REAL-TIME RETAIL OUT-OF-SHELF DETECTION
卷宗概要
发明人
Shiran Abadi; Noa Shmulevich
IPC 分类
CPC 分类
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.
原文（中文）
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.