Digital Twins Market Estimated to Surpass $16.7 Billion Marks by 2026

Digital Twins Market Estimated to Surpass $16.7 Billion Marks by 2026

“Digital Twins Market”
Rising Data Collection Points Due to IoT and Significant Computing Requirements is Driving the Growth of Digital Twins Market

The global Digital Twins Market is estimated to surpass $16.7 billion marks by 2026 growing at an estimated CAGR of more than 24.7% during the forecast period 2021 to 2026. Companies find various use cases to utilize the technology as there will be billions of connected devices in next few years, thereby billions of digital twins can be represented. These digital representations of real devices will lead to new opportunities like remote periodic checkup and maintenance of all devices, new product development based on user preference, service upgrade, safety, and better customer experience. To get a competitive advantage, companies look at digital twin as the opportunity to understand the customer needs, improve the existing products or services and to develop new products, thereby enhancing the customer experience. Not just in Smart cities and Factory 4.0, digital twin concept has penetrated health care in telemedicine technology to create personalized solutions to anticipate and prevent disease.

Digital Twins Market Segment Analysis – By Application

The applications of digital twins that are covered in this study are product design and development, performance monitoring, predictive maintenance, inventory management, and business optimization. In 2020, predictive Maintenance is the leading segment at 24% share followed closely by performance monitoring. Though it can provide a complete real-time performance of an asset which allows technicians to look for inconsistencies or any problems which are not easily identified through traditional methods, it is not bounded by the limitations of time.

The situations and environment can be simulated and run in advance to predict how the asset will be affected based on factors like life cycle, runtime or working with exposure to tough environment. Using the results from these tests, the companies can predict when the asset is likely to fail and how it can fail before it actually fails. Though Business optimization is at a nascent stage now considering the benefits digital twin can help to business when integrated to the business operations. Inventory management is a small and slow-growing market. However as reduced downtime is the primary objective of digital twin, inventory management plays a key role in supply of spare parts and other components.

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Digital Twins Market Segment Analysis – By End-Use Industry

Increasing market volatility, customer expectations and technology impacts are driving businesses everywhere towards transformation. Initiatives to digitize assets, machines, devices, workflows and processes are being launched under different technologies and all the industries use them for their advantage. Most of these digital transformations are targeted at increasing automation, operational intelligence and connectedness. The method by which companies are doing this is to develop a digital twin of their business that surface the information they need to improve decision making, de-risk planning and delight customers with new experiences and business models. Manufacturing sector in factory 4.0 is the biggest user of digital twin. In the cloud, a machine’s digital twin makes its current state and prior operating history available at a level of detail that’s only possible with the Industrial Internet of Things. Earlier, these machines were programmed to monitor relatively few parameters, like high temperatures or high vibration levels. Now using digital twin everyone in the company from maintenance engineers to the CEO of a company has a vision into the life of the machine, from the granular detail of specific machines or the aggregated metrics.

Digital Twins Market Segment Analysis – By Geography

The global market for digital twin in 2019 was dominated by North America followed by Europe as the adoption rate was higher. As more and more companies in Asia Pacific are expected to learn the best practices, it is expected to gain significant market share. In North America, the recent developments in digital twins give companies throughout the product chain, product designers, manufacturers, and suppliers, more ways to optimize operations, speed products to market, increase competitiveness and expand business opportunities. So companies that add capabilities in simulation using digital twin are better equipped to deal with cost pressures, offshore competition and shifting consumer market trends that mandate product.

Digital Twins Market Drivers

Rising Data Collection Points due to IoT

The high growth in internet has created way for many innovative technologies and applications, which as a business generates billions of dollars. Internet connects people from all parts of world through their devices, thereby generating huge amount of data. With addition of sensors to the devices, additional data can be collected which is termed as Internet of Things. With the data collected and digital modelling such as computer-aided design (CAD), a digital twin can be created for billions of the connected devices. Digital Twins allows companies to trace their products throughout their life cycle. In addition to product performance and maintenance it also provides opportunity to the companies to understand customer’s needs and improve customer experience. The companies or vendor can inspect and monitor which part of the product needs replacement considering the safety or operational efficiency, without being in proximity to the products. This helps them in the decision-making process that is associated with physical products.

Becoming an Integral Part of Digital Factory Strategies

With more and more companies getting digitalized, digital twins are becoming a part of the strategy, Companies can replace the current reactive static model, with a dynamic proactive model. The data analyzed in a digital twin is used for prevention of issues rather than diagnosing post an issue. Digital twin technology facilitates virtual prototyping, the digital representation of a product before it is physically created and also simulating the operation of the product in its intended environment. This leads to precise product design at higher quality at a faster pace and low cost. Industrial robots and other complex machinery operate by combining different components and their interactions with other systems and behaviors are extremely difficult to anticipate using the traditional design process. Hence these complex systems need multiple improvements before they reach higher efficiency, which adds cost to the design process. However, using the simulation in a virtual model a superior product can be designed without any additional cost. Moreover, in a digital factory setup, the Industrial IoT, many sensors are integrated to the complex machines and their interactions and behaviours at different operating conditions can be easily tracked. The data not only helps in superior design but also supports predictive maintenance. With multiple benefits like superior product or process design, predictive maintenance and reduced operating cost, the digital factories have started to integrate digital twins in their operations.

Digital Twins Market Challenges

Significant Computing Requirements

Machine learning, Analytics and Artificial intelligence are the backbones of the new technologies by adding intelligence to new or existing systems to form a smarter network. They are getting embedded into regular equipment such as home appliances, electronic devices, medical devices and industrial appliances. This shows the emergence of conversational systems with the support of IoT into a digital network that is progressing towards digital twins. With billions of IoT devices, there is a need to track the operations of billions of digital twins. In addition, there is a need for a robust simulating software which should be up and running continuously without any delay or disturbances. Considering the need for processing huge amount of data and billions of digital representations, scalable massive cloud infrastructures integrated to millions of physical servers is required. The financial resources that is required to operate this computing and storage infrastructures is high, making it impossible for small enterprises to get the complete benefits of digital twins. Moreover, the need for advanced security protocols to protect the data adds cost and require additional computation.

Digital Twins Market Landscape

Product launches, acquisitions, and R&D activities are key strategies adopted by players in the Digital Twins Market. ANSYS, PTC, Dassault Systèmes, General Electric (GE), Bosch, IBM, Microsoft, Oracle and Infosys, Siemens and others are considered to be the key players of the Digital Twins Market.

Acquisitions/Technology Launches/Partnerships

In February 2020, Huawei Launches Industry’s First Site Digital Twins Based on 5G Digital Engineering Solution
Bentley Systems Commits $100 Million of Venture Funding to Accelerate Infrastructure of Digital Twins

Key Takeaways

Digital twin consists of software including CAD, FEM and analytics; products like computing devices, AR / VR, other wearable devices, location-based systems, sensing technologies including sensors and gauges, coordinate Measuring Machines, lasers, vision systems and scanning devices and related services

Digital twin eliminates guesswork from determining the best course of action to service critical physical assets, from engines to power turbines. Easy access to this intelligence about assets paves the road to wider optimization and business transformation.

Digital twin technology spans across all industries where the value is in assets and more generally complex systems. Its ability to deliver early warnings, predictions, and optimization is currently used with external operating assets.

The digital twin idea has additional benefits too, as it can use historical data and current data to provide a complete picture of a particular asset, its past performance, what it should be achieving now and its likely end of life date, when it would be predicted to be less efficient.

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