Volume 7 • Issue 1 • PP: 40-49 • 2023
Innovations at the Nexus of Sustainability and Industry 4.0: Data-Driven Approach for Preemptive Equipment Management in Smart Factories
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The convergence of Industry 4.0 and sustainability has brought forth a new era of manufacturing, where data-driven approaches play a pivotal role in achieving operational efficiency while minimizing environmental impact. This paper presents an innovative framework for sustainable smart manufacturing through data-driven predictive maintenance planning. By integrating advanced analytics and machine learning, we propose a preemptive equipment management approach that not only optimizes production processes but also fosters environmental responsibility. Our methodology combines the power of Long Short-Term Memory (LSTM) networks for pattern modeling and the Sea Lion Optimization Algorithm for feature selection. We demonstrate the effectiveness of our approach through a comprehensive empirical analysis conducted on a real case study, where the results indicate significant improvements over baseline studies, as evidenced by reduced Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), along with higher R-squared (R2) values. Our findings emphasize the synergy between technological innovation and sustainability imperatives, positioning our approach as a catalyst for reshaping modern manufacturing practices.
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References
[1] Hassoun, A., Aït-Kaddour, A., Abu-Mahfouz, A. M., Rathod, N. B., Bader, F., Barba, F. J., ... & Regenstein, J. (2022). The fourth industrial revolution in the food industry—Part I: Industry 4.0 technologies. Critical Reviews in Food Science and Nutrition, 1-17.
[2] Gu, F., Guo, J., Hall, P., & Gu, X. (2019). An integrated architecture for implementing extended producer responsibility in the context of Industry 4.0. International Journal of Production Research, 57(5), 1458-1477.
[3] Roy, M., & Roy, A. (2019). Nexus of internet of things (IoT) and big data: roadmap for smart management systems (SMgS). IEEE Engineering Management Review, 47(2), 53-65.
[4] Samadhiya, A., Agrawal, R., & Garza-Reyes, J. A. (2022). Integrating industry 4.0 and total productive maintenance for global sustainability. The TQM Journal.
[5] Pandey, S., Twala, B., Singh, R., Gehlot, A., Singh, A., Montero, E. C., & Priyadarshi, N. (2022). Wastewater Treatment with Technical Intervention Inclination towards Smart Cities. Sustainability, 14(18), 11563.
[6] Mona Mohamed, Abduallah Gamal, Toward Sustainable Emerging Economics based on Industry 5.0: Leveraging Neutrosophic Theory in Appraisal Decision Framework, Neutrosophic Systems with Applications, Vol. 1, (2023): pp. 14-21
[7] Ghenai, C., Husein, L. A., Al Nahlawi, M., Hamid, A. K., & Bettayeb, M. (2022). Recent trends of digital twin technologies in the energy sector: A comprehensive review. Sustainable Energy Technologies and Assessments, 54, 102837.
[8] Big data supply chain analytics: ethical, privacy and security challenges posed to business, industries and society. Production Planning & Control, 33(2-3), 123-137.
[9] O’Donovan, Peter, et al. "An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities." Journal of big data 2.1 (2015): 1-26.
[10] Rossit, D. A., Tohmé, F., & Frutos, M. (2019). A data-driven scheduling approach to smart manufacturing. Journal of Industrial Information Integration, 15, 69-79.
[11] Medojevic, M., Villar, P. D., Cosic, I., Rikalovic, A., Sremcev, N., & Lazarevic, M. (2018). ENERGY MANAGEMENT IN INDUSTRY 4.0 ECOSYSTEM: A REVIEW ON POSSIBILITIES AND CONCERNS. Annals of DAAAM & Proceedings, 29.
[12] Jasiulewicz-Kaczmarek, M., Legutko, S., & Kluk, P. (2020). Maintenance 4.0 technologies–new opportunities for sustainability driven maintenance. Management and production engineering review, 11.
[13] Chinchorkar, S. (2022). Data-Driven Paradigm for Smart Manufacturing in the Context of Big Data Analytics. In Big Data Analytics in Smart Manufacturing (pp. 21-34). Chapman and Hall/CRC.
[14] Revathy, G., Selvakumar, K., Murugapriya, P., & Ravikumar, D. (2022). Smart manufacturing in Industry 4.0 using computational intelligence. Artificial Intelligence for Internet of Things: Design Principle, Modernization, and Techniques, 31.
[15] Çınar, Z. M., Zeeshan, Q., & Korhan, O. (2021). A framework for industry 4.0 readiness and maturity of smart manufacturing enterprises: a case study. Sustainability, 13(12), 6659.
[16] Sheth, A., & Kusiak, A. (2022). Resiliency of smart manufacturing enterprises via information integration. Journal of Industrial Information Integration, 28, 100370.
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