Industrial Automation

Using Digital Twins in Manufacturing’s Digital Transformation

Digital twins allow manufacturers to simulate outcomes based on real conditions—without having to carry them out in real life. Learn more about their benefits. 


Today, most manufacturers are building innovations like cloud, edge, and other concepts into their digital journeys—but is something missing?


Using digital twins in manufacturing isn’t a new idea, but it’s one that’s becoming more prevalent in manufacturing.


According to a McKinsey survey, factory digital twins are becoming a realistic way to solve manufacturing problems. In the survey, 86% of respondents say digital twins are applicable to their organization, while 44% have already implemented a digital twin and 15% are planning to deploy one in the future.


What is a Digital Twin?

A digital twin is a visual representation of an industrial asset: a PLC, an industrial machine, an assembly line, a factory floor, or a complete site, for example.


Just like scientists test theories and replicate and design experiments, the digital world follows a similar process that allows manufacturers to simulate and test processes, workflows, designs, and changes before deploying them across the plant. Digital twins allow you to simulate outcomes based on real factory conditions—without having to carry them out in real life.


Enterprise data buffs can use digital twins to simplify the creation and maintenance of purpose-driven data models. For example, the same assembly line could have three data models:

  • Energy monitoring
  • Predictive maintenance
  • Production optimization

Each model uses static and dynamic data specific to the assembly line. Ideally, the model follows a certain data hierarchy, has application-specific contextual information (like metadata) and produces valuable outputs, whether those are calculations, KPIs, alerts or events. Those outputs can also act as an input to a larger data model (a digital twin of the entire site, for example).


Each digital twin model can also have multiple instances. Ideally, if you use a centralized management platform for multiple sites, you can clone an instance at the enterprise level and easily deploy it on similar assets across those sites.


How Digital Twins Are Used

Digital twins can provide a big-picture view of what’s happening on the factory floor from start to finish, replicating sensors and systems and modeling various scenarios.


For instance, manufacturers can use digital twins to:

  • Enable “what-if” analyses and scenario planning, exploring the impact of things like process changes or layout adjustments.
  • Identify problems before they drain finances, time, and resources.
  • Recognize opportunities for better product design.
  • Test options in product variety with minimal time and cost commitments.


A Digital Twin Use Case: Automobile Manufacturing

One sector blazing the trail with digital twins is automotive. It uses various features of digital twins to improve vehicle design, manufacturing, performance monitoring, maintenance, and autonomous driving development.


Vehicle Design and Manufacturing

In the vehicle design phase, manufacturers create detailed virtual models of cars before they’re physically built. Engineers simulate and analyze various aspects of the vehicle and production process to replicate, test, and identify issues before real-world implementation.


Vehicle Performance and Maintenance

For real-time data monitoring, sensors help model battery life, motor function, and driving dynamics. This information feeds into a digital twin model of each vehicle.


The data collected can also be used to predict maintenance needs by comparing real-time data with the expected performance from the digital twin.


Autonomous Driving and AI

Testing and refining systems through simulation and digital twin technology for a wide range of conditions is safer than running real tests. It also provides more robust testing than what’s possible on real roads.


Digital twins are integral to training the machine-learning algorithms for autonomous vehicles as well. Vast amounts of collected data are used to continuously train and improve the artificial intelligence (AI) systems responsible for autonomous driving.


The Potential Pitfalls of Digital Twins

While digital twins offer manufacturers the opportunity to make substantial advancements in digital transformation, there are also a few considerations to keep in mind.


Data Privacy and Security

Digital twin technology requires extensive data collection, which can raise data privacy and security concerns. As these systems gather and store vast amounts of detailed information, it can become difficult to protect and preserve sensitive data. Robust cybersecurity measures are required to prevent unauthorized access and data breaches, which could have far-reaching consequences.


Complexity and Cost

Digital twins can be costly and complex to set up and maintain, especially for small and medium-sized manufacturers. Requirements like specialized sensors and distinct datasets call for significant time and money investments. For this reason, their use may be more justified for complex products or in industries with high-value assets.


Data Accuracy

Digital twins rely on accurate data. Meticulous data gathering and validation practices are critical to ensure the effectiveness of digital twins. Otherwise, inaccuracies can significantly impact the reliability of the predictions and decisions made using these models.


Technological Limitations

Digital twins have limitations when it comes to what they can replicate in the real world. While they provide valuable insights, they also come with a threshold surrounding how intricately they can mirror real-life scenarios. This gap between the world of simulation and real-world intricacies can sometimes create problems.


Digital Twins: Just Part of Your Digital Transformation

There’s no debate that the digital twin movement in manufacturing is here to stay.


Digital twins are just one example of the many positive benefits that result from digital transformation and industrial automation, helping manufacturers save time, resources, and money.



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