In today’s physical retail, inventory distortions in the form of out-of-stocks, overstocks, and non-compliance with planograms (product placement plans) cost retailers and suppliers a staggering 1.75 trillion USD loss in annual revenue worldwide. As a countermeasure, stores constantly task their personnel to frequent manual checks at the shelves to replenish them when necessary. But the sheer diversity, amount and pace of fast-moving goods makes this manual process very overwhelming, untimely and error-prone, and arguably a waste of valuable human resources given the current state of AI and IoT technologies. Shelf integrity inspection is almost entirely visual, allowing for its automation using Computer Vision.
We propose the Vispera ShelfSight, a shelf-monitoring and management system that can analyze retail shelves for out-of-stocks, planogram compliance and many other KPIs in real-time. ShelfSight uses IoT cameras mounted on shelf aisles, and is powered by state-of-the-art deep learning algorithms developed by Vispera. Our deep models are customized for specific visual tasks such as detecting-recognizing product facings, detecting-reading price tags, tracking customers and more, and operate with almost perfect accuracy on edge/near-edge enabling real-time execution. ShelfSight can provide retailers instant access to the digitized view of their store and give them an operational excellence with to-the-point store actions.