Application of Predictive Analytics in Engineering Management for Sustainable Industrial Performance

Abstract

Industrial sectors worldwide are undergoing a technological shift driven by digitalization, sustainability pressures, and the requirements of Industry 4.0. Predictive analytics, enabled by machine learning, big data, and sensor-based monitoring, has emerged as a powerful tool for engineering managers to optimize maintenance, improve energy efficiency, minimize system downtime, and strengthen sustainability outcomes. This paper provides a comprehensive analysis of how predictive analytics can be applied within engineering management systems to achieve sustainable industrial performance. A conceptual framework is proposed that links data collection, modelling, decision support, and feedback loops to organizational sustainability metrics. Real-world applications, including predictive maintenance, energy forecasting, quality control, and supply chain optimization, are examined. Findings suggest that predictive analytics significantly enhances asset reliability, reduces waste, lowers operational costs, and enables proactive decision-making. Challenges such as data infrastructure limitations, workforce skill gaps, and organizational readiness are discussed. The article concludes by recommending directions for future research, including integration with circular economy practices and longitudinal impact measurement.