As products, processes, and systems become increasingly complex, pinpointing areas of concern and identifying their interdependencies poses ever-greater challenges. That’s where digital-twin technology comes in: By mirroring real-world entities in real time, this tech delivers invaluable insight into the present and future states of physical assets – enabling more accurate predictions, well-founded decisions, and informed planning.
Virtual Modelling: from the Drawing Board to Ongoing Operations
Digital twins are exact virtual representations or models of physical objects, processes, and systems that are used throughout the product lifecycle. During the design phase, for example, standalone digital twins (without physical counterparts) enable companies to simulate, predict, and optimize products and production systems before investing in costly physical prototypes.
By combining simulation, data analytics, and machine-learning capabilities, digital twins can demonstrate the impact of diverse factors, including design changes, usage scenarios, and environmental conditions. The bottom line? Greater transparency, shorter development times, and higher-quality end products and processes.
Tracking Real-World Counterparts Every Step of the Way
Once products go into operation, digital twins continue to play a pivotal role. Sensors on the physical objects collect real-time data on performance, operating conditions, and changes over time. This data is then relayed to powerful processing systems and applied to the digital copy, delivering 360-degree visibility into how products behave in real-world conditions.
But it doesn’t stop there. Digital twins can also be used to run simulations, study performance issues, and generate ideas for improvements. Lessons learned in the virtual space can be applied in the physical world, helping companies drive transformation, achieve better business results, and ultimately improve their overall performance.
A Deeper Dive into the World of Digital Twins
To better understand how this tech helps master complexity, let’s look at the different types of digital twins available. First, there are digital component twins. These are the basic units of virtual representation. As their name suggests, they’re used to model the physical, mechanical, and electrical characteristics of individual components.
Next, we have digital asset twins. An asset consists of two or more components that interact. Their digital twins enable you to study the interplay of these components and deliver a wealth of performance data that can be processed and transformed into actionable insights.
Moving up a level of complexity, digital system (or unit) twins let you see how different assets combine to form an entire functioning system. Here, you gain visibility into how assets interact and can identify potential scope for enhancing performance.
Finally, at the very highest level of complexity, we have digital process twins. These allow you to analyze your entire value creation process or even the entire value creation network. As a result, you can not only identify inefficiencies, but also rapidly resolve critical situations in processes.
When Does it Make Sense to Deploy Digital Twins?
If you’re considering using digital twin technology, there are two key factors you should bear in mind: the nature of your products and whether the objects you want to model will change over time. Right from the design phase, digital twins are best suited for cost-intensive, critical, and long-lived products.
Deploying a digital twin also makes sense if the object in question is liable to undergo changes – both during design and its useful life. In such cases, virtual modeling lets you monitor the object’s current state, predict future developments, and rapidly take corrective action, where necessary.
Better, Faster, More Proactive
If you decide to introduce digital twin technology, what benefits can you expect to see for your business? One area where the tech clearly has great advantages is performance tracking. Digital twins enable you to monitor assets’ performance and compare current and previously captured data – providing a firm foundation for highly effective continuous-improvement processes.
What’s more, by moving the entire product lifecycle into the digital environment, the virtual-modelling approach enables enhancements to be designed and implemented much more quickly and easily. And this significantly reduces time to market for your products.
Finally, by allowing you to spot issues before they become outages, digital twins can slash maintenance costs. By uncovering potential problems, the tech enables you to promptly order parts and schedule repairs for times that won’t impact production. The result: a 40% reduction in reactive maintenance.
Digital Twins: Real-Word Use Cases
And it’s not only analysts and forecasters who see advantages in digital-twin technology. Large organizations with complex products and processes are already reaping tangible benefits. For example, Boeing has achieved a 40% increase in the quality of parts and systems used in aircraft production since deploying what it calls model-based engineering.
Another global manufacturing player has created virtual models of its factories. Unilever is using digital twins to create what-if scenarios, which help the company identify optimal operational conditions for a more efficient and flexible production process.
And it’s not just manufacturers who’re getting in on the action: A 1:1 virtual representation of the city Shanghai enables specialists to monitor everything from traffic to bridge maintenance, right through to flood simulations for disaster planning.
Want to Find Out More?
As these examples show, when it comes to mastering complexity, digital twins deliver the goods. If you face the challenge of complex products, systems, or processes and would like to find out more about this technology and its potential benefits for your business, feel free to reach out to me. As ever, I’ll be happy to answer any questions you may have.