AI USE CASE
AI Freight Capacity Matching Platform
Match available freight capacity to shipment demand in real time, cutting empty miles and boosting load factors.
What it is
An ML-powered matching engine continuously pairs available truck or container capacity with incoming shipment requests, optimising routes and consolidation in real time. Automated matching replaces manual broker calls and spreadsheet coordination, cutting dispatch time from hours to minutes.
Data you need
Historical shipment records, real-time carrier capacity feeds, route and lane data, and freight pricing history are required for training and live matching.
Required systems
- erp
- data warehouse
Why it works
- Onboard a critical mass of carriers and shippers early to ensure sufficient liquidity for meaningful matching.
- Integrate directly with TMS and ERP via APIs so capacity and demand data flow in real time without manual input.
- Build dispatcher trust through transparent match explanations and an easy manual override workflow.
- Implement continuous model retraining pipelines using fresh shipment and pricing data.
How this goes wrong
- Sparse or siloed carrier capacity data leads to poor match quality and low adoption by dispatchers.
- Real-time data integration with legacy TMS or ERP systems stalls deployment for months.
- Carriers distrust algorithmic assignments and revert to manual broker relationships, undermining platform ROI.
- Model performance degrades during demand shocks (e.g. peak seasons) if not retrained on recent data.
When NOT to do this
Do not build a custom matching platform if your monthly shipment volume is below a few hundred loads, off-the-shelf freight exchange networks will deliver better results at a fraction of the cost.
Example vendors
This page describes an idea. It gives no budget or timeline: those depend on your company. The free diagnostic tells you which ideas fit your situation.