## 4 Discussion  
The results demonstrate the potential of the proposed intelligent assistant to support expert decision-making across departments by replicating their logic and anticipating quality issues in advance. The performance varied depending on the department, with the steel mill stage yielding some of the most promising results. Specifically, the model achieved a sensitivity of 75% for the \"SCRAP\" label, correctly identifying three out of four billets that should be rejected before processing. Additionally, the system demonstrated a precision of over 72%, meaning most predictions for rejection were valid. Out of 59,578 analyzed billets, 403 would have been correctly flagged as scrap before production, potentially avoiding unnecessary processing. Only 97 billets (0.16%) would have been wrongly flagged. These results are in line with the findings in [7], where the application of ML-based classifiers for hot-rolling quality grading also resulted in high precision and effective early rejection of defective outputs. A similar direction is observed in [9], where a hybrid model combining AdaBoost and LSTM outperformed other approaches in a real manufacturing setting, highlighting the value of ensemble deep learning architectures for anticipating quality issues based on production data. In the logistics department, the intelligent system also showed strong performance, particularly in terms of early detection. With a sensitivity above 70%, the model would have correctly flagged 2536 defective billets (4% of total production) before entering the billet park. This level of anticipation, although with 641 false alarms, represents a valuable opportunity for reducing downstream defects. The combination of temporal models and decision emulation, such as LSTM with MLP, has demonstrated high predictive power across complex industrial datasets, as validated in real steel manufacturing environments. The rolling mill posed a more complex challenge, especially for the \"DOWNGRADE\" label. The system struggled with this class, likely due to the limited number of labeled samples and the subtle differences distinguishing it from the \"OK\" and \"SCRAP\" categories. These types of difficulties are common in industrial settings, particularly when  
Figure 14: More examples of confusion matrix and SHAP values of the interdepartmental expert. dealing with visually ambiguous or infrequent defects. The integration of explainable AI techniques, like those explored in [11], enhances transparency by translating random forest models into semantic rules. This interpretability supports informed decision-making in steel production and highlights the value of combining domain knowledge with machine learning. Such findings highlight the importance of expert knowledge, data balance, and interpretable models when dealing with rare or borderline defect classes in steel manufacturing. Nonetheless, for the critical \"SCRAP\" cases in the rolling stage, the AI achieved 79% sensitivity and 81% precision, correctly identifying 598 defective coils and issuing only 56 false positives. This suggests that the temporal modeling performed by the LSTM-based architecture is well suited to detect anomalies in sequential data where quality evolves along the production line. The sales department presented additional complexity. The model achieved an F1-score of 72%, lower than the performance in upstream departments. This is likely due to the narrower data diversity and the influence of commercial criteria that are not always encoded in sensor data. Similar conclusions are drawn in [11], where integrating domain knowledge and contextual semantic reasoning was essential to enhance the interpretability and robustness of machine learning decisions in steel production. The formalization of expert knowledge into SWRL rules, when combined with data-driven classification, demonstrated a significant improvement in decision support capabilities.