Digital twin vs. simulation: Understanding the key differences

Digital Twin vs. Simulation: Key Differences Explained

Have you ever listened to someone explaining the benefits of a digital twin, and desperately wanted to say: “But… isn’t that just a simulation? Surely we’ve had those for years?”

If so, you’re far from alone. The line between digital twins and simulations can easily become blurred — but it is there, and this blog post will help you see it much more clearly.

I’ll illustrate the differences between simulations and digital twins by explaining how each technology can be applied during the lifecycle of a car. I’ve chosen this example because a car is a discrete, easy-to-visualize, physical object, and because its lifecycle is one that most of us can readily understand.

Just know that the differences I’ll highlight are also exhibited by simulations and digital twins of other things, such as systems and processes.

The key characteristics of a simulation

For decades, computer-based simulations have been an invaluable tool for product designers and R&D engineers. Imagine you’re one of them.

During new product development you follow an iterative process: you create a version of the product, test how well it performs in different scenarios, tweak the product, and test again.

For many products, like a new car, repeatedly following this loop with a physical prototype would require a huge amount of time and resources. So, you use software to build a virtual model of the car and simulate the scenario. You learn from the results, make changes to the virtual representation of your product, and rerun the simulation.

A simulation:

• Uses predefined data inputs to test specific, “what if” scenarios
• Provides insights for engineers and designers
• Primarily used during R&D and design phases

How is digital twin technology different from simulation?

Having validated key aspects of your car’s design through simulation, you build a physical prototype. As it flies around the test circuit, you have its onboard sensors send real-time data to your virtual model.

Suddenly, your virtual model has become a digital twin. It shows how the real car is performing, in real life, in real time. You can use this insight to further refine your car’s design.

At this point, you might even decide to simulate a specific scenario using data from your digital twin. After all, your twin contains much more accurate information about the car’s performance than you had available during your earlier stages of purely computer-based R&D.

While you run your “what if” scenario, your digital twin doesn’t stop mirroring the state of the real car. So you now have a simulation and a digital twin, two different sources of insight, running side by side.

Your digital twin can continue to deliver insight, even after your cars leave the production line. Let’s say, like Tesla, you duplicate it to create multiple digital twins — one for every individual vehicle you sell.

Fed by real-time data from your customers’ cars, these digital twins can be a source of valuable insight for everything from future R&D efforts to timely customer communications. They allow you, for example, to contact customers about issues before they impact the driving experience.

A digital twin:

• Uses real-time data to reveal what’s actually happening
• Provides insight for multiple business units
• Useful throughout the product lifecycle
• Can also be used to run simulations

Is the line between “digital twin” and “simulation” starting to blur?

As digital twins expand to find new applications in new industries, these definitions are becoming a little more elastic.

Strictly, a virtual model only becomes a digital twin when it starts exchanging information with its real-world counterpart. But if the virtual model you’re building is intended, from the very outset, to perform the function of a digital twin — well, you’re going to tell people you’re building a digital twin. Because you are.

This is reflected in Wikipedia’s current definition, “A digital twin is a digital model of an intended or actual real-world physical product, system…” (our emphasis). It’s also reflected in how digital twin pioneers like Siemens use the term when discussing sophisticated virtual models used in place of physical prototypes.

The truth is that, like the virtual models themselves, ideas of what constitutes a digital twin are evolving over time. So, rather than asking, “Is this solution a digital twin?” it’s usually more worthwhile to ask, “What can it do for my business?”

A few of the advantages of digital twins

According to McKinsey, 70% of C-suite tech execs at large enterprises are already “exploring and investing” in digital twins. It’s easy to see why. Digital twins are empowering organizations of all kinds to:

See what’s actually happening.

A digital twin can show you things you wouldn’t think to simulate. This is a huge advantage of digital process twins, in particular, given how rarely business processes operate in the way an organization imagines.

Gain remote control.

A digital twin lets you monitor the operation of an object, system, or process, and take steps to optimize its performance, without having to physically visit every machine in your factory, or team in your business.

Collaborate easily.

A digital twin can provide a shared reference for everyone working on a project. And with the rise of generative AI-powered natural language interfaces, a digital twin’s insights are becoming even more readily available to non-technical users.

Prevent problems.

A digital twin can help you identify issues that could, in time, compromise safety, productivity, customer experience, and any number of other things your organization cares about.

From possible process bottlenecks to impending gearbox failures, a digital twin solution can alert you to the danger, so you can add extra resources and strategic automation, perform predictive maintenance, or do whatever else is necessary to make sure potential issues don't become actual ones.

Spot opportunities.

A digital twin can also help you surface opportunities you’re currently missing. These could be opportunities to improve product quality, boost efficiency, reduce energy consumption, or create cost savings. In the world of business process digital twins, they could be opportunities to improve your cashflow, or accelerate order fulfilment and increase customer satisfaction.

How a process digital twin benefits organizations like yours

Aerospace and automotive organizations have been instrumental in pioneering digital twin solutions, but today, the technology is finding a home in every industry.

Healthcare and pharmaceutical companies are developing digital twins to accelerate medical device R&D and drug discovery. Public authorities are creating digital twins of entire cities to support smarter urban planning.

At the same time, Process Intelligence is empowering every organization to create digital twins of its key business processes, see how they really run, and optimize their operations.

You can learn more about Process Intelligence here, or just talk to an expert.