## 5 Conclusions
The framework's effectiveness depends on the quality, availability, and granularity of sensor and event data, so deployment in data-scarce environments would be challenging. In addition, real-time execution of the MOO engine is computationally intensive and demands substantial cloud resources. This study also focused on a specific set of operational and environmental KPIs; a more comprehensive analysis could include other factors like water usage, social metrics, or detailed supply chain impacts. Future research could focus on extending the framework by incorporating machine learning for predictive analytics, particularly leveraging the observed 1-3 h lag between sensor anomalies and significant downtimes. The successful correlation between sensor anomalies and operational performance (predictive precision 76%) suggests the potential for advanced predictive maintenance strategies. For instance, future work could explore the use of deep learning on temporal data for more robust characterization and prediction, building on techniques from recent studies. In conclusion, this research establishes a robust, scalable, and adaptable model for sustainable manufacturing, validated through real-world application that achieves both operational efficiency improvements (up to 5.1%) and environmental impact reductions (up to 12.4%). The framework provides a powerful toolkit to help industries navigate the dual challenges of the 21 st century, demonstrating that operational excellence and environmental responsibility are not mutually exclusive but can be achieved synergistically through integrated, data-driven optimization. Conceptualization, M.M.M. and I.B. ; Methodology, M.M.M. and I.B. ; Software, M.M.M. ; Validation, M.M.M. ; Formal Analysis, M.M.M. ; Investigation, M.M.M. ; Resources, I.B. ; Data Curation, M.M.M. ; Writing Original Draft Preparation, M.M.M. ; Writing Review and Editing, I.B. ; Visualization, M.M.M. ; Supervision, I.B. ; Project Administration, I.B. ; Funding Acquisition, I.B. All authors have read and agreed to the published version of the manuscript. This research was supported by the Department of Informatics, the Faculty of Engineering of Debrecen University, and supported by the University of Debrecen Program for Scientific Publication. **Data Availability Statement:** The dataset supporting the conclusions of this article was generated from proprietary operating systems and sensor readings within a private industrial manufacturing facility. Due to confidentiality agreements with the collaborating company and the commercially sensitive nature of detailed operational performance data, the raw data set cannot be made publicly available. The aggregated findings, process models, and key performance indicators derived from the data are presented in this paper. Further inquiries regarding the data may be directed to the corresponding author, subject to the limitations imposed by the confidentiality agreement. **Acknowledgments:** The authors wish to express their sincere gratitude to the management and operational staff of the collaborating tube manufacturing facility for granting access to their facilities, providing invaluable operational insights and making the necessary data available for this investigation. Their cooperation was essential for the successful validation of the proposed framework. We also thank colleagues at the Doctoral School of Informatics and the Faculty of Engineering, Debrecen University, for their constructive feedback and supportive academic environment. **Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.