Digital twins and AI for end-to-end sustainable pharmaceutical supply chain management

Ali, Shahid and Khan, Shifa Saleem Ahmed and Khan, Bushra Fatima and Ali, Adib Syed Hifazat (2025) Digital twins and AI for end-to-end sustainable pharmaceutical supply chain management. World Journal of Biology Pharmacy and Health Sciences, 21 (3). pp. 678-687. ISSN 2582-5542

Digital Twin (DT) and Artificial Intelligence (AI) technologies rapidly transform the pharmaceutical industry by enabling intelligent, data-driven systems across manufacturing, supply chain logistics, sustainability initiatives, and personalized medicine. This review synthesizes findings from 29 peer-reviewed studies published between 2020 and 2024, highlighting the capabilities of DTs to optimize processes, enhance decision-making, and support regulatory compliance. The analysis categorizes DT applications into four core domains—manufacturing, logistics, sustainability, and clinical care—while identifying emerging trends, research gaps, and integration challenges. The discussion covers key enablers such as IoT, machine learning, and simulation platforms, along with critical limitations like data interoperability, scalability, and regulatory readiness. A novel contribution of this review is the conceptualization of an integrated DT hub that enables closed-loop pharmaceutical intelligence, offering real-time, end-to-end optimization across the drug lifecycle. The findings underscore the strategic importance of AI-powered Digital Twins in shaping the future of sustainable and patient-centric pharmaceutical systems.