Posted by Hyderabad: GlobalData forecasts the global digital twin market to grow at a compound annual growth rate (CAGR) of 35.6%, rising from $5 billion in 2019 to $154 billion by 2030.
Digital twin are increasingly transforming industries such as manufacturing, healthcare, and aerospace, offering solutions to optimise operations, improve efficiency, and enable predictive capabilities across various sectors.
Low-cost sensors used in Internet of Things (IoT) devices, a decline in the cost of high-performance computing (HPC), and cloud accessibility, will drive the global digital twin market growth reveals GlobalData’s latest Strategic Intelligence report “Digital Twins.”
the Advances in data analytics and artificial intelligence (AI) will also drive the growth.
“Large companies such as Amazon have tapped into their reach and reputation to partner with firms such as Matterport and Anthropic to enhance their digital twin offerings, and smaller companies such as Aerogility are providing services to specific industries such as aerospace and defense,” commented Aisha U-K Umaru, Strategic Intelligence Analyst – GlobalData.
Digital twins: Diverse use cases
Digital twin conceptually exists around for decades. In NASA’s Apollo 13 mission to the moon in 1970 had a digital twin. While far from ubiquitous today, adoption is increasing across industries.
“Various industries employ digital twins including oil and gas, power, sport, and government. They serve a wide range of purposes within these fields, from enhancing the efficiency of a factory to providing an enriched viewing experience for sports fans,” added Umaru.
AI’s impact on digital twin industry
Digital twin are increasingly harnessing AI to provide more context to the users. This approach has created a hybrid technology called semantic twins, which can provide a deeper level of understanding by letting users ask large language models (LLMs) questions about a twin and its components.
In response to these questions, the LLM can draw from its knowledge of the twin, the twin’s aims and objectives, and its broader understanding of systems and the world.
For example, a user might ask a semantic twin of a city, “How can I update this twin to align with other cities of similar population and transport systems that manage traffic congestion more effectively?” Semantic twins also benefit from other features of generative AI, including advanced predictive analytics and information retention.
“AI is pervading almost every industry, and it can offer more depth to digital twins. Semantic twins can allow users to draw deeper meaning from their digital twins, using LLMs for support,” concluded Umaru.