Artificial Intelligence Techniques for Leveraging Blockchain with Integrated Finite Element Neural Network-Based Drug Supply Chain Management for Pharmaceutical Industries: Trends and Challenges

Abstract

The pharmaceutical supply chain is a highly complex and safety-critical system requiring transparency, traceability, and efficiency to ensure reliable drug delivery. Traditional supply chain frameworks face challenges such as data fragmentation, counterfeit drugs, and inefficiencies in logistics and monitoring. These issues necessitate advanced technologies capable of integrating diverse data sources and improving real-time decision-making across global pharmaceutical networks. This paper presents a comprehensive review of integrated technologies, including artificial intelligence, blockchain, and Finite Element Neural Networks (FENN), for pharmaceutical supply chain management. Artificial intelligence techniques enable predictive analytics, anomaly detection, and demand forecasting, while blockchain provides a secure and immutable infrastructure for ensuring transparency and regulatory compliance. FENN introduces physics-informed modeling, enabling accurate representation of spatial and temporal dynamics within supply chain systems, enhancing optimization and reliability. Applications include counterfeit detection, cold chain monitoring, inventory optimization, and automated compliance verification across multi-tier supply networks. The review also highlights emerging trends such as federated learning, edge computing, and quantum-enhanced optimization for large-scale systems. While these integrated approaches demonstrate improved efficiency, transparency, and resilience, challenges related to data quality, interoperability, scalability, and regulatory constraints remain, emphasizing the need for further research in intelligent pharmaceutical supply chain management systems.