한글목차

산업용 메타버스는 제조, 물류, 운송, 유틸리티 등의 부문을 보다 현명하고, 보다 효율적으로, 보다 지속 가능하게 함으로써 혁명을 일으킬 가능성을 지니고 있습니다. 세계의 산업용 메타버스 시장 규모는 2035년까지 1,500억 달러를 초과할 가능성이 있어 생산성을 향상시키고 AI/ML 기능에 의해 지원되는 VR/AR/MR 및 5G 기술에 의해 그린이행을 가속화하고 고객에게 부가가치를 창출하는 실현 기술과 프로세스에 대규모 투자가 이루어지고 있습니다.

산업용 메타버스는 물리적 산업 경영과 몰입형 디지털 기술의 융합을 의미하며, 제조, 유지보수, 트레이닝, 콜라보레이션의 새로운 패러다임을 창조합니다. 산업용 메타버스는 물리적 자산, 생산 프로세스, 공급망이 가상 복제본으로 미러링되는 디지털 에코시스템으로, 이러한 디지털 트윈을 통해 기업은 실시간으로 산업 경영을 시뮬레이션, 모니터링 및 최적화할 수 있습니다.

산업용 메타버스를 지원하는 기술 스택에는 VR/AR, IoT 센서, AI, 클라우드 컴퓨팅, 5G 연결이 포함되어, 물리적 환경과 디지털 환경의 원활한 상호작용이 가능해져 작업자가 복잡한 데이터를 시각화하고 지리적인 경계를 넘어 협업할 수 있는 몰입형 체험을 실현합니다.

산업용 메타버스의 주요 용도는 다음과 같습니다.

• 원격으로 유지 보수 및 수리 기술자가 AR을 사용하여 설비 수리 중에 시각적 지침을 받음으로써 첫회 수리율을 향상시켜 이동 비용을 절감합니다.
• 위험한 처리나 복잡한 처리를 향한, 안전성이나 설비를 위험에 노출시키지 않는 몰입형 트레이닝 시뮬레이션
• 세계 팀이 공유한 가상 공간의 3D 모델상에서 콜라보레이션하는 가상 디자인 리뷰
• 실시간 모니터링과 예측 분석을 통한 생산 최적화
• 분산형 업무에서 공급 체인의 가시화와 관리

Siemens, GE, Boeing 등 산업의 주요 기업은 이미 메타버스 기술을 도입하여 상당한 업무 개선을 실현하고 있습니다. 산업용 메타버스는 산업 경영의 구상, 실행, 관리 방법의 근본적인 전환을 의미합니다. 기술이 성숙하고 표준이 진화함에 따라 산업용 메타버스는 미래의 개념이 아니라 점점 본질적인 경쟁 우위를 갖게 될 것으로 보입니다. 상호운용성, 보안, 노동자의 적응 등 분야에서 과제가 남아 있는 반면, 산업용 메타버스는 산업변혁의 다음 프론티어가 되고 있으며, 물리세계의 설계, 구축, 유지의 방법에 새로운 가능성을 창출하고 있는 것은 분명합니다.

본 보고서에서는 급속히 진화하는 산업용 메타버스의 정세를 상세하게 분석하고, 이 기술적 패러다임 시프트가 제조, 엔지니어링, 의료 등 주요 산업 부문에 어떤 변화를 가져오는지를 조사했습니다.

목차

제1장 주요 요약

• 산업용 메타버스의 정의
• 인더스트리 4.0에서 산업용 메타버스로의 진화
• 산업용 메타버스 에코시스템
• 메타버스를 실현하는 기술
• 산업용 메타버스의 구현
• 현재 시장 상황

제2장 시장 개요

• 시장의 진화
• 시장 규모와 성장률
• 관련 시장(IoT, AR/VR 등)과의 비교
• 투자 상황
• 주요 시장 성장 촉진요인
• 기술의 진보
• 효율성과 생산성 향상 수요
• 원격근무와 콜라보레이션의 동향
• 지속가능성과 환경상의 우려
• 시장의 과제와 장벽
• 산업용 메타버스의 기회

제3장 기술 상황

• 산업용 메타버스를 실현하는 핵심 기술
• 신기술과 그 잠재적 영향
• 기술 채용의 동향과 예측

제4장 최종 용도 시장

• 하드웨어
• AI, 애널리틱스 툴
• 품질 관리, 검사
• 산업별
  • 자동차
  • 항공우주
  • 화학, 재료 제조
  • 에너지
  • 의료, 생명 과학
  • 건설, 엔지니어링
  • 공급망 관리, 물류
  • 소매

제5장 규제

• 데이터 프라이버시, 보안 규제
• 지적재산에 관한 고려
• 표준과 상호 운용성의 대처
• 환경과 지속가능성에 관한 규제

제6장 사회적/경제적 영향

• 노동력의 변화와 기술 요건
• 경제 성장과 생산성 향상
• 지속가능성과 환경에 미치는 영향
• 윤리적 고려와 사회적 영향

제7장 과제와 위험 인자

• 기술적 과제
• 구현 및 통합 문제
• 사이버 보안 위험
• 경제와 시장의 위험

제8장 기업 프로파일

• VR, AR, MR(햅틱스 포함)(71사의 기업 프로파일)
• AI(136사의 프로파일)
• 블록체인(31사의 프로파일)
• 엣지 컴퓨팅(31사프로파일)
• 디지털 트윈(48사의 프로파일)
• 3D 이미징, 센싱(132사의 프로파일)

제9장 조사 방법

제10장 용어집

제11장 참고문헌

영문목차

The Industrial Metaverse has the potential to revolutionize sectors such as manufacturing, logistics, transportation, and utilities by making them smarter, more efficient, and more sustainable. The market for industrial metaverse applications could grow to >$150 billion by 2035, with major investments being made in enabling technologies and processes to enhance productivity, accelerate green transitions through VR/AR/MR and 5G technologies supported by AI/ML capabilities, and create additional value for their customers.

The Industrial Metaverse represents the convergence of physical industrial operations with immersive digital technologies, creating a new paradigm for manufacturing, maintenance, training, and collaboration. Unlike consumer-focused metaverse applications, the industrial metaverse prioritizes practical business outcomes and operational efficiency. At its core, the industrial metaverse is a digital ecosystem where physical assets, production processes, and supply chains are mirrored as virtual replicas. These digital twins allow organizations to simulate, monitor, and optimize industrial operations in real-time. Engineers can manipulate virtual models before implementing changes to physical systems, significantly reducing costs and risks associated with physical prototyping.

The technology stack powering the industrial metaverse includes virtual and augmented reality (VR/AR), Internet of Things (IoT) sensors, artificial intelligence, cloud computing, and 5G connectivity. This enables seamless interaction between physical and digital environments, creating immersive experiences where workers can visualize complex data and collaborate across geographical boundaries.

Key applications of the industrial metaverse include:

• Remote maintenance and repair, where technicians use AR to receive visual guidance while servicing equipment, improving first-time fix rates and reducing travel costs
• Immersive training simulations for dangerous or complex procedures without risking safety or equipment
• Virtual design reviews where global teams collaborate on 3D models in shared virtual spaces
• Production optimization through real-time monitoring and predictive analytics
• Supply chain visualization and management across distributed operations

Major industrial firms like Siemens, GE, and Boeing have already implemented metaverse technologies to achieve significant operational improvements. For example, some manufacturers report 30% reductions in design time and 25% improvements in maintenance efficiency. The industrial metaverse represents a fundamental shift in how industrial operations are conceived, executed, and managed. By creating persistent digital environments that mirror physical operations, organizations can achieve unprecedented levels of collaboration, efficiency, and innovation. As technologies mature and standards evolve, the industrial metaverse will increasingly become an essential competitive advantage rather than a futuristic concept. While challenges remain in areas of interoperability, security, and workforce adaptation, the trajectory is clear: the industrial metaverse is becoming the next frontier of industrial transformation, creating new possibilities for how we design, build, and maintain the physical world.

The Global Industrial Metaverse Market 2025-2035" provides an in-depth analysis of the rapidly evolving industrial metaverse landscape, exploring how this technological paradigm shift is transforming manufacturing, engineering, healthcare, and other key industrial sectors. This 658-page analysis examines the convergence of extended reality (XR), artificial intelligence, digital twins, IoT, and other emerging technologies that are creating immersive, collaborative industrial environments with unprecedented capabilities for optimization, training, and innovation.

Report contents include:

• Market Growth Projections: Detailed forecasts of the industrial metaverse market from 2025 to 2035, including compound annual growth rates, regional analysis, and segment-specific growth patterns.
• Market Overview: Detailed examination of market evolution, size, growth rate by component/technology/industry/region, investment landscape, drivers, challenges, and opportunities.
• Technology Landscape: Comprehensive examination of core enabling technologies including XR (AR/VR/MR), artificial intelligence, industrial IoT, 5G/6G networks, edge computing, blockchain, and 3D scanning/modeling.
• Industry Adoption Analysis: Sector-by-sector breakdown of industrial metaverse implementation across automotive, aerospace, chemicals, energy, healthcare, construction, supply chain, and retail industries.
• End Use Markets: Comprehensive breakdown by hardware components, AI tools, and industry-specific applications with current commercial examples.
• Investment Trends: Analysis of venture capital, corporate investments, and government funding initiatives driving industrial metaverse development globally.
• Technological Challenges: Critical assessment of current technological limitations, integration complexities, skill gaps, security concerns, and cost barriers.
• Future Opportunities: Exploration of emerging business models, sustainability applications, enhanced customer experiences, and novel use cases in non-traditional industries.
• Regulatory Landscape: Analysis of data privacy, intellectual property, standards development, and environmental regulations affecting industrial metaverse deployment.
• Implementation Case Studies: Real-world examples of successful industrial metaverse applications across manufacturing, product development, training, maintenance, and quality control.
• Market Evolution Timeline: Projected adoption curves from 2025-2035 across short-term, medium-term, and long-term implementation horizons.
• Societal and Economic Impact: Assessment of workforce transformation, economic growth potential, sustainability implications, and ethical considerations.
• Challenges and Risk Factors: Critical examination of technological, implementation, cybersecurity, and economic barriers to adoption.
• Company Profiles: Detailed analysis of over 460 companies including AAC Technologies, ABB, Accelink, Acer, Acuity, Advantech, Aeva, AEye, Ag Leader, Airy3D, Aistorm, Aize, Akselos, Alphabet (Google), Altair, Amazon Web Services (AWS), AMD, AnonyBit, Ansys, Apple, Arm, ArborXR, Artec 3D, Artilux, Axelera AI, Axera Semiconductor, Baidu, Balyo, Baraja, Basemark, Beamagine, BenQ, bHaptics, BlackShark.ai, Blaize, Blippar, BlockCypher, Bosch, BrainChip, Cambridge Mechatronics, Cambricon, Casper Labs, Celestial AI, Cepton, Cerebras Systems, Certik, Chainalysis, Circulor, Clique, Cognite, Cognizant, ConsenSys, Cosmo Tech, Coupa Software, CyDeploy, Dassault Systemes, DataMesh, Deep Optics, DeepX, DeGirum, Dexory, Dexta Robotics, DigiLens, Dispelix, d-Matrix, Dune Analytics, EdgeConneX, EdgeCortix, Edge Impulse, Emersya, EnCharge AI, Enflame, Expedera, Expivi, FARO Technologies, Fetch.ai, Finboot, Flex Logix, FuriosaAI, Gauzy, General Electric, GrAI Matter Labs, Graphcore, GreyOrange, Groq, Hailo, HaptX, Headspace, Hexa 3D, Hexagon, Hikvision, HOLOGATE, Hololight, Horizon Robotics, HTC Vive, Huawei, IBM, ImmersiveTouch, Infinite Reality, Inkron, Intel, Intellifusion, IoTeX, JigSpace, Kalima, Kalray, Kentik, Kinara, Kneron, Kongsberg, Kura Technologies, Leica Geosystems, Lenovo, LetinAR, Leucine, Lightmatter, Limbak, Litmus, Locusview, Loft Dynamics, LucidAI, Lumen Technologies, Lumibird, Luminar, Luminous XR, Lumus, Lynx, Magic Leap, MathWorks, Matterport, MaxxChain, MediaTek, Medivis, Meta, MicroOLED, Microsoft, MindMaze, Mojo Vision, Moore Threads, Morphotonics, Mythic, Native AI, NavVis, Neara, Nextech3D, Niantic, NVIDIA, NXP Semiconductors, Oculi, Omnivision, Oorym, Optinvent, Orbbec, Ouster, PassiveLogic, pgEdge, Photoneo, Pimax, Plexigrid, Presagis, Prevu3D, Prophesee, Q Bio, Qualcomm, Quanergy, Rain, Rapyuta Robotics, RealWear, Red 6, RoboSense, Rokid, R3, Rypplzz, Samsung, SambaNova Systems, Sapeon, Sarcos, Scantinel Photonics, Schott AG, Seeq, Sentera, SiLC, Siemens, SiMa.ai, Solitorch, Space and Time, Spherity, Story Protocol, Swave Photonics, Tachyum, Taqtile, TensorFlow, Tenstorrent, Tesla, Threedium, TRM Labs, TruLife Optics, TWAICE, TwinUp, Unity, Varjo, Veerum, vHive, VividQ, VNTANA, VRelax, Vuzix, Web3Firewall, Windup Minds, Worlds, Xaba, Xpanceo, Yizhu Technology, Zama, ZEDEDA, Zebra Technologies, Zivid, zkPass, and Zvision, spanning hardware manufacturers, software developers, system integrators, connectivity providers, AI specialists, blockchain innovators, XR device makers, sensor companies, robotics firms, edge computing providers, and digital twin platforms.

TABLE OF CONTENTS

1. EXECUTIVE SUMMARY

• 1.1. Definition of the Industrial Metaverse
  • 1.1.1. Key Characteristics
  • 1.1.2. Differentiation from the Consumer Metaverse
• 1.2. Evolution of Industry 4.0 to the Industrial Metaverse
  • 1.2.1. Technological Convergence
• 1.3. Industrial metaverse ecosystem
• 1.4. Metaverse enabling technologies
  • 1.4.1. Artificial Intelligence
  • 1.4.2. Cross, Virtual, Augmented and Mixed Reality
  • 1.4.3. Blockchain
  • 1.4.4. Edge computing
  • 1.4.5. Cloud computing
  • 1.4.6. Digital Twin
  • 1.4.7. 3D Modeling/Scanning
  • 1.4.8. Industrial Internet of Things (IIoT)
• 1.5. Industrial Metaverse Implementations
• 1.6. Current Market Landscape

2. MARKET OVERVIEW

• 2.1. Market Evolution
  • 2.1.1. Precursors to the Industrial Metaverse
    • 2.1.1.1. Virtual Reality in Industrial Design
    • 2.1.1.2. Augmented Reality in Manufacturing
    • 2.1.1.3. Digital Twin Concepts in Industry 4.0
  • 2.1.2. Transition from Industry 4.0 to Industrial Metaverse
  • 2.1.3. Unmet business needs addressed by the metaverse
  • 2.1.4. Convergence of Physical and Digital Realms
  • 2.1.5. Shift from Connectivity to Immersive Experiences
  • 2.1.6. Evolution of Human-Machine Interaction
• 2.2. Market Size and Growth Rate
  • 2.2.1. Total market
  • 2.2.2. By component
  • 2.2.3. By technology
  • 2.2.4. End-User Industry
  • 2.2.5. Regional Market Dynamics
• 2.3. Comparison with Related Markets (e.g., IoT, AR/VR)
• 2.4. Investment Landscape
  • 2.4.1. Venture Capital Funding
  • 2.4.2. Corporate Investments
  • 2.4.3. Government and Public Funding Initiatives
• 2.5. Key Market Drivers
• 2.6. Technological Advancements
  • 2.6.1. Improvements in XR Hardware
  • 2.6.2. Advancements in AI and Machine Learning
  • 2.6.3. 5G and Edge Computing Proliferation
  • 2.6.4. Industry 4.0 Initiatives
    • 2.6.4.1. Smart Factory Implementations
    • 2.6.4.2. Digital Transformation Strategies
    • 2.6.4.3. Industrial IoT Adoption
• 2.7. Demand for Increased Efficiency and Productivity
  • 2.7.1. Cost Reduction Imperatives
  • 2.7.2. Quality Improvement Initiatives
  • 2.7.3. Time-to-Market Acceleration
• 2.8. Remote Work and Collaboration Trends
  • 2.8.1. Impact of Global Events
  • 2.8.2. Distributed Workforce Management
  • 2.8.3. Cross-border Collaboration Needs
• 2.9. Sustainability and Environmental Concerns
  • 2.9.1. Carbon Footprint Reduction Goals
  • 2.9.2. Resource Optimization Efforts
  • 2.9.3. Circular Economy Initiatives
• 2.10. Market Challenges and Barriers
  • 2.10.1. Technological Limitations
    • 2.10.1.1. Hardware Constraints (e.g., Battery Life, Comfort)
    • 2.10.1.2. Software Integration Complexities
    • 2.10.1.3. Latency and Bandwidth Issues
  • 2.10.2. Integration Complexities
    • 2.10.2.1. Legacy System Compatibility
    • 2.10.2.2. Interoperability Standards
    • 2.10.2.3. Data Integration and Management
  • 2.10.3. Skill Gaps and Workforce Readiness
    • 2.10.3.1. Technical Skill Shortages
    • 2.10.3.2. Change Management Challenges
    • 2.10.3.3. Training and Education Needs
  • 2.10.4. Data Security and Privacy Concerns
    • 2.10.4.1. Cybersecurity Risks
    • 2.10.4.2. Intellectual Property Protection
    • 2.10.4.3. Regulatory Compliance Challenges
  • 2.10.5. High Initial Investment Costs
    • 2.10.5.1. Infrastructure Setup Expenses
    • 2.10.5.2. Software Licensing and Development Costs
    • 2.10.5.3. ROI Justification Challenges
• 2.11. Opportunities in the Industrial Metaverse
  • 2.11.1. New Business Models
    • 2.11.1.1. Industrial Metaverse-as-a-Service
    • 2.11.1.2. Virtual Asset Marketplaces
    • 2.11.1.3. Subscription-based Digital Twin Services
  • 2.11.2. Sustainability and Green Initiatives
    • 2.11.2.1. Virtual Prototyping for Reduced Material Waste
    • 2.11.2.2. Energy Optimization through Digital Twins
    • 2.11.2.3. Sustainable Supply Chain Simulations
  • 2.11.3. Enhanced Customer Experiences
    • 2.11.3.1. Immersive Product Demonstrations
    • 2.11.3.2. Virtual Factory Tours
    • 2.11.3.3. Customized Product Configuration in VR
  • 2.11.4. Emerging Markets and Applications
    • 2.11.4.1. Industrial Metaverse in Developing Economies
    • 2.11.4.2. Integration with Emerging Technologies (e.g., Quantum Computing)
    • 2.11.4.3. Novel Use Cases in Non-Traditional Industries

3. TECHNOLOGY LANDSCAPE

• 3.1. Core Technologies Enabling the Industrial Metaverse
  • 3.1.1. Extended Reality (XR): AR, VR, and MR
    • 3.1.1.1. Head-Mounted Displays (HMDs)
    • 3.1.1.2. Haptic Devices
    • 3.1.1.3. Companies
  • 3.1.2. Artificial Intelligence and Machine Learning
    • 3.1.2.1. Deep Learning in Industrial Applications
      • 3.1.2.1.1. Convolutional Neural Networks (CNNs)
      • 3.1.2.1.2. Recurrent Neural Networks (RNNs)
      • 3.1.2.1.3. Generative Adversarial Networks (GANs)
    • 3.1.2.2. Natural Language Processing
    • 3.1.2.3. Computer Vision
    • 3.1.2.4. Companies
  • 3.1.3. Internet of Things (IoT) and Industrial IoT (IIoT)
    • 3.1.3.1. Sensor Technologies
    • 3.1.3.2. Data Collection and Analysis
    • 3.1.3.3. Edge Computing in IIoT
    • 3.1.3.4. Companies
  • 3.1.4. 5G and Beyond (6G) Networks
    • 3.1.4.1. Ultra-Low Latency Communication
      • 3.1.4.1.1. Network Slicing
      • 3.1.4.1.2. Mobile Edge Computing (MEC)
    • 3.1.4.2. Massive Machine-Type Communications
    • 3.1.4.3. Enhanced Mobile Broadband
    • 3.1.4.4. Companies
  • 3.1.5. Edge Computing and Cloud Infrastructure
    • 3.1.5.1. Hybrid Cloud Solutions in Edge Computing
    • 3.1.5.2. Edge AI in Edge Computing and Cloud Infrastructure
    • 3.1.5.3. Companies
  • 3.1.6. Blockchain and Distributed Ledger Technologies
    • 3.1.6.1. Smart Contracts in Blockchain and Distributed Ledger Technologies
    • 3.1.6.2. Supply Chain Traceability in Blockchain and DLT
    • 3.1.6.3. Decentralized Finance in Industry
    • 3.1.6.4. Companies
  • 3.1.7. 3D Scanning/Modeling
    • 3.1.7.1. Overview
    • 3.1.7.2. Companies
• 3.2. Emerging Technologies and Their Potential Impact
  • 3.2.1. Quantum Computing
    • 3.2.1.1. Companies
  • 3.2.2. Brain-Computer Interfaces
    • 3.2.2.1. Non-invasive BCI Technologies
    • 3.2.2.2. Neural Control of Industrial Systems
    • 3.2.2.3. Cognitive Load Monitoring
    • 3.2.2.4. Companies
  • 3.2.3. Advanced Materials and Nanotechnology
    • 3.2.3.1. Smart Materials for Sensors
    • 3.2.3.2. Nanotech in Manufacturing
    • 3.2.3.3. Self-healing Materials
  • 3.2.4. Human-Machine Interfaces in the Industrial Metaverse
  • 3.2.5. Edge Computing in the Industrial Metaverse
  • 3.2.6. Autonomous Systems and Robotics
    • 3.2.6.1. Collaborative Robots (Cobots)
    • 3.2.6.2. Swarm Robotics
    • 3.2.6.3. Biomimetic Robots
    • 3.2.6.4. Companies
• 3.3. Technology Adoption Trends and Forecasts
  • 3.3.1. Short-term Adoption (2025-2028)
    • 3.3.1.1. Technology Readiness Levels
    • 3.3.1.2. Early Adopter Industries
  • 3.3.2. Medium-term Adoption (2029-2032)
    • 3.3.2.1. Scaling Successful Implementations
    • 3.3.2.2. Cross-industry Technology Transfer
    • 3.3.2.3. Standardization and Interoperability Efforts
  • 3.3.3. Long-term Adoption (2033-2035)
    • 3.3.3.1. Mainstream Integration
    • 3.3.3.2. Disruptive Business Models
    • 3.3.3.3. Societal and Economic Impacts

4. END USE MARKETS

• 4.1. Hardware
  • 4.1.1. XR Devices
  • 4.1.2. Sensors and Actuators
  • 4.1.3. Industrial PCs and Servers
  • 4.1.4. Communication Infrastructure for the Industrial Metaverse
  • 4.1.5. AR/VR/MR Solutions
• 4.2. AI and Analytics Tools
• 4.3. Quality Control and Inspection
• 4.4. By industry
  • 4.4.1. Automotive
    • 4.4.1.1. Overview
    • 4.4.1.2. Current commercial examples
  • 4.4.2. Aerospace
    • 4.4.2.1. Overview
    • 4.4.2.2. Current commercial examples
  • 4.4.3. Chemicals and materials manufacturing
    • 4.4.3.1. Overview
    • 4.4.3.2. Current commercial examples
  • 4.4.4. Energy
    • 4.4.4.1. Overview
    • 4.4.4.2. Current commercial examples
  • 4.4.5. Healthcare and life sciences
    • 4.4.5.1. Overview
    • 4.4.5.2. Current commercial examples
  • 4.4.6. Construction and engineering
    • 4.4.6.1. Overview
    • 4.4.6.2. Current commercial examples
  • 4.4.7. Supply Chain Management and Logistics
    • 4.4.7.1. Overview
    • 4.4.7.2. Current commercial examples
  • 4.4.8. Retail
    • 4.4.8.1. Overview
    • 4.4.8.2. Current commercial examples

5. REGULATIONS

• 5.1. Data Privacy and Security Regulations
• 5.2. Intellectual Property Considerations
• 5.3. Standards and Interoperability Initiatives
• 5.4. Environmental and Sustainability Regulations

6. SOCIETAL AND ECONOMIC IMPACT

• 6.1. Workforce Transformation and Skill Requirements
• 6.2. Economic Growth and Productivity Gains
• 6.3. Sustainability and Environmental Impact
  • 6.3.1.1. Energy Consumption
  • 6.3.1.2. E-Waste
  • 6.3.1.3. Virtual Economies and Blockchain
  • 6.3.1.4. Reduction in Pollution
• 6.4. Ethical Considerations and Social Implications

7. CHALLENGES AND RISK FACTORS

• 7.1. Technological Challenges
• 7.2. Implementation and Integration Issues
• 7.3. Cybersecurity Risks
• 7.4. Economic and Market Risks

8. COMPANY PROFILES

• 8.1. Virtual, Augmented and Mixed Reality (including haptics)(71 company profiles)
• 8.2. Artificial Intelligence 428 (136 company profiles)
• 8.3. Blockchain (31 company profiles)
• 8.4. Edge computing. 561 (31 company profiles)
• 8.5. Digital Twin(48 company profiles)
• 8.6. 3D Imaging and Sensing(132 company profiles)

9. RESEARCH METHODOLOGY

10. GLOSSARY OF TERMS

11. REFERENCES