Supply Chain Visibility and Transparency: Enabling Traceability and Accountability through Machine Learning Technologies

Abstract

This paper explores the role of machine learning (ML) technologies in enabling traceability and accountability within supply chains. By harnessing data analytics, ML algorithms offer innovative solutions to enhance visibility across complex supply networks, thereby fostering trust and efficiency. This abstract presents an overview of key concepts and methodologies employed in leveraging ML for supply chain transparency. It highlights the significance of traceability, which involves tracking the movement of goods and information throughout the supply chain, and accountability, which entails identifying responsible parties for actions or events within the chain. Machine learning techniques such as supervised learning, unsupervised learning, and reinforcement learning are discussed in the context of supply chain management. These algorithms enable predictive analytics, anomaly detection, and optimization, thereby facilitating real-time monitoring and decision-making. Issues such as data quality, interoperability, and privacy concerns are addressed, alongside potential benefits including improved efficiency, reduced waste, and enhanced sustainability. This abstract serves as a foundation for further research and practical applications aimed at revolutionizing supply chain management in the digital era.