Some of the most fascinating and complex business environments are those where competition centres on innovation and technology. Just as is the case with digital twin, every exciting innovation has its sceptics:

 “I can think of no conceivable reason why an individual should wish to have a computer in their own home”

Kenneth Olson, Chairman, Digital Equipment Corporation, 1977

 That’s why it takes courage. It means being willing to change. But where technology is our primary medium of competition, we must nurture and embrace innovation with an open mind:

“Computers in the future may have just 1000 vacuum tubes and weigh less than 1.5 tons”

Popular Mechanics Magazine, March 1949

To say that innovation creates upheaval is an understatement. Innovation creates entirely new industries. The history of manufacturing is a history of pure innovation and change. In a Hexagon article on digital twins published on forbes.com last year, we wrote that manufacturers are facing a “perfect storm”.

By that, we meant that design and engineering companies are having to find a balance between competing demands. On the one hand, technology and products are becoming increasingly complex which creates slower times to market. On the other hand, organisations simply cannot afford to slow down. The pace of innovation is increasing, and we operate in a market where competitive advantage comes primarily from innovation.

Digital twins are representations of complex systems that empower engineers with greater freedom that ever before. Yes, we can call that a perfect storm, but I would add that manufacturing was born in such a storm as this. We are not strangers to upheaval. The world of design and engineering is a lasting storm and the way we weather this turbulence is with a rational, data-driven approach.

A perfect replica of this perfect storm – the digital twin

It’s always been that way, but now we have so much data and so much processing power, we can create a perfect replica of this perfect storm and find a perfect route through it. We can do so without ever facing a drop of rain or turning our faces to the wind. This is what we call a digital twin or the broader, more immersive digital reality.

By creating a virtual model of a design, a machine, or an entire assembly line, companies can simulate real-life situations and make predictions supported by data.

Now we can see the strengths and weaknesses of a design before it’s left the drawing board. We can foresee potential problems or sources of failure in manufacturing processes. With a digital twin, manufacturers can design and test products, processes, even complete production lines in a virtual sandbox.

Digital twins are representations of complex systems that empower engineers with greater freedom that ever before

Digital representations can incorporate artificial intelligence (AI) and real-world measurements, such as machine sensor data, to enhance simulations. Data inputs can come from anywhere in the production process and beyond. With the industrial internet of things (IIoT), cloud computing and concepts like a digital product passport on the horizon, potential data sources could be innumerable.

The benefits of this technology vary based on the different contexts of each industry, but the potential is enormous. The challenges of that perfect storm can be consigned to a teacup.

Currently around half of automotive manufacturers are investing in smart manufacturing techniques. Those companies are able to simulate and test new components, even complete vehicles, then find and fix potential flaws before they’ve spent a penny on materials or dedicated any machine time on the production, and there’s no reason why your organisation can’t do the same.

Hexagon’s computer-aided engineering (CAE) software solutions enable designers, engineers and analysts to simulate product and process performance in a variety of ways.

Navigate vast amounts of data quickly and easily

Digital twin vs digital reality

Digital reality and digital twins are similar concepts. Both are digital representations of physical things like components, products, processes or facilities, but there is a big difference between the two.

A digital twin primarily works as a mirror of a real-world counterpart, using data and insights to improve operations or make predictions.

Digital reality elevates this idea to a whole new dimension. Instead of mirroring, it’s more immersive.  It replicates the physical world digitally with real-time data updated continuously. This platform gives users a deeper understanding and allows manipulation, predictions and ‘What If’ scenarios of intricate systems. Digital realities can give far more nuanced insights and immersive experiences than what is usually achievable with a digital twin, accelerating innovation and decision-making wherever they are applied. We’ve also written about the difference between a digital twin and a digital thread here.

Getting started with digital twin in 5 stages

If you have yet to invest in this technology, there are five broad steps you need to take to get started and you can read more about digital transformation for manufacturing here.

Step 1. Define your objectives

Identify the core activities that you want to optimise with a digital twin. Consider where digital twins could make the best impact. Begin by conducting a comprehensive assessment of your current operations. Identify pain points and inefficiencies, establishing a baseline measurement of your pre-digital twin state.

Consider the value that a digital twin could offer your organization. Will it enhance productivity, reduce costs, improve quality, or enable new capabilities? Clarify the expected benefits and set clear goals for improvement. Determine what you want to achieve and define specific metrics to measure success.

Think about scalability. Are you looking to pilot digital twin technology with existing legacy systems, or do you plan to scale up across multiple areas? Ensure your objectives align with your long-term strategy.

By defining your objectives clearly, you’ll create a solid foundation for a successful implementation. This clarity will guide your initial efforts and provide a framework for evaluating the effectiveness of the project as it progresses.

Step 2. Evaluate readiness

Once you’ve defined your objectives, the next step is to evaluate the readiness of your current infrastructure to support the integration.

How will the technology be integrated into your existing infrastructure and connected to other tools within your processes? You’ll need to ensure compatibility between the digital twin and the equipment it will need to interface with.

Determine if your current infrastructure can accommodate the integration and work out if you need any upgrades to support the implementation. That must extend to your staff too. Does your team have the skills to install and operate the technology? If not, identify any training requirements to bridge the gap.

Make a list of the data sources that will feed into the digital twin. This will typically include databases, sensor logs and a range of connected devices. Then you’ll need to determine their compatibility and how data from legacy systems can be leveraged to enhance the capabilities.

By conducting a thorough assessment of readiness and infrastructure at this early stage, you can mitigate risks and potential challenges and ensure a smoother integration process. This approach sets the stage for maximising the value derived from your digital twin implementation.

Step 3. Architecture and interaction planning

It’s crucial to establish the architectural framework for your digital twin. Whether it’s a component-level, machine-level, or factory-level digital twin, understanding the scale is essential.

Be clear about how often your digital twin needs to be updated to fulfill its purpose effectively, and then make a decision about your time stamping requirements. Some will need to be updated in real-time, while others may be updated once a week, or with the click of a button on demand. This decision significantly influences the architecture, data structure, and visualisation capabilities of your digital twin. Real-time updates demand short feedback loops and robust graphical interfaces, while less frequent updates allow for simpler architectures.

Ask yourself who will interact with the digital twin, and what will be the nature of their interactions. Will it primarily serve factory managers, or will operators also engage with it for decision-making? Understanding these interactions is crucial for designing a digital twin that effectively supports decision-making and enhances operational efficiency.

Step 4. Implementation and testing

Don’t forget, this is an iterative process. It’s a series of steps in a logical sequence: identifying your goals, refining and continuously improving. Now is the stage where you can begin to see insights from the digital twin. Use these insights to optimise your processes, identifying areas for enhancement. Employ the technology to detect and address quality issues in the production line before they escalate into significant problems.

This phase involves implementation and testing, step by step, measuring against your initial metrics. As you progress, refine the integration to ensure the best functionality. The intention is to implement and correct any problems in advance, simulating ‘What If’ scenarios to enhance efficiency and productivity.

Step 5.  Industrialisation, adoption and upgrades

Congratulations on reaching this stage. By now, you’ve successfully implemented the technology and are likely beginning to reap its benefits. As you move forward, consider the industrialisation of the digital twin — meaning that you’re transitioning from testing and initial implementation to widespread adoption across your organisation.

During this adoption phase, focus on ensuring that the digital twin is embraced by all stakeholders. This involves ongoing maintenance to keep the technology working smoothly and meeting the evolving needs of your organisation.

You must be prepared for the digital twin to grow and evolve. Upgrades will be an important factor at this stage. As new data becomes available and as new technologies emerge, be ready to enhance your digital twin accordingly. This may involve integrating new tools, accommodating new users or even extending the digital twin for use in other factories or sites.

Industrialisation entails expanding access to the digital twin, potentially providing access to a broader audience or connecting with suppliers. By giving suppliers access to your digital twin, you can streamline supply chain operations and proactively address delays or disruptions.

Ultimately, industrialisation, adoption, and upgrades are integral parts of using a digital twin, ensuring that your investment continues to deliver value for years to come.

Read more about Hexagon’s smart digital reality: tapping the potential of the most dynamic and powerful use of data in history.

Any Questions?

Don’t forget that here at Hexagon we’ve got experts on hand ready to answer your questions and offer advice. Contact us for more information