FedEx reduces pickup and delivery costs 10% using AI-driven network consolidation and the Hold-to-Match system
FedEx's Network 2.0 consolidation and AI-driven Hold-to-Match system — which holds packages to consolidate multi-package deliveries to the same address — cut pickup and delivery costs by approximately 10% in key markets, with a $2 billion savings target by end of 2027.
Background
FedEx operates one of the world's largest logistics networks, with hundreds of millions of package movements annually. Last-mile delivery is the most expensive segment of the delivery chain, and stop density — how many packages can be delivered per driver stop — is a primary driver of unit economics. Duplicate stops (two packages to the same address on different days) represent a compressible inefficiency at scale.
What Was Implemented
- Network 2.0: consolidation of FedEx Express and FedEx Ground operations; 360+ facilities optimized
- AI-driven routing for the consolidated network, improving stop density
- Hold-to-Match: system that holds packages one day when a next-day same-destination match exists, combining deliveries and reducing stops
- Shipment Eligibility Orchestrator (AI tool identifying which packages can be held without violating delivery commitments)
- 25% of eligible volume in consolidated network by 2024; target 65% by 2026 peak season
Results
FedEx reduced pickup and delivery costs by approximately 10% in key markets (U.S. and Canada). The Hold-to-Match system increases delivery stop density, lowering per-package costs. The full Network 2.0 program targets $2 billion in savings by end of 2027 . More than 360 facilities have been optimized, with 25% of eligible daily volume flowing through the consolidated network as of 2024.
Lessons
- Consolidating delivery network infrastructure (facility and operational) enables AI routing to function on a more efficient substrate
- Hold-to-Match demonstrates that deliberately holding a package one day to increase stop density can reduce total cost — a counterintuitive choice that requires AI to identify and execute at scale
- Phased rollouts (25% → 65% of eligible volume) allow operational learning before full deployment