Real results from industry leaders
From dairy to consumer robotics, from global CPG to automotive — see how leading companies use Convect to plan with confidence.
They chose Convect
Consumer goodsUnilever cuts network inventory by 10%+ at the same service level
For a nationwide, multi-echelon distribution network, Convect combined probabilistic demand forecasting, network-wide deployment optimization and simulation to set the right safety stock for every SKU at every DC — cutting inventory by more than 10% in simulation, with service unchanged.
10%+
Lower inventory (simulated)
Same
Service level maintained
Every DC
SKU-level safety stock
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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
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RetailDecathlon lifts forecast accuracy from 55% to 85%+
With new products making up more than a third of the range and strong seasonality, Decathlon used Convect’s Algoflow engine to automatically select and blend the right model for each item — giving replenishment and production a forecast they can trust.
55% → 85%+
Forecast accuracy
1/3+
Of range is new products
Auto
Model selection per item
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ElectronicsDreame finds material gaps across complex, shared-component BOMs
Fast product cycles, deep BOMs and heavily shared components made material shortages hard to spot. Convect models Dreame’s production and transport network over time and solves for the lowest-cost purchasing, production, shipping and inventory plan — with what-if scenarios on demand.
Multi-level
BOM shortages found automatically
What-if
Scenario comparison in minutes
Lower
Inventory cost and obsolescence
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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
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