Friday, April 26, 2024

Global Digital Twin Market: Smart Factory & Digital Twin

The digital twin technology is garnering significant market prominence mainly due to the growing smart factory trend. Organizations use this technology to use real-time data, algorithms, and AI to create a virtual depiction of physical objects, processes, or services. This visualization technology complements sensors used by manufacturers to collect important production data.

Smart factory solutions utilize scanning tools to measure components in new, faster ways. In manufacturing, the digital twin plays a key role in measuring a product’s or building’s performance accurately, rapidly, and efficiently to enhance productivity. Digital twins are emerging as suitable virtual duplicates to investigate a part’s characteristics and flaws.

Replicas are practically more affordable than physical prototypes to create and test a number of versions. Engineers use data generated by visualization equipment like cameras, lasers, and sensors to create and sustain such 3D models helpful in the mass production of various components and parts and test them in a less risky way.

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Using digital twin technology and virtual reality glasses, technicians can view the most sophisticated models of machines and updated specs. They also use digital twins to improve system designs and strategize & experiment with production lines, testing out possible glitches before creating a physical line and maintaining them in a virtual environment. Lastly, they use twins to address asset lifecycle management requirements.

The wide penetration of Industry 4.0 in the manufacturing sector that requires consistent reproduction with equal repeatability and higher flexibility creates vast demand for the digital twin. Considering the rising use of this technology across industries, Market Research Future (MRFR) expects the global digital twin market to create a revenue pocket worth USD 63.23 Billion by 2030, growing at a massive 33.31% CAGR during 2022-2030.

The popularity of the digital twin is led by the rapid adoption of 3D printing and 3D simulation. The twin technology witnesses huge demand from the electronic and electrical/machine manufacturing sector, escalating the market valuation on the global platform.

Besides, digital twins offer multiple benefits for product-based companies and users, aiding in design optimization, reducing costs, and time to market accelerating the organization’s response to new customer needs. This technology also enables new revenue streams, such as remote maintenance & support offerings ‘as a service’ business models.

Digital Twins’ Uses beyond Product Development 

Digital twin technology has already transformed the way products are designed, manufactured, and improved. Companies across different industries are implementing the technology to capture real value in product development, manufacturing, and through-life support. However, digital twins are increasingly finding more and more application areas, such as automotive, aircraft, environment monitoring, and so on.

Automotive OEMs use the digital-twin approach to create a digital concept configurator for complex products required in early phase development, which is challenging for simultaneous evaluation of customer requirements, technical concepts, and product costs. With digital twins, the implications for meeting customer requirements or product cost targets become comprehensively transparent.

In aircraft, digital twins are installed in cockpits to help planes look after themselves and book themselves in for a repair if needed. The digital version helps plan maintenance, detect a potential issue, and simulate the effect of upgrades and design changes.

Digital Twins are increasingly used in environmental monitoring to respond to climate change crises. City municipalities and corporations are rapidly implementing this technology to gain real-time insights into city management. Digital twins are paired with IoT devices to obtain real-time data-driven insights that benefit property managers, guests, travelers, airport operations, city officials, etc.

For instance, in 2021, San Francisco used digital twins on how CO2 emissions led to air-quality problems and public health issues, studying the mobility patterns of people, vehicles, and movements at the port. This helped the city improve mobility, air quality, and public health and create a systematic plan for change, identifying poor air quality and hot spots in the city.

In 2021, Accenture identified digital twins as a leading tech trend growing businesses by driving investments in data & artificial intelligence (AI) to boost new generation business analytics and intelligence. In 2022, Accenture identified two new tech trends associated with digital twins: the programmable world and the AI-generated data and synthetic content to mimic what’s real.

Industry Players’ Strategies to Follow

Major players make significant investments in developing and improving the technology with wearable scanning and robust document control. Using wearable scanners and document management systems in the cloud, faster time to value for Greenfield and Brownfield digital twins can be achieved. Key technology providers form strategic partnerships and acquire small yet promising companies to gain a larger competitive advantage.

For instance, on May 17, 2022, Gradiant, a leading water solutions provider, announced the acquisition of Synauta to accelerate the digital twins’ use in the water treatment industry. The acquisitions would enable Gradiant to advance digital twins, combining its expertise in digitizing and optimizing water treatment with Synauta’s practical machine learning expertise for the water industry.

Gradiant, a spun out of MIT, integrates its experience into a digital twin platform that keeps all water treatment procurement, safety, and maintenance data in one place to help enterprises build more efficient water treatment systems. Gradiant as an end-to-end water solution provider improves the design, operations, and asset optimization for industrial water treatment.

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