For years, the greatest business minds have had to operate in a VUCA (volatile, uncertain, complex, and ambiguous) context. In a post-pandemic world, each of these aspects has been amplified. We’re in a new world order, and knowing how to maximize resources to benefit business is more challenging than ever. Added to this are the increasing customer demands and expectations, requiring businesses to develop an agile approach digital twin technology has emerged as an intelligent solution that helps decision-makers explore various
What are digital twins?
Digital twins are a virtual render of a physical object, person, or process. The recreation simulates the real-world context of whatever it mirrors, allowing for better decision-making. Digital twins enable organizations to fully recreate a situation, allowing leaders to run multiple scenarios and make an optimal choice.
Digital twins provide businesses with a proving ground for new ideas, product testing, and ventures. For leaders trying to make decisions before going to market or iterating a piece of technology or a process, the virtual replica provides a sandpit environment; an arena where options can be explored using real data to generate accurate outcomes.
This technology uses data-driven behavioral insights and boasts an accuracy rate of up to 90%. Combining the required data with an algorithm creates what’s known as an enterprise metaverse, an immersive virtual environment that recreates and links the various aspects of a business. This allows organizations to plan, anticipate, and optimize their strategies.
Types of digital twin technology
There are various types of digital twins, each with a purpose adapted to business needs. A product twin can be used across the full life cycle of development, from concept design to engineering and modifications of the final product. Siemens conducted research looking at product development and has shared that 59% of companies find product development increasingly complex, while 46% struggle to meet their launch or product delivery deadlines.
Digital twin technology can reduce the go-to-market timeframe while improving quality and sustainability. Apart from improving products for businesses, digital twins can themselves be the result. The best example of this is Google Maps, which provides real-time data (traffic) while simulating the route between two places.
As the name suggests, production plant twins are virtual representations of manufacturing facilities. In procurement and supply chain, network twins are used to improve operations and logistics, while infrastructure twins aid civil engineers and town planners in construction and spatial development.
CEOs recognize the importance of digital twins and are eager adopters of this technology for efficient decision-making. According to McKinsey, 70% of senior executives at tech-driven enterprises are investing and exploring the benefits of digital twins.
How digital twins deliver value
Companies are increasingly looking to capitalize on the First Mover Advantage by debuting a new product or service. Digital twins offer immense value in this regard by reducing the go-to-market time. They allow teams to make multiple iterations and optimize product design in a fraction of the time.
This also has a knock-on effect on product quality, as designers are able to identify vulnerabilities at an earlier stage in development. According to Will Roper, senior adviser at McKinsey, “The companies that harness this first will really shake up the markets they’re in.”
He’s seen their clients’ post revenue increase by as much as 10% when using digital twins to simulate the end customer’s use of a product. Car manufacturer Daimler, for example, successfully employs digital twins to produce an immersive test-drive experience without ever needing to physically sit in a car.
How can digital twins affect an organization’s environmental sustainability?
Sustainability is an important factor for organizations; not only is it an indicator of environmental responsibility, but it also forms part of regulatory compliance in many countries. Digital twins can offer support in improving sustainability by reducing the amount of material required for product design. Additionally, with the ability to enhance product traceability, digital twins can reduce waste. Studies show that manufacturers of consumer electronics have used digital twin technology to reduce waste by approximately 20%.
How can an organization get started on building its first digital twin?
Digital maturity is an important factor in digital twin development. Organizations that have successfully implemented boast sophisticated data infrastructure with the capacity to deliver data from both testing and live phases. These organizations also employ the human resources required to build and maintain the infrastructure.
A common misconception is that these organizations exist in complex environments, and only the most challenging issues demand digital twin technologies. Many users have simple requirements and use these products to gain customer feedback in real-time. Once they have the initial outcomes, they can build a more complex simulation by adding and testing for different data insights, one layer at a time.
The best-practice method for building and scaling a digital twin takes a three-step approach:
- Design a blueprint. The blueprint is the foundation for the end result: a multi-functional digital twin that can simulate the entire production process for a product or system. This should be inclusive of ownership and governance structures.
- Build the base digital twin. This requires project teams to put together the first version of their digital twin. It needs to incorporate visualizations and enable data scientists to create at least two case studies. This usually requires three to six months.
- Enhance capabilities. The final step in establishing digital twin technology is to boost capabilities by layering data and analytics. This supports new use cases and allows organizations to add complexity to the twinning technology. At this stage, digital twins can represent more than just products and people; with the integration of AI and machine learning, digital twins can evolve into advanced simulations.
How digital twin technology empowers the metaverse
On the more advanced end of the digital twin spectrum are enterprise metaverses. These are real-time simulations with multiple layers of interconnected data fields; in essence, multiple digital twins, each with their own purpose, connected to each other. They generate deep and accurate insights. Here’s an example of how this could work:
An organization interested in developing customer behavior insights could connect a digital twin of its customers with that of retail stores, sales and inventory, and customer process flaws. By building an enterprise metaverse, businesses can achieve a number of objectives.
First, through simulation, they’re able to replicate the end-to-end impact that changes in the business or the market have on retail stores. This creates an omnichannel approach that affords a seamless overview of the customer journey across channels.
Additionally, enterprise metaverse technology can use digital twins to recreate store layouts. This is essential to the process of optimizing setups to respond to changing customer preferences. With this simulation, they’re also able to view the relationships between sales, employee performance, and the various characteristics of local stores, against compensation and staffing models.
How are some companies already using digital twins?
With rapid technological advancements, comes the increasing interest in digital twins. According to experts, investment in this is set to exceed $48 billion by 2026. Global leaders across various industries have already generated several use cases for digital twins; some of the best examples include:
- Emirates Team New Zealand. A digital twin of sailing environments, boats, and crew members enables Emirates Team New Zealand to test boat designs without the costly process of building them. This has allowed the champion sailing team to evaluate thousands of hydrofoil designs.
- Anheuser-Busch InBev. Working with Microsoft, they’ve developed a brewing and supply chain digital twin that enables brewers to adjust inputs based on active conditions. They’re also able to automatically compensate for production bottlenecks (for instance, when vats are full).
- SoFi Stadium. The stadium debuted a first-of-its-kind digital twin that helps optimize stadium management and operations. Their technology acts as an aggregator across multiple data sources and includes information about the stadium’s structure and real-time football data.
- Space Force. Space exploration and research is a costly exercise, and the US Armed Forces is creating a digital twin of space. This includes simulations of extraterrestrial bodies and satellites.
- SpaceX. SpaceX created a digital twin of their Dragon capsule spacecraft which allows them to monitor and make changes to trajectories, loads, and propulsion systems. This maximizes safety and improves reliability during transport.
Conclusion
Digital twin technology has become a key element for C-suite executives looking to harness the power of simulations for decision-making. Organizations are able to replicate their environments to generate real-time insights into how products, assets, customers, or systems respond to changes.
Businesses need to adapt to changing market conditions, and being able to predict these changes and act accordingly improves resilience. Digital twin technologies also offer businesses quicker turnaround times from product development to entering the market, allowing them to make significant gains against competitors.
With rich, predictive analytics and powerful data-driven insights, digital twin technology is empowering leaders to fine-tune decision-making.
