Integrating Big Data Analytics into Supply Chain Management: Overcoming Data Silos to Improve Real-Time Decision-Making

Abstract

Big data analytics (BDA) has emerged as a transformative force in supply chain management (SCM), offering unprecedented insights into operations, demand forecasting, and risk mitigation. However, the integration of BDA into SCM remains hindered by persistent data silos, limiting the potential for real-time decision-making. This paper explores strategies to overcome data fragmentation and leverage analytics-driven decision-making in SCM. We examine the role of cloud computing, data lakes, and interoperability standards in facilitating seamless data integration. Additionally, we discuss how machine learning algorithms and predictive analytics enhance supply chain visibility and responsiveness. The study highlights key challenges such as data security, privacy concerns, and the need for organizational change management. Through an analysis of recent advancements and industry trends, we propose a framework for breaking down data silos, enabling firms to optimize their supply chain operations and improve resilience in dynamic market environments. The findings suggest that firms embracing integrated data strategies can significantly enhance their decision-making capabilities, reduce inefficiencies, and gain a competitive edge. Future research should focus on the development of standardized frameworks for data governance and interoperability, ensuring scalable and sustainable BDA integration in SCM.