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Case Study
Data ScienceArtificial Intelligence Model for Retail Shelf Monitoring
Overview
JashDS revolutionized retail shelf management for a major grocery chain by developing an AI-powered real-time monitoring system. The solution utilized advanced computer vision techniques and deep learning models to detect out-of-stock and misplaced products, significantly improving inventory accuracy and enhancing the customer shopping experience while reducing manual labor costs.
About the Client
A grocery store or big mart chain seeking to optimize shelf management and improve customer experience.
The 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
Our 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
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