How many warehouses — and where? Let the data answer
Optimization finds the network that balances cost and service, from a greenfield region to reshaping the network you have today.
- 4
- Ready-made network scenarios
- $M / yr
- Line-haul savings at Yili
- Minutes
- To solve large site-selection models
Why Convect for network design
Network Design, done right
Decisions grounded in data
Comprehensive analysis makes sure network decisions rest on facts, not intuition or the last consultant’s spreadsheet.
How many, and where
Advanced optimization answers it all at once: number of sites, locations, service assignments and flows.
Scenarios side by side
Run many scenarios in parallel and compare cost, lead time and coverage in one view — ready for the executive review.
Platform
How the three layers power network design
Network Design isn’t a standalone tool. It runs on the same ontology, optimizer and agents as production scheduling, so plans connect end to end.
AI Planning Agents
A scenario agent builds “add a site / close a site / pick one” what-ifs on request and explains the differences between them.
Contour Ontology
Plants, warehouses, customers, lanes, rates and lead times form one network; business rules like service radius and capacity caps become explicit constraints.
Optimization Engine
Large-scale mixed-integer optimization solves location and allocation together, including fixed costs, freight rate breaks and service time.
Customer results
Proven by our customers
Food & beverageYili cuts master planning from 5 days to half a day and unlocks 5% more capacity
Asia’s largest dairy company moved sales, production, logistics and procurement onto one planning platform. Optimization replaced rules of thumb — adding 5% total capacity (half a plant’s worth, with no new investment) and cutting line-haul transport costs by millions of dollars a year.
5 days → ½ day
Master planning cycle
+5%
Total capacity, no capex
−17%
Transport cost
Read the story
AutomotiveGeely redesigns a multi-tier automotive parts network
Geely modeled suppliers, parts warehouses and vehicle plants as one network and used Convect’s optimization engine to decide how many parts warehouses to run, where, and which flows each should serve — lowering transport cost while keeping plants supplied on time.
Multi-tier
Supplier → DC → plant model
End-to-end
Inbound flows optimized
Lower
Transport cost
Read the story
Get started with network design
One ontology and one optimizer from demand to supply — so every plan connects to production scheduling.