Digital twin is one of the most exciting technologies of our time.
It’s not new – it was first used by NASA in the Apollo flights – but technology is finally at a place where the concept is no longer a “shoot for the moon” endeavor.
For those who work in the industrial markets, digital twin technology is the means to leverage other increasingly prevalent technologies, like Internet of Things (IoT) cloud computing, and sensors, to unlock higher value insights and analytics.
If you’re looking to learn more about digital twin, you’ve found the right place. There’s both misinformation and different interpretations out there of what digital twin technology is and the potential it has. We’ve consolidated the most frequently asked questions about digital twin in one place – let’s dive in!
Here’s how we broadly define digital twin technology:
Digital twins are digital models that virtually represent their physical counterparts. This virtual representation of a physical product, an operational process, or a person’s task is used to understand or predict the physical counterpart by leveraging both the business system data that defines it and its physical world experience captured through sensors.
In many instances, the digital twin is the key to unlock siloed data sets and bring them together to present a contextualized representation of a product or process. Industrial digital twins are gaining traction for this reason; disparate data sets are brought together to uncover new insights and possibilities. With digital twin, enterprises are driving business outcomes like operational effectiveness, product differentiation, and increased quality and productivity.
Yes, digital twins are created at the convergence of the physical and digital – and that isn’t limited to physical things or products, people and processes, such as a workflow, can have a digital twin enabled by augmented reality. A manufacturing process – even an entire factory – can have a digital twin.
With the increasing prevalence of IoT in industrial spaces, machines are now producing incredible amounts of data. Not only that, they’re able to communicate with each other, and with the right technologies in place, this data can be converted to insights that help businesses better understand the inner workings of product or process within its native environment. Ultimately, this information can be leveraged to drive efficiency in daily tasks and processes and inform broader business decisions.
Digital twins range from basic to complex, depending on the goals of the initiative. As we mentioned, both physical products and entire processes, even factories, can have digital twins. Different technologies enhance the fidelity of a digital twin. Here’s a quick overview of digital twin technologies for consideration:
At a basic level, to build a digital twin the physical asset(s) there should be some access to compute power to generate data and access to servers (on-premises, cloud, or edge) and reliable connectivity. A digital definition provides the backbone for the digital twin, for example a CAD model, product lifecycle management data, and real-time sensor data.
From there, enterprises are building on the complexity and fidelity of digital twins with IIoT platform and analytics integrations, ERP & MES, augmented reality, artificial intelligence, additive manufacturing, and more.
Most industrial enterprises are already using many of these technologies already, a digital twin strategy unifies them in a way that brings additional value.
In industrial enterprises, we see three distinct digital twin use cases categories emerging across the value chain: Engineering, Manufacturing and Operations, and Maintenance and Service.
For discrete manufacturers, digital twins enable products to “talk” – in other words, it replaces usage assumptions with data and insights, ultimately accelerating time-to-market for optimized designs and features. With digital twin, product designers are better understanding how the products are being used in the field, and with a full lifecycle feedback loop, customer-centric optimizations (or completely new products/features) are made faster and more efficiently.
Digital twins of processes are making a big difference in the factory. Enterprises are gaining deeper visibility and insights into their current operations that uncover ways for increased operational efficiency and agility throughout the supply chain. We’re seeing examples of digital twins being used effectively in quality assurance processes and delivery of work instructions as well.
This use case is all about enabling customer success in the “wild”. Before the advent of IoT, businesses needed to rely on customer feedback and reactive data to understand the performance of their products.
With digital twin, there is capability for real-time feedback – and even predictive monitoring and insights. This is leading to new revenue opportunities, such as enhanced service delivery. Outcomes of these use cases are increased customer satisfaction and loyalty, driven by improved asset uptime and faster time-to-resolution.
Our ebook, “Top Use Cases for Digital Twin Technology to Drive Digital Transformation”, goes into much more detail and offers real-world examples from Volvo Group, Polaris, and Elekta.
This is, of course, the next obvious question after looking at use cases. Within each of the broad use cases described above there are significant opportunities for role-based digital twins. A single digital twin can be used in different ways – through different lens, or views – to drive value for specific roles and/or tasks.
For example, a service technician uses a digital twin to access work order information to perform machine maintenance, while an operator uses the same digital twin to monitor and analyze key metrics, to ensure utilizations, available and quality. Beyond the factory floor, an executive has greater visibility into operations, which can inform day-to-day and long-term business decisions.
The benefits of digital twin are nearly universal in the enterprise space. In fact, a 2019 IDC report, Digital Transformation FutureScape, 30 percent of G2000 companies will have implemented advanced digital twins to optimize operations by 2020. The majority of adopters will be industrial enterprises.
The appeal for industrial enterprises is digital twin is likely possible with the existing technologies already in play. With a focused digital twin strategy, enterprises can start leveraging existing data and technologies in new and powerful ways.
The value of digital twin is individual to each company and the extent and use case for which they are leveraging the technology. We can say with certainty that digital twin initiative makes an impact on people, products, and process.
By getting contextualized information to the right personnel, it streamlines how workers do their daily tasks. Twins of products are creating new customer-centric business models, such as product-as-a-service. Manufacturers are driving significant efficiencies, while improving flexibility and agility across their processes.
Identifying the desired business outcome of a digital twin and identifying measurable ROI is an integral part of strategy development, even before the technology and implementation discussion.
Check out this whitepaper for more detail on the business outcomes we’re seeing from digital twin implementations at the enterprise level.
New technologies are emerging that will enable high-fidelity digital twins, as well as connections to transformative manufacturing processes, from generative design to additive manufacturing. Digital twins when combined with artificial intelligence capabilities, such as machine learning and deep learning algorithms, will derive new operational insights. Increased transparency and security will be made possible with blockchain as well.
We outlined several digital twin predictions here, but as digital twins become more prevalent across the enterprise, there is great potential in orchestrating twin networks and enablement of communications between two distinct digital twins.
A digital thread is the term used to describe simple, universal access to data. It’s the connection synchronizing related upstream and downstream information from multiple sources and systems. A digital thread enables a more complete, real-time representation of a product, process, or person across enterprise functions.
This data unification of digital thread is a pre-requisite to building a robust digital twin.
Now, of course! The potential for digital twins to bring increased value to your digital transformation initiatives should not be ignored or pushed off to next year. As we mentioned above, many of the technologies enabling digital twins are already in use within industrial enterprises. Leveraging these technologies and building connections with a digital twin strategy drives further measurable value for your business.
Our Digital Twin Primer for Industrial Enterprises walks through the essential steps to a digital twin strategy to get you started.
We hope to have answered all your most pressing digital twin questions. The opportunity to drive real business outcomes and value for industrial enterprises is there for the taking. If you haven’t already, start looking into how digital twin technology could help remove silos within your organization and deliver value to multiple teams.
Did we miss any of your most pressing digital twin questions?