In the present landscape, digital twins are arising as a groundbreaking paradigm to revolutionize process and innovation. Predictive maintenance held the largest share of the digital twin market in 2023, however, the technology is now expanding its realms to various domains, from healthcare to smart cities. Healthcare is anticipated to exhibit the highest growth in the years to come.
The global digital twin market is projected to grow significantly exhibiting an average CAGR of 40.7%. This growth is primarily attributed to its rising demand in the healthcare industry and the increasing focus on predictive maintenance. Digital twins are increasingly being used in predictive maintenance for cost reduction and improved supply chain operations.
For instance, LocLab, a part of Hexagon, has introduced LocLab Cloud, utilizing HxDR, a secure cloud-based platform for 3D digital twins. The platform allows partners to upload, access, and share their digital 3D content, enhancing data integration for various systems. Users can efficiently manage assets, optimize processes, conduct simulations, and gain insights. It offers end-to-end solutions for data-enriched digital twins, facilitating predictive maintenance, sustainability analysis, and improved decision-making.
North America is estimated to account for the largest share of the digital twin market. This is attributed to the technologically mature ecosystem, robust digital infrastructure, advanced data analytics capabilities, and skilled workforce. The major market players include General Electric, Microsoft, Amazon Web Services, ANSYS, and PTC. Other key players around the globe include Siemens (Germany), Dassault Systems (France), and Robert Bosch (Germany).
Applications of Digital Twins
The healthcare industry is one of the major contributors to the digital twin market. Digital twins in this industry are the virtual replicas of patients, organs, or medical devices. These aid in the development of personalized medicines, disease modeling and simulation, remote patient monitoring, surgery planning, surgical training, device development and testing, optimization of healthcare operations, preventive care, and clinical trials.
In October 2023, Leucine, secured $7.0 million in series A funding to expand its AI-generated digital twin platform, developed to assist manufacturers in streamlining regulation compliance for drugmakers’ production floors. The platform employs AI to digitize manufacturing workflows and enhance compliance, speed, and cost-effectiveness.
Additionally, the utilization of digital twins for predictive maintenance is widespread across various industries. Predictive maintenance involves the real-time tracking and utilization of historical data to foretell failure modes to optimize maintenance. A digital twin in the context of predictive maintenance refers to a virtual representation of a physical asset or system.
For predictive maintenance digital twins are used for data collection, digital replica creation, analytics, decisioning, simulation, and system integration of IoT platforms. These tools revolutionize processes across industries including manufacturing, energy, aerospace, and defense. They are also increasingly being used for the development of smart cities for urban planning and development and traffic management.
In the manufacturing industry, digital twins are useful for product designing and prototyping. It also has applications in production planning and optimization. Digital twins can also regulate the supply chain for the manufacturing industry by enabling end-to-end visibility and risk management. For the aerospace and defense sector, the technology can be leveraged for simulation testing and mission training.
In October 2023, the Sovereign Manufacturing Automation for Composites CRC (SoMAC) collaborated with the University of NSW and Omni Tankers to advance the creation of a digital twin of Omni Tanker's production plant. The digital twin operates alongside the physical plant, enabling virtual testing of various production methods for efficiency improvement. The project supports the future growth of Omni Tanker and Industry 4.0 advanced manufacturing in Sydney.
Similarly, NTT DATA's Innovation Centre is leveraging digital twins in the industrial metaverse to create new business opportunities through client collaborations, such as developing virtual data center replicas and enhancing product configurators for automotive and manufacturing clients. This technology optimizes performance, efficiency, and safety, particularly in remote control and maintenance scenarios leading to flexible and resource-efficient production environments.
In the energy setup, digital twins can assist in smart grid management and asset performance management. IoT systems can effectively utilize the technology for device optimization and remote monitoring and control. Digital twin technology also has applications in building management (for facility optimization), retail industry (for customer experience optimization and inventory management), and environmental monitoring (for natural resource management and climate change analysis).
For instance, ConocoPhillips, a Houston-based hydrocarbon exploration and production company, embraced digital twin technology to enhance safety and efficiency in its operations. The organization aims to achieve four times ROI and expand its use cases.
Also, Nemetschek Group, with its array of software brands, facilitated the AEC industry's digital transformation by creating seamless digital twins. These allow real-time data access, better decision-making, and improved project planning to reduce costs and minimize errors. The use of OPEN BIM and Building Lifecycle Intelligence fosters collaboration and ensures real-time data accessibility, transforming the construction industry. Several real-world projects including children’s hospital in Norway and the Kohlbrand Bridge in Germany highlight the efficiency of this project.
Encapsulating, the implementation of digital twins offers several benefits, including improved asset performance, reduced maintenance costs, increased operational efficiency, improved efficiency, enhanced product development, and enhanced overall reliability.
For instance, January AI introduced its digital twin technology, which utilizes Continuous Glucose Monitoring (CGM) data and AI, to aid both food companies looking for product transformation and customers to make informed dietary choices. Food companies like Nestle are exploring this innovation to create specialized, nutritious products for specific population. The product enables the food industry to optimize the glycemic response, taste, and texture of food at scale.
Challenges and Considerations
As other technologies, digital twin technology also comes with its own set of challenges and considerations. The greatest challenge associated with digitization is data security and privacy. The creation of a digital twin involves a collection of huge amounts of sensitive data about the physical assets and processes. Ensuring the security of such data from unauthorized access or malicious activities is crucial. This is possible by the implementation of robust policy measures and strict regulation compliance.
Another issue can be the lack of standardized protocols and data formats, which can hinder interoperability between different systems and platforms. This could be addressed by establishing industry-wide standards for the seamless integration of such systems. Challenges can also be evident in the integration of digital twin technology with legacy systems in certain organizations.
The accuracy and reliability of data used to create and update digital twins are crucial. Inaccurate or outdated data can lead to incorrect predictions and decisions, potentially causing operational issues. Yet another challenge arises with the management and processing of the volumes of data. Scalability considerations are crucial to ensure the system can handle increased workloads.
The two most important challenges in the establishment of any new systems in any organization are the initial investment and user training. The initial investment includes making arrangements for the upfront cost of hardware, software, and expertise. Also, the estimation of training costs for users and operators is crucial. Thus, a careful examination of long-term ROI is essential to the establishment of any new setup.
Furthermore, automated decision-making based on digital twin data raises ethical considerations. Organizations need to establish ethical guidelines and frameworks to ensure responsible and transparent use of technology. Additionally, planning for long-term support and updates is essential to keep the technology effective and secure.
In a nutshell, digital twin technology is rapidly advancing, supported by its vast array of potential benefits. However, proper regulatory checks should be ascertained to unravel the full potential of the technology. Studies anticipate the integration of artificial intelligence, machine learning, and advanced analytics into the digital twin ecosystem, in the coming years, to develop more sophisticated and autonomous systems. The adoption of this technology can leverage a competitive edge in the ever-evolving digital landscape.