An AI-driven retail shelf analytics system using in-store cameras to detect out-of-stock items and planogram compliance, achieving 95%+ detection accuracy, integrating with ERP for auto-replenishment, reducing stockouts, and boosting sales.
AI-based Retail Shelf Analytics System Computer Vision Solutions — Case Study
An AI-driven retail shelf analytics system using in-store cameras to detect out-of-stock items and planogram compliance, achieving 95%+ detection accuracy, integrating with ERP for auto-replenishment,
Category
Computer Vision Solutions
Industry
FMCG/Retail
Key Impact
Reduced stockouts by 30%, increased sales by 12%.
Challenge
Retailers lacked real-time visibility on stock levels.
Solution
In-store cameras + AI detect out-of-stock items and planogram compliance.
Key Achievements
- 95%+ shelf detection accuracy
- API to ERP for auto-replenishment
Tech Stack
- YOLOv8
- OpenCV
- Python
- AWS
Industry
FMCG/Retail
Impact
Reduced stockouts by 30%, increased sales by 12%.
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Industry Context
Where this project sits in the bigger market picture
AI use cases for retail, commerce, personalization, pricing, and customer support.
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