Advancing data-driven decision-making processes using big data analytics in procurement, production and distribution networks

Big data analytics has revolutionized decision-making across procurement, production, and distribution networks, reshaping supply chain management. This review explores how advanced tools process vast datasets, transforming them into actionable insights that drive operational efficiency and strategic planning. Predictive analytics and machine learning optimize procurement processes such as supplier selection and demand forecasting. In production, real-time monitoring and quality control systems enhance manufacturing efficiency, while route optimization and last-mile delivery innovations improve logistics performance in distribution. These implementations have delivered significant gains in efficiency, cost savings, and customer satisfaction.

However, challenges such as data quality, integration complexities, and resistance to change persist. Overcoming these obstacles requires robust data governance frameworks, scalable technologies, and organizational adaptability. Emerging technologies, including artificial intelligence, blockchain, and edge computing, are positioned to further transform supply chain analytics by enhancing transparency, predictive accuracy, and operational agility.

This review provides a comprehensive framework for understanding the role of big data analytics in supply chain management. By examining current applications, challenges, and emerging trends, it offers valuable insights into successful implementation strategies. These findings underscore the evolving nature of supply chains and the critical role of analytics in shaping their future, fostering innovation and competitiveness in a dynamic global landscape.