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Letting Data Speak!

Case Study

AI Model for Retail Shelf Monitoring

A grocery store or big mart chain seeking to optimize shelf management and improve customer experience.

About the Client

A grocery store or big mart chain seeking to optimize shelf management and improve customer experience.

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Challenge

The client faced two primary issues that were causing revenue loss:

  1. 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.

  2. Product misplacement: Disarranged or misplaced products negatively impact the customer shopping experience, particularly in high-end fashion outlets where presentation is crucial.

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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


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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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