Research Article
How Data and AI Can Optimise Pharmaceutical Supply Chains
Abstract
Pharmaceutical supply chains are highly complex, global, and tightly regulated systems that are increasingly exposed to disruption from geopolitical instability, pandemics, and demand volatility. This report examines how data and Artificial Intelligence (AI) can optimise pharmaceutical supply chains by improving forecasting accuracy, manufacturing efficiency, inventory management, logistics, quality assurance, and regulatory compliance. It argues that the industry is transitioning from isolated AI pilots toward integrated, intelligent, and potentially autonomous supply chain systems underpinned by data-driven decision-making, digital twins, and agentic AI. The report highlights the foundational role of data, emphasising that its value depends on contextualisation, integration, and governance across fragmented systems. AI applications such as machine learning-based demand forecasting, predictive inventory optimisation, and real-time analytics enable more responsive and efficient supply chain operations. In manufacturing, digital twins and predictive maintenance systems improve yield, reduce downtime, and enhance quality consistency, while AI-enabled logistics and warehouse optimisation strengthen cold-chain integrity and distribution efficiency. The report also explores AI applications in quality management and regulatory compliance, including anomaly detection, computer vision inspection, and automated documentation aligned with Good Manufacturing Practice (GMP) standards. Ma