Authors

Ishaan Rajasekar

Department of Computer Science and Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India

Sanjana Balakrishnan

Department of Computer Science and Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India

Advik Chandrasekar

Department of Computer Science and Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India

Abstract

Healthcare supply chains operate under substantial uncertainty arising from fluctuating medical demand, supplier disruptions, transportation constraints, inventory shortages, and uneven resource availability across healthcare facilities. Conventional capacity planning methods may struggle to accommodate these uncertainties because decisions are frequently based on historical averages or predefined assumptions about future operating conditions. This study examines how Bayesian scenario-based capacity planning can strengthen healthcare supply chain resilience by integrating probabilistic reasoning, alternative disruption scenarios, and counterfactual assessment of capacity allocation decisions. Drawing on causal Bayesian networks, supply chain risk management, and resilience research, the study develops an analytical framework for evaluating how changes in demand, supplier reliability, inventory availability, and transportation capacity may affect healthcare service continuity. The framework treats capacity planning as a decision problem under uncertainty, allowing planners to compare alternative interventions before implementing them in operational settings. The analysis also considers the importance of reliable Bayesian inference when estimating uncertain outcomes and interpreting low-probability, high-impact disruptions. The literature indicates that scenario-based planning can support more informed capacity allocation, while causal analysis can help distinguish predictive relationships from the potential effects of operational interventions. The study concludes that integrating Bayesian scenario analysis with resilience-oriented capacity planning can improve the transparency, adaptability, and risk sensitivity of healthcare supply chain decisions. The framework offers a foundation for future empirical evaluation using hospital inventory, procurement, supplier performance, and distribution data.

Keywords

Bayesian networks scenario-based planning healthcare supply chain capacity allocation supply chain resilience counterfactual analysis uncertainty quantification disruption management

Citation of this Article

Ishaan Rajasekar, Sanjana Balakrishnan, & Advik Chandrasekar. (2025). AI-Driven Low-Light Image Enhancement Using Deep Neural Networks. Journal of Artificial Intelligence and Emerging Technologies. 2(12), 42-49. Article DOI: https://doi.org/10.47001/JAIET/2025.212006 

Licence Copyright (c) 2026 Journal of Artificial Intelligence and Emerging Technologies. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

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