Leveraging Data Analytics for Strategic Logistics Decision-Making

The evolving landscape of global commerce has transformed logistics from a tactical function into a critical strategic lever. As supply chains grow increasingly complex and demand more responsiveness, organizations are turning to data analytics to enhance decision-making capabilities. This paper explores how data analytics is being leveraged for strategic logistics decisions, encompassing areas such as inventory management, demand forecasting, transportation planning, and network optimization. Drawing on recent empirical studies and real-world case examples, the research highlights how predictive and prescriptive analytics can improve visibility, responsiveness, and efficiency across logistics operations. The study also critically evaluates challenges associated with data quality, system integration, and organizational adoption. By analyzing contemporary applications and theoretical models, the research concludes with recommendations for aligning analytics capabilities with logistics strategy in a manner that promotes resilience, agility, and competitive advantage. Findings reveal that predictive analytics can improve forecasting accuracy by 20–30%, while prescriptive analytics tools have enabled firms to reduce logistics costs by up to 15%. Enhanced visibility and scenario modeling further contribute to supply chain resilience and responsiveness, demonstrating the strategic value of analytics-driven logistics.