Artificial Intelligence and Pharmaceutical Supply Chain Resilience: A Proposed Administrative Decision-Support Model for Improving Efficiency and Managing Operational Risk
DOI:
https://doi.org/10.54536/ajlsc.v1i1.8637Keywords:
AI Governance, Artificial Intelligence, Operational risk, Pharmaceutical Supply Chain, Supply Chain ResilienceAbstract
Pharmaceutical supply chains face demand uncertainty, multi-tier dependencies, strict quality controls, cold-chain requirements, and serious consequences when medicines are unavailable. Existing studies examine AI applications, supply-chain risk, resilience, visibility, and governance, but do not consistently integrate these strands into a pharmaceutical-specific administrative decision-support model. This study develops such a model through an integrative review and framework synthesis of 24 purposively selected scholarly and authoritative sources published between 1955 and 2026. Sources were chosen for their relevance to supply-chain theory, AI applications, pharmaceutical quality, risk, resilience, and governance. The synthesis identifies five findings: AI creates value as an organizational capability; visibility and data quality connect analytics with efficiency; risk sensing and scenario analysis support resilience when linked to accountable workflows; governance and human-AI collaboration shape decision quality; and pharmaceutical quality, traceability, and patient availability constrain acceptable decisions. The model’s theoretical contribution is an integrated explanation linking AI capability to efficiency and resilience through visibility, risk sensing, scenario analysis, and decision quality, with data quality, digital integration, governance, and human oversight as enabling conditions. Its practical value is a decision process for translating AI outputs into accountable action while preserving professional judgment. The review does not estimate pooled effects or empirically validate the model. Future empirical research should test the framework using organizational and operational evidence.
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Copyright (c) 2026 Mohanad El Assadi (Author)
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