.png){kind=icon}
返回
Misplaced product detection using sensor data without planograms
.png){kind=icon}
PRE
AI
DOI：10.1016/j.dss.2018.06.006.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Accurate and timely provisioning of products to the customers is essential in retail environments to avoid missed sales opportunities. One cause for missed sales is that products are misplaced in the store. This can be addressed by fast and accurately detecting those misplacements. A problem of current detection methods for misplaced products is their reliance on up-to-date planogram information, which is often missing in practice. This paper investigates the effectiveness and efficiency of outlier detection methods for finding misplaced products without planograms. To that end, we conduct simulation studies with realistic parameters for different store parameters and sensor infrastructure settings. We also evaluate the detection methods in a real setting with an RFID inventory robot. The findings indicate that our proposed MiProD aggregation of individual detection methods consistently outperforms individual techniques in detecting misplaced products.
Keyword:
Data analysis
Sensors
Outlier detection
Inventory management
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.8
论文数:
3.8K
被引数:
1.5W
机构
引用论文
.png){kind=icon}
查看更多内容