ENHANCING SUPPLY CHAIN INTEGRITY: MACHINE LEARNING APPROACHES FOR REAL-TIME IDENTIFICATION OF COUNTERFEIT PRODUCTS
Keywords:
Supply Chain Integrity, Counterfeit Detection, Machine Learning, Artificial Intelligence, Computer Vision, Natural Language Processing, Blockchain, Spectroscopy, Real-time Authentication, Global TradeAbstract
This study examines machine learning (ML) approaches for real-time identification of counterfeit products in global supply chains. Through analysis of literature, case studies, and expert interviews, we investigate primary ML techniques, their integration into supply chain processes, and potential economic impact. Findings reveal that computer vision, natural language processing, spectroscopy-based methods, and blockchain-enhanced ML systems demonstrate high accuracy in counterfeit detection. While challenges such as model drift and integration complexities persist, the potential impact is significant, with projections suggesting a possible 31% reduction in global counterfeit trade over five years. The study highlights synergies between technologies, discusses legal and ethical considerations, and outlines future research directions, emphasizing the need for adaptive systems and ethical frameworks to realize ML's full potential in enhancing supply chain integrity.
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