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System Sizing in Modern Automation
In modern automation, system sizing is one of the most critical — and most underestimated — phases of machine design.
system-sizing-in-modern-automation-from-assumptions-to-simulation-driven-decisions
Last updated:
January 21, 2026
The biggest benefit of simulation-driven system sizing isn’t just accuracy — it’s confidence ✅. Confidence that a system will hit throughput targets, scale as expected, and behave the way it was designed to, long before hardware is committed.
Before a single motor is ordered or a PLC program is written, engineers must answer fundamental questions:
- How many movers, carriers, or transport units are required?
- Will the layout meet the target throughput?
- Where will congestion occur as the system scales?
- What happens when cycle times, payloads, or priorities change?
Traditionally, these questions are answered through experience, spreadsheets, rough calculations, and multiple iterations of CAD and PLC logic. As systems grow more complex and heterogeneous, this approach no longer scales.
Most sizing exercises rely on static assumptions:
- Average cycle times
- Idealized paths
- Perfect synchronization
- No congestion
👉 This is where simulation becomes more than visualization.
With Champion, powered by NVIDIA Omniverse, system sizing shifts from static estimation to dynamic experimentation. Instead of guessing, engineers can simulate how a system behaves under realistic operating conditions — long before physical hardware exists.
The key idea is simple: Treat system sizing as a traffic and scheduling problem, not just a mechanical one.
What Champion enables across heterogeneous OEMs 🏭
Champion is designed to be architecture-agnostic. It does not assume a specific transport technology, PLC brand, or control strategy. Instead, it focuses on the common abstractions shared by all automated systems:
- Agents (movers, carriers, shuttles, robots)
- Graphs (tracks, tiles, paths, zones)
- Tasks (pickup, processing, delivery)
- Constraints (timing, spacing, priorities, energy)
Using Omniverse as the simulation backbone, Champion allows OEMs to:
1. Size agent count with confidence 🔢
Determine how many movers or carriers are required to meet throughput targets — and identify the point of diminishing returns.
2. Validate layouts early 📐
Test station placement, routing options, and shared resources before committing to mechanical designs.
3. Identify congestion and bottlenecks 🚦
Reveal where agents interfere with each other, even when individual paths look correct.
4. Explore trade-offs ⚖️
Compare throughput-focused designs against energy- or thermal-aware configurations.
5. Support presales and concept validation 🚀
Rapidly evaluate “what-if” scenarios during early customer discussions, without requiring full PLC logic or hardware models.
Why Omniverse matters
NVIDIA Omniverse provides a physically coherent, scalable simulation environment where:
- Large numbers of agents can be simulated together
- Layouts and assets can evolve without rework
- Timing, motion, and interaction effects emerge naturally
- AI-assisted workflows reduce setup time
This makes it possible to run many system sizing experiments quickly, rather than relying on a single “best guess” design.
From sizing to confidence
The biggest benefit of simulation-driven system sizing is not just accuracy — it’s confidence.
OEMs can walk into design reviews, presales discussions, or internal gate meetings with data-backed answers to questions like:
- “Will this system scale?”
- “What happens if throughput increases by 20%?”
- “Where are we most sensitive to delays?”
- “Is adding more movers actually helping?”
⚡ Bottom line: Isaac Sim trades some physical purity for real-time performance. Most of the time that’s a smart call, but knowing where the engine cuts corners helps you tune your simulations and spot engine limits—not physics limits.