Do you have a Digital Twin?

There’s a new tech in town to help enterprises and governments. But what about individuals?

There once was a city so fine,

It had a digital twin online.

It could simulate and optimize,

With algorithms so wise,

But it crashed when someone spilled some wine.

If that limerick made you snigger, these statistics should make you smile: The amount being invested to set up and manage digital twin projects, including digital cities, is set to cross a whopping US$110 billion by 2028, up from US$10 billion in 2023, growing at a 61.3% annual clip during the period, according to research house MarketsAndMarkets Inc. “The growth is being driven by an increasing demand from the healthcare industry and a focus on predictive maintenance,” the firm says.

That’s a massive growth in a technology that’s just about taking off. But then, what exactly is a digital twin? Put simply, a digital twin is a digital representation of a physical object, person, or process, in a digital version of its environment. Digital twins can help companies simulate real situations and outcomes, helping them make better and more informed decisions.

The Potential

Digital twins also promise to deliver more agile and resilient operations. That potential isn’t lost on CEOs. McKinsey research indicates that 70% of C-suite technology executives at large enterprises are already exploring and investing in digital twins. Daimler, for example, has developed customer twins that allow customers to test drive a vehicle without getting behind the wheel.

Using digital twins can result in significant boosts in product quality, for example, by simulating the product throughout the manufacturing process and identifying flaws in the design much earlier. “The companies that harness this first will really shake up the markets they’re in,” says a McKinsey senior adviser Will Roper. Moreover, by mirroring a product as a digital twin, it’s possible to create a single source of truth for on how the design is functioning, allowing for real-time adjustment or redesign.

According to a recent Gartner survey, most CSCOs have overlooked the customer perspective in their digitalization plans. Gartner says only 27% of CSCOs plan to implement a DToC, a new tech that can enhance demand forecasting, customer experience, and AI apps.

According to a recent Gartner survey, most CSCOs (chief supply chain officers) have overlooked the customer perspective in their digitalization plans. Gartner says only 27% of CSCOs plan to implement a DToC (digital twin of the customer), a new tech that can enhance demand forecasting, customer experience, and AI apps. By contrast, 60% of CSCOs are piloting or planning to use a DSCT (digital supply chain twin), which focuses on the internal aspects of the supply chain.

“Without an effective digital representation of their customers, such as retailers, consumers, patients, and other customers, CSCOs will be disadvantaged in driving growth,” says Beth Coppinger, a Gartner senior director and analyst. “Expected benefits of DToC include boosting demand forecast accuracy, increasing responsiveness, and improving the customer experience.”

The Challenges

Digital twins underpin smart city development, but should be used wisely, cautions PwC. “While the application of digital twins is promising, it comes with a whole set of requirements that both public and private organizations need to address when developing and executing any strategy,” PwC advises.

Here are ten challenges to consider, in alphabetical order:

• Articulate: A coherent vision with quantifiable metrics. Digital twins require a significant investment of resources and collaboration among various stakeholders. Therefore, it is essential to have a shared understanding of the purpose and expected outcomes of these initiatives and a consistent set of indicators to measure their performance.

• Build: Trust, which is a vital ingredient. Digital twins are virtual representations of physical assets, systems, and processes that can be used to simulate, monitor, and optimize their performance. Stakeholders from the public and private sectors need to collaborate and share data, technology, processes, talent, and protocols, all of which involve trust.

• Coordinate: Outcomes. Having an unclouded vision is just the start; delivering it can be tough. Complex digital twin projects involve an overload of decisions and processes that need multiple stakeholders to coordinate a slew of variables. That’s because priorities need to be set, and impact on customer segments need to be assessed.

• Develop: Data quality and standards. To create and operate digital twins, historical data from multiple sources must be fed to the model for AI algorithms to analyze. However, historical data may need to be scrubbed to remove duplicates, incompletes, and inaccurate. Else, data quality issues may adversely impact the reliability and validity of the digital twin model and AI solutions.

• Enable: Data trust. Stakeholders may be hesitant to share data with other parties, especially sensitive or personal data. A strong accountability framework should be set up to assure adequate protection for the data providers. The problem: A lack of consistent standards and guidelines for collecting data can make it tough for different units to share information.

• Foster: Talent. To create and operate digital twins, AI tools are needed to process and analyze large amounts of data generated by digital twins. However, these technologies cannot replace the human expertise and skills to make sense of the data. Data scientists, engineers, and architects would be vital to help design, develop, and maintain digital twins and their applications.

• Generate: Information updates. To ensure the digital twins reflect real-world dynamics, it is vital to have a systematic and real-time updating mechanism to constantly update the static objects, as well as scan new objects in the physical environment. Multiple sources of data needs to be used, including IoT sensors, cameras, drones, satellites, or human inputs to capture the dynamic and complex nature of the physical world.

• Harvest: Tech platforms. They’re vital for data integration and analysis. However, different units may have diverse legacy systems or software that have limited functionality and compatibility for data interoperability. This could result in challenges to rapidly identify useful information from mounds of data, integrate different datasets such as geo-information systems and BIM (building information management), to build the digital twin model and its applications.

• Integrate: Sensor data, essential for monitoring of digital twins. However, deploying sensors in existing infrastructure and networks (called brownfield deployment) may pose more challenges than deploying sensors in new infrastructure and networks (called greenfield deployment). That’s because brownfield deployments may face issues of compatibility, interoperability, security, scalability, and reliability of sensor devices and networks.

• Justify: Environmental concerns. With technologies such as AI, IoT, and BDA (big data analytics), it’s possible to collect, analyze, and utilize various forms of data that were previously inaccessible. Digital twin tech can be used to cut costs, reduce carbon emissions of new construction projects, and avoid costly modifications after completion. It can also help cities evaluate the effectiveness of various measures and enhance the sustainability and resilience of urban planning and management.

Human Digital Twin

Which companies are leading in creating digital twins? According to MarketsAndMarkets, the leaders in the enterprise digital twin field include Microsoft, IBM, Siemens, SAP AG, Dassault Systèmes, Ansys, General Electric, Oracle, and Robert Bosch.

We’re conducting R&D on creating digital twins with the internal and external aspects of human beings, simulation tech that uses digital twins of humans and ‘things’, and constructing digital spaces that use digital twins.

So far so good. But how about creating your personal digital twin? The Metaverse offered high hopes, but it has not taken off. NTT Data says it is creating digital twins that reproduce not only the outer aspects of a human, such as physical and physiological characteristics, but also inner qualities such as personality, sensibilities, thoughts, and skills.

What use is a digital twin? NTT suggests using your avatar to set up proxy meetings with other digital twins, creating a personal agent to work on your behalf for multi-tasking, and endowing your digital self with skills you lack, such as being able to communicate in foreign languages using AI. “We’re conducting R&D on creating digital twins with the internal and external aspects of human beings, simulation tech that uses digital twins of humans and ‘things’, and constructing digital spaces that use digital twins,” NTT Data says.

Since I started this column with a “cool” limerick, let me end with a “hot” one:

There once was a city so neat,

It had a digital twin so complete.

It could manage and improve,

With data and apps so smooth,

But it still had to deal with the heat.

Raju Chellam

He is a former editor of Dataquest and is currently based in Singapore, where he's the chief editor of the AI Ethics & Governance Body of Knowledge and Chair of Cloud & Data Standards.

maildqindia@cybermedia.co.in