Digital Twins Are Optimizing Supply Chains And More. Here's Why Enterprises Should Care

When organizations develop a new product, revamp their supply chains, or want more visibility into their operations, disrupting existing processes and systems to experiment can be daunting, risky, and costly.

They can now mitigate these risks with a digital twin, which has emerged as a feasible solution due to the rise of the Internet of Things (IoT), sensors, edge computing, cloud technology, wireless communications, and data.

What is digital twin technology?

A digital twin is a virtual representation or replica of a physical system or environment. It enables organizations to test whether an operational change or investment is right for them, before deciding whether to proceed.  

The Digital Twin Consortium's official definition describes a digital twin as "a virtual representation of real-world entities and processes, synchronized at a specified frequency and fidelity."

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Digital twins are exact replicas of real-world assets, modeled to simulate how the original assets operate in the physical world. These could include equipment on the factory floor, prototype vehicles, aircraft, manufacturing systems, and even entire supply chains. 

One replicated asset may seem insignificant, but used to create a digital network comprising an organization's full set of real-world assets, digital twins can give businesses a realistic overview of existing equipment and systems -- and their performance.

These replica systems, generated through data gleaned from real-world assets, can be experimented with digitally without real-world risks. Data is one of the world's most valuable currencies today, and implementing digital twin technologies allows enterprises to harness this information to generate actionable insights.

Types of digital twins

Digital twins can be as varied as the assets they represent. They can be entity, system, or process-based. They can be built to simulate existing systems and supply chains, check asset interoperability, or test physical materials and components. This type of setup is known as a composite digital twin.

Many businesses today benefit from digital twins developed for predictive maintenance and to operate as virtual clones of complex systems. One key type of digital twin is used to envision and refine new product lines, which can be invaluable in product design and subsequent testing stages.

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Another potential application of digital twin technologies is the creation of virtual representations of customers. According to Gartner, creating a "digital twin of the customer" (DToC) could improve demand forecast accuracy and behavioral analytics.

The benefits of digital twins

The benefits of digital twin technologies are vast.

Digital twins provide an environment for organizations to design, test, and tweak product designs or operational processes, without disrupting active supply chains and workflows.

Let's consider the following example: a manufacturer wants to see if changing a specific set of settings on a production line would improve results. Rather than ground operations to a halt on the factory floor, the manufacturer can instead run the experiment using performance data and system status information -- pulled from sensors imbued in the equipment -- to create a virtual replica for the test.

This provides a safe way for the company to identify whether or not the changes will work, while also saving them the cost associated with having to pause production. The same premise can be applied to product designs, where businesses can evaluate prototypes without fully investing in their development or manufacture.

Also: How digital twins and XR will transform product development in virtually every industry

According to McKinsey research, senior research and development leaders say that digital twin technologies are already making a "significant" impact on product development, often cutting total development times by 20% to 50% and reducing expenditure.

The benefits of digital twin technologies also extend to the remote monitoring of assets in real time. Digital twins can pull data from physical equipment, tracking performance and alerting users to potential issues so they can be remediated quickly.

Digital twins also have an important role in environmental design. While creating virtual product designs in systems such as CAD is nothing new, digital twins can create realistic replicas to support urban planning and the development of critical infrastructure.

The potential downsides of digital twin technologies

Digital twin technologies can drive business growth and provide valuable, data-driven insights into existing processes, but they are not without potential drawbacks.

The adoption of digital twin technologies must be considered in conjunction with the returns on potentially high levels of investment. Creating exact replicas of real-world assets can be a time-consuming process with a high start-up cost, and an ROI may not materialize until an organization can draw useful information from its digital twin and put changes into practice.

Also: Deploying digital twins: 7 challenges businesses can face and how to navigate them

Furthermore, creating the right environment for a digital twin setup can be complex, especially if interoperability is problematic. These systems must also be constantly monitored and maintained.

Moreover, there are security risks to consider. Digital twins need a network of backend access, cloud storage, and data, and will typically require entry points into real-world assets and environments.

If an organization's security posture is lacking, cyber attackers could infiltrate the digital twin system to learn about the victim's full stack of technologies and assets, steal information, or cause widespread destruction.

Analyst insights and predictions

• Gartner analysts estimate that the digital twin market will reach a value of approximately $183 billion by 2031. According to the research firm, composite digital twins present the largest opportunity.
• MarketsandMarkets research suggests that the global digital twin market will grow from an estimated $10.1 billion in 2023 to $110.1 billion by 2028, driven by the need to reduce manufacturing costs alongside increasing interest in the healthcare industry.
• Comparatively, Fortune Business Insights predicts that the global digital twin market, valued at $12.91 billion in 2023, will grow to $17.73 billion in 2024 and $259.32 billion by 2032.
• IDC forecasts that by 2027, 35% of G2000 companies will employ supply chain orchestration tools featuring digital twin capabilities.
• McKinsey & Company survey data indicates that close to 75% of companies have already adopted digital twin technologies with at least "medium" complexity levels.

Real-world examples of digital twin adoption

Awareness of digital twin technology is quickly spreading across industries, including manufacturing, aerospace, transport, retail, and healthcare.

A survey conducted by IDC revealed an increasing familiarity with digital twin technologies in these industries and others. In total, 52% of respondents in resources and construction said they were familiar with the technology, followed by 40% in manufacturing and professional services, 37% in transportation and logistics as well as in energy, and 36% in finance.

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Digital twin technologies appear to be growing most rapidly in asset-heavy industries, including manufacturing, oil and gas, aerospace, and automotive. However, digital twin technologies are also used in retail, healthcare, and smart city pilot schemes.  

Real-world cases of digital twin technology today include:

• Swisscom: Swisscom worked with Ericsson to roll out a project that utilized network digital twins to produce network recommendations. It helped the Swiss telco achieve a 20% overall reduction in transmit power, lower base station power consumption, and improved customer speeds.
• Mayo Clinic: Through data including digital imaging, genetics, and wearable devices, healthcare provider Mayo Clinic has used digital twins to create custom patient models for diagnostics and treatment.
• Siemens: German tech conglomerate Siemens is utilizing a virtual power plant digital twin to map out power plant infrastructures, including components such as solar panels and wind turbines.
• NTT Indycar: US auto racing body NTT Indycar Series uses digital twin technologies, combined with AI (artificial intelligence) and data analytics, to produce real-time racing insights. 
• E.ON: E.ON, a German energy company, appointed DNV to implement digital twins to monitor its assets and collect performance data.
• BMW: Automaker BMW is working with SAP to create virtual models of all of its active factories.
• Orlando Economic Partnership: The OEP, a not-for-profit economic and community development organization, engaged SAP's help to roll out digital twins to develop an urban planning tool (OEP), including 3D representations of metropolitan areas.

As far back as 2018, Deloitte explored real-world cases of adoption in a report, saying, "Companies are using these 'digital twins' in a growing number of industries, making it easier to design and operate complex products and processes ranging from wind turbines to supermarket aisles. Digital twins are accelerating product and process development, optimizing performance, and enabling predictive maintenance."

The 2018 Deloitte study outlines how these companies have benefited from digital twins: 

• Maserati: The automaker is using digital twin technologies to accelerate product design. Virtual modeling and simulation are reducing the number of expensive, real-world prototypes required, as well as the need to launch physical wind tunnel tests and test drives, cutting vehicle development time by 30%.
• GE: The tech giant is using digital twins to model supply chain and factory processes at its Nevada facility to improve inventory management.
• Dassault Systems: The healthcare company is building a library of realistic human heart simulations that physicians can consult to better understand a patient's condition in real time.

Alongside the cloud, AI, machine learning, and data analytics, digital twin technologies have the potential to vastly improve production, manufacturing, and supply chains. Digital twins are not static; instead, they can integrate a host of modern technologies to give businesses a holistic, transparent overview of their assets and how they work together -- hence, improving business outcomes, allowing informed decision-making, and increasing efficiency.

With interest and understanding of digital twin technologies increasing every year, we can expect that many more organizations will create and capitalize on their own digital twins in the future.