Digital Twin Example 1: The Asset Digital Twin

It’s easy to think of Industry 4.0 as a technology-led initiative, but the goal is to boost customer value and operational efficiency. Using digital twins with various applications and capabilities can help you achieve this. In this 3-part series, we look at different digital twin examples to give you a clear idea of what they do and how they drive real business value.

When defining the digital twin concept, I always emphasise the importance of considering the 3 levels in which Industry 4.0 aims to impact performance:

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

Digital twins play a different role at each level – and can work together to deliver maximum business impact. In this article, I discuss the asset digital twin. In the other 2 posts in this series, I cover the operational process and enterprise levels.

What is an asset digital twin?

An asset digital twin is an extension of the expertise traditionally covered by CAD/CAE and product life-cycle management (PLM). It focuses on optimising the life-cycle design and manufacture of complex assets, usually at a product or machine level.

This digital twin captures detailed engineering data to visualise, simulate and analyse asset functions. It’s capable of capturing performance data across a variety of operating contexts and geographies – for example, for wind turbines, heavy mining machinery or aero-engines. A good example is collecting reliability data to better understand failures so they can be managed more predictably.

What you can do with an asset digital twin

Asset digital twins can be used to represent hardware (devices, machines, vehicles), resources (people, finances, energy, water) or even products so you can maintain a functional view.

You can use them to mimic the functionality of existing product and machine assets. Or capture and visualise real-time sensor information. The data you gain from the digital twin can then be used to improve design and speed up time to market while reducing quality costs (for instance).

Asset digital twins also enhance predictive maintenance, particularly in situations where multiple asset populations are deployed across different geographies and/or operating conditions. This is because the additional insight helps you organise and schedule field service and engineering resources more effectively, which in turn enables further cost reductions.

Do you need an asset digital twin?

You will gain significant value from an asset digital twin if your business strategy requires robust asset management – to provide product performance monitoring, distributed field services and training, or connected services, for example.

Start with a structured scoping study that defines the required asset boundaries, data level, technology architecture, process logic assumptions and analytics metrics. Then you’re in a strong position to create a twin that can be deployed quickly and provides answers to the right questions.

Learn more about digital twin implementations and how you should use them to drive real business value by downloading our executive briefing – Industry 4.0: Demystifying Digital Twins:

Download Briefing Paper