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Letting Data Speak, AI Act!
Case Study
Artificial Intelligence Model for Retail Shelf Monitoring
About the Client
A grocery store or big mart chain seeking to optimize shelf management and improve customer experience.
Challenge
The client faced two primary issues that were causing revenue loss:
Product out of stock: Products were unavailable on shelves despite being in store inventory. The manual process of checking stock was labor-intensive and time-consuming.
Product misplacement: Disarranged or misplaced products negatively impact the customer shopping experience, particularly in high-end fashion outlets where presentation is crucial.
Key Results
Improved real-time stock monitoring, reducing out-of-stock instances by 15%
Enhanced product placement accuracy, increasing customer satisfaction
Reduced manual labor costs for inventory checks
Solution
JashDS developed a deep learning-based model to detect out-of-stock or misplaced products in real-time:
Implemented continuous video stream capture using existing CCTV infrastructure
Created a custom deep learning model to analyze the live video feed
Developed a system to predict and identify out-of-stock and misplaced items in real-time
Integrated automated alerts (SMS/email) to notify relevant personnel for immediate resolution
Utilized state-of-the-art learning-based models and custom datasets from local marts for training
Technologies Used
RetinaNet architecture with ResNet-101 backbone
Focal Loss function
Custom dataset collection and preparation
Computer Vision and Image Processing
Deep Learning frameworks (likely TensorFlow or PyTorch)
Video streaming and real-time processing
Automated alert systems (SMS/email integration)
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