DefinePK

Abstract

The pharmaceutical supply chain is a complex, multi-layered system that faces unique challenges, including fluctuating demand, stringent regulatory requirements, and logistical constraints. This paper explores the role of AI-driven predictive analytics in optimizing pharmaceutical supply chain management, presenting a conceptual framework that enables companies to leverage advanced data analytics for improved decision-making, risk management, and operational efficiency. Key AI techniques, such as machine learning, data mining, and predictive demand forecasting, are discussed as tools for addressing critical supply chain issues, including inventory optimization, demand variability, and regulatory compliance. The proposed framework details essential components—data collection, processing, integration with existing systems, and real-time decision-making—and outlines critical success factors for effective implementation. By offering insights into the transformative potential of predictive analytics, this paper provides actionable recommendations for pharmaceutical companies seeking to build resilient and responsive supply chains. Future research directions are also proposed, emphasizing the need for adaptable predictive models and the ethical implications of AI applications in pharmaceuticals.
Keywords: Pharmaceutical Supply Chain, AI-driven Predictive Analytics, Machine Learning, Demand Forecasting, Inventory Optimization, Regulatory Compliance.