AI-enabled Product Authentication and Traceability in Global Supply Chains

Abstract

This paper presents a comprehensive analysis of artificial intelligence applications for product authentication and traceability within global supply chains. Counterfeiting and supply fraud represent significant challenges across industries, with annual losses exceeding $4.2 trillion globally. Traditional authentication approaches demonstrate inherent limitations in accuracy, scalability, and implementation feasibility against increasingly sophisticated counterfeiting techniques. This research evaluates advanced AI methodologies including computer vision techniques, machine learning algorithms, and multi-modal data fusion approaches for enhancing authentication capabilities. Performance analysis demonstrates that AI-enabled authentication systems achieve 15.7-26.0% accuracy improvements compared to conventional methods, while reducing verification time from 47.3 minutes to 2.8 seconds on average. Implementation case studies across luxury goods, pharmaceutical, and food supply chains reveal industry-specific optimization strategies and quantifiable benefits, including counterfeit reduction rates between 64-82%. Cross-border implementations face additional challenges related to regulatory frameworks, infrastructure variability, and environmental factors affecting authentication performance. The research identifies critical success factors for global deployments, including edge computing architectures, adaptive calibration algorithms, and standards-based interoperability frameworks. The findings provide a foundation for organizations implementing AI-enabled authentication systems while highlighting remaining challenges in data availability, privacy regulations, infrastructure limitations, and standards harmonization.