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AI-Powered Smart Shelf Management System for Retail Optimization

lionwood.software

Retail

Logistics

Consumer products & services

Challenges in Retail Operations and Merchandizing Efficiency

Manual shelf inventory checks, lack of real-time stock visibility, inefficient product placement strategies, and delayed response to stock shortages lead to reduced sales efficiency and suboptimal supply chain performance.

About the Client

A mid-tier supermarket chain operating in Central and Northern Europe, focused on leveraging AI to enhance merchandizing and supply chain efficiency in a competitive market.

Project Objectives for AI-Driven Retail Management

Develop a scalable computer vision model for automated product recognition on shelves
Enable real-time monitoring of stock levels and brand popularity analytics
Integrate AI-driven insights with existing ERP systems for seamless operations
Reduce manual labor through crowdsourced data collection and automated alerts
Improve sales efficiency and supply chain optimization through actionable dashboards

Core System Functionalities and Key Features

YOLO-based product recognition (brand, package, quantity)
Real-time stock level monitoring and out-of-stock alerts
Brand popularity analytics and heatmap visualization
Push notifications for merchandizers based on predefined triggers
Integration with ERP systems for inventory synchronization

Preferred Technology Stack

.NET

MongoDB

Angular

Xamarin

Identity Server

Required System Integrations

ERP systems
Push notification services
Inventory databases

Non-Functional Requirements

Scalable architecture for multi-store deployment
Real-time processing with sub-second latency
High-accuracy computer vision model (95%+ recognition rate)
Role-based access control and data encryption

Expected Business Impact of Smart Shelf Management System

25% increase in sales efficiency through optimized stock management, 40% reduction in manual inventory tasks, enhanced brand visibility analytics, and improved supply chain responsiveness via real-time alerts and data-driven merchandizing decisions.