Blockchain-Integrated Finite Element Neural Networks for Intelligent Pharmaceutical Supply Chain Management

Abstract

The pharmaceutical supply chain is a critical and complex system requiring high levels of transparency, traceability, and quality assurance to ensure safe drug delivery. Traditional systems are vulnerable to issues such as counterfeit drugs, cold chain failures, and lack of real-time monitoring, posing significant risks to patient safety and regulatory compliance. These challenges have driven the adoption of advanced technologies to enhance security and operational efficiency.  This paper presents a systematic review of blockchain technology integrated with Finite Element Neural Networks (FENN) for intelligent pharmaceutical supply chain management. Blockchain provides a secure and immutable framework for tracking drug provenance and ensuring compliance, while FENN enables high-fidelity modeling of complex processes such as temperature variations, drug degradation, and demand forecasting. The integration of these technologies creates a unified system combining transparency with predictive intelligence. Applications include cold chain monitoring, counterfeit detection, and supply chain optimization supported by IoT-based data collection. The review highlights optimization techniques such as federated learning, metaheuristic algorithms, and consensus mechanisms to improve system performance. Empirical findings demonstrate enhanced reliability, efficiency, and trust among stakeholders. However, challenges related to scalability, computational cost, and system integration persist, indicating the need for further research in developing robust and scalable intelligent pharmaceutical supply chain systems.