AI-Driven Shelf Product Recognition System for Retail Monitoring

A retail client faces inefficiencies in manually monitoring product placement on shelves, leading to inaccuracies in stock status and compliance, potentially impacting sales and customer experience. The client requires a scalable solution to automate SKU identification and stock level tracking within retail environments.

A large retail chain seeking to automate product placement monitoring across numerous store locations using computer vision technology.

• Develop an AI-powered mobile application capable of recognizing and tracking a wide range of product SKUs on retail shelves.
• Automate identification of product presence, placement accuracy, and stock levels to ensure compliance with merchandising standards.
• Enhance accuracy of product recognition beyond manual checks, reducing errors and operational costs.
• Enable detailed reporting of product stock status at each retail location to facilitate better inventory management.
• Support integration with existing retail management systems to streamline data flow and reporting.

• Image capture via mobile device camera for real-time shelf analysis.
• Detection and localization of all potential product packs based on predefined patterns.
• Application of convolutional neural networks (CNN) trained to recognize over 50 product types or SKUs.
• Automated identification of product presence, placement, and stock status within captured images.
• Generation of comprehensive reports per retail location detailing SKU counts, missing items, and placement accuracy.
• User interface for sales representatives or staff to review recognition results and inventory status.

• Retail inventory management systems to synchronize stock data
• Reporting and analytics dashboards for monitoring operational metrics

• Real-time processing capabilities for on-site shelf analysis
• High accuracy in SKU recognition (>95%) to ensure reliable data
• Scalability across numerous store locations
• Data security and privacy compliance, especially when handling product and inventory data
• Ease of use for retail staff with minimal training

Implementation of this AI-powered recognition system is expected to significantly improve stock accuracy and shelf compliance, reducing manual audit time by up to 70%, increasing SKU recognition accuracy to over 95%, and enabling real-time inventory insights. These improvements aim to enhance sales, improve customer satisfaction, and optimize inventory management across retail locations.