Autonomous Quality Release Decisioning Using SAP QM, Computer Vision, and Warehouse Event Correlation

Abstract

Manufacturing plants generate quality signals from many different sources at once: visual inspection data, warehouse handling telemetry, environmental sensor streams, and enterprise resource planning transactional records, to name the main ones. Pulling these mismatched signal types into one decisioning framework that can autonomously recommend batch release or rejection remains an unresolved problem in industrial quality management. This paper introduces an autonomous quality release decisioning framework that combines SAP Quality Management (QM) inspection lot processing, convolutional neural network (CNN)-based computer vision defect detection, SAP Extended Warehouse Management (EWM) event correlation, and IoT environmental monitoring into a single Release Confidence Score (RCS) computation engine. The resulting five-layer architecture was validated on an experimental dataset comprising 120,000 warehouse events, 48,000 inspection images, 9,500 pallet movements, and 3,200 environmental deviations drawn from a regulated food manufacturing environment. It reached a CNN-based defect detection accuracy of 94.7%, a false release prevention rate of 96.2%, a release cycle time reduction of 38%, a traceability correlation accuracy of 97.1%, an environmental deviation detection sensitivity of 91.8%, and an autonomous recommendation precision of 93.5%. Taken together, these results show that fusing multi-modal signals under SAP QM governance can match the release decisioning accuracy of dedicated manual review panels while cutting cycle time by more than a third.