Data-Driven Resource Allocation and Causal Decision Support in Healthcare Supply Chains

Abstract

Healthcare supply chains operate in complex environments characterized by uncertain demand, limited resources, supplier disruptions, transportation constraints, inventory shortages, and changing service requirements. These challenges make effective resource allocation and timely decision-making essential for maintaining the availability of medicines, medical equipment, and other critical healthcare resources. This study examines a data-driven framework for resource allocation and causal decision support in healthcare supply chains. The proposed approach combines data analytics, artificial intelligence, predictive modelling, and causal reasoning to identify resource requirements and evaluate the potential effects of alternative allocation decisions. Operational data on demand, inventory, supplier performance, transportation, facility capacity, and resource utilization can be analysed to identify emerging shortages and areas of operational pressure. Causal decision support further enables decision-makers to assess how specific interventions may influence supply-chain outcomes rather than relying only on predictive estimates. The framework also incorporates counterfactual scenario analysis to compare alternative resource-allocation strategies under normal, high-demand, and disruption conditions. By integrating data-driven analysis with causal reasoning, the proposed approach can support more targeted resource allocation, improved preparedness, and stronger healthcare supply-chain resilience.