Bibliometric Analysis of Supply Chain Analytics: Trends, Research Impact and Evolution (2001-2025)

This research presents a comprehensive bibliometric evaluation of the scholarly work on supply chain analytics from 2001 to 2025. The study utilises tools such as VOSviewer and R Programming to map the intellectual landscape, identify trends, and assess the research impact and evolution within the field of supply chain analytics. The analysis is based on a systematic review of 1,405 publications indexed in the Scopus databases. Using VOSviewer, the study visualises citation and keyword co-occurrence networks to uncover thematic clusters, while R Programming is employed for statistical analysis and trend identification. The findings indicate significant growth in supply chain analytics research, driven by advancements in big data as well as artificial intelligence (AI) and machine learning (ML), which have transformed decision-making processes in supply chains. Key themes identified include predictive analytics, sustainability, risk management, and digital supply chain integration. Emerging trends point to a shift towards real-time analytics, IoT-driven decision-making, and AI-powered supply chain optimisation. Furthermore, the study identifies influential authors and highlights the global distribution of research contributions. The analysis reveals an evolution in research focus, shifting from traditional optimisation models to more data-centric, realtime analytics. This research offers valuable insights for academics, practitioners, and policymakers by identifying research gaps and suggesting future directions in supply chain analytics. It provides a roadmap for future research and emphasises the interdisciplinary nature of the field. By synthesising 25 years of research, this work illuminates the development of supply chain analytics and its significance for supply chain management in the era of industry 4.0 and beyond.