Explainable Artificial Intelligence for Continuous Supplier Risk Assessment and Proactive Mitigation Across United States Supply Networks

Author

Igbinabor Okunbor


Abstract

 United States supply networks increasingly depend on complex ecosystems of manufacturers, logistics providers, technology vendors, contractors, and specialized service organizations whose changing conditions can create substantial operational exposure. Traditional supplier assessments based on periodic scorecards, questionnaires, audits, and static risk indicators may provide limited visibility into rapidly evolving financial, operational, cybersecurity, and dependency-related vulnerabilities. This study develops an Explainable Artificial Intelligence (XAI) framework for continuous supplier risk assessment and proactive mitigation across United States supply networks. The framework integrates multi-source supplier data with temporal machine learning, anomaly detection, dependency analysis, and dynamic risk scoring to identify emerging deterioration before significant disruption occurs. Explainability mechanisms provide supplier-specific attribution of risk to underlying indicators, enabling procurement and risk teams to understand why risk classifications change and evaluate model-generated warnings. Predicted probability, deterioration persistence, operational criticality, substitutability, and network exposure are subsequently combined to prioritize intervention. The framework links risk intelligence with supplier engagement, inventory protection, alternative sourcing, enhanced monitoring, and contingency activation, establishing a transparent, continuously learning approach to predictive supplier resilience management.



Keywords

Explainable Artificial Intelligence; Supplier Risk Assessment; Supply Chain Resilience; Predictive Analytics; Third-Party Risk; Proactive Risk Mitigation



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References


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