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  • By Lanner
  • In Blog
  • Posted 05/12/2018

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.

In the first article in this series, we discussed the bottom-level digital twin – the asset twin. Here we move up the organisational pyramid to the operational process level and look at how twins facilitate more effective decision-making.

3 types of operational process digital twin

Remember: it’s best to think in terms of a digital twin ecosystem instead of a one-off initiative, as twins can work together to maximize visibility and value. With that in mind, it’s helpful to think of operational processes in 3 ways when it comes to the use of digital twins:

  • Supervisory capabilities
  • Diagnostic and control capabilities
  • Predictive capabilities

Let’s go through each example in turn.

The supervisory digital twin

A supervisory digital twin (sometimes called an emulation twin) mimics the running of real-life processes, either at the operational or business level. This twin may meet your needs if you need support with macro-level design and monitoring or detailed operator/maintenance training.

At its most basic, a supervisory digital twin offers visual emulation (or animation) to support your understanding of static or dynamic operational characteristics – and how they relate to performance outcomes. For example, they can help ensure consistent operation and ongoing standards; plan and test the impact of critical scenarios; or visually test new conceptional business models.

The diagnostic and control twin

This digital twin gives you dynamic diagnostic capabilities, enabling you to analyse end-to-end process performance in real time. They can deliver impressive results by supporting the optimization of real-world asset and process performance.

These twins link sensor data from the physical world to analytical and data-mining algorithms, giving you detailed, accurate and actionable insight. This allows you to trigger alerts to prompt closer monitoring, diagnose issues and identify performance improvements. You can also link physical assets and control systems so you can automatically adjust process parameters via an actuation loop.

As a result, you can execute operational plans more effectively. You can also increase productivity, improve control and drive more stable and reliable operational performance.

The predictive digital twin

predictive digital twin goes a step further – it unlocks opportunities beyond using current-state data to drive asset and operational process performance. It’s created using specially designed predictive simulation software and can be used to evaluate future scenarios.

Using this digital twin, decision-makers can test and understand the impact of different scenarios, identifying opportunities and risks without incurring any cost. You can ask questions like “What if?”, “What’s best?” and “How do we?’”– and get answers using dynamic models of real business and operational processes. You essentially gamify short, medium and long-term decision-making. You can then build an informed investment case for technology implementations because you’re taking a strategy-led, rather than technology-led, approach.

In other words, you’re in a strong position to design the business to meet future challenges.

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

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