Development of an AI-Driven Inventory Optimization and Store Assortment System

The client operates a network of over 2,000 retail stores across 30 regions, facing difficulties in creating optimal product assortment lists tailored to each store's local demographic, location, and customer demand patterns, which affects sales and operational efficiency.

A large retail chain managing thousands of storefronts across multiple regions, seeking to optimize product assortment and inventory management based on location-specific factors.

• Develop an AI-powered system to generate optimal product assortment lists for each store considering location, demographic density, transportation hubs, healthcare facilities, and nearby centers.
• Enhance sales performance by tailoring product offerings to local demand, aiming for measurable improvements.
• Integrate with existing POS systems for real-time assortment management and inventory control.
• Achieve targeted improvements such as a 10% increase in productivity and a 7% uplift in sales, based on projected outcomes.

• Modeling of store location data including geographic coordinates, housing density, transportation hubs, and nearby institutional facilities.
• Clustering analysis based on historical sales data (e.g., 48 months) to identify store segments with similar characteristics.
• Recommendation engine generating tailored product lists for each store based on clustering and external data inputs.
• Integration interfaces with POS terminals to monitor stock levels, sales, and assortment adjustments.
• Real-time data processing and updates, including predictive insights for inventory planning.

• POS systems for sales and inventory data synchronization
• Geospatial data sources for location context
• External data on transportation, healthcare, and commercial centers

• High scalability to process data across 2,000+ stores
• Performance target of near real-time updates to support dynamic assortment adjustments
• Data accuracy and integrity for decision-making
• Secure integration with POS and external data sources

The implementation of this AI-driven assortment and inventory management system aims to boost productivity by 10%, increase sales by approximately 7%, and streamline supply chain operations, leading to enhanced market competitiveness and adaptive local marketing strategies.