In an era characterized by data proliferation, a deeper exploration is required to investigate the application of advanced analytics methodologies within the supply chain domain. This chapter explains the technical dimensions of leveraging big data analytics to optimize strategic supply chain management decision-making. Focusing on predictive analytics, the work here utilizes machine learning algorithms and statistical models to improve the precision of demand forecasting, optimize inventory levels, and refine supply chain processes. Moreover, the chapter delves into integrating real-time data streams and analytics tools to augment decision agility and responsiveness. Through analyzing technical frameworks and case studies, an effort is made to contribute to the technical discourse surrounding the implementation and implications of big data analytics in supply chain management, offering insights into the enhanced efficiency, resilience, and adaptability achievable through advanced data-driven strategies.