Summary of Ai in Supply Chain Risk Assessment: a Systematic Literature Review and Bibliometric Analysis, by Md Abrar Jahin et al.
AI in Supply Chain Risk Assessment: A Systematic Literature Review and Bibliometric Analysis
by Md Abrar Jahin, Saleh Akram Naife, Anik Kumar Saha, M. F. Mridha
First submitted to arxiv on: 12 Dec 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comprehensive review of supply chain risk assessment (SCRA) by integrating systematic literature reviews with bibliometric analysis. The study examines 1,903 articles from 2015-2025, focusing on the application of artificial intelligence (AI) and machine learning (ML) in SCRA. The findings show that ML models, including Random Forest, XGBoost, and hybrid approaches, improve risk prediction accuracy and adaptability in post-pandemic contexts. The bibliometric analysis highlights key trends, influential authors, and institutional contributions, with China and the United States emerging as leading research hubs. The study emphasizes the importance of integrating explainable AI (XAI) for transparent decision-making, real-time data utilization, and blockchain for traceability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how to make sure that supply chains are safe from risks. It looks at all the ways that artificial intelligence and machine learning can help with this. The study found that using these technologies makes it easier to predict and prepare for risks in the future. The researchers also looked at who is doing the most work in this area and where they are based, finding that China and the United States are leading the way. The paper suggests that it’s important to make sure that AI is transparent and explains its decisions, and that data is used in real-time. It also emphasizes the importance of working together and constantly updating our approaches to stay ahead of risks. |
Keywords
* Artificial intelligence * Machine learning * Random forest * Xgboost
