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Hospital supply chains face unprecedented vulnerabilities from demand shocks, such as pandemics or natural disasters, which disrupt the availability of critical consumables like personal protective equipment and medications. This conceptual manuscript proposes a resilience analytics blueprint leveraging artificial intelligence (AI) to detect and mitigate these shocks in healthcare systems. Drawing on clinical AI architectures, healthcare analytics infrastructures, and electronic health record (EHR) intelligence ecosystems, we introduce the demand-shock adaptive resilience network (DSARN), a novel framework for proactive monitoring and orchestration. DSARN integrates decision support pipelines with AI governance mechanisms to enable real-time anomaly detection without empirical data or model training. Key components include layered interoperability frameworks for data exchange across hospital nodes and workflow integration models that prioritize critical consumables. Conceptual formulas illustrate risk propagation through supply networks and governance load on monitoring systems. By synthesizing recent literature on AI deployment in healthcare, this blueprint emphasizes theoretical infrastructures for enhancing supply chain resilience, addressing interoperability challenges, and ensuring ethical governance. The architecture fosters adaptive feedback topologies to anticipate disruptions, offering a pathway for hospitals to build robust analytics ecosystems. Ultimately, DSARN provides a theoretical foundation for transforming reactive supply management into predictive resilience, safeguarding patient care amid volatility.

Unpredictable hospital supply shortages can compromise patient care, interrupt clinical workflows, and force costly emergency purchasing. These shortages are often preceded by signals embedded in procurement activity, consumption patterns, vendor behavior, and inventory movement. Current hospital inventory practices frequently depend on manual review, static reorder points, and retrospective averages. These methods cannot adequately synthesize predictive data from procurement systems, clinical demand sources, supplier feeds, and inventory thresholds. This article proposes an AI-based early warning system that ingests procurement logs, real-time consumption rates, vendor delay patterns, procedure forecasts, and inventory thresholds. The system computes a dynamic shortage risk score for each monitored hospital supply item. The framework includes data ingestion pipelines, a machine learning prediction engine, a vendor risk assessor, a procedure-to-supply demand mapper, and an alert dashboard. These components convert fragmented operational data into actionable shortage risk intelligence. The system would provide actionable lead time for proactive reordering, substitution planning, vendor escalation, and redistribution across hospital locations. It could also learn from historical shortage events to support more resilient supply chain planning. A predictive, AI-augmented supply chain could transform hospital materials management from reactive replenishment to anticipatory risk mitigation. Such a system would support safer, more resilient, and more efficient hospital logistics.