Digital Twins vs Simulations: My Experience Testing the Two

Last updated on October 29th, 2025 at 01:28 pm

I really didn’t see any difference between digital twins and simulations. Whoa, whoa: Fancy tech language for “making a virtual copy of something,” isn’t that all? Wrong. And then, after really digging into both of them, I found out that they’re completely different. Here’s what I discovered.

The Difference No One Talks About

And that’s the crux of it: digital twins vs simulations, really depends only on one factor connection to reality.

I experimented with this approach in industrial machinery. A simulation? That’s like taking a snapshot. You create a model, run some tests, observe what happens under certain conditions. It’s a one-shot test in a controlled setting.

A digital twin? That’s a living, breathing replica. It is hooked up to the real thing via sensors and Internet of Things gadgets, and updates constantly with live data. If anything, it’s a shadow that moves precisely when you move.

The catch? A simulation can veer off from what is real. You run your model once, things change out in the real world, and all of a sudden your model doesn’t fit. A digital twin is always synchronized if you connect it correctly, it won’t drift.

When I’d Use Each One

By testing both ways, I learned they solve different problems.

Simulations work best when you’re:

• Testing ‘what if’ scenarios before creating anything tangible.
• Teaching people in safe, predictable situations
• Validating designs without wasting materials

I was fascinated by simulations, a tool I started to use vigorously in product development you can damage stuff virtually without material consequences. Plus, they’re way cheaper upfront.

Digital twins are great when you require:

• On-line monitoring of equipment or systems
• Predictive maintenance that actually works
• Adaptive optimization for the current situation

The manufacturing sector was the perfect exemplar of this. With predictive maintenance, companies using digital twins reduce downtime a lot because they are catching the problems before they happen, not simply modeling potential failures.

The Part That Surprised Me

Here’s what surprised me: Digital twins solve for both “what’s happening now” and “what will happen next.” Simulations “just consider what could take place given these specific conditions.

I saw this in healthcare apps. Specific patient digital twins.They can use information from the real-time vitals of a patient to predict disease progress. A simulation would model just one scenario at a given time useful, but rather narrow.

The flip side? Digital twins require serious infrastructure. You need sensors, connectivity and data-processing power. It’s not cheap. In the meantime, simulations can be run on basic software without any of those hardware bets.

What About the Costs?

This matters. Begin with digital twins and you’re talking about a potentially big upfront investment IoT sensors, connectivity infrastructure, analytics platforms. Smaller entities have more difficulty overcoming such a barrier.

But get this: Virtual simulations have saved companies about $200 million in design validation alone. You’re doing all this testing virtually, instead of having to make multiple physical prototypes.

The smart move? Begin with simulations for design and testing, then graduate to using digital twins for ongoing operations once you’ve demonstrated their worth.

The Half-Loaf Compromise That Really Works

Testing the two against each other, I came to the conclusion that it’s not choosing between them that we need. They combine them.

You do similations within your digital twin environment. The digital twin provides the real-world data, and you run simulations against that data in order to test out different scenarios. It’s as though you would be able to have a laboratory inside your live operations.

This enables organizations doing this to develop feedback loops that lead them in the direction of both innovation and optimization. This is taken a step further with the Industrial Metaverse — offloading physics simulations to be running constantly in the background, similar to how graphics rendering operates.

My Bottom Line

Digital twins vs. simulations isn’t a question of one or the other. But then, it’s just about appreciating each for what he does well.

Want to check your code before it’s final? Simulation.

The need to monitor and optimize what’s already in operation? Digital twin.

Want the best of both? Simulate in the context of a digital twin.

The technology’s maturing fast. By 2025, half of industrial companies will use digital twins, with the market expanding at more than 30% per year. Not hype real use solving real problems.

Just don’t conflate the two anymore. They’re different tools for different jobs, and when to use one or the other is what matters, not the tech itself.