Artificial Intelligence-Based Predictive Maintenance and Energy Optimization in Smart Manufacturing: A Comparative Engineering Systems Study of Japan and Canada

Abstract

This article examines how artificial intelligence-based predictive maintenance and energy optimization systems influence operational performance, engineering resilience, and sustainable industrial transformation in advanced smart manufacturing ecosystems. Using a comparative engineering systems analysis of Japan and Canada, the study investigates how different industrial structures, digital infrastructures, energy systems, and technology-governance models shape the implementation of AI-enabled manufacturing optimization. Japan represents a highly automated, robotics-intensive manufacturing ecosystem with strong cyber-physical integration in automotive, electronics, and precision machinery sectors. Canada represents a resource-integrated and sustainability-oriented industrial ecosystem where AI, industrial IoT, and energy optimization are increasingly applied across advanced manufacturing, mining equipment, aerospace, and clean-technology production. Drawing on OECD digital transformation evidence, International Energy Agency industrial energy-efficiency data, International Federation of Robotics indicators, World Economic Forum smart manufacturing reports, IEEE and Scopus-indexed engineering literature, and institutional technology-policy documents, the article demonstrates that AI-driven predictive maintenance improves system reliability, reduces operational downtime, enhances energy visibility, and supports sustainability-oriented production. However, the comparison also reveals that engineering outcomes depend on data interoperability, workforce capability, computational infrastructure, cybersecurity governance, and energy-system integration. The article contributes to engineering and applied science scholarship by proposing a conceptual model linking AI-enabled sensing, predictive analytics, operational optimization, system resilience, and sustainable socio-technical development.