Journal of Industrial and Systems Engineering

Journal of Industrial and Systems Engineering

AI and Machine Learning for Predictive Maintenance in Green Supply Chains

Articles in Press

Document Type : Research Paper

Author
Koorosh Pouri
PhD. Student, Industrial engineering, Faculty of Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran
Abstract
The integration of artificial intelligence (AI) and machine learning (ML) in predictive maintenance (PM) has transformed maintenance strategies, optimized operational efficiency while supported green supply chain (GSC) sustainability. Traditional maintenance methods, including reactive and preventive maintenance, often lead to excessive costs, energy waste, and unplanned downtime. This study explores the application of AI-driven predictive maintenance using advanced machine learning models, such as Long Short-Term Memory (LSTM), Deep Q-Learning (DQL), and digital twin simulation, to enhance maintenance scheduling and sustainability. Findings reveal that AI-based predictive maintenance reduces downtime by up to 59.2%, cuts energy consumption by 27.6%, and lowers maintenance costs by 35.4%, significantly improving supply chain resilience. This research contributes to the development of AI-powered predictive maintenance frameworks, optimizing both economic and environmental performance in industrial operations. The study also highlights challenges and future research directions, particularly in model interpretability and scalability.
Keywords
Predictive Maintenance
Artificial Intelligence
Green Supply Chain
Digital Twin
Subjects
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Journal of Industrial and Systems Engineering

Articles in Press, Accepted Manuscript
Available Online from 01 March 2025

  • Receive Date 27 August 2024
  • Revise Date 23 November 2024
  • Accept Date 06 February 2025