SCAP: An AI-Powered Supply Chain Compliance Framework for Deep Network Visibility and Risk Prediction
DOI:
https://doi.org/10.15662/IJEETR.2026.0802041Keywords:
Artificial Intelligence, Supply Chain Visibility, Compliance Automation, Machine Learning, Optical Character Recognition, Predictive Risk Scoring, Textile Industry, Regulatory ComplianceAbstract
Supply chain visibility beyond Tier 1 suppliers remains a critical vulnerability for global brands, particularly in textiles, where regulatory mandates like the 2026 EU Corporate Sustainability Due Diligence Directive (CSDDD) demand comprehensive compliance oversight. Current barriers—prohibitive certification costs, manual verification timelines, and language barriers—systematically exclude small and mid-tier suppliers from formal networks, creating hidden compliance risks. Existing network mapping approaches, while theoretically robust, lack operational implementation capabilities. We propose the Supply Chain AI Compliance Platform (SCAP), an integrated framework that automates sub-tier compliance verification through generative AI and predictive machine learning. SCAP employs a Vision-Language Model (VLM)-based optical character recognition (OCR) pipeline to extract structured certificate data multilingually, coupled with an XGBoost regression model augmented by SHAP explainability to generate real-time, interpretable risk scores with three-month predictive horizons. Experimental validation demonstrates an 99.75× reduction in processing time (four hours to 0.04 seconds), 95–98% OCR accuracy, 85% prediction accuracy, and 87% cost reduction in compliance management. By enabling continuous, transparent data sharing across four supply chain tiers, SCAP delivers scalable automation for regulatory adherence and network resilience.
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