To address the problem of products being out of stock on retail shelves, Trax created the Retail Watch solution in China. The autonomous shelf-monitoring robot was created with local robotics company Ecovacs and it relies on proprietary computer vision and machine learning technologies, an IoT-enabled shelf-edge and ceiling cameras.
The retail robot has the potential to save store associates from having to complete simple repetitive tasks, as the system boasts the ability to offer alerts to pricing errors, stocking opportunities and even planogram non-compliance. As the autonomous robot can move about the store like a shopper, it offers a new layer of insights by tapping into what shoppers see in real-time.
Retail Watch is anticipated to boost supply chain replenishment, on-shelf availability, picking for online orders and overall, an elevated shopper experience.
Shelf-Monitoring Retail Robots
Retail Watch Uses Computer Vision & Machine Learning In-Store
Trend Themes
1. Autonomous Retail Robots - The use of autonomous robots in retail stores can save time and reduce repetitive tasks for store associates.
2. Computer Vision in Retail - Computer vision technology can help retail stores monitor shelves, identify pricing errors, and improve stocking efficiency.
3. Machine Learning in Retail - Machine learning algorithms can analyze retail data to provide insights on planogram compliance, shopper behavior, and supply chain optimization.
Industry Implications
1. Retail Technology - The development of shelf-monitoring robots and advanced technologies like computer vision and machine learning can disrupt the retail industry by improving efficiency and enhancing the shopping experience.
2. Robotics - The collaboration between robotics companies and retail businesses can lead to disruptive innovations in the field of autonomous retail robots and shelf-monitoring systems.
3. Supply Chain Management - The integration of autonomous retail robots and AI-powered technologies can revolutionize supply chain replenishment, on-shelf availability, and picking for online orders.