In complex manufacturing ecosystems, tracking component defects and understanding supply chain interconnections is critically challenging. This project proposes traceability methods by developing a node-based tracking system using Neo4j graph database and Docker containerization technologies. By implementing/adopting an advanced visualization framework, we created a comprehensive tool that maps supply chain relationships across enabling precise identification and rapid diagnosis of defective components. Our implementation significantly enhances a manufacturer’s ability to trace product origins, isolate potential failure points, and streamline quality control processes. Our implemented solution demonstrates the potential for significant reductions in defect investigation time and offers unprecedented visibility into intricate supply chain networks, providing a scalable and adaptable methodology for comprehensive component tracking.