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September 17, 2025International Journal of Computational and Experimental Science and EngineeringOpen Access

Autonomous Supplier Evaluation and Data Stewardship with AI: Building Transparent and Resilient Supply Chains

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Authors

CBChandra BonthuGGGanpati Goel

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Overview

This approach demonstrates improved risk identification and compliance in supply chains, suggesting enhanced governance methods.

Key Points

  • Experiments show better early warning of risks in delivery, quality, and compliance using advanced data features.
  • Calibrated ranking strategies effectively detect adverse issues without increasing false positives in limited review capacities.
  • The methodology combines tabular data, unstructured evidence, and network-based features to improve decision-making.
  • Governance processes like data contracts enhance accountability and audit-readiness in autonomous systems.

Cite This Study

Bonthu et al. (2025) studied this question.

synapsesocial.com/papers/68d4604031b076d99fa5f46chttps://doi.org/10.22399/ijcesen.3854
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1AI Ethics in Supply Chain Decisions2026
  2. 2Supplier Risk Intelligence with AI2026
  3. 3AI-Augmented Decision-Making Agility in Supplier Evaluation: Insights from a Qualitative Procurement Case Study2026 · 1 citations
  4. 4An Intelligent Analytics Framework for Supplier Selection and Delay Risk Management in Sustainable Supply Chains2026 · 1 citations
  5. 5EXPLAINABLE AI FOR SUPPLIER CREDIT APPROVAL IN DATA-SPARSE ENVIRONMENTS2025 · 2 citations