How AI strengthens pharma supply chains against global disruptions

A new systematic review unveils the potential of artificial intelligence (AI) and machine learning (ML) in revolutionizing the pharmaceutical supply chain, especially in the face of global disruptions such as pandemics.

Published in Sustainability, the study “A Systematic Review of Artificial Intelligence (AI) and Machine Learning (ML) in Pharmaceutical Supply Chain (PSC) Resilience: Current Trends and Future Directions”  provides a consolidated view of how AI and ML are being deployed across critical pharmaceutical logistics functions. Based on a review of 89 studies, 32 of which met rigorous selection criteria using PRISMA guidelines, the research identifies both current applications and persistent gaps in AI/ML-driven PSC systems.

How are AI and ML transforming the pharmaceutical supply chain?

The pharmaceutical supply chain, vital for the continuous flow of life-saving drugs and treatments, has historically been vulnerable to external shocks and internal inefficiencies. The study reveals that AI and ML are becoming integral tools in addressing these vulnerabilities through predictive analytics, data-driven decision-making, and real-time monitoring.

The review categorizes AI/ML applications in PSCs into five major domains: demand forecasting, risk management, logistics optimization, supplier coordination, and regulatory compliance. In demand forecasting, AI tools are being used to model disease outbreaks, predict demand surges, and automate inventory replenishment. In logistics, route optimization algorithms are helping reduce transit times and cut waste, particularly for temperature-sensitive pharmaceuticals.

Risk management solutions powered by ML are enabling real-time disruption detection by analyzing both internal operations data and external variables such as geopolitical tensions and weather patterns. Meanwhile, supplier collaboration systems are integrating machine learning to automate vendor risk profiling, identify alternate sources, and simulate contingency scenarios.

The use of AI in quality control and compliance is growing, particularly in automating batch testing, auditing documentation trails, and flagging anomalies that may lead to non-compliance. These innovations collectively aim to minimize delays, reduce costs, and strengthen the responsiveness of pharmaceutical distribution networks under normal and crisis conditions.

What gaps exist in current research and deployment?

Despite promising trends, the study highlights significant limitations in both the academic literature and real-world adoption of AI/ML in pharmaceutical logistics. A major concern is the lack of practical deployment of these technologies at scale. Many of the systems remain at the pilot or proof-of-concept stage, with limited movement toward industry-wide integration.

Notably, regulatory and ethical considerations are underexplored. Only 40.7% of the reviewed studies addressed regulatory compliance, and even fewer considered the implications of AI-driven decision-making in contexts where human oversight is critical. This regulatory blind spot raises concerns about the feasibility of AI adoption in highly controlled pharmaceutical environments.

The study also emphasizes that managerial and organizational perspectives are often ignored in the AI/ML discourse. Without clear frameworks for training, adoption, and accountability, many promising technologies fail to gain traction beyond R&D labs. There is a pressing need for research that examines how leadership buy-in, change management, and cross-departmental collaboration can accelerate adoption and trust in AI systems.

Moreover, data standardization and system interoperability continue to hinder broader implementation. The lack of uniform digital infrastructure across pharmaceutical ecosystems makes it difficult to scale AI/ML solutions that require clean, real-time, and comprehensive datasets.

What strategic directions should future research take?

The authors propose several targeted recommendations to bridge the gaps and unlock the full potential of AI and ML in PSCs. First, they call for stronger integration of regulatory frameworks into AI/ML system design. Algorithms must be trained to comply with global standards such as Good Distribution Practices (GDP) and Good Manufacturing Practices (GMP) to ensure safe, ethical, and lawful deployment.

Next up, the authors recommend a shift toward predictive modeling and prescriptive analytics. Rather than just analyzing past trends, future systems should be capable of simulating scenarios, suggesting solutions, and proactively adjusting operations to minimize risk. This requires embedding real-time data processing and feedback loops into PSC architectures.

Third, empirical validation and cross-sector case studies are necessary to establish benchmarks, prove efficacy, and reduce skepticism. Building open-access repositories of AI/ML deployment outcomes in PSCs could accelerate learning across companies and regions.

The study also advocates for greater collaboration between academia, industry, and regulatory bodies. This would support the creation of ethical guidelines, data-sharing agreements, and co-designed systems that are both innovative and compliant. Interdisciplinary projects and innovation hubs can play a pivotal role in this ecosystem development.