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Real-Time Computer Vision Inventory Management System

intuz.com

Retail

eCommerce

Challenges in Inventory Management and Customer Experience

The client faces significant operational inefficiencies including frequent stockouts due to manual shelf monitoring, excessive labor costs from human inventory checks, customer dissatisfaction from misplaced/damaged products, and lack of real-time inventory visibility impacting supply chain decisions.

About the Client

Leading multinational retail corporation with integrated online and offline sales channels seeking to optimize operations through AI-driven inventory solutions

Goals for Inventory Management Optimization

Automate product tracking across 1000+ stores using computer vision
Reduce inventory discrepancies by 85% through real-time monitoring
Decrease labor costs by 40% via AI-powered automation
Improve customer satisfaction scores by ensuring product availability
Enable real-time inventory analytics for supply chain optimization
Create scalable framework for global store expansion

Core System Capabilities

Real-time image processing pipeline for shelf monitoring
Product detection and classification using CNN models
Empty shelf space identification with geolocation tagging
Integration with existing warehouse management systems
Interactive dashboard for inventory insights (Databricks SQL + Power BI)
Automated restocking alerts and reporting functionality

Technology Stack Requirements

Databricks platform for big data processing

Apache Spark for distributed image data pipelines

Delta Lake for optimized data storage

Convolutional Neural Networks (ResNet/EfficientNet)

MLflow for model versioning and tracking

AWS cloud infrastructure

System Integration Needs

Existing ERP systems for inventory updates
In-store camera networks API endpoints
Power BI dashboard for visualization
Supply chain management software

Operational Requirements

Horizontal scalability for 10,000+ concurrent camera streams
Real-time processing latency under 500ms
99.99% system availability with auto-scaling clusters
GDPR-compliant image data processing
Model retraining pipeline with performance monitoring

Expected Business Impact of Real-Time Inventory Management

Implementation of this solution is projected to reduce inventory management costs by 40%, increase stock availability by 85%, and improve customer satisfaction metrics by 30% within the first year. The system will enable data-driven restocking decisions with real-time inventory visibility across all distribution channels, supporting 20% faster supply chain response times and creating a foundation for AI expansion into demand forecasting.