International Journal of Scientific Research and Engineering Development

Supply chain management faces significant challenges in ensuring transparency, detecting anomalies, and optimizing operations amid increasing complexity and fraud risks. This paper presents an integrated forensic data analytics system that combines classical data analysis, blockchain for immutable transaction tracking, and quantum-inspired algorithms for advanced optimization and anomaly detection. The proposed framework processes supply chain datasets to perform quality assessments, supplier evaluations, cost analyses, and logistics optimizations while leveraging blockchain for tamper-proof records and quantum computing simulations for superior route planning and supplier selection. Implemented in Python with modules for data analytics, blockchain simulation, and quantum annealing, the system was tested on a synthetic dataset of 4609 records. Results demonstrate a 15.2% improvement in route optimization costs compared to classical methods, detection of 8.7% anomalous transactions, and enhanced traceability through blockchain validation. This integration offers a robust solution for forensic auditing, reducing defect rates by up to 22.7% and enabling real-time compliance via smart contracts. The approach advances supply chain resilience in Industry 4.0 contexts, with implications for scalable quantum-blockchain hybrids.

Sree Mathi V, Mrs.N.Vaishnavi,"Quantum and Blockchain Integrated Supply Chain Forensic Data Analytics" International Journal of Scientific Research and Engineering Development, V9(1): Page(1867-1871) Jan-Feb 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.