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@article{190247,
        author = {Dhruv Toshniwal and Dev Agrawal and Reeya Kumari Yadav and Lavina Sitlani},
        title = {Artificial Intelligence in Circular Supply Chains: Enabling Intelligent Resource Loops for Sustainable Value Creation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {647-662},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190247},
        abstract = {The global economy's reliance on the linear "take–make–dispose" model is environmentally and economically unsustainable, necessitating a rapid transition to the regenerative principles of the Circular Economy (CE) and its operational counterpart, the Circular Supply Chain (CSC). This transition is fundamentally challenged by the inherent complexity, uncertainty, and data requirements of closed-loop systems. This paper examines the critical role of Artificial Intelligence (AI) in overcoming these barriers, positioning AI as the essential Circular Intelligence Engine (CIE) for enabling intelligent resource loops and sustainable value creation. We systematically analyze how AI technologies including Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Computer Vision (CV), and Large Language Models (LLMs) enable, optimize, and scale circular supply chains. The paper proposes a novel, three-layered architectural framework for an AI-Enabled CSC, detailing the necessary Data Layer (e.g., Digital Product Passports, IoT), the AI Layer (processing and decision-making), and the Execution Layer (automated operations). Furthermore, we present a comprehensive taxonomy that maps specific AI techniques to key circular functions (reuse, repair, recycle, remanufacture) across the supply chain lifecycle. The analysis reveals that AI shifts CSC from reactive recovery to predictive circularity, significantly improving resource efficiency, reverse logistics, and material recovery yield. Finally, we synthesize the critical challenges including data fragmentation, algorithmic bias, and the AI energy footprint and derive clear managerial and policy implications, emphasizing the need for strategic investment in digital infrastructure and robust ethical governance to ensure a transparent and equitable AI-driven circular transition.},
        keywords = {},
        month = {January},
        }