Big data analytics value creation in operations and supply chain management: a review of capabilities, conditions and constraints

Purpose Big data analytics (BDA) can transform operations and supply chain management (OSCM), yet firms often struggle to convert analytics investments into consistent value. This review examines how BDA capabilities shape OSCM outcomes and identifies the conditions under which value is realized or constrained. Design/methodology/approach The study combines bibliometric mapping of 508 Scopus-indexed publications from 2015 to 2026 with qualitative synthesis of 145 studies. Keyword co-occurrence and co-citation analyses are triangulated with thematic coding to identify the field’s intellectual structure, dominant themes and blind spots. Findings Six knowledge clusters structure the field: analytics capability, supply chain visibility, Industry 4.0 transformation, resilience, sustainability and governance. BDA can enhance decision-making, operational performance, resilience and sustainability, but value depends on data-resource orchestration, governance maturity, analytical capability and organizational readiness. Persistent constraints include poor data quality, fragmented systems, cybersecurity exposure, weak absorptive capacity and resistance to change. The review develops a contingent capability framework explaining how BDA creates, limits or fails to create OSCM value. Practical implications Managers should strengthen data governance, analytical capabilities and decision-process alignment before scaling advanced analytics. The proposed framework supports assessment of BDA readiness, analytics maturity and governance risk. Originality/value By framing BDA value creation as a contingent capability pathway rather than a direct technology–performance relationship, this review explains why similar analytics investments produce uneven returns and advances a more critical theoretical foundation for future BDA–OSCM research.