Writer: Mohona Sarkar
Drug shortages create disruptions in treatment plans, potentially causing life-threatening situations for the patient or the public during an epidemic or outbreak. For years, they were treated as temporary interruptions that were inconvenient, but manageable. That assumption no longer holds. Today, supply failures can be frequent, prolonged, and systemic, affecting everything from cancer therapies to basic injectable vaccines to mitigate an outbreak. Pharmaceutical supply chain is influenced by a global world, defined by pandemics, geopolitical instability, climate shocks, and highly concentrated manufacturing in a few hubs across the globe
In the United States, the Food and Drug Administration (FDA) maintains a continuously updated drug shortage database, a public record of how often supply fails to meet demand.1 These shortages signal a deeper fragility: dependence on a small number of manufacturers, limited transparency and interactions across supply networks, and regulatory systems that intervene only after disruptions occur.
Not only do drug shortages affect treatment plans, but counterfeit or adulterated drugs circulating in the market possess a serious risk to the public. Globally, the World Health Organization estimates that at least one in ten medical products in low- and middle-income countries is substandard or falsified2—an alarming figure that translates directly into treatment failure, preventable deaths, and erosion of trust in health systems.
Despite this reality, drug supply chains are still governed by reactive and retrospective analysis for compliance. Manufacturers report problems after production falters. Regulators respond after shortages emerge. Health systems scramble once shelves are already empty. This approach may have worked when supply chains were simpler, more localized and perturbations in the global market did not cause an impediment, however with interdependent manufacturing and interconnected supply chains, a reactive approach is inadequate.
Artificial intelligence (AI) offers a way out of this cycle—but only if it is treated as essential public health infrastructure, not a corporate optimization tool.3,4
At its core, the problem is neither a lack of regulation nor a lack of intent to integrate AI. There exists information such as data trending and analytical tools around manufacturing capacity, quality deviations, prescribing trends, epidemiological patterns, logistics disruptions, and regulatory inspections, but these factors or variables do not communicate with each other. AI’s most significant contribution lies in its capacity to integrate disparate data streams, identify complex interdependencies, and detect emerging risks before they escalate, generating predictive models that map how changes in one variable propagate through secondary and tertiary factors across the supply chain. Predictive modeling has already demonstrated this potential. Machine-learning systems that combine historical shortage data with demand patterns and external signals can forecast supply disruptions earlier and more accurately than traditional statistical approaches.5 Studies across pharmaceutical manufacturing and hospital pharmacy settings show that AI-driven forecasting improves early detection of demand spikes and impending shortages, enabling earlier intervention.6
It is indeed important to shift this dynamic from a reactive to an anticipatory strategy. Early warnings allow manufacturers to adjust production, diversify sourcing, or build inventory buffers. Regulators can prioritize inspections or expedite approvals from forecasting. Health systems can plan substitutions before patient care is compromised. None of this requires deregulation; it requires better intelligence and policy to enforce guardrails. FDA first addressed AI applications in process design, advanced process control, real-time release testing, and predicting product quality attributes, including for complex biologics such as cancer vaccines, cellular, and gene therapies.7 AI also addresses one of the most dangerous blind spots in global medicine: counterfeit and diverted drugs. Expansive therapies and online marketplaces have made treatments more accessible but have also introduced the risks of substandard and fraudulent therapies. Patients increasingly encounter products that look legitimate but are ineffective or unsafe. Here, too, AI offers practical tools. Computer-vision systems can detect subtle packaging deviations invisible to the human eye. Transactional analytics can flag unusual distribution patterns across borders. When paired with blockchain based traceability8, these technologies strengthen end-to‑end verification and help regulators focus enforcement where risk is the highest. FDA initiated a few pilot programs as part of the Drug Supply Chain Security Act to assess the blockchain and its interoperability. Such programs using AI and blockchain for counterfeit detection already show measurable improvements in supply chain transparency and oversight.9
Inside manufacturing facilities, AI enhances quality rather than undermining it. Predictive maintenance, real-time monitoring of critical quality attributes, and deviation trend analysis reduce the likelihood that localized failures escalate into national shortages. These tools are particularly important for advanced therapies—such as cell and gene treatments—where production is patient specific and intolerant of error. Importantly, these approaches align with existing regulatory principles, including internationally recognized quality-risk management frameworks.
Logistics in the drug supply chain can be another point of vulnerability. Vaccines, biologics, and gene therapies depend on strict temperature control where even brief cold-chain failures can render products unusable. AI driven systems that combine real-time data from multiple sources with predictive routing models can anticipate disruptions due to inclement weather, infrastructure failures, or transit delays and proactively reroute shipments before damage occurs. Evidence from vaccine and biologics distribution during Covid-19 pandemic has shown that these systems reduce temperature excursions and waste10,11, especially during largescale public health campaigns.
For regulators, AI is not a replacement for human judgment but an augmentation tool. Natural language processing tools can analyze inspection reports, recalls, and adverse-event data at a scale no agency workforce can match, surfacing recurring manufacturing deficiencies or geographic risk areas earlier than traditional review processes. While AI has great potential to improve pharmaceutical supply chains, there are still several challenges. Poor data quality, cybersecurity threats, algorithmic bias, and “black box” models could undermine trust and produce fabricated outputs or “hallucinations”. Furthermore, there are always economic risks around new emerging technology and the ways it can cause inequities in societies. Public policy must set clear guardrails: transparency in model validation, visibility into decision logic that can be easily queried by regulators, secure public-private data sharing frameworks, and explicit equity considerations are just a few examples. AI should serve resilience and patient safety—not merely efficiency.
The question, then, is no longer whether AI belongs in the drug supply chain. It already does. The real question is whether governments and regulators will treat it as optional—or recognize it as essential infrastructure for protecting access to safe, effective medicines. In the decade ahead, compliance alone will not secure the drug supply, but intelligent forecasting based on inter-connected data and supply chains will. The schematic given below shows how AI can integrate multiple players and variables in creating a robust supply chain.
This schematic illustrates how AI capabilities (center) can be embedded across the five stages of the pharmaceutical supply chain — from manufacturing to patient — to convert reactive, siloed data streams into integrated, forward-looking risk tolerance and predictions. Each AI module feeds a central integration hub that synthesizes cross-domain signals to enable early shortage detection, targeted regulatory enforcement, cold chain integrity, and end-to-end counterfeit traceability. Policy guardrails are presented as necessary conditions for responsible deployment. Arrows indicate bidirectional data flows between supply chain stages, AI capabilities, and resilience outcomes. (AI: Artificial Intelligence; NLP: Natural Language Processing; ML: Machine Learning). Anthropic, Sonnet 4.6 was used to generate the schematic of using AI in drug supply chain with the draft as an input.
AI disclosure
Disclosure of AI use: Claude (Anthropic, Sonnet 4.6) was used to improve grammar, brevity and sentence constructs of the draft. Anthropic was also used to generate the schematic representation given here.
References:
1. FDA Drug Shortages. Published online October 23, 2025. https://www.fda.gov/drugs/drug-safety-and-availability/drug-shortages
2. WHO Substandard and Falsified Medical Products. December 3, 2024. https://www.who.int/news-room/fact-sheets/detail/substandard-and-falsified-medical-products
3. Majumdar K, Jain J, Mohan D. AI-Driven Optimization of Pharmaceutical Supply Chains: Enhancing Forecasting, Inventory, and Transparency. In: Proceedings of Data Analytics and Management. 2025. https://doi.org/10.1007/978-3-032-03072-6_18
4. Dzogan R. Reducing drug shortages: The power of AI in pharma supply chain management. September 13, 2024. https://pharmaphorum.com/rd/reducing-drug-shortages-power-ai-pharma-supply-chain-management
5. Zhang J, Yuyang W, Zidu W. Enhancing Supply Chain Forecasting with Machine Learning: A Data-Driven Approach to Demand Prediction, Risk Management, and Demand-Supply Optimization. J Fintech Bus Anal. 2024;2(1):1-5. doi:10.54254/3049-5768/2024.18321
6. Pall R, Gauthier Y, Auer S, Mowaswes W. Predicting drug shortages using pharmacy data and machine learning. Health Care Manag Sci. 2023;26(3):395-411. doi:10.1007/s10729-022-09627-y
7. Center for Drug Evaluation and Research. Artificial Intelligence in Drug Manufacturing. Discuss Pap. Published online 2023. https://www.fda.gov/media/165743/download
8. Md Saifur Rahman, Nazmun Nahar, Md Hasan Imam, Mohammad Nuruzzaman Bhuyian, Md Auhidur Rahman, Mayeen Uddin Khandaker, Shams Forruque Ahmed,. A blockchain-based framework for drug security: Leveraging EdDSA to prevent counterfeiting. Array. 2025;28(100604). https://doi.org/10.1016/j.array.2025.100604.
9. Drug Supply Chain Security Act Pilot Project Progran. FDA; 2023. https://www.fda.gov/media/168307/download
10. Fusco T. Using the Cold Chain to Safely Deliver COVID-19 Vaccines. March 25, 2021. https://www.unicefusa.org/stories/using-cold-chain-safely-deliver-covid-19-vaccines
11. Advancements in Cold Chain Logistics for Pharmaceuticals. https://www.opex.com/insights/cold-chain-logistics-advancements-for-pharmaceuticals/#elementor-toc__heading-anchor-0