Jiani Zhang, Executive Vice President, Chief Software Officer at Capgemini Engineering.
Over the last decade, the concept of a digital twin has both excited and perplexed the technology landscape. With digital twins, the potential to fundamentally transform many industries has left enterprises vying to take this technology from concept to reality.
As someone who has worked in the digital twin space for over a decade, I have seen firsthand the value that digital twins can provide, as well as the challenges associated with implementation at scale. It all boils down to a core truth: humans are visual beings. We connect better to concepts and ideas when there is a visual representation.
The Evolution Of Digital Twins
Digital twins have undergone many evolutions. They first hit a hype cycle in 2015 when the concept of “Industry 4.0” became prominent. During this time, the growing focus on IoT and data gave way to the idea of products and industries digitizing, where previously unintelligent products and services could become intelligent through connectivity.
Digital twins entered the limelight once again in 2022 during the metaverse boom. Suddenly, enterprises and consumers alike were drawn to the idea of building a digital universe where the physical world could be better understood. During this time, the focus was less on data and more on the digital “being” living within the metaverse.
While it may seem that digital twins have been removed from the limelight, their value has never diminished. Digital twins are often pulled to the forefront with the growth of new technologies. Like with IoT and the metaverse, the value proposition associated with digital twins—cost savings, reduced failures and more—offers a longstanding appeal that pairs well with innovative technologies.
However, when evaluating the history of digital twins, the true heart of the story is often overlooked. Since the inception of digital twins, the key has always been to develop a better understanding of the physical world. Across industries, developers have sought the ability to understand a physical asset, process, or environment and modify its behavior.
By bringing together the entire value chain of a product from design through manufacturing to the service line to enhance decision-making, businesses can work toward providing better experiences and creating better outcomes. However, stringing together this digital thread has often been unattainable as a result of silos that exist within organizations.
Focusing On The Value Chain
One of the largest challenges on the road to establishing the digital thread is defining a consistent digital twin that serves all elements of the value chain. Because each function has different use cases for a digital twin, each team is focused on different sets of data. The contextual nature of data makes it difficult for designers, manufacturers and service personnel alike to focus on one digital twin that serves all stakeholders.
However, when a visual representation is introduced that realistically portrays the physical product or service, each of these business areas has a discernible focus and can then assess how data can truly make an impact on that product or service. For example, imagine staring at a spreadsheet of all the physical components of a vehicle, and this spreadsheet is different across design, manufacturing and service. Now picture a fully realistic digital replica of the car that can be modified, tested and altered in tandem across all three stages.
When looking to implement virtual twins and leverage their existing resources, enterprises should focus on investments that are aimed at driving business value. This also involves clearly defining the use cases for the virtual twin as well as outlining measurable KPIs to showcase value. Above all, top-down support is key for driving virtual twin initiatives. When leadership is passionate about breaking down business silos and achieving pre-determined goals, the rest of the organization will shift its priorities to ensure success.
What’s further, leveraging technologies such as real-time capture and generative AI can help alleviate some of these challenges and allow digital products to be created faster and easier.
The Importance Of Virtual Twins
Seeing is believing. Virtual twins enable the entire enterprise and value chain to focus on a common goal. More importantly, having a holistic, end-to-end view of a product can prevent many issues down the road. A virtual twin can help increase understanding of the physical asset before resources, time and costs are committed. It provides a sustainable way to model or simulate a physical element so that back-end issues can be preemptively avoided.
When selecting a car, for example, consumers often choose a color from the list of options without thinking twice. From an R&D perspective, altering the color of a car on a simulation is an easy change. However, the process of painting a car on the manufacturing line is highly complex. Manufacturers must account for factors such as humidity and how that will impact paint curing or pollen counts during certain seasons.
By simulating the various factors that impact these types of processes, enterprises can make smarter business decisions. The virtual twin is one visual representation that carries fidelity from design into manufacturing and all the way into the consumers’ hands. That one visual entity can bring continuity across the organization, helping to eliminate business silos and increase knowledge.
Final Thoughts
The potential of achieving digital continuity of products across the value chain is a goal of many enterprises. However, business silos and contextual data often prevent this goal from becoming a reality and result in challenges across R&D, manufacturing and service.
With a strategic approach to virtual twins, achieving a consistent digital twin can better be reached. Organizations can position themselves to eliminate silos faster and easier while working together to prevent breakdowns in production by proactively weaving data into design and leveraging insights across the product lifecycle.
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