production Data Characteristics  
Initial input data for the simulation model-including factory layout, selected product types, routing sequences, and partial workstation cycle times, provided by the company in Excel format based on exports from their ERP system. These data formed the baseline for constructing the virtual factory simulation. Excel also served as the company's primary tool for production planning and operational feedback. Throughout the project, this initial dataset was iteratively refined to improve the realism and fidelity of the simulation model. To validate and enrich the simulation inputs, selected production workstations were instrumented with DIMUSA hardware for real-time monitoring. On the plotter workstation, current sensors were successfully used to detect active plotting periods, enabling an accurate view of operational cycles. However, on the heat-based press workstation, current-based monitoring proved ineffective, as the heating system remained continuously powered during the entire shift. To overcome this limitation, a part-counting sensor was installed on the press to identify the start and end of each print cycle by detecting the movement of physical materials. In addition to sensor-based monitoring, a manual reporting phase was conducted during one week of the pilot, during which operators logged  
Figure 5: Time-series analysis of workstation OEE based on one-month virtual factory simulation data. The results serve as input for subsequent clustering analysis.  
task start and completion events via the DIMUSA interface. Although limited in duration, this experiment helped assess data quality and train staff on accurate input procedures. DIMUSA data were collected continuously over a six-month period from selected workstations, while simulation data covered a three-month virtual period. Bidirectional data flow was established between Excel and the DIMUSA platform. Task orders were imported into DIMUSA for execution monitoring, and actual start and end times were exported back to Excel for further analysis. Collected data included equipment usage, cycle time durations, idle intervals, and manually logged exceptions. To ensure reliability, post-processing steps were applied to filter out simulation artifacts, correct manual input errors, and align reported events with equipment-level OEE indicators [21]. Before applying cluster analysis, the dataset was cleaned to improve interpretability and accuracy. Based on the simulation and monitoring results, the plotter and press workstations were selected as focal points for deeper analysis, given their high load, complexity, and integration with both DIMUSA sensors and manual reporting channels. These workstations exhibited the highest OEE scores and output volumes during the monitoring period within one month, as shown in Table 2. Their central role in the company's mini-batch production process, starting from plot file generation to material preparation and pressing, further justified the focus. Table 1 provides a summary of workstation-level KPIs for October 2024, highlighting the relative performance of each monitored station in terms of availability, performance, quality, OEE, Total Effective Equipment Performance (TEEP), and production results.