Data-Driven Decision-Making in Global Supply Chain Management: Transforming Operations through Analytics and Intelligence

Global supply chain management has evolved from intuition-based decision-making to sophisticated data-driven approaches that leverage advanced analytics, artificial intelligence, and real-time information systems. This paper examined the critical role of data-driven decision-making in modern supply chain operations, analysing key technologies, implementation strategies, and performance outcomes. Through comprehensive literature review and case study analysis, we explore how organizations utilize big data analytics, predictive modelling, and machine learning to optimize inventory management, demand forecasting, risk mitigation, and operational efficiency. The research identified significant benefits such as cost reduction, improved customer satisfaction, and enhanced supply chain resilience, while also addressing implementation challenges such as data quality, integration complexity, and organizational change management. Our findings demonstrate that companies adopting comprehensive data-driven approaches achieve 15-25% improvements in operational efficiency and 20-30% reduction in supply chain costs. The paper concludes with recommendations for successful implementation and future research directions in the rapidly evolving landscape of intelligent supply chain management.