Edge–cloud integration for logistics quality assurance

Abstract

In today’s fast-paced and highly interconnected logistics networks, quality assurance (QA) requires real-time responsiveness, data accuracy, and scalable technological infrastructure. This study investigates the integration of edge computing and cloud platforms to enable real-time quality data analytics across distributed logistics systems. By deploying sensor-based edge devices for immediate data processing and anomaly detection, and synchronizing with cloud environments for centralized analytics and decision support, the proposed architecture significantly enhances the responsiveness and transparency of QA operations. The paper presents a modular system design suitable for warehouse quality control and transport condition monitoring, emphasizing low-latency performance, data security, and system scalability. Through application scenarios and performance evaluation, the study demonstrates how digital QA systems can support proactive interventions, reduce product damage, and optimize quality compliance in logistics. Future directions include AI-based predictive analytics and the integration of digital twins to further improve QA intelligence and automation