## 3 Results
### Cluster Analysis Results  
A clustering-based analytical workflow was applied to the preprocessed production dataset to identify performance patterns and anomalies across workstations. The methodology combined DBSCAN-based prefiltering with K-means clustering to enhance robustness and interpretability. The dataset was first cleaned and normalized. Key performance indicators were selected as clustering features, including OEE, availability, performance, quality, and state durations (produced, off, short, long, and working). Categorical workstation labels were encoded numerically, and timestamps were converted into a consistent datetime \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Date** & **Workstation** & **Availability \%** & **Performans \%** & **Quality \%** & **OEE \%** & **TEEP \%** & **Result/pcs** \\ \hline
2024/10 & Plotter KJ-PL040 & 67\% & 100\% & 100\% & 67\% & 16\% & 9200 \\
2024/10 & Press KJ-PR050 & 29\% & 100\% & 100\% & 29\% & 7\% & 9200 \\
2024/10 & Quality control & 24\% & 100\% & 100\% & 24\% & 6\% & 2300 \\
2024/10 & KJ-VD031 & 22\% & 101\% & 100\% & 22\% & 5\% & 2622 \\  & White parts & & & & & & \\
2024/10 & KJ-VD032 & 22\% & 101\% & 100\% & 22\% & 5\% & 2622 \\
2024/10 & White parts & 21\% & 101\% & 100\% & 22\% & 5\% & 2599 \\  & KJ-VD034 & 21\% & 101\% & 100\% & 22\% & 5\% & 2599 \\
2024/10 & Quality control & & & & & & \\
2024/10 & KJ-KK062 & 21\% & 100\% & 100\% & 21\% & 5\% & 2300 \\
2024/10 & Quality control & 21\% & 100\% & 100\% & 21\% & 5\% & 2300 \\  & KJ-KK064 & 21\% & 100\% & 100\% & 21\% & 5\% & 2300 \\
2024/10 & Cutting KJ-JL020 & 15\% & 100\% & 100\% & 15\% & 4\% & 20,355 \\
2024/10 & Sewing KJ-QM070 & 14\% & 100\% & 100\% & 14\% & 3\% & 9200 \\
2024/10 & Packaging KJ-PA080 & 14\% & 100\% & 100\% & 14\% & 3\% & 9200 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of OEE-related performance metrics for monitored workstations (during October 2024). format. DBSCAN was used to remove noise and outliers, including negative values and unrealistic cycle durations [22]. Afterward, K-means clustering was applied with k = 5, producing five representative operational states [23]. The most representative workstation and a median timestamp were identified for each cluster to support interpretation. Cluster centroids were computed and enriched with metadata, enabling performance comparison across stations and time windows. Average OEE scores then ranked workstations to identify top and bottom performers, while problematic stations were flagged for deeper investigation. The entire process, from parameter definition to final visualization, followed a structured analysis pipeline [24]. This included feature selection, validation checks, outlier handling (e.g., logarithmic transformations), and cluster labeling. Both the DBSCAN and K-means clustering algorithms were implemented in Python (version 3.12) and integrated into the DIMUSA system as part of its analytical backend. The results were visualized through the DIMUSA dashboard as interactive time-series views and color-coded status overlays, enabling planners and supervisors to identify root-cause opportunities for improvement. The complete clustering workflow is illustrated in Figure 6, which served as the basis for implementing data-driven diagnostics in a live production setting. The clustering results were further visualized using a two-dimensional scatter plot to map the relationship between availability and performance across all monitored workstations. Figure 7 presents the cluster distribution based on the simulation data collected throughout October 2024. Each data point represents the aggregated performance of a workstation for a given time window, with color-coded labels indicating the different stations. The visualized output closely corresponds to the quantitative results shown in Table 1, providing a fast and intuitive overview of workstation utilization. The chart effectively highlights performance disparities, such as the consistently high workload of the plotter and press stations.