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

• URL: http://arxiv.org/abs/2401.10895v3
• Date: Sat, 23 Nov 2024 12:41:32 GMT
• Title: AI in Supply Chain Risk Assessment: A Systematic Literature Review and Bibliometric Analysis
• Authors: Md Abrar Jahin, Saleh Akram Naife, Anik Kumar Saha, M. F. Mridha,
• Abstract summary: Supply chain risk assessment (SCRA) has witnessed a profound evolution through the integration of artificial intelligence (AI) and machine learning (ML) techniques. Previous reviews have outlined established methodologies but have overlooked emerging AI/ML techniques. This paper conducts a systematic literature review combined with a comprehensive bibliometric analysis.
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• Abstract: Supply chain risk assessment (SCRA) has witnessed a profound evolution through the integration of artificial intelligence (AI) and machine learning (ML) techniques, revolutionizing predictive capabilities and risk mitigation strategies. The significance of this evolution stems from the critical role of robust risk management strategies in ensuring operational resilience and continuity within modern supply chains. Previous reviews have outlined established methodologies but have overlooked emerging AI/ML techniques, leaving a notable research gap in understanding their practical implications within SCRA. This paper conducts a systematic literature review combined with a comprehensive bibliometric analysis. We meticulously examined 1,439 papers and derived key insights from a select group of 51 articles published between 2015 and 2024. The review fills this research gap by addressing pivotal research questions and exploring existing AI/ML techniques, methodologies, findings, and future trajectories, thereby providing a more encompassing view of the evolving landscape of SCRA. Our study unveils the transformative impact of AI/ML models, such as Random Forest, XGBoost, and hybrids, in substantially enhancing precision within SCRA. It underscores adaptable post-COVID strategies, advocating for resilient contingency plans and aligning with evolving risk landscapes. Significantly, this review surpasses previous examinations by accentuating emerging AI/ML techniques and their practical implications within SCRA. Furthermore, it highlights the contributions through a comprehensive bibliometric analysis, revealing publication trends, influential authors, and highly cited articles.

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