A digital twin is a technically exact, virtual replica of an object or process that’s twinned to its real-world counterpart via data and information feeds. It’s a design and testing approach that’s attracting a growing number of adopters.
At their core, digital twins use mathematical- or physics-based models to simulate or predict likely behaviors, performance attributes, or outcomes of their analogues in the physical world, explains Kenneth Norton, a senior manager in Deloitte Consulting’s smart manufacturing group. “Sensors and real-time performance data from the physical products and systems can be used to provide feedback and tune the virtual models,” he says. “Advances in the computational and visualization capabilities of commercially available software applications have led to a proliferation of digital twin use cases across industries, as companies seek to improve performance of their products, production processes, and end-to-end supply chains.”
By creating sophisticated insights that drive value through improved business performance, decreased risk, and better-informed decision making, digital twins have opened the door to the advanced integration, analysis, and visualization of potentially millions of disparate data streams that make up the world around us, says Sandy Marshall, a principal at management consulting firm Booz Allen Hamilton.
Growing Interest In Digital Twin Tech
Digital twin technology has been available since NASA piloted the concept in the 1960s. “We’ve started to see an uptick in interest for digital twins recently,” Marshall says. She observes that as more organizations harness their data to conduct optimized decision making, vendors have arrived to offer solutions, prompting a shift toward digital twin adoption.
Supporting and sustaining this shift toward broader adoption will require digital twins that run in secure cloud environments, while enabling open and agile development, Marshall says. “This will allow organizations to safely deploy and begin harnessing the value of digital twins sooner while continuing to build on and mature the technology to support additional integrations and use cases across a longer lifecycle.”
Digital twin technology allows organizations to better simulate a product, design, and architect in a digital state, and thereby reduce development costs, explains Jiani Zhang, executive vice president and chief software officer at engineering consulting firm Capgemini Engineering. “Adding physical attributes to the digital twin also enables engineers to simulate how these physical attributes will react to specific environments or scenarios.”
Getting Started With Digital Twins
For organizations lacking the necessary in-house knowledge, the first step in building a successful digital twin strategy starts by finding the right partner with the appropriate expertise. “From there, determining your current needs, such as your use case and users, is a great place to start,” Marshall says. “Having a grasp of your use case will allow you to demonstrate ROI and involve stakeholders.”
The best way to get started with digital twin technology is to identify specific improvement opportunities, Norton says. Potential uses should have the desired outcomes defined, along with the business value to be achieved. “Evaluating the available datasets and ability to model the desired outcomes across the portfolio can inform decisions on specific partners or technology vendors to work with, as well as a logical sequence for how to proceed,” he explains.
Norton adds that organizations that excel at digital twin technology typically exhibit a common pattern of deploying robust, foundational technologies, such as Product Lifecycle Management and Manufacturing Execution Systems, which provide the enterprise-wide datasets that digital twin technologies require.
Digital Twins at Work
Digital twins present an opportunity to impact product development in an extremely positive way, Marshall says. “For example, by enabling data transparency and collaboration across all stakeholders throughout the lifecycle of a product or process.”
Digital twin technology accelerates product development while reducing time to market and improving product performance, Norton says. The ability to design and develop products using computer-aided design and advanced simulation techniques can also facilitate collaboration, enable data driven decision making, engineer a market advantage, and reduce design churn. “Furthermore, developing an integrated digital thread can enable digital twins across the product lifecycle, further improving product design and performance by utilizing feedback from manufacturing and the field.”
Using digital twins and generative design upfront allows better informed product design, enabling teams to generate a variety of possible designs based on ranked requirements and then run simulations on their proposed design, Marshall says. “Leveraging digital twins during the product use-cycle allows them to get data from users in the field in order to get feedback for better development,” she adds.
Digital twin investments should always be aimed at driving business value. “A use case must be clearly defined, with measurable KPIs to demonstrate the value,” Zhang advises. “Once the value is proven, leadership backing is necessary to ensure that the proof of concept becomes scaled to truly achieve ROI.” She adds that typical mistakes include conducting too many proof of concept projects without a clear definition of success and failing prove potential business benefits.
Digital twin technology, when properly operationalized and implemented, can give adopters a significant competitive advantage, Zhang says. Yet she warns that while many industry leaders are embracing the approach, many others remain reluctant, hesitant adopters, or sideline spectators. “This is due to obstacles, such as delayed ROI realization or company culture,” Zhang notes. “In those cases, the full benefits of digital twin aren’t realized.”
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