Leveraging AI-Driven Supply Chain Analytics for Accelerating the Circular Economy: A Focus on Real-Time Resource Management and Extended Product Life-Cycles - Lödar-Miculeac, Renata Silvia; Szeberényi, András; Gallego-Nicholls, José Fernando; Guijarro-García, María (9th FEB International Scientific Conference: Sustainable Management in the Age of ESG and AI: Navigating Challenges and Opportunities, 2025)
Digital supply chains are increasingly critical to advancing circular‑economy models because they couple artificial intelligence (AI), the Internet of Things (IoT) and big‑data analytics to provide real‑time resource tracking, predictive maintenance and data‑driven end‑of‑life product management. Purpose – This article investigates how such AI‑enabled analytics accelerate the shift from linear to circular supply chains. Design/methodology/approach – A systematic literature review followed by a Summative Content Analysis (SCA) was conducted on eight Web of Science‑indexed articles published between December 2024 and March 2025. Findings – The evidence shows that integrating AI‑based analytics strengthens supply‑chain resilience, cuts resource inefficiencies and supports closed‑loop flows, provided that organisational capabilities and policy frameworks are in place. Research implications – The study consolidates emerging knowledge on the enablers and barriers of AI‑driven circular supply chains and proposes future research avenues. Practical implications – Managers can leverage real‑time analytics to extend product life‑cycles and minimise waste. Originality/value – This is one of the first reviews to apply SCA to recent empirical work at the intersection of AI, supply‑chain analytics and the circular economy.