Innovations at the Nexus of Sustainability and Industry 4.0: Data-Driven Approach for Preemptive Equipment Management in Smart Factories

Ahmed Hatip ^1,*, Karla Zayood ², Rabah Scharif ³

¹ Gaziantep university, Turkey

² Online Islamic University, Department Of Science and Information Technology, Doha, Qatar

³ Applied Engineering Department, Institute of Applied Technology, UAE

Emails: Kollnaar5@gmail.com; zayyyoood134@gmail.com ; rabah.scharif@aths.ac.ae

Abstract

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.

Keywords: Sustainable manufacturing; green technologies; Environmental efficiency; Sustainable operations; Sustainable supply chain; Life cycle assessment; Equipment Management; Smart Factories; Artificial Intelligence.