Warehouse simulation and the future of supply chain visibility

Supply chain visibility isn’t optional anymore. Climate shocks, labor strikes, geopolitical shakeups - disruptions keep coming and they’re getting harder to predict.

That’s why the conversation is shifting from dashboards to decisions. And warehouse simulation is leading that shift.

By creating a digital twin - a virtual replica of your warehouse - teams can test scenarios, identify bottlenecks, and model performance before it ever impacts the real world. It's not just visibility. It's rehearsal. It's foresight.

In this post, we explore how warehouse simulation is moving visibility from passive observation to strategic advantage and what it takes to turn those insights into action.

Warehouse simulation goes beyond “seeing”

Yes, digital twins improve visibility. But that’s only the starting point.

The real value lies in what you do once you see. Warehouse simulation empowers teams to move from reactive firefighting to proactive design.

With the right simulation model, you can:

• Predict the impact of volume surges before they hit
• Stress-test contingency plans under realistic conditions
• Coordinate workflows across people, systems and sites
• Learn continuously as new data flows in

This isn’t about more data. It’s about faster, smarter decisions.

A dashboard shows you the traffic. Simulation shows you how to re-route before you hit the jam.

Where most visibility efforts fall short

A lot of digital twin discussions stop at live monitoring. But the most strategic use cases dive deeper - solving for execution, not just observation.

Warehouse simulation lets you:

• Model ripple effects: What happens if a critical dock goes offline?
• Run what-if scenarios: How would a supplier delay affect throughput next week?
• Test network changes: Should we switch carriers or rebalance volumes before peak season?

Companies like Unilever and DHL are alreay reporting measurable gains using simulation to accelerate decision cycles, reduce downtime and optimize resources.

What they’ve learned: it’s not about avoiding disruption. It’s about designing for it.

What makes simulation actually work

You can’t plug simulation into a rigid supply chain stack and expect results. For warehouse digital twins to drive real change, your tech has to keep up.

That means:

• Modular, API-friendly systems that let simulation outputs feed real-time changes
• Configurable tools that allow for rapid layout or logic adjustments
• Shared data standards so every system speaks the same language

And behind the scenes, it all runs on:

• AI + ML for intelligent modeling and anomaly detection
• IoT and edge devices to pull real-world signals from your assets
• Cloud platforms for global collaboration and scalability
• Blockchain (optional) for secure, multi-party data sharing

At Infios, we see digital twins as a natural extension of adaptive execution. When paired with modular, cloud-based supply chain systems, they help you not just respond to change but get ahead of it.

Case study: how John Lewis de-risked warehouse design before breaking ground

When UK retailer John Lewis planned a new 600,000 sq. ft. distribution center to support omni-channel growth, they turned to simulation.

Using Infios and the CLASS simulation platform, they built a digital twin of the facility - long before construction started.

What it enabled:

• Testing multiple layout and labor scenarios
• Identifying high-congestion zones
• Visualizing the full design in 3D to align internal stakeholders

The result: Confidence to move forward with a design backed by real data, not just best guesses.

From simulation to execution 

Visibility is changing. It’s no longer about seeing more. It’s about knowing what to do next. 

Digital twins give you that advantage. They don’t just report. They rehearse. They help you stress-test today’s plan against tomorrow’s disruption.