Fleet and Route Optimization for Last-Mile Delivery
The Logistics AI Suite ingested real-time traffic, orders, and capacity to optimize routes and dispatch. Manual planning was replaced by continuous optimization with human override for exceptions.
A regional logistics provider was planning routes and dispatch manually, leading to inefficiency and slow adaptation to demand spikes. They wanted to move to continuous optimization while keeping drivers and operations managers in control of exceptions.
We deployed the Logistics AI Suite with real-time ingestion of orders, traffic, and fleet capacity. The system generated and updated routes and dispatch recommendations around the clock. Dispatchers could override or adjust when needed; the AI learned from those overrides to improve future suggestions.
Route efficiency improved by 30%, and dispatch could run 24/7 without manual replanning. Exception handling (e.g., breakdowns, priority orders) remained human-led, which increased trust and adoption. The client has since expanded the same approach to a second region.
Recommendation: Begin with a single depot or region. Prove that the AI's recommendations are safe and actionable before scaling. Keep human-in-the-loop for all exception and override decisions.