Big Data Analytics and Supply Chain Operations: Trends, Gaps and Future Research Directions

Abstract

Purpose: Big Data Analytics has emerged as a transformative force in addressing the complexities of modern Supply Chain Operations. Despite its growing significance, scholarly attention on this knowledge intersection has remained marginal with noticeable scarcity of systematic literature reviews. The purpose of this study is to understand existing knowledge and explore the areas to be evolved in in the intersection of Big Data Analytics (BDA) and Supply Chain Operations (SCO).

Design/methodology/approach: This study presents comprehensive systematic review of meticulously selected fifty-two articles published from 2016 to 2024 whereas article selection was executed as per the PRISMA guidelines. We conducted multifaceted descriptive, bibliometric and content analysis with two research objectives (1) to understand current state of the knowledge and (2) to propose future research landscape in this domain.

Findings: This comprehensive analysis revealed the increasing trend of scholarly attention; whereas noticeable gaps persist, including imbalances of contextual focus, methodological bias, inadequate industrial focus, limited theoretical foundations, and focus on key terms. Through rigorous content analysis, we clustered existing research gaps under eight themes exploring the necessity of future inquiries.

Originality: This study contributes to the knowledge domain by setting five clearly defined potential directions for future researchers with starting points. It advances the academic discourse by exploring current state of knowledge and unveiling marginally explored dimensions in BDA applications in SCO.

Implications: Findings offer valuable understandings for researchers and practitioners seeking to enhance SCO through BDA. Ultimately, current study explores the role of Big Data Analytics in contemporary Supply Chain Operations.