Authors

Nikhil Varadarajan

Department of Electronics and Communication Engineering, GMR Institute of Technology, Rajam, Andhra Pradesh, India

Priya Narayanan

Department of Electronics and Communication Engineering, GMR Institute of Technology, Rajam, Andhra Pradesh, India

Harshith Ramesh

Department of Electronics and Communication Engineering, GMR Institute of Technology, Rajam, Andhra Pradesh, India

Abstract

Pharmaceutical supply chains operate within increasingly complex environments characterized by supplier uncertainty, transportation disruptions, inventory shortages, demand fluctuations, manufacturing interruptions, regulatory requirements, and interconnected global networks. Conventional risk monitoring systems frequently rely on historical reporting and correlation-based analytics, which can identify where problems occur without adequately explaining why they occur or how alternative interventions could change future outcomes. The combination of cloud computing and causal analytics provides an opportunity to develop scalable risk-monitoring systems capable of integrating heterogeneous pharmaceutical supply-chain data and supporting continuous decision-making. This article examines the application of cloud-based causal analytics to pharmaceutical supply chain risk monitoring and develops a conceptual framework that integrates cloud data infrastructure, real-time risk monitoring, causal inference, counterfactual analysis, and decision support. The framework incorporates state-of-the-art counterfactual supply-chain modeling and modular cloud-tiered pipeline architectures. Recent research on causal machine learning, pharmaceutical supply-chain disruption modeling, digital transformation, artificial intelligence, and supply-chain resilience is incorporated to establish the theoretical foundation. The analysis indicates that cloud-based causal analytics can strengthen pharmaceutical supply-chain risk monitoring by connecting continuously updated data with causal explanations and intervention-oriented decision support. The approach can assist organizations in identifying underlying risk drivers, evaluating alternative mitigation strategies, improving supply-chain visibility, and responding more effectively to emerging disruptions. The study also emphasizes that technological scalability must be accompanied by data quality, security, interoperability, governance, and human oversight. The proposed framework provides a foundation for developing more proactive and decision-oriented pharmaceutical supply-chain risk-monitoring systems.

Keywords

Cloud computing causal analytics pharmaceutical supply chain risk monitoring causal artificial intelligence supply chain resilience machine learning digital transformation pharmaceutical logistics counterfactual analysis

Citation of this Article


Nikhil Varadarajan, Priya Narayanan, & Harshith Ramesh. (2025). Cloud-Based Causal Analytics for Pharmaceutical Supply Chain Risk Monitoring. Journal of Artificial Intelligence and Emerging Technologies. 2(12), 50-59. Article DOI: https://doi.org/10.47001/JAIET/2025.212007 

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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