Schedules that fix themselves when the plant changes
Not an algorithm bolted onto a spreadsheet. Convect understands your lines, routings, materials and constraints, builds the best schedule in minutes, and lets agents replan around every disruption.
- 90%+
- Faster plan creation
- 15–30%
- Better on-time delivery
- 5–15%
- Higher capacity utilization
Sound familiar?
Is this still how you schedule?
Spreadsheet scheduling
Planners iterate by hand across workbooks; change one order and everything else moves.
Impact: Hours per schedule, frequent errors, slow to react
An APS that didn’t stick
The system never understood the plant’s real constraints, so plans weren’t executable — and everyone went back to Excel.
Impact: Big spend, little value, IT and ops blaming each other
Every rush order is a fire drill
A hot order means half a day of rescheduling — and the knock-on effects still get missed.
Impact: Late deliveries, wasted capacity, unhappy customers
Siloed plans
Production, materials and purchasing are planned by different people in different tools and never quite line up.
Impact: Low kit-complete rates, excess stock, line stoppages
The root cause usually isn’t the algorithm. It’s that the system doesn’t understand your plant. Convect starts there.
How it works
Three layers that make scheduling genuinely intelligent
AI Planning Agents
Handle exceptions on their own
Agents monitor execution and automatically respond to rush orders, breakdowns and late materials — routine exceptions no longer need a human to notice them first.
Technical detailsContour Ontology
Understands your plant
Line capabilities, routings, changeover rules, substitutions, equipment constraints and skills — not a form to fill in, a working model of how your plant runs.
Technical detailsOptimization Engine
Finds the best schedule in minutes
Scheduling becomes a large-scale optimization model, solved under every capacity, material, due-date and routing constraint for lowest cost or best service.
Technical detailsCapabilities
End-to-end planning and scheduling
From the master schedule to the shop floor to replanning when things go wrong — every step covered.
Master production scheduling
Capacity allocation and balancing across plants and periods, with scenario comparison
Finite-capacity scheduling
Line- and workstation-level schedules, down to the hour or minute
Material kitting checks
Material availability verified before release; schedules adjust automatically on shortage
Capacity load analysis
See bottlenecks and simulate adding or removing capacity
Multi-plant coordination
Orders allocated across plants to balance capacity and logistics cost
Dynamic replanning
Rush orders, breakdowns and late materials replanned automatically, with minimal disruption
Industry fit
Every industry schedules differently
Same engine, a different ontology for each.
| Industrial equipment | Electronics | Machining | Food & beverage | CPG | |
|---|---|---|---|---|---|
| Production type | Engineer-to-order / discrete | Discrete / mixed-mode | Discrete job shop | Process / batch | Process / batch |
| Key constraints | Project milestones, assembly kitting | SMT capacity, alternate parts | Flexible routings, tooling | Shelf life, CIP / allergen cleans | Promotions, freshness |
| Planning granularity | Week / day | Day / shift | Day / hour | Batch / shift | Day / week |
| BOM complexity | Deep, multi-level | Deep, many alternates | Shallow (operation-driven) | Medium (recipes) | Shallow (packaging-driven) |
| Rush-order frequency | Low (but high impact) | High | High | Medium (seasonal) | High (promotion-driven) |
| Ontology highlights | Project WBS + material network | Alternates graph + ECO versions | Routing graph + capability matrix | Recipes + lot tracing + clean rules | Channels + promo calendar + freshness |
Customer results
Results our customers have measured
Food & beverageYili cuts master planning from 5 days to half a day and unlocks 5% more capacity
- Master planning cycle
- 5 days → ½ day
- Total capacity, no capex
- +5%
Read the story
ElectronicsDreame finds material gaps across complex, shared-component BOMs
- BOM shortages found automatically
- Multi-level
- Scenario comparison in minutes
- What-if
Read the story
Consumer goodsUnilever cuts network inventory by 10%+ at the same service level
- Lower inventory (simulated)
- 10%+
- Service level maintained
- Same
Read the story
RetailDecathlon lifts forecast accuracy from 55% to 85%+
- Forecast accuracy
- 55% → 85%+
- Of range is new products
- 1/3+
Read the story
15–30%
Average cost reduction
90%+
Faster plan creation
15–30%
Better on-time delivery
Multi-agent workflow · Rush order
See it work
What you’ll see in a demo
- 1Load data from Excel or your ERP → the ontology is built from it
- 2The optimizer generates the schedule → shown as an interactive Gantt
- 3Drop in a rush order → agents replan and compare before vs. after
- 4Review the impact → cost, on-time delivery and utilization side by side
Fast rollout
From spreadsheets to AI scheduling in 6 weeks
Connect your data, let the system model your constraints, and see optimized schedules — results in as little as six weeks.
- 1
Connect your data
Import Excel or connect your ERP
- 2
Model your plant
The ontology captures your scheduling constraints
- 3
Get optimal schedules
Optimized in minutes, kept current by agents
Start scheduling with AI agents
Get our production scheduling playbook, or book a demo on a scenario from your plant.