Big data analytics in supply chain management: uncovering emerging trends through a bibliometric network analysis and a systematic literature review

Abstract

Purpose This study aims to investigate the intersection of supply chain management (SCM) and big data analytics (BDA) through a multidimensional approach that incorporates bibliometric and network analysis (BNA) and a systematic literature review (SLR). Design/methodology/approach BNA and SLR are academic research methods, each with distinct purposes and limitations. BNA manages large datasets, while SLR focuses on smaller ones for in-depth review. As of January 2023, we analysed 851 articles retrieved from the Web of Science (WoS) core collection via BNA. To mitigate against BNA's limitations, we performed an SLR of 194 articles in 2023–2024, unveiling new themes in the “BDA in SCM” domain that were not apparent through BNA alone. Findings The findings demonstrate global collaboration patterns, highlighting China's lead in publications but lower international engagement than the USA's. BDA and the emerging discipline of supply chain resilience are closely interlinked; Industry 4.0 intertwines with sustainability and circular economy (CE) themes, and both are contributions that have been underexplored in previous reviews. Future studies will explore how BDA and other digital technologies enhance Supply Chain Ambidexterity by improving information processing in uncertain environments. Healthcare 4.0 technologies – specifically BDA, AI and Blockchain – boost efficiency, innovation, and risk management. However, the full potential of an intelligent Food Supply Chain (IFSC) lies in the integration of AI-driven systems, BDA and advanced analytics – a step that is still in its early stages. Originality/value Big data analytics in supply chain management is a rapidly evolving research domain without a review paper since 2020 and with more than 500 papers published in 2021–2022. Our novel methodology of supplementing BNA with an SLR allows us to capture newer or niche contributions in the domain of big data analytics in supply chain management. This approach sets the study apart, ensuring insights into the field's current state and future directions. Integrating quantitative (BNA) and qualitative (SLR) approaches provides a well-rounded perspective on the field.