Healthcare and pharmaceutical supply chains are vulnerable to inefficiencies, stockouts, and counterfeit drug circulation, affecting patient safety and health outcomes globally. Integrating geospatial analytics (GIS) with business intelligence (BI) provides a pathway for real-time monitoring and data-driven decision-making. This study developed a cross-continental GIS-BI framework combining data acquisition, spatial clustering, predictive demand modeling, and interactive BI dashboards. Data sources included Nigerian regulatory compliance records, distribution logs, and U.S. hospital workflow data. Predictive models forecasted demand patterns, while route optimization algorithms minimized delivery times. Stakeholder workshops were used to validate usability and operational relevance. Implementation of the framework reduced delivery lead times by 18–22%, decreased stockouts by up to 30%, and improved compliance inspection coverage by 18%. Dashboards provided real-time visualization of inventory levels and geospatial risk hotspots, improving cross-departmental coordination. Predictive alerts anticipated over 80% of stockout events, enabling proactive replenishment and reducing patient service disruptions. The study demonstrates that GIS-BI integration strengthens supply chain resilience and regulatory oversight in both low-resource and highly digitized health systems. This scalable framework supports equitable access to medicines, cost efficiency, and improved global health security.

Geospatial analytics, business intelligence, pharmaceutical supply chain, healthcare optimization, predictive modeling, global health

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