What Is a Digital Twin?

If you’ve been anywhere adjacent to technology, you may have heard the term “Digital Twin” and wondered what it meant? Is it like that time your great aunt’s Facebook account was recreated and she said, “DO NOT accept any new friend requests from me?” Or is it your AI counterpart in the digital world that thinks and acts just like you and is how the internet knows exactly how to market to you? Well, I’m sorry to tell you that these examples, while tongue-in-cheek, are not what a digital twin stands for.

So, what does Digital Twin mean?

Digital Twin Definition

In the world of technology, a digital twin is a virtual model of something physical that can be used to simulate real use cases, testing, or be put in place as a monitoring tool.

NASA originated the concept with their attempt to improve their simulation of spacecraft in 2010, even though the idea of using virtual models to simulate the real world has been around for a lot longer. As you can imagine, it can be difficult to test spacecraft without some sort of virtual model in place to give an accurate representation of what will happen when the spacecraft launches.

The digital twin is meant to replicate data from the actual physical system to help predict failure, help optimize the physical machine/equipment without taking it out of commission, and mitigate issues before they have the opportunity to occur.

There are three types of digital twins:

• Digital twin prototype – Exists before there is a physical product
• Digital twin instance – Virtual/digital instance of the product once it exists
• Digital twin aggregate – Data from multiple digital twin instances that can be used together to compile information about a stream of products

Digital Twin Use Cases

Now, your brain may already be coming up with ideas for when a digital twin might make sense within your organization. Here are some common use cases for digital twins:

• Product lifecycle management – Using digital models of products to design, develop, and test products before they exist and throughout their lifecycle to model issues and improve the next model.
• Inventory management – Define optimal stocking levels and reduce stockouts.
• Warehouse management – Simulate a physical warehouse to optimize space and material flow.
• Preventive maintenance – Observe a digital model of equipment to determine when to expect the next round of preventive maintenance based on data from the equipment to support the model.
• Finance – Model financial data for products and inventory cost to determine feasibility of products for your organization.
• Urban planning – Determine feasibility of planning a city using digital models that take population, crime, and other factors into account.
• Construction – Review feasibility of a construction project by using digital twins to build and review outside factors that may affect the construction project.
• Healthcare – Consider a digital twin for a human being that allows for experimental treatment and multiple outcomes based on data.

Conclusion

The concept of a digital twin is very interesting, and can be used within organizations now to capture data and use that data to model potential changes or shifts in customer trends, forecasting, and cost. As these concepts continue to mature, expect to see more about digital twins in your business systems to simulate outcomes and prepare for all common, and uncommon, scenarios.

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