7 minute read 19 Mar 2024
Tech Trend: Digital Twin
EY Tech Trends series

Tech Trend 04: Digital twins: Creating intelligent industries

By Ram Deshpande

EY India Technology Consulting Partner

Experienced professional with experience in business strategy, digital transformation, market development, business growth and large program delivery.

7 minute read 19 Mar 2024

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Digital twins will redefine industries but require meticulous implementation.

In brief

  • The global digital twin market, valued at nearly US$9 billion in 2022, is projected to reach US$137.67 billion by 2030.
  • Component twins analyze, optimize designs; system twins predict component interactions; process twins reduce defects and enhance productivity.
  • Digital twins are applied across sectors with significant adoption by original equipment manufacturers (OEMs) and in new manufacturing operations.
  • Organizations should develop concrete roadmaps, conduct proof-of-concept (POC) projects, and ensure full-scale implementation with continuous monitoring.

If the consumer metaverse was creating all the buzz in 2022, the year that followed has seen Generative AI (GenAI) take center stage. But away from the limelight, digital twins, the foundation of the enterprise metaverse, has matured as a technology and its use cases are going up in multiple sectors. 

In fact, the global digital twin market had reached almost US$9 billion in 2022, with 29% of global manufacturing companies having either fully or partially implemented their digital twin strategies — an increase from 20% in 2020, according to research agencies. However, it is projected to soar to US$137.67 billion by 2030, which translates into a CAGR of 42.6%, as reported by Fortune Business Insights. The Asia-Pacific region is projected to grow faster, at a CAGR of 45.9%, led by countries such as South Korea, Japan, India, and China.

Unlocking efficiency

While there are many ways in which digital twin technology can be used, three main types have emerged, depending on purpose and scope.

The first is the component digital twin that helps in experimenting with new component designs before choosing the final one for production. These twins utilize digital counterparts to analyze, predict, and optimize the performance of individual components. They excel at assessing factors such as stress and strain in mechanical components, electrical load in electrical ones, or flow characteristics in fluid components, thus preventing premature malfunctions or breakdowns.

The second type can be termed as the system digital twins, and they provide a predictive analysis by offering insights into how components interact and perform together. They serve as comprehensive digital replicas crucial for predictive analysis and understanding of the complex interplay among various components.

Finally, the process digital twins intricately model specific segments or entire manufacturing processes, providing highly detailed virtual representations. These advanced models play a key role in optimizing processes, increasing productivity, reducing defects, and ultimately adding significant value to operations.

It is critical to identify use cases relevant for the business and ensure early prioritization based on business benefits, availability of technology and skills. In addition, change management’s impact to mitigate adoption risk is a critical consideration as organizations embark on this journey.

Diverse digital twin applications

Digital twins' applications vary by sector due to factors such as technology, network connectivity, skills, interoperability, data standardization, and governance. But OEMs are the biggest adopters, especially in new manufacturing operations. The automotive sector holds more than 15% of the market share of digital twin adoption, with significant demand in the electric vehicle (EV) segment. In fact, a global EV major employs component digital twins to monitor vehicle parts in real time and predict issues before they manifest. This enhances the overall lifecycle, safety, and performance of its EV cars.

Other early and heavy adopters include manufacturing, healthcare, infrastructure, smart cities, and agriculture sectors.

Manufacturing: Organizations are using virtual replicas of production lines, machinery, and factories to simulate and optimize processes, improving production planning, minimizing downtime, and reducing maintenance costs. A global aviation company is using component digital twins to predict 99.9% of anomalies in its jet engine parts, while a manufacturing company is using process digital twins to optimize various parameters, resulting in a reduction of defective products by 75%. An oil company has deployed process digital twins to optimize the drilling process on its oil rigs, resulting in reported savings of up to US$1 million per day.

Healthcare: A medical center in the US is developing digital twins of patients' kidneys to improve surgical outcomes and provide enhanced training for surgeons. Similarly, an Indian healthcare company is utilizing this technology to develop patient-specific heart models, enabling treatment simulation and evaluation without invasive procedures. Personalized treatments are now possible by creating virtual patient models for precise diagnosis and treatment planning. To improve skills and minimize errors, surgeons are using process digital twins to simulate complex procedures.

Infrastructure and smart cities: Digital twins are being utilized to simulate human behavior, including crowd dynamics in urban environments or emergency scenarios, addressing critical political and societal decision-making needs. The Survey of India is actively creating digital twins of major cities that accurately mirror the urban landscapes and physical assets. These detailed models not only aid in city planning and policymaking, but also enhance disaster management efforts. Recently, the Indian government launched Sangam, an initiative focused on digital twins for future infrastructure planning and design, leveraging innovative integration of advanced technologies such as AI and 5G.

Agriculture and precision farming: While manufacturing and healthcare were early adopters of digital twin, agriculture is an emerging sector. Digital twins empower farmers with data-driven insights for precision farming. Virtual models of crops and farmland help optimize irrigation, fertilization, and pest control or can continuously monitor and predict a milch animal’s poor health, equipment malfunction, soil dryness, or temperature change. With a significant portion of the Indian population reliant on agriculture for employment, adopting digital twins in this sector has the potential to modernize traditional farming practices and bolster food security.

A comprehensive table below describes more industry applications and various use cases that are getting implemented. 

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Type of digital twin use cases

Challenges in adoption

While digital twins offer significant opportunities, there are several challenges to successful implementation. Upgrading infrastructure and enhancing connectivity, particularly in rural areas, is crucial as unreliable or slow internet access hinders real-time data exchange. Security and regulatory compliance also pose hurdles, with rising cyber threats necessitating prioritization of cybersecurity measures. Interoperability and standardization across OEMs is critical for adoption and success of digital twin solutions. The shortage of skilled professionals in data analytics, simulation modeling, and cybersecurity underscores the importance of aligning educational programs with market demands. Moreover, the substantial upfront costs can be especially daunting for small and medium-sized enterprises (SMEs). Demonstrating return on investment and integrating with legacy systems add to the complexity, requiring meticulous planning and execution. Addressing these challenges comprehensively is vital for widespread adoption of digital twin technology in India.

Building a future roadmap

As the ecosystem matures, digital twins will redefine industries and innovation. Integration of GenAI and Internet of Things (IoT) will enhance predictive capabilities, further boosting effectiveness. We can expect increased collaboration between technology providers, businesses, and research institutions. Organizations should develop a concrete roadmap, understanding advantages and challenges, evaluating requirements and resources, partnering with experts, conducting POC projects, and testing and reviewing efficacy. Full-scale implementation with continuous monitoring and maintenance will ensure sustained functionality. 

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Digital twins have matured as a foundational technology, with diverse applications across industries. Digital twins, categorized into component, system, and process types, optimize efficiency and predictive analysis. Major adopters like automotive and healthcare leverage digital twins for enhanced outcomes, while infrastructure and smart cities utilize it for urban planning, enhancing disaster management, and future infrastructure planning and design. However, challenges such as infrastructure upgrades, security, and skill shortages must be addressed for widespread adoption. With GenAI and IoT integration, digital twins are poised to reshape industries, demanding meticulous planning and collaboration for successful implementation.

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About this article

By Ram Deshpande

EY India Technology Consulting Partner

Experienced professional with experience in business strategy, digital transformation, market development, business growth and large program delivery.