The global digital twin market size is projected to reach $155,84-billion by 2030, registering a compound annual growth rate (CAGR) of 35,7% from 2024 to 2030, according to a new study by Grand View Research.
Increasing public and private investments in digital transformation solutions, rising significance of smart factories, and global proliferation of cloud-based platforms are collectively contributing to market growth.
The digital twin facilitates enterprises to efficiently reduce expenses and increase revenue. Various factors, such as social media, cloud computing, and process automation, also contribute to the use of digital twins to increase process efficiency and propel market expansion.
The emergence of advanced technologies, such as robotic process automation (RPA), the Internet of Things (IoT), artificial intelligence (AI), cloud computing, and big data analytics is creating a positive outlook for the market.
Implementing IoT in manufacturing allows electronic devices to communicate with each other without any human interference within a prevailing internet infrastructure. Therefore, IoT could have a profound impact on the digital twin industry. IoT allows connected devices to interact with each other and exchange critical notifications, such as defective or damaged ping, supporting market growth. Several industries have adopted digital twin technology.
For instance, a new packaging machine can be tested virtually before being introduced commercially. At the same time, a fan motor of an industrial HVAC unit can also be tested virtually before it is installed. Digital twins enable end-users to conduct tests on the product while enhancing the digital world’s decision-making capabilities.
Digital twins are rapidly gaining momentum in the healthcare, automotive, and manufacturing industries. Various digital twin platform-developing companies have launched different solutions to cater to specific business areas. High demand for automation in various industries is anticipated to trigger market growth over the forecast period.
Highlights from the Digital Twin Market Report include:
* In terms of solution, the process segment is anticipated to grow at the fastest CAGR of 37,5% from 2024 to 2030. The digital twin technique can be applied to several tasks, including tracking device performance, anticipating maintenance needs, and identifying potential issues, all of which will contribute to the segment’s growth.
* The cloud-based deployment segment is expected to register the fastest CAGR from 2024 to 2030. Cloud-based systems lower maintenance costs and installation expenses for physical equipment, giving businesses greater flexibility and cost-effectiveness.
* The small enterprises segment is expected to register the fastest CAGR from 2024 to 2030. SMEs are increasingly using digital twin technologies to reduce expenses associated with product development and to easily access reasonably priced solutions.
* The product design & development segment is expected to register the fastest CAGR from 2024 to 2030. A digital twin solution is in demand in product design and development due to various factors, such as helping engineers and designers visualise design concepts, reviewing manufacturing processes with computer-aided manufacturing (CAM) software, and simulating design performance.
* The automotive and transportation industry is expected to register the fastest CAGR from 2024 to 2030. The automotive and transportation industries use digital twin solutions to maximize vehicle performance while reducing maintenance costs and downtime.
* Market players are adopting various business strategies to attract potential clients and achieve higher profitability from this potential market. For instance, in January 2023, IBM and Adobe Inc. announced a strategic partnership to offer next-generation digital transformation solutions for streamlining and optimising organisations’ supply chain & order management process by offering digital commerce experience to the customer from real-time tracking of inventory to the tracking of customer data for better end-user experience.