What is the definition of ‘digital twin’?

Industry 4.0 is moving from the hype-cycle to the investment phase, with manufacturing firms launching innovative development projects to future-proof their competitive position.

Digital twins are discussed as a key to success, but there’s lots of confusion in the market about what a digital twin actually is. Here we cut through the uncertainty and settle on a basic definition.

Why is there so much confusion about digital twin definitions?

The confusion is because today’s digital world encompasses a broad spectrum of technologies and market needs, which has spawned many interpretations of the digital twin concept.

Digital twins gained initial momentum within high-value product manufacturing industries such as automotive and aerospace, however the concept has expanded rapidly, and you can now find digital twins across FMCG, food and beverage, construction, retail, energy and more.

General Electric (GE) defines 3 levels in which the Industrial Internet of Things (IIoT or  Industry 4.0 or 4IR) aims to digitally create a performance impact:

1. Asset level
2. Operational process level
3. Enterprise level

Many of the digital twin definitions you see from researchers and industry commentators relate solely to physical objects like products and machines. In other words – the definitions confine digital twins to the asset level, however this approach is unnecessarily restrictive and limits the digital twins’ potential.

The best digital twin definition

We get a more concise yet holistic definition of a digital twin from Marc Thomas Schmidt, Chief Architect at GE Predix. In his presentation at Minds & Machines 2017, he defined digital twins as:

“Dynamic digital representations that enable companies to optimise the performance of their assets, processes and business.”

It’s important to note that at the operational and enterprise levels, digital twins will necessarily be process-based rather than asset-based. The higher the level, the more the game moves away from the product life cycle management domain and towards the business process management domain.

Remember: there’s a place for multiple digital twins

We’ve helped hundreds of businesses develop digital twins that deliver measurable business impact. The ones that reap the greatest benefit think in terms of a digital twin ecosystem, in which different twins interact with (and inform) one another.

A good example of this is when Lanner worked with a well-known  chocolate confectionery manufacturer in North America. The company used a detailed predictive twin of chocolate bar packing lines to streamline performance. A factory-level twin was subsequently created and connected to this, drawing key data assumptions from the packing line twin to test investment strategies. Subsequently, a digital twin of the entire North American supply chain was developed to enable broader production planning and strategic business decision validation.

The 3 digital twins informed different stakeholders and answered different questions, but they worked together to facilitate holistic business and operational performance optimisation.

There’s no one-size-fits-all approach

When you approach the digital twin concept in this comprehensive way, you’re in a position to gain maximum value from it (and related IIoT and Industry 4.0 investment).

After all, digital twins come into their own when they’re tailored to your needs – helping you understand your data, complex processes and resources in a way that helps move the business forward.

Learn more about digital twin implementations and how you should use them to drive real business value

Download our Executive Briefing – Industry 4.0: Demystifying Digital Twins:

Download Briefing Paper