Purpose This study aims to undertake a comprehensive and in-depth review of published research studies spanning from January 2001 to December 2023, focusing on big data analytics–based research across supply chain management, Logistics Management and Inventory Management. Design/methodology/approach The examination delves into the conceptual framework, research methodologies and big data analytics techniques, uncovering the original contributions of esteemed authors. In the dynamic landscape of supply chain management, the integration of big data analytics represents a transformative force, offering unparalleled insights and decision-making capabilities for businesses seeking to advance to the next level. This study’s scope extends to elucidating how big data analytics augments performance, mobility and integrity within the supply chain in a timely manner. Findings The findings from the review not only illuminate existing research gaps but also propose strategies for expediting big data analytics research and fostering its widespread adoption. The implications of big data analytics in supply chain management, Logistics Management and Inventory Management domains are explored, with an emphasis on its potential benefits, unexplored best practices and diverse applications across sectors. Critical factors for effective big data analytics implementation, such as collaborative stakeholder training, standardized information flow and the reduction of redundant data, are identified as pivotal components of success. Originality/value This study underscores the imperative of meticulously examining fundamental questions surrounding big data generation and delves into the complexities of data inheritance within increasingly intricate supply chain facilities as a fresh attempt.
Kayani et al. (Thu,) studied this question.
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