Capacity Planning

Find the real bottleneck before you invest in capacity

One capacity model of your lines, operations, shifts and equipment — with optimization and simulation that answer: do we have enough, where are we short, and what’s the cheapest fix?

+5%
Capacity at Yili, no capex: What-if
New line vs. overtime vs. subcontract: Week → year
From weekly load to annual plans

Root-Cause Agent

Answers “why can’t we ship this?” by tracing the real constraint
Separates true capacity limits from tooling, material or skill gaps
Quantifies the marginal value of each capacity option

Runs continuously on the same ontology and optimizer as the rest of your planning.

Why Convect for capacity planning

Capacity Planning, done right

Bottlenecks at a glance

Load heatmaps by line, operation, machine and shift flag overloaded and idle resources automatically.

Simulate before you spend

New lines, shift patterns, overtime or subcontracting — compare the impact of each on delivery, cost and inventory.

Tied to the demand forecast

Annual capacity plans use the forecast directly, exposing peak-season gaps and off-season slack before you commit capital.

Platform

How the three layers power capacity planning

Capacity Planning 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

The Root-Cause Agent traces “why can’t this order ship?” through the ontology to find the real binding constraint.

Contour Ontology

The capacity model includes line capability, routings, changeover rules, shift calendars and skills — any change propagates automatically.

Optimization Engine

Capacity is allocated under demand, material and due-date constraints; what-if analysis compares the marginal return of each expansion option.

How the platform works →

Scenarios

Typical scenarios

Annual capacity planning

Match capacity to the forecast and plan for peak season early

Load analysis

Identify bottleneck operations and resource conflicts

Investment cases

Quantify the output and payback of new lines or equipment

Multi-plant balancing

Allocate orders across plants intelligently

Customer results

Proven by our customers

Food & beverage

Yili 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

Electronics

Dreame 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