BIG DATA ANALYTICS FOR SUPPLY CHAIN MANAGEMENT

Supply chain management (SCM) is a dynamic and intricate process that requires the integration of multiple operations across multiple entities in the modern day. There are benefits as well as constraints associated with the growing amount, diversity, and velocity of data generated throughout supply chains. Businesses may now optimize their supply chains by using Big Data Analytics (BDA), a potent tool for turning vast amounts of data into actionable insights. The integration of big data analytics with supply chain management will be investigated in this study, with an emphasis on how data-driven insights can improve forecasts, lower risks, better decision-making, and streamline procedures. By using machine learning methods, predictive analytics, and real-time data analysis, BDA enables businesses to comprehend their supply chains, increase the accuracy of demand forecasting, lower operating costs, and boost overall efficiency. Additionally, the research explores ways Big Data might be used to address important issues including demand-supply mismatches, inventory management, and supply chain interruptions. Businesses can increase supply chain agility, improve customer satisfaction, and allocate money effectively by utilizing BDA. The future of big data in supply chain management and its effects on the global supply chain are examined in the paper's conclusion.