A Comprehensive Review of Green AI Applications for Sustainable Manufacturing and Supply Chain Management

This paper presents a comprehensive review of Green AI applications in sustainable manufacturing and supply chain management. As environmental concerns and resource scarcity intensify, manufacturing industries are increasingly adopting innovative approaches to reduce their ecological footprint while maintaining competitiveness. The evolution of sustainability in manufacturing has progressed from basic compliance to integrated sustainable practices, with artificial intelligence emerging as a powerful enabler of this transformation. This review systematically examines how Green AI contributes to various aspects of sustainable manufacturing, including supply chain optimisation, energy efficiency, waste reduction, predictive maintenance, carbon emission management, and resource optimisation. For each domain, conventional practices and their environmental impacts are analysed, followed by an examination of how AI-based solutions are implemented and the resulting sustainability improvements. Empirical evidence from various studies indicates that Green AI applications can achieve significant environmental benefits, including 15-20 percent reductions in resource wastage, 10-15 percent decreases in energy consumption, up to 20 percent lower carbon emissions, and 15-25 percent improvements in material recovery rates. Additionally, the implementation of AI in specialised areas, such as sustainable cutting tool manufacturing, green packaging, and reverse manufacturing, is explored. This review identifies promising research directions and highlights challenges in the widespread adoption of Green AI for manufacturing sustainability. The findings suggest that integrating artificial intelligence with sustainable manufacturing practices represents a promising pathway toward environmentally responsible and economically viable industrial operations in an increasingly resource-constrained world.