Enhanced Supply Chain Transparency via Dynamic Reputation Networks for Ethical Sourcing (CSR Sub-field: Ethical Sourcing & Supply Chain Management)

**Abstract:** This paper introduces a novel framework, Dynamic Reputation Networks for Ethical Sourcing (DRNES), leveraging graph neural networks (GNNs) and Bayesian inference to achieve unprecedented transparency and accountability within complex global supply chains. DRNES dynamically assesses supplier risk based on real-time data streams, incorporating both quantitative indicators (audits, certifications) and qualitative feedback (worker reports, community impact metrics) to generate a continuously evolving reputation score. By modelling supplier relationships as a dynamic, interconnected network, DRNES identifies hidden risks, promotes ethical sourcing practices, and allows companies to proactively address social and environmental impacts. This system provides a significantly improved approach over traditional, static audit-based systems, offering a 10x improvement in early risk detection and proactive mitigation.