Digital Twins - Thematic Research
Summary
Digital twins are digital representations of physical assets, systems, people, or processes. They help detect, prevent, predict, and optimize the physical environment using artificial intelligence (AI), real-time analytics, visualization, and simulation tools. Conceptually, digital twins have been around for decades; a forerunner was used in NASA’s Apollo 13 mission to the moon in 1970. While far from ubiquitous today, adoption is increasing across industries, although challenges around security and interoperability still need to be addressed.
GlobalData forecasts that the global digital twins market will reach $154.3 billion by 2030, driven by advances in underlying technologies such as the Internet of Things (IoT), cloud, AI, and data analytics. The number of use cases for digital twins is increasing and includes remote asset monitoring, 3D design, and modeling of the effects of drugs on human patients.
Interoperability remains a key concern for digital twins. For widespread adoption of digital twins, it is important to ensure they can communicate effectively with each other. This requires standardizing data formats, communication protocols, and interfaces for seamless integration across different platforms, software, and hardware. Efforts are underway to address these challenges; however, achieving full interoperability requires collaboration among industry stakeholders, technology providers, and standardization bodies.
Scope
This report provides an overview of the digital twins theme.
It identifies the key trends impacting growth of the theme over the next 12 to 24 months, split into three categories: technology trends, macroeconomic trends, and regulatory trends.
It includes a comprehensive industry analysis, including use cases for digital twins across various industries, including manufacturing, power, oil and gas, healthcare, construction, automotive, aerospace and defense, government, and sports.
The detailed value chain comprises six layers: a physical layer, a connectivity layer, a data layer, a platform layer, a delivery layer, and a services layer.
Reasons to Buy
The range of potential use cases for digital twins is extensive. They range from design and architecture to engineering, smart cities, aerospace and defense, power, oil and gas, and, probably the most advanced, a digital twin of the human body. This report tells you everything you need to know about digital twins, including identifying the current leaders in some of the most important segments of the digital twins value chain.
- Executive Summary
- Players
- Technology Briefing
- The forerunner to today’s digital twins
- What are digital twins?
- A single version of the truth
- Data management
- The evolution of digital twins
- Challenges facing digital twins
- Creating a common language
- Standards and interoperability
- Security
- Data validation
- Complexity and cost
- The different types of digital twins
- The maturity of digital twins
- Trends
- Technology trends
- Macroeconomic trends
- Regulatory trends
- Industry Analysis
- Market size and growth forecasts
- Use cases
- Manufacturing
- Power
- Oil and gas
- Healthcare
- Construction
- Automotive
- Aerospace and defense
- Government
- Sports
- Timeline
- Signals
- M&A trends
- Venture financing trends
- Patent trends
- Company filing trends
- Value Chain
- Physical layer
- Connected things
- Cameras and lenses
- Sensors and microcontrollers
- Microprocessors
- Connectivity layer
- Edge infrastructure
- Cloud infrastructure
- Networking equipment
- Telecom networks
- Data layer
- Data integration
- Data aggregation
- Data processing
- Data storage
- Data validation
- Data governance and security
- Platform layer
- Delivery layer
- Licensed software
- Digital twin as a service
- Services layer
- Companies
- Sector Scorecards
- Application software sector scorecard
- Who’s who
- Thematic screen
- Valuation screen
- Risk screen
- Industrial automation sector scorecard
- Who’s who
- Thematic screen
- Valuation screen
- Risk screen
- Glossary
- Further Reading
- GlobalData reports
- Our Thematic Research Methodology
- About GlobalData
- Contact Us
- List of Tables
- Table 1: The maturity of digital twins
- Table 2: Technology trends
- Table 3: Macroeconomic trends
- Table 4: Regulatory trends
- Table 5: Digital twins in the manufacturing sector
- Table 6: Digital twins in the power sector
- Table 7: Digital twins in the oil and gas sector
- Table 8: Digital twins in the healthcare sector
- Table 9: Digital twins in the construction sector
- Table 10: Digital twins in the automotive sector
- Table 11: Digital twins in the aerospace and defense sector
- Table 12: Digital twins in the government sector
- Table 13: Digital twins in the sports sector
- Table 14: M&A trends
- Table 15: Key venture financing deals associated with the digital twins theme since January 2022
- Table 16: Companies
- Table 17: Glossary
- Table 18: GlobalData reports
- List of Figures
- Figure 1: Who are the leading players in the digital twins theme, and where do they sit in the value chain?
- Figure 2: “Houston, we’ve had a problem.”
- Figure 3: Digital twins create live virtual models of the real world
- Figure 4: Digital twins have a hunger for data
- Figure 5: The global digital twins market will be worth $154 billion by 2030
- Figure 6: Digital twin use cases range in sophistication and visibility
- Figure 7: Manufacturing operations are a key area for digital twins
- Figure 8: Developing the world’s first digital twins for floating offshore wind turbines
- Figure 9: BP and Chevron use digital twins to optimize assets
- Figure 10: NTT and Harvard are creating a digital twin of the heart
- Figure 11: Digital twins offer a single source of the truth
- Figure 12: Automotive companies are starting to exploit digital twins
- Figure 13: Digital twins are transforming the aerospace industry
- Figure 14: Singapore puts its Virtual Singapore digital twin to good use
- Figure 15: Digital twins are being used to plan the 2024 Olympic Games
- Figure 16: The digital twins story
- Figure 17: Digital twin financing deal volume increased between 2019 and 2023
- Figure 18: Digital twin patent publications have accelerated since 2016
- Figure 19: Siemens, General Electric, and IBM lead the way in digital twins-related patents
- Figure 20: Digital twin mentions in company filings across sectors grew significantly between 2016 and 2022
- Figure 21: The digital twin value chain
- Figure 22: The digital twins value chain - Physical layer: leaders and challengers
- Figure 23: The digital twin value chain - Connectivity layer: leaders and challengers
- Figure 24: The digital twin value chain - Data layer: leaders and challengers
- Figure 25: The digital twins value chain - Platform layer: leaders and challengers
- Figure 26: The digital twin value chain - Delivery layer: leaders and challengers
- Figure 27: The digital twin value chain - Services layer: leaders and challengers
- Figure 28: Who does what in the application software space?
- Figure 29: Thematic screen - Application software sector scorecard
- Figure 30: Valuation screen - Application software sector scorecard
- Figure 31: Risk screen - Application software sector scorecard
- Figure 32: Who does what in the industrial automation space?
- Figure 33: Thematic screen - Industrial automation sector scorecard
- Figure 34: Valuation screen - Industrial automation sector scorecard
- Figure 35: Risk screen - Industrial automation sector scorecard
- Figure 36: Our five-step approach for generating a sector scorecard
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