REAL-TIME RETAIL OUT-OF-SHELF DETECTION

原文（中国語）

A robust, data-driven model for detecting Out-of-Shelf (OOS) events in retail environments in real-time. Utilizing transactional logs, the system employs a statistical model to analyze sales data across specific intervals, identifying significant deviations from expected sales patterns. By harnessing noise within the data, the model generates a probability density function for each item, facilitating the detection of unlikely sales drops. Alerts are triggered when sales fall below a predefined significance level, enabling immediate remedial action. This innovative approach offers a cost-effective, scalable solution to minimize sales interruptions and enhance item inventory management.