Causal Inference and Scenario-Based Capacity Planning for Healthcare Supply Networks

Abstract

Healthcare supply networks operate under substantial uncertainty caused by fluctuating demand, supply disruptions, transportation constraints, limited inventories, and uneven distribution of resources. Effective capacity planning therefore requires methods that can evaluate alternative scenarios while accounting for complex relationships among supply-chain variables. This study examines the application of causal inference and scenario-based analysis to capacity planning in healthcare supply networks. The proposed framework integrates causal reasoning with scenario modelling to evaluate how changes in supply, demand, inventory, and distribution capacity may influence healthcare resource availability. Artificial intelligence provides an important technological foundation for healthcare innovation and data-driven decision-making, while scenario-based optimization supports the evaluation of alternative healthcare logistics and medical equipment management conditions. Bayesian scenario analysis provides an additional structured approach for examining uncertain relationships and alternative system conditions. The framework also incorporates causal Bayesian reasoning and counterfactual analysis to assess how specific capacity interventions may influence healthcare supply-network outcomes. Pharmaceutical supply-chain distribution structures are considered in relation to their potential role in reducing disruption propagation and strengthening network resilience. The proposed framework considers demand, supplier reliability, inventory availability, transportation capacity, facility requirements, and distribution structure as key determinants of healthcare capacity. Alternative scenarios are evaluated to identify potential shortages and resource constraints, while causal inference supports assessment of the likely effects of different capacity interventions.