A Security-Centric Warehouse Management Framework for Mitigating Product Abuse and Cybersecurity Risks

This study investigates product abuse, reconciliation challenges, and cybersecurity risks in warehouse management systems (WMS) within increasingly digitized supply chain environments. As warehouses evolve into data-driven operational hubs, vulnerabilities such as data manipulation, insider threats, and fraudulent activities pose significant risks to financial accountability and system integrity. To address these challenges, this research proposes a security-centric WMS framework that integrates blockchain-based immutable logging, Internet of Things (IoT)-enabled tracking, and artificial intelligence (AI)-driven anomaly detection. The methodology follows a hybrid iterative–incremental development approach, supported by real-world deployment of a prototype WMS implemented using a scalable microservices architecture. Over a five-year operational period, the system processed more than 10 million transactions with no recorded successful cybersecurity incidents leading to data breaches, operational compromise, or unauthorized system access, while achieving improvements in reconciliation accuracy, operational efficiency, and fraud detection capabilities. Results demonstrate reductions in manual reconciliation efforts, mispricing incidents, and operational losses, while maintaining high system availability and low latency. In addition, the reported 18–22% improvement associated with AI-assisted anomaly detection is presented as a simulation-based projection rather than a production-validated measurement. The findings indicate that combining secure software engineering practices with automation, auditability, and advanced analytics can significantly enhance transparency and resilience in warehouse operations. The study concludes that integrating decentralized and intelligent technologies provides a viable pathway toward secure, privacy-preserving, and abuse-resistant warehouse ecosystems.