Case Study: Design & Implementation of Intelligent Shelf Management using AI/ML for a Leading Retail Firm

Client: Leading Retail Firm Consulting Company: VerticalServe

Objective The objective of this case study is to demonstrate the successful design and implementation of an Intelligent Shelf Management system using AI/ML for a leading retail firm. The system aimed to optimize shelf space allocation, streamline product catalog management, and enhance the overall efficiency of inventory management.

Background The retail firm faced challenges in managing their product catalog, keeping track of inventory levels, and optimizing shelf space allocation. This led to inefficiencies in store operations, resulting in lost revenue and reduced customer satisfaction. The firm engaged VerticalServe to design and implement an Intelligent Shelf Management system using AI/ML to address these challenges.

Solution VerticalServe designed and implemented an Intelligent Shelf Management system using AI/ML for the retail firm. The solution encompassed the following components:

1. Product Catalog Management: A system for managing the firm’s product catalog, including product images, descriptions, and attributes.
2. Labeling Workbench: A tool for annotating product images with relevant labels, enabling the AI/ML models to identify and classify products accurately.
3. Warehouse Data: Integration with the firm’s warehouse management system to access real-time inventory data.
4. Model Training for Product Detection: Development of AI/ML models to detect and classify products in shelf images, using the annotated product images and warehouse data.
5. Shelf Count Detection: Implementation of AI/ML models to analyze shelf images and detect shelf count discrepancies, enabling proactive inventory management.
6. POS Data: Integration with the firm’s Point of Sale (POS) system to access real-time sales data and inform shelf space allocation decisions.
7. Model Endpoints: Deployment of AI/ML models to endpoints for real-time analysis of shelf images and generation of live shelf reporting.
8. Live Shelf Reporting: A dashboard for visualizing live shelf data, providing insights into inventory levels and shelf space allocation.
9. MLOps Pipeline: Implementation of a Machine Learning Operations (MLOps) pipeline to streamline the deployment, management, and monitoring of AI/ML models.

Implementation VerticalServe began by setting up a system for product catalog management, enabling the firm to efficiently manage product images, descriptions, and attributes. They then developed a labeling workbench to annotate product images with relevant labels, laying the foundation for accurate AI/ML model training.

The team integrated the firm’s warehouse management system to access real-time inventory data and incorporated this data into the model training process. They also integrated the firm’s POS system to access real-time sales data and inform shelf space allocation decisions.

Next, VerticalServe developed AI/ML models for product detection and shelf count detection using the annotated product images and warehouse data. They deployed these models to endpoints for real-time analysis of shelf images, generating live shelf reporting.

The team implemented a dashboard for visualizing live shelf data, providing insights into inventory levels and shelf space allocation. This enabled the retail firm to make data-driven decisions and optimize store operations.

Finally, VerticalServe implemented an MLOps pipeline to streamline the deployment, management, and monitoring of AI/ML models, ensuring optimal performance and scalability.

Results With the successful implementation of the Intelligent Shelf Management system using AI/ML, the retail firm experienced the following benefits:

1. Optimized Shelf Space Allocation: The AI/ML models enabled the firm to allocate shelf space more effectively, maximizing revenue and improving customer satisfaction.
2. Streamlined Product Catalog Management: The product catalog management system allowed the firm to efficiently manage product images, descriptions, and attributes, reducing manual effort and errors.
3. Enhanced Inventory Management: The shelf count detection

About:

VerticalServe Inc — Niche Cloud, Data & AI/ML Premier Consulting Company, Partnered with Google Cloud, Confluent, AWS, Azure…50+ Customers and many success stories..

Website: http://www.VerticalServe.com

Contact: contact@verticalserve.com

Successful Case Studies: http://verticalserve.com/success-stories.html

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