Anomaly Detection and Control in Supply Chains Based on Image Feature Modeling

Ensuring process quality in supply chains is a critical challenge due to the complexity and variability of production processes. Conventional methods often struggle to accurately detect and address quality issues in such dynamic environments. This paper proposes a novel anomaly detection and control method based on image feature modeling to enhance process monitoring and decision-making in supply chain operations. By leveraging advanced image processing techniques, key features of production processes are extracted and modeled, enabling accurate identification of deviations and anomalies. Experimental results demonstrate that the proposed method significantly improves detection accuracy and response time compared to traditional approaches. This study contributes to the development of intelligent quality control solutions, offering scalability and robustness for real-world supply chain applications.