AI-Driven Causal Scenario Analysis for Healthcare Supply Chain Resilience
DOI:
https://doi.org/10.31004/anthor.v5i1.1352Keywords:
Artificial Intelligence, Causal Analysis, Scenario Analysis, Healthcare Supply Chains, Resilience, Machine Learning, Bayesian Networks, Counterfactual AnalysisAbstract
Healthcare supply chains operate in highly uncertain environments where supplier disruptions, fluctuating demand, inventory shortages, transportation failures, and capacity constraints can threaten the continuous availability of essential medical resources. Artificial intelligence provides new opportunities for monitoring these conditions, predicting emerging risks, and supporting more responsive supply-chain management. This study develops an AI-driven causal scenario-analysis framework for healthcare supply-chain resilience. The framework combines machine-learning prediction, causal reasoning, Bayesian scenario modelling, and counterfactual analysis to examine how supply-chain conditions may change under alternative disruption and intervention scenarios. Key variables include demand, supplier reliability, inventory levels, transportation capacity, facility requirements, procurement conditions, and resource availability. Predictive models are used to identify potential risks, while causal analysis is used to examine the likely effects of interventions such as inventory adjustments, supplier substitution, transportation support, and resource reallocation. Scenario analysis allows healthcare organizations to compare alternative responses before implementation and prepare for both individual and compound disruptions. The proposed framework provides a structured approach for moving healthcare supply-chain management from reactive disruption response toward proactive and evidence-based resilience planning.
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Copyright (c) 2026 Chinedu Okafor
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