A Data-driven Review: Bibliometric Insights into AI-enhanced Resource Optimization in Supply Chain Management

This study presents a comprehensive bibliometric analysis of scholarly literature at the intersection of artificial intelligence (AI), resource optimization, and supply chain management (SCM). Employing the PRISMA protocol for systematic data selection, the research investigates the intellectual structure, thematic evolution, and collaborative networks within this interdisciplinary domain over the period 2021–2025. A curated dataset of peer-reviewed publications was retrieved from established academic databases and analyzed using advanced bibliometric techniques. The majority of the analysis—including publication trends, author productivity, citation networks, and collaboration patterns—was conducted using R Studio, while VOSviewer was specifically employed for keyword co-occurrence analysis. Key findings reveal a notable increase in research output, reflecting the growing integration of AI technologies—such as machine learning, deep learning, and big data analytics—into SCM for improved operational efficiency and sustainability. Temporal keyword analysis uncovers emerging research themes such as blockchain, generative AI, and Industry 4.0. The study further identifies leading authors, highly cited works, and prominent journals shaping the field. Geographical analysis indicates that technologically advanced nations are at the forefront of research, often engaging in international collaborations. Visualizations of author and country networks highlight the global and interdisciplinary nature of the research landscape. This bibliometric review offers critical insights into the evolution and trajectory of AI-enabled resource optimization in supply chains. It serves as a valuable reference for academics, industry practitioners, and policymakers aiming to understand current research trends and identify strategic directions for future innovations in smart and sustainable SCM.