## 4 Discussion
Our results suggest that enriching the sales decision module with data from earlier stages--as proposed in the interdepartmental model--could improve classification accuracy by providing a broader context. This approach also aligns with the hybrid methodology validated in [11], where expert knowledge was formalized into SWRL rules and combined with data-driven classifiers. From a detailed comparative perspective, ref. [11] presents a hybrid decision support tool for quality classification in steel production, combining semantic rules written in SWRL with random forest classifiers. Their system was evaluated on real cold-rolled steel data to assist human experts in interpreting quality outcomes using structured knowledge and machine learning inference. While effective, the system operates after production, classifying finished products rather than predicting quality before fabrication as we do. In their results, the reported weighted precision and recall reached 78%, and the weighted F1-score was 77%, demonstrating robust classification capabilities in a static setting. In contrast, our approach tackles a significantly more complex scenario, as it focuses on anticipating product quality prior to completion by integrating heterogeneous, sequential data from multiple departments. Despite this increased difficulty, our expert modules achieved strong performance across departments: the steel mill module reached a precision of 78.01%, recall of 80.50%, and F1-score of 79.18%; the logistics module showed 75.67% precision, 74.54% recall, and 75.10% F1-score; the rolling mill achieved 77.63% precision, 74.57% recall, and 76.06% F1-score; and the sales module reported 74.59% precision, 71.71% recall, and 72.71% F1-score. These values confirm the robustness of our anticipatory and hybrid methodology, delivering performance that matches or exceeds post-production models like [11] under more challenging predictive conditions. The interdepartmental model offers significant potential to compensate for local uncertainties by aggregating information from multiple departments. While [8] focuses on a single-stage prediction task, their results show that leveraging temporal sensor data across various positions within a production line enhances the accuracy of downstream property estimation. This suggests that broader integration of production data may further improve predictive performance in multi-stage industrial systems. Moreover, by adopting explainable components, such as feature importance ranking (from random forests), it becomes possible to identify which departments or variables are most responsible for quality degradation--a principle validated by [9] in real-world rolling fault detection scenarios. The hybrid use of LSTM networks for temporal prediction and random forests for final decision logic proved to be a robust and interpretable combination for industrial applications. To bridge the gap between research and industrial deployment, a pilot-scale validation plan is proposed. It involves selecting a production line with sufficient sensor coverage and historical data and initially operating the AI system in parallel to existing decision-making processes without influencing real-time operations. The system's outputs would be systematically compared against expert judgment and current quality control protocols to build trust. Depending on performance, low-risk decisions would be progressively automated, ultimately leading to full integration into Level 2 automation and continuous monitoring for iterative refinement. In parallel, the adaptability of the proposed system to different industrial environments must be addressed. While many data types and sensor configurations are shared across steel plants, transferring the model to new facilities would likely require some degree of retraining to reflect site-specific conditions. Currently, the absence of a systematic identification of the key variables that influence model transferability represents a limitation.