Role of Big Data Analytics in Enhancing Supply Chain Decision-Making and Organizational Productivity

Abstract

Big Data Analytics (BDA) has emerged as a transformative force in modern Supply Chain Management (SCM), significantly improving decision-making capabilities and organizational productivity. In an increasingly complex and globalized business environment, supply chains generate massive volumes of structured and unstructured data from multiple sources such as suppliers, customers, logistics systems, and market platforms. Traditional decision-making approaches often fail to process such large-scale data effectively, leading to inefficiencies, delays, and suboptimal performance outcomes. Big Data Analytics enables organizations to extract meaningful insights from real-time and historical data, thereby supporting predictive decision-making, operational optimization, and strategic planning. This study explores the role of Big Data Analytics in enhancing supply chain decision-making and improving organizational productivity across industries. It examines how data-driven insights improve forecasting accuracy, inventory management, supplier coordination, and logistics efficiency. The study adopts a descriptive and analytical research design based on secondary data sources including journals, industry reports, and case studies. The findings suggest that organizations adopting Big Data Analytics experience improved operational efficiency, reduced costs, faster decision cycles, and higher customer satisfaction levels. However, challenges such as data integration issues, lack of skilled professionals, and high implementation costs continue to hinder full-scale adoption. The study concludes that Big Data Analytics is a critical enabler of intelligent supply chain systems and plays a vital role in enhancing organizational competitiveness in the digital economy.