Abstract

Personalized medicine, which tailors medical treatments to individual patient characteristics, represents a transformative shift in healthcare. However, its unique requirements pose significant challenges to traditional pharmaceutical supply chains, necessitating innovative solutions to optimize logistics and ensure the efficient delivery of patient-specific therapies. This paper explores the complexities involved in supply chain optimization for personalized medicine, focusing on the unique logistical demands of therapies such as gene and cell-based treatments, biologics, and the impact of patient-specific treatment variability. It examines the role of advanced technologies, including Artificial Intelligence (AI), Machine Learning (ML), Blockchain, Internet of Things (IoT), and predictive analytics, in enhancing visibility, security, and real-time tracking within the supply chain. Furthermore, it investigates strategic frameworks for improving supply chain efficiency and resilience, with an emphasis on adaptive logistics models, decentralized manufacturing approaches, and collaborations between pharmaceutical companies, logistics providers, and healthcare systems. Finally, the paper provides recommendations for future research and policy development to further strengthen the resilience and scalability of personalized medicine supply chains, ensuring the efficient and timely delivery of these transformative therapies.