Automated Blockchain-Based Secure Data Provenance System for Supply Chain Management

Abstract

This study sets forth a blockchain-driven system for safe, traceable, and effective supply chain management in manufacturing. It combines Ethereum blockchain, AI-powered fraud detection, and real-time tracing. A CNN-LSTM hybrid model was found most useful for anomaly detection, supplemented by smart contracts for automatic monitoring. The system was constructed with a React front-end, SQLite, Web3, Solidity, PyTorch, and an external API for geographical mapping. The outcomes demonstrate improvements in fraud detection, transparency, and operational efficiency, highlighting the feasibility of a scalable, tamper-evident architecture for intelligent supply chain systems.