Manuel Raffel

Vienna University of Economics and Business

Papers

Total Citations

H-Index

About

Manuel Raffel is a researcher whose work sits at the intersection of computer vision, sensor data analysis, and retail automation. His most notable contribution, "Misplaced product detection using sensor data without planograms" (2018), addresses a critical challenge in inventory management: identifying incorrectly shelved items without relying on predefined store layouts. By leveraging sensor data and machine learning, Raffel’s approach reduces the need for costly planogram updates and manual audits, offering retailers a scalable, real-time solution for shelf compliance. Though his citation count—17 for this key paper—reflects a niche but growing field, his work has direct implications for smart retail environments, where accuracy in product placement drives both customer satisfaction and operational efficiency. Raffel’s research bridges the gap between theoretical sensor fusion and practical retail applications, making him a valuable contributor to the emerging domain of intelligent store management. His focus on data-driven, planogram-free detection positions him as an innovator in retail technology, with potential for broader impact as IoT and computer vision systems become more prevalent in everyday commerce.