AI logistics optimization cut automotive planning time by 80% across supplier and plant networks.
- Logistics planning time
- -80%
- Transportation efficiency
- Improved
- Fuel and transport costs
- Reduced
What we shipped
A vehicle routing and demand prediction platform that automates transportation planning across supplier, plant, and dealership flows.
Automotive supply chains involve thousands of components, suppliers, and logistics routes. Many decisions are made manually or through legacy planning tools that cannot adapt to changing conditions such as demand shifts, capacity changes, or disruptions.
Blueprint → AI Pilot → Production launch → Scale and operate.
We followed the Datablooz Delivery Model. See our process.
- Blueprint
Mapped supplier locations, plant demand, fleet capacity, delivery constraints, and planning rules currently held in legacy tools.
- AI Pilot
Prototyped vehicle routing optimization and predictive demand models on a single supplier-plant flow.
- Production launch
Deployed dynamic route planning and operational data pipelines across supplier, factory, and dealership distribution.
- Scale and operate
Extended to spare-parts logistics, added real-time re-planning on disruptions, and continuously retrained demand models.
Business, technical, and governance outcomes.
- 80% less time spent on logistics planning.
- Improved transportation efficiency across suppliers.
- Reduced fuel consumption and transportation costs.
- More reliable just-in-time manufacturing.
- Python
- OR-Tools
- Machine Learning
- Airflow
- PostgreSQL
- Kafka
Constraint-based optimization with planner override, versioned plans, and audit logs on automated routing decisions.
Working on something similar?
Schedule a call. We will tell you honestly whether AI is the right move.
Reference calls available under NDA after the second working session.