Real Estate and Facilities Cost Modeling

A western apparel and boot retailer accelerated store expansion from nine new locations in 2024 to 27 new locations in 2025 by adopting a data-driven site selection process that combined AI-powered scoring with demographic and foot traffic analysis. According to a 2025 case study published by the retailer's analytics partner, the structured five-phase framework enabled the real estate team to evaluate hundreds of potential sites rapidly, with AI handling data aggregation and scoring while human analysts provided final go or no-go recommendations. The approach reduced per-site evaluation time and allowed the company to triple its expansion pace without proportional increases in real estate staff.

At the enterprise scale, a large mass-market retailer reported in 2024 that more than half of online orders are now fulfilled from local stores, a strategy that required AI-driven network optimization to determine which locations should serve dual roles as retail and fulfillment nodes. According to a Supply Chain Dive report from October 2025, the retailer uses agentic AI tools to provide a unified view of inventory across stores, fulfillment centers, and supply chain facilities, with systems that automatically detect, diagnose, and correct issues in real time. A major logistics provider separately used an AI-powered digital twin to increase warehouse capacity by nearly 10% without adding new real estate, according to McKinsey research published in November 2024, demonstrating how simulation-based modeling can defer or eliminate costly facility expansions.