Real Time Shelf Inventory Management Solution Powered by Azure ML & Cognitive Services: 6-week PoC

Affine’s 6-week PoC consulting offer will provide Retail customers with a real-time shelf inventory monitoring solution using a CCTV camera video feed, powered by Azure ML and cognitive services. This solution will enable retailers to track their inventory in real-time, ensuring accurate and up-to-date records at all times, and enabling store operations staff to manage the inventory levels and avoid stockouts.

Affine’s automated real-time shelf inventory management solution powered by Azure Machine Learning (ML) and Cognitive Services uses advanced machine learning algorithms to monitor shelf inventory levels in real-time, using computer vision to analyze video frames from the CCTV camera feed. This allows retailers to automatically generate replenishment orders when inventory levels are low, ensuring that products are always in stock and available to customers.

Benefits:

• Improved customer satisfaction: By avoiding stockouts, this solution will ensure that customers can always find the products they need on the shelves. This will improve their shopping experience and increase customer satisfaction.
• Increased operational efficiency: Real-time monitoring of shelf inventory will enable store operations staff to quickly and accurately identify when items need to be replenished, reducing the time and effort required to manage the inventory.
• Enhanced data analysis: By integrating the solution with a central data warehouse, retailers can capture and analyze additional data sources, such as customer purchasing patterns, to gain insights into the effectiveness of their inventory management processes.
• Flexible product detection: The in-built product detection framework can be easily adapted to identify new products, allowing retailers to quickly and easily add new items to their inventory management system.
• Cost savings: With improved shelf inventory monitoring, this solution will enable lesser stock out thus improving overall sales & revenue.

Agenda – 6 weeks:

• Week 1: Initial consultation and requirements gathering
• Week 2-3: Development of the Azure ML model and integration with cognitive services
• Week 4-5: Implementation of the inventory management framework and the development of dashboards for the operations staff
• Week 6: Testing and deployment of the solution

Outcome:

Solution output integrates with a web tool that allows users to view the CCTV video feeds and see the results of the machine learning analysis in real-time. This could involve displaying the detected items on the shelves, along with information about their quantities and any changes seen over time. We will also provide a report summarizing the results of the proof of concept, including any challenges or limitations encountered during the project. This report could also include recommendations for the next steps and potential improvements to the system.

Why Affine

Enabling business-focused data science, AI, and BI development with deep domain expertise. Affine believes in faster design to faster deployment through key differentiators- Experimentation Focus and Speed to Value.