Abstract

This paper presents a comprehensive, data-driven framework for improving supply chain resilience in large-scale U.S. retail operations. Leveraging over 50 million rows of transactional and inventory data processed through Google BigQuery and Python, the framework identifies bottlenecks in Regional Distribution Centers (RDCs), predicts disruption points, and drives measurable operational improvements. Key interventions include real-time inventory flow management, exposure reduction (swell and shrink), and product allocation optimization. Dashboards built in Tableau and Power BI give executives actionable, real-time intelligence to reduce stockouts, improve compliance, and mitigate operational risk. Results demonstrate a 15.2-percentage-point improvement in inventory accuracy, a 32.4% reduction in stockout rates, and a 12% minimization in total exposure — outcomes that underscore the national significance of data-driven supply chain management.