If You Build Products, You Should Be Using Digital Twins

Categories: Digital TransformationTesting and QAManufacturing and Industrial

Moving from concept to market faster than your competitors is one of the hallmarks of a successful, sustainable product development strategy. Digital twins are proving to have an oversized impact on businesses using them to curate data from multiple sources and activate it to improve outcomes at every step through design, manufacturing, and support.

The IoT enables engineers to test and communicate with integrated sensors within a company’s operating products, delivering real-time insights about the system’s functionality and ensuring timely maintenance. Digital twins can also help businesses analyze data to identify underperforming parts of the plant, and even replicate that “golden batch.” They give manufacturers a tool to predict likely outcomes before investing in changes. They use real-world data and artificial intelligence (AI) to create scenarios and test product outcomes given various inputs.

While this technology has useful applications in many industries, it’s crucial for product manufacturers. Let’s look at the benefits of using a digital twin model, what you should consider before adopting one, and real-world examples of how companies deploy them to improve performance, accelerate production, and achieve faster time-to-value.

What is a Digital Twin?

A digital twin is a comprehensive digital model of an environment, product, or system used for testing, integration, and simulations without impacting its real-world counterpart.

Where a simulation typically replicates a single scenario or process, a twin can run multiple simulations simultaneously, studying various processes and outcomes at scale.

It’s no wonder the global digital twin industry was valued at $6.5 billion in 2021 and is projected to reach $125.7 billion by 2030, growing at a CAGR of 39.48% from 2022 to 2030. Growth in IoT and cloud — and the goal to cut down costs and reduce the time for product development — are key factors driving this growth.

Recommended reading: The Future of Cloud-Driven Manufacturing: Built to Scale

Digital Twins in Action: Real-World Use Cases to Inspire Your Strategy

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The Value & Benefits of Digital Twins

Accelerated risk assessment and production time

This technology enables companies to test and validate a product before it even exists in the real world. By creating a replica of the planned production process, a digital twin enables engineers to identify any process failures before the product goes into production.

Engineers can disrupt the system to synthesize unexpected scenarios, examine the system’s reaction, and identify corresponding mitigation strategies. This new capability improves risk assessment, accelerates the development of new products, and enhances the production line’s reliability.

Predictive maintenance

Since the twin system’s IoT sensors generate big data in real time, businesses can proactively analyze their data to identify problems within the system. This ability enables businesses to more accurately schedule predictive maintenance, thus improving production line efficiency and lowering maintenance costs.

Real-time remote monitoring

It is often very difficult or even impossible to get a real-time, in-depth view of a large physical system. However, a twin can be accessed anywhere, enabling users to monitor and control the system performance remotely.

Improved team collaboration

Process automation and 24/7 access to system information allow technicians to focus more on inter-team collaboration, improving productivity and operational efficiency.

Data-backed financial decision-making

A virtual representation of a physical object can integrate financial data, such as the cost of materials and labor. The availability of a large amount of real-time data and advanced analytics enables businesses to make better and faster decisions about whether or not adjustments to a manufacturing value chain are financially sound.

What Types of Digital Twins Are There?

Component twins

A component twin is a representation or simulation of a single part of a product or process. It can be used to test the impact of weight, heat, or other stressors on an individual product part such as a screen or mechanical subassembly, for example.

Asset twins

This dynamic virtual model of an existing physical asset is kept up-to-date and accurate with ongoing, real-time data while being used to test how two or more components work together. An asset twin could provide a replica of assembly line machinery, for example, enabling the business to test multiple configurations to maximize production and reduce error.

System twins

The system twin is a level up from the asset twin because it is a digital representation of the larger system in which critical assets function – in this example, the entire factory floor. This twin not only tests multiple outcomes and analyzes data but may recommend performance improvements, as well.

Infrastructure twins

An infrastructure digital twin is a 3D digital representation of an object or system with engineering-grade accuracy. According to the Digital Twin Consortium, this subtype is unique in that it must have millimeter precision, geospatial alignment, and support for complex 3D engineering schemas.

How Do You Create a Digital Twin?

There are three essential factors to consider before implementation.

1. Update your data security protocols

According to Gartner’s estimation, 75% of the digital twins for IoT-connected OEM products will utilize at least five different kinds of integration endpoints by 2023. The amount of data collected from these numerous endpoints is huge, and each endpoint represents a potential area of security vulnerability. Therefore, companies should assess and update their security protocols before adopting digital twin technology. The areas of highest security importance include:

  • Data encryption
  • Access privileges, including a clear definition of user roles
  • Least privilege principles
  • Addressing known device vulnerabilities
  • Routine security audits

2. Manage your data quality

Digital twin models depend on the data from thousands of remote sensors communicating over unreliable networks. Companies that want to implement digital twin technology must be able to exclude bad data and manage gaps in the data streams.

3. Train your team

Users of digital twin technology must adopt new ways of working, which can potentially lead to problems in building new technical capabilities. Companies must ensure their staff has the skills and tools to work with digital twin models.

The Future of Digital Twins

Digital twins have proven an important enabler of data-driven change, particularly in product development, where they are helping designers and manufacturers reduce costs, scale testing, go to market faster, and improve customer experiences.

What does the future of digital twins look like in your organization?

You cannot just mimic every single process there is; rather, your existing pain points and goals must inform your digital twin strategy. What are you trying to achieve with your implementation, and what outcomes will provide the best ROI?

Do you have the actionable data required to mimic a product or process in a digital simulation?

And what is the anticipated business impact of your digital twin(s) implementation? Will you mimic just one island of data or your entire ecosystem in the digital environment?

Let’s explore how digital twins can help you accelerate production time, product quality, and time-to-value. Book a free consultation call.

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Yana Arnautova

Manager, Sales Enablement

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A digital twin is a virtual model of a physical object, process, or system that is used to simulate and analyze real-world conditions.

They are used to test, optimize, and predict performance, enabling improvements and innovations before physical deployment.

Industries like manufacturing, automotive, healthcare, and urban planning are significantly benefiting from digital twin technology.

Yes, they can predict potential failures and maintenance needs, improving operational efficiency and reducing downtime.

The future involves more integrated, real-time data analysis, and wider application across industries, enhancing decision-making and innovation.

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