Supply chain cybersecurity is a growing concern for businesses as they utilize increasingly interconnected digital systems. This systematic literature review examines how machine learning (ML) and business intelligence (BI) may be used in conjunctions to improve supply chain cyber security risk management. This review followed PRISMA guidelines. A quality evaluation was performed based on CASP to evaluate 35 peer-reviewed articles published in 2016–2025. The review analysis indicates that although ML has been extensively utilized for threat detection, BI utilization is fragmented. Additionally, there is a lack of integrated ML-BI frameworks, specifically for small–medium enterprises (SMEs) and developing economies. As such, this literature review provides a conceptual four-layer framework of predictive and analytical capabilities for threat detection, risk assessment, and decision-making. It also identifies a structured research agenda with which to advance the field of research.
Aljaafreh et al. (Tue,) studied this question.