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

arXiv: https://arxiv.org/abs/2401.10895 · HTML: https://mlsanity.com/paper/2401.10895/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 117 references that are in the map.
- First posted on arXiv 12 Dec 2023.

## Builds on (most cited first, 1 of 1)

- [Machine learning and deep learning](https://mlsanity.com/paper/2104.05314/) (2021)

## Built on by (most cited first, 2 of 2)

- [Leveraging Large Language Models for Risk Assessment in Hyperconnected Logistic Hub Network Deployment](https://mlsanity.com/paper/2503.21115/) (2025)
- [From Noise to Insights: Enhancing Supply Chain Decision Support through AI-Based Survey Integrity Analytics](https://mlsanity.com/paper/2601.17005/) (2026)

## Abstract (arXiv)

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), with 54 studies selected through PRISMA guidelines. Our findings reveal that ML models, including Random Forest, XGBoost, and hybrid approaches, significantly improve 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.