AI Last-Mile Facility Planning
The Problem
“Optimizing last-mile facility sites amid volatile demand”
Organizations face these key challenges:
1
Fragmented data across brokers, GIS, WMS/TMS, and public records makes comparable, city-by-city decisions inconsistent and slow
2
High lease and build-out commitments (often 5-10 year terms) amplify the cost of siting errors when demand shifts by submarket
3
Manual modeling cannot reliably capture neighborhood-level delivery times, labor constraints, zoning/permitting risk, and competitive saturation simultaneously
Impact When Solved
5-12% reduction in last-mile transportation spend via optimized coverage and drive-time routing assumptions3-8% lower occupancy cost and 10-20% higher utilization through right-sized footprints and better submarket selectionPlanning cycles cut from 6-10 weeks to 3-7 days, enabling continuous re-optimization as rents, vacancy, and demand change