A Survey of Methods and Architectures for Leveraging Blockchain with Integrated Finite Element Neural Network-Based Drug Supply Chain Management for Pharmaceutical Industries

Abstract

The pharmaceutical supply chain is a critical and complex system where ensuring drug authenticity, traceability, and timely delivery is essential for patient safety. Traditional supply chain frameworks are vulnerable to issues such as counterfeit drugs, data tampering, and inefficiencies in multi-tier distribution networks. These challenges have driven the need for advanced technological solutions that enhance transparency, reliability, and operational efficiency. This paper presents a comprehensive review of integrated approaches combining blockchain technology with neural network models, particularly Finite Element Neural Networks (FENN), for pharmaceutical supply chain management. Blockchain provides a decentralized and immutable infrastructure for secure data recording and traceability, while FENN enables detailed modeling of distributed supply chain systems by capturing both local and global dynamics. The integration of these technologies allows for intelligent decision-making through predictive analytics and automated processes via smart contracts. Applications include counterfeit detection, inventory optimization, demand forecasting, and automated compliance monitoring across pharmaceutical networks. The review highlights optimization techniques such as evolutionary algorithms, reinforcement learning, and federated learning to improve system performance and scalability. Empirical findings demonstrate enhanced transparency, efficiency, and reliability. However, challenges related to computational cost, interoperability, and regulatory constraints remain, emphasizing the need for further research in developing scalable and intelligent pharmaceutical supply chain systems.