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AI in Supply Chain Risk Assessment: A Systematic Literature Review and Bibliometric Analysis

Md Abrar Jahin, Saleh Akram Naife, Anik Kumar Saha, M. F. Mridha

TL;DR

This paper addresses AI-driven supply chain risk assessment (SCRA) by conducting a systematic literature review (SLR) combined with bibliometric analysis. Analyzing 1,903 articles from 2015–2025 across Google Scholar and Web of Science, it narrows to 54 studies for content review and 778 for bibliometric mapping, revealing that machine learning models such as Random Forest and XGBoost, along with hybrid approaches, improve risk prediction and adaptability in post-pandemic contexts. The study highlights the rise of explainable AI (XAI) and blockchain as practical enablers, identifies China and the United States as leading research hubs, and characterizes co-authorship and citation networks to map the field’s intellectual structure. A concrete AI-based framework for SCRA is proposed, detailing data collection, preprocessing, model selection, interpretation, and preventive actions, while offering managerial guidance and future research directions that address data quality, interpretability, and cross-organizational collaboration. Overall, the work synthesizes AI techniques with resilience frameworks to provide actionable guidance for resilient, data-driven SCRM in evolving risk landscapes.

Abstract

Supply chain risk assessment (SCRA) is pivotal for ensuring resilience in increasingly complex global supply networks. While existing reviews have explored traditional methodologies, they often neglect emerging artificial intelligence (AI) and machine learning (ML) applications and mostly lack combined systematic and bibliometric analyses. This study addresses these gaps by integrating a systematic literature review with bibliometric analysis, examining 1,903 articles (2015-2025) from Google Scholar and Web of Science, with 54 studies selected through PRISMA guidelines. Our findings reveal that ML models, including Random Forest, XGBoost, and hybrid approaches, significantly enhance risk prediction accuracy and adaptability in post-pandemic contexts. The bibliometric analysis identifies key trends, influential authors, and institutional contributions, highlighting China and the United States as leading research hubs. Practical insights emphasize the integration of explainable AI (XAI) for transparent decision-making, real-time data utilization, and blockchain for traceability. The study underscores the necessity of dynamic strategies, interdisciplinary collaboration, and continuous model evaluation to address challenges such as data quality and interpretability. By synthesizing AI-driven methodologies with resilience frameworks, this review provides actionable guidance for optimizing supply chain risk management, fostering adaptability, and informing future research in evolving risk landscapes.

AI in Supply Chain Risk Assessment: A Systematic Literature Review and Bibliometric Analysis

TL;DR

This paper addresses AI-driven supply chain risk assessment (SCRA) by conducting a systematic literature review (SLR) combined with bibliometric analysis. Analyzing 1,903 articles from 2015–2025 across Google Scholar and Web of Science, it narrows to 54 studies for content review and 778 for bibliometric mapping, revealing that machine learning models such as Random Forest and XGBoost, along with hybrid approaches, improve risk prediction and adaptability in post-pandemic contexts. The study highlights the rise of explainable AI (XAI) and blockchain as practical enablers, identifies China and the United States as leading research hubs, and characterizes co-authorship and citation networks to map the field’s intellectual structure. A concrete AI-based framework for SCRA is proposed, detailing data collection, preprocessing, model selection, interpretation, and preventive actions, while offering managerial guidance and future research directions that address data quality, interpretability, and cross-organizational collaboration. Overall, the work synthesizes AI techniques with resilience frameworks to provide actionable guidance for resilient, data-driven SCRM in evolving risk landscapes.

Abstract

Supply chain risk assessment (SCRA) is pivotal for ensuring resilience in increasingly complex global supply networks. While existing reviews have explored traditional methodologies, they often neglect emerging artificial intelligence (AI) and machine learning (ML) applications and mostly lack combined systematic and bibliometric analyses. This study addresses these gaps by integrating a systematic literature review with bibliometric analysis, examining 1,903 articles (2015-2025) from Google Scholar and Web of Science, with 54 studies selected through PRISMA guidelines. Our findings reveal that ML models, including Random Forest, XGBoost, and hybrid approaches, significantly enhance risk prediction accuracy and adaptability in post-pandemic contexts. The bibliometric analysis identifies key trends, influential authors, and institutional contributions, highlighting China and the United States as leading research hubs. Practical insights emphasize the integration of explainable AI (XAI) for transparent decision-making, real-time data utilization, and blockchain for traceability. The study underscores the necessity of dynamic strategies, interdisciplinary collaboration, and continuous model evaluation to address challenges such as data quality and interpretability. By synthesizing AI-driven methodologies with resilience frameworks, this review provides actionable guidance for optimizing supply chain risk management, fostering adaptability, and informing future research in evolving risk landscapes.
Paper Structure (61 sections, 14 figures, 4 tables)

This paper contains 61 sections, 14 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Methodology
  4. Study Design
  5. Search Strategy
  6. Article Selection Process
  7. Bias Mitigation
  8. Final Selection
  9. Data Extraction and Synthesis
  10. Bibliometric Analysis
  11. Publication Trend Over Time
  12. Geographical Distribution of Research
  13. Analysis of Authors
  14. Publisher Contributions
  15. Co-citation Analysis
  16. ...and 46 more sections

Figures (14)

  • Figure 1: Prisma flow diagram for systematic literature review.
  • Figure 2: Annual publication trend (2015-2025) of the 778 published literature on AI in SCRA topic.
  • Figure 3: Geographical distribution of the authors affiliated with 778 published papers on AI in SCRA topic.
  • Figure 4: Publisher-wise distribution of 778 articles.
  • Figure 5: Co-citation network graphs visualization using (a) authors, (b) cited references, and (c) sources as the unit of analysis.
  • ...and 9 more figures