Digital Twins: Analyzing Growth and Emerging Trends

Posted by Tom C on May 10th, 2024

The global digital twin market size in terms of revenue was estimated to be worth USD 10.1 billion in 2023 and is poised to reach USD 110.1 billion by 2028, growing at a CAGR of 61.3% from 2023 to 2028. The new research study consists of an industry trend analysis of the market.

The growth of the digital twin market is driven by the growing demand for digital twin in the healthcare industry and the increasing focus on predictive maintenance.

Digital Twin Market Dynamics:

Driver: Growing focus on predictive maintenance

The utilization of digital twins for predictive maintenance is widespread across various industries. Predictive maintenance based on digital twins involves collecting real-time sensor data that provides information about the condition and performance of a product, process, or system. This data is then analyzed and compared to historical records of failure modes and their criticality. The insights gained from this analysis are used to predict maintenance needs. Apart from predicting failures, this technology offers several advantages that optimize maintenance. Digital twins enable the calculation of maintenance-related key performance indicators and forecast how machines perform under different conditions. By serving as accurate real-time models of a product, process, or system's condition and performance, digital twins facilitate simulations and predictions of behavior under specific factors such as runtime, exposure to extreme operating conditions, and temperature.

Opportunity: Rising trend of 3D modeling and scanning across industries

IIoT combines machinery, advanced analytics, and human involvement in specific processes or products, creating a network of interconnected industrial devices through communication technologies. This network enables the development of systems that monitor, collect, exchange, analyze, and deliver valuable insights, enhancing decision-making capabilities. With its ability to cover the entire lifecycle of physical systems, processes, or products, IIoT provides businesses with a powerful analytical tool to thoroughly evaluate key performance indicators and identify areas for enhancements or upgrades. In the long run, the lessons and suggestions derived from digital twins are anticipated to generate significant opportunities for innovation and growth. Furthermore, by integrating digital twins with field performance monitoring, businesses gain constant visibility into actual product usage compared to the anticipated use based on design parameters. This approach offers valuable real-time feedback for improving the design process. It also improves product development efficiency by reducing the time and costs associated with manufacturing and testing prototypes in a physical environment. Virtual simulation models of products, processes, or units facilitate collaboration among experts from diverse fields during product development. For example, software developers, mechanical engineers, and user interface designers can simultaneously collaborate on the exact digital twin. Deploying IoT and digital twins enhances efficiency by predicting production failures and allowing engineering teams to address issues before they impact manufacturing targets.

Challenge: Lack of skilled workforce and awareness regarding cost benefits offered by digital twins

Companies need concrete implementation plans and significant investments for integrating digital twins into product management. Due to the novelty of the technology and the substantial changes it entails, end users are still determining the economic benefits, investment requirements, and future cost savings. Assessing the potential of a digital twin is considered complex and multifaceted, further impeding its widespread adoption. Creating a digital twin for assets or processes involves leveraging various technologies and skill sets alongside a workforce equipped to handle the latest equipment and software systems. The digital transformation necessitates a shift in employee skill requirements across the entire value chain, from development to sales and marketing. As processes increasingly rely on data and efficiency gains are expected, new employees must possess updated skill sets and higher qualifications. This situation can result in a skills gap between newly hired and experienced employees. Furthermore, industries are grappling with the dual challenge of embracing emerging technologies while also contending with a need for a more highly skilled and proficient workforce, creating a dynamic environment for implementation.