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全球油气数位孪生市场：依类型、应用、部署模式和公司规模划分 - 市场规模、行业趋势、机会分析和预测（2025-2033 年）

Global Digital Twin in Oil & Gas Market: By Type, Application, Deployment, Enterprise Size - Market Size, Industry Dynamics, Opportunity Analysis and Forecast for 2025-2033
出版日期: | 出版商: Astute Analytica | 英文 234 Pages | 商品交期: 最快1-2个工作天内

价格
简介目录

油气数位孪生市场正经历快速成长，反映出该产业越来越依赖先进的数位技术来优化复杂的营运。 2024 年，该市场规模约为 1.3672 亿美元，预计将显着扩张，到 2033 年达到 11.3732 亿美元。这一令人瞩目的成长意味着 2025 年至 2033 年的复合年增长率 (CAGR) 为 26.54%。这种强劲的成长表明，在最具挑战性的工业环境之一中，对创新解决方案的需求不断增长，以提高营运效率、提升安全标准并实现预测性维护。

该市场发展的核心是创建先进的虚拟模型，以复製钻井平台、管道和炼油厂等实体资产。这些数位孪生体作为动态表示，会根据嵌入资产的广泛感测器网路收集的即时数据不断更新。人工智慧 (AI) 的整合透过实现进阶分析、模式识别和预测洞察，进一步提升了这些模型的价值。

市场动态

石油和天然气数位孪生市场的主要参与者包括 IBM、西门子和 AVEVA 等知名科技公司，它们为该技术的广泛应用和发展做出了重大贡献。随着企业从有限的试点计画转向全面、全企业范围地采用数位孪生解决方案，这些公司正在见证产业行为的决定性转变。

2025 年 11 月，华为及其合作伙伴宣布推出一项联合解决方案，旨在促进石油和天然气作业的智慧化，这是一项重要的进展。此次合作的重要参与者之一是中国石油天然气集团公司 (CNPC) 旗下的地球物理勘探公司 BGP。两家公司共同向世界展示了其在油气勘探领域的成就，重点介绍了整合数位孪生技术在变革勘探活动和营运流程方面的潜力。

同时，横河电机株式会社旗下公司KBC于2025年8月发布了其旗舰数位孪生流程模拟平台 "Petro-SIM® v7.6" 的最新版本。更新后的平台支援油气产业的上游和下游领域，涵盖炼油、石化、聚合物生产以及永续航空燃料（SAF）等新兴领域。

核心成长驱动因子

推动油气市场对数位孪生技术需求的关键因素是该行业对降低勘探活动中地下不确定性的强烈需求。这一领域的风险极高，深水钻井作业中一口干井的成本可能超过1.5亿美元。如此庞大的成本使得人们迫切需要更精确、更可靠的地质模型，以显着降低钻井失败的风险。为了应对这项挑战，各公司正越来越多地转向下一代地下数位孪生技术，这代表着勘探技术的重大飞跃。

新机遇

将量子运算启发的方法应用于数位孪生技术中高度复杂的最佳化问题，正在创造巨大的机会。传统的计算方法往往难以处理某些油气製程中固有的大量变数和复杂计算。然而，受量子启发的演算法提供了一个很有前景的解决方案，使其能够有效率地处理传统电脑难以即时解决的复杂问题。例如，炼油厂的催化裂解製程需要同时优化数千个相互依存的变量，以最大限度地提高效率和产量。

优化障碍

将数位孪生技术与现有传统营运系统整合的复杂性是一个重大挑战，可能会阻碍数位孪生市场的成长。许多油气公司仍然依赖过时的基础设施和软体平台，这些平台并非为支援先进的数位技术而设计。这为数位孪生的实施带来了巨大的技术障碍。新系统必须与各种各样的旧式硬体和软体解决方案相容并有效通讯——这个过程通常需要大量的客製化、资料迁移和系统升级，既耗时又昂贵。

目录

第一章：研究架构

  • 研究目标
  • 产品概述
  • 市场区隔

第二章：研究方法

  • 质性研究
    • 一手和二手资料来源
  • 量化研究
    • 一手和二手资料来源
  • 依地区划分的一手调查受访者组成
  • 研究假设
  • 市场规模估算
  • 资料三角验证

第三章：摘要整理：全球石油天然气数位孪生市场

第四章：全球石油天然气数位孪生市场概论天然气

  • 产业价值链分析
    • 开发商
    • 技术整合商
    • 服务提供者
    • 公司规模
  • 行业展望
    • 数位孪生技术在石油和天然气产业的影响
    • 数位孪生技术在海上平台上的应用
  • PESTLE 分析
  • 波特五力分析
    • 供应商议价能力
    • 买方议价能力
    • 替代品威胁
    • 新进入者威胁
    • 竞争强度
  • 市场动态与趋势
    • 成长推动因素
    • 阻碍因素
    • 机遇
    • 主要趋势
  • COVID-19 对市场成长趋势的影响评估
  • 市场成长与展望
    • 市场收入估计与预测（2020-2033 年）
    • 价格分析
  • 竞争格局概览
    • 市场集中度
    • 公司市占率分析（价值，2024 年）
    • 竞争格局图

第五章 全球石油天然气产业数位孪生市场（依类型划分）

  • 主要发现
  • 市场规模与预测（2020-2033 年）
    • 描述性孪生
    • 资讯性孪生
    • 预测性孪生
    • 综合性孪生
    • 自主型孪生数位孪生

第六章：全球油气产业数位孪生市场（依组件划分）

  • 主要发现
  • 市场规模及预测（2020-2033 年）
    • 产品数位孪生
    • 流程数位孪生
    • 系统数位孪生

    第七章：全球油气产业数位孪生市场（依应用划分）

    • 主要洞察
    • 市场规模及预测（2020-2033 年）
    • 钻井
    • 紧急疏散
    • 管道
    • 智慧油田
    • 虚拟学习与训练
    • 资产监控与维护
    • 专案规划与生命週期管理
    • 协作与知识分享
    • 海上平台和基础设施
    • 勘探和地质调查

第八章：全球石油天然气数位孪生市场（以部署模式划分）

  • 主要见解
  • 市场规模及预测（2020-2033 年）
    • 云端部署
    • 本地部署

第九章：全球石油天然气数位孪生市场（依公司规模划分）

  • 主要发现
  • 市场规模及预测（2020-2033 年）
  • 大型企业
  • 中小企业 (SME)

第十章：全球石油天然气数位孪生市场分析（依…划分）区域

  • 主要发现
  • 市场规模及预测，2020–2033
    • 北美
    • 欧洲
    • 亚太地区
    • 中东和非洲 (MEA)
    • 南美

第11章：北美油气数位孪生市场分析

第12章：欧洲油气数位孪生市场分析

第13章：亚太油气数位孪生市场分析

第14章：中东与非洲油气数位孪生市场分析

第15章：南美油气数位孪生市场分析

第16章：英国油气数位孪生市场分析

第17章：德国油气数位孪生市场分析

第18章：义大利油气数位孪生市场分析

第19章：西班牙油气数位孪生市场分析

第20章：法国油气数位孪生市场分析

第21章：波兰油气数位孪生市场分析

第22章：俄罗斯油气数位孪生市场分析

第23章 企业简介

  • Ansys, Inc.
  • General Electric
  • IBM Corporation
  • Microsoft Corporation
  • Oracle Corporation
  • PTC Inc.
  • Robert Bosch GmbH
  • SAP SE
  • Siemens AG
  • SWIM.AI
  • Other prominent players
简介目录
Product Code: AA1023630

The digital twin market within the oil and gas industry is experiencing rapid growth, reflecting the sector's increasing reliance on advanced digital technologies to optimize complex operations. Valued at approximately US$ 136.72 million in 2024, this market is projected to expand significantly, reaching an estimated valuation of US$ 1,137.32 million by 2033. This impressive expansion corresponds to a compound annual growth rate (CAGR) of 26.54% during the forecast period from 2025 to 2033. Such robust growth highlights the escalating demand for innovative solutions that enhance operational efficiency, improve safety standards, and enable predictive maintenance in one of the most challenging industrial environments.

At the core of this market's development is the creation of sophisticated virtual models that replicate physical assets, including drilling rigs, pipelines, and refineries. These digital twins serve as dynamic representations that are continuously updated with real-time data collected from a wide network of sensors embedded in the equipment. The integration of artificial intelligence further enhances the value of these models by enabling advanced analytics, pattern recognition, and predictive insights.

Noteworthy Market Developments

Key players in the digital twin in oil and gas market include prominent technology companies such as IBM, Siemens, and AVEVA, which have been instrumental in advancing the adoption and capabilities of this technology. These companies are witnessing a decisive shift in industry behavior, as organizations move beyond limited pilot programs to embrace comprehensive, enterprise-wide deployments of digital twin solutions.

In November 2025, a significant development occurred with Huawei and its partners launching joint solutions aimed at promoting intelligent oil and gas operations. One notable participant in this collaboration was BGP, a geophysical exploration specialist operating under the China National Petroleum Corporation (CNPC). Together, they showcased their achievements in oil and gas exploration to a global audience, highlighting the potential of integrated digital twin technologies to transform exploration efforts and operational workflows.

In a related advancement, August 2025 KBC, a Yokogawa Company, announced the release of Petro-SIM(R) v7.6, the latest version of its flagship digital twin process simulation platform. This updated platform caters to both upstream and downstream sectors of the oil and gas industry, encompassing refining, petrochemical, polymer production, and emerging areas such as sustainable aviation fuel (SAF).

Core Growth Drivers

A primary driver of demand in the digital twin in oil and gas market is the industry's intense focus on reducing subsurface uncertainty during exploration activities. The stakes are incredibly high in this area, as the cost of drilling a single deepwater dry hole can surpass 150 million dollars. Such enormous expenses create an urgent imperative for more precise and reliable geological models that can significantly reduce the risk of unsuccessful drilling. To meet this challenge, companies are increasingly turning to next-generation subsurface digital twins, which represent a sophisticated leap forward in exploration technology.

Emerging Opportunity Trends

A significant opportunity is arising from the application of quantum-inspired computing to address highly complex optimization challenges within digital twin technology. Traditional computational methods often struggle to handle the enormous variables and intricate calculations involved in certain oil and gas processes. Quantum-inspired algorithms, however, provide a promising solution by enabling the efficient processing of problems that are otherwise too complex for classical computers to solve in real time. For example, in refinery operations, catalytic cracking processes involve thousands of interdependent variables that must be optimized simultaneously to maximize efficiency and output.

Barriers to Optimization

The complexity involved in integrating digital twin technology with existing legacy operational systems presents a significant challenge that may hinder the growth of the digital twin market. Many oil and gas companies still rely on older infrastructure and software platforms that were not originally designed to support advanced digital technologies. This creates substantial technical barriers when attempting to implement digital twins, as the new systems must be compatible with, and able to effectively communicate with, a wide range of outdated hardware and software solutions. The process often requires extensive customization, data migration, and system upgrades, which can be both time-consuming and costly.

Detailed Market Segmentation

By Type, the informative twin segment stands out within the global market, securing an impressive 27% share. This dominance is largely driven by the segment's ability to transform vast volumes of raw data into actionable intelligence, which is critical for optimizing operations and decision-making in a complex industry. Informative twins serve as sophisticated digital replicas that go beyond mere visualization, offering a comprehensive and contextualized view of both assets and overall operational processes.

By Component, the process digital twin segment commands a leading position in the global market, accounting for over 46% of the revenue. This segment distinguishes itself by enabling companies to simulate and optimize entire operational workflows rather than focusing on individual assets. By creating comprehensive virtual models of complex, interconnected systems, process digital twins provide a holistic view of critical industry operations such as drilling activities, refining processes, or complete liquefied natural gas (LNG) production chains. This broader perspective allows operators to analyze how different components interact and influence overall performance, which is essential for improving efficiency and reducing operational risks.

By Application, the asset monitoring and maintenance segment holds a prominent position in the market, capturing over 19% of the total market share. This segment addresses one of the most pressing challenges faced by the industry: unplanned downtime, which can result in costly disruptions, safety risks, and operational inefficiencies. By leveraging digital twin technology, companies create precise virtual replicas of critical equipment such as pumps, turbines, and pipelines. These digital models are continuously fed with real-time sensor data, allowing for constant monitoring of the equipment's health and performance.

By Deployment, the cloud segment has emerged as the undisputed leader in the market, commanding an overwhelming market share of more than 70.9%. This dominance is largely attributed to the cloud's inherent scalability, which allows companies to easily expand or reduce their digital twin operations based on fluctuating demands. The flexibility offered by cloud platforms is particularly valuable in the oil and gas industry, where operational scales can vary dramatically and projects often require rapid deployment of advanced technologies across geographically dispersed sites.

Segment Breakdown

By Type

  • Descriptive twin
  • Informative twin
  • Predictive twin
  • Comprehensive twin
  • Autonomous twin

By Application

  • Drilling
  • Emergency evacuation
  • Pipelines
  • Intelligent Oil Fields
  • Virtual Learning and Training
  • Asset Monitoring and Maintenance
  • Project Planning and lifecycle management
  • Collaboration and knowledge sharing
  • Offshore platforms and infrastructure
  • Exploration and geological study

By Component

  • Product Digital Twin
  • Process Digital Twin
  • System Digital Twin

By Deployment

  • On-Premise
  • Cloud

By Enterprise Size

  • Large Enterprises
  • Small and Medium-sized Enterprises (SMEs)

By Region

  • North America
  • The US
  • Canada
  • Mexico
  • Europe
  • Western Europe
  • The UK
  • Germany
  • France
  • Italy
  • Spain
  • Rest of Western Europe
  • Eastern Europe
  • Poland
  • Russia
  • Rest of Eastern Europe
  • Asia Pacific
  • China
  • India
  • Japan
  • South Korea
  • Australia & New Zealand
  • ASEAN
  • Rest of Asia Pacific
  • Middle East & Africa (MEA)
  • UAE
  • Saudi Arabia
  • South Africa
  • Rest of MEA
  • South America
  • Brazil
  • Argentina
  • Rest of South America

Geography Breakdown

  • North America holds a commanding position in the digital twin market for the oil and gas sector, capturing a significant share of over 32.80%. This leadership is largely driven by the extensive deployment of digital twin technologies in the region's prolific shale plays, which are among the most active and technologically advanced oil production areas in the world. A prime example is the Permian Basin, where Chevron is pioneering its "digital factory" initiative.
  • The impact of digital twin adoption is further exemplified by a major operator in the region that processes over 20 terabytes of production data daily from its shale operations. This vast amount of data is analyzed to improve efficiency, predict equipment failures, and enhance overall operational decision-making. Supporting this technological advancement is Houston's vibrant tech ecosystem, which plays a crucial role as an enabler of innovation in the oil and gas digital twin space.

Leading Market Participants

  • Ansys, Inc.
  • General Electric
  • IBM Corporation
  • Microsoft Corporation
  • Oracle Corporation
  • PTC Inc.
  • Robert Bosch GmbH
  • SAP SE
  • Siemens AG
  • SWIM.AI
  • Other prominent players

Table of Content

Chapter 1. Research Framework

  • 1.1. Research Objective
  • 1.2. Product Overview
  • 1.3. Market Segmentation

Chapter 2. Research Methodology

  • 2.1. Qualitative Research
    • 2.1.1. Primary & Secondary Sources
  • 2.2. Quantitative Research
    • 2.2.1. Primary & Secondary Sources
  • 2.3. Breakdown of Primary Research Respondents, By Region
  • 2.4. Assumption for the Study
  • 2.5. Market Size Estimation
  • 2.6. Data Triangulation

Chapter 3. Executive Summary: Global Digital Twin in Oil & Gas Market

Chapter 4. Global Digital Twin in Oil & Gas Market Overview

  • 4.1. Industry Value Chain Analysis
    • 4.1.1. Developer
    • 4.1.2. Technology Integrator
    • 4.1.3. Service Provider
    • 4.1.4. Enterprise Size
  • 4.2. Industry Outlook
    • 4.2.1. Impact of digital twins in Oil & Gas
    • 4.2.2. Offshore Platform in Digital Twins
  • 4.3. PESTLE Analysis
  • 4.4. Porter's Five Forces Analysis
    • 4.4.1. Bargaining Power of Suppliers
    • 4.4.2. Bargaining Power of Buyers
    • 4.4.3. Threat of Substitutes
    • 4.4.4. Threat of New Entrants
    • 4.4.5. Degree of Competition
  • 4.5. Market Dynamics and Trends
    • 4.5.1. Growth Drivers
    • 4.5.2. Restraints
    • 4.5.3. Opportunities
    • 4.5.4. Key Trends
  • 4.6. Covid-19 Impact Assessment on Market Growth Trend
  • 4.7. Market Growth and Outlook
    • 4.7.1. Market Revenue Estimates and Forecast (US$ Mn), 2020 - 2033
    • 4.7.2. Pricing Analysis
  • 4.8. Competition Dashboard
    • 4.8.1. Market Concentration Rate
    • 4.8.2. Company Market Share Analysis (Value %), 2024
    • 4.8.3. Competitor Mapping

Chapter 5. Global Digital Twin in Oil & Gas Market, By Type

  • 5.1. Key Insights
  • 5.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 5.2.1. Descriptive twin
    • 5.2.2. Informative twin
    • 5.2.3. Predictive twin
    • 5.2.4. Comprehensive twin
    • 5.2.5. Autonomous twin

Chapter 6. Global Digital Twin in Oil & Gas Market, By Component

  • 6.1. Key Insights
  • 6.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 6.2.1. Product Digital Twin
    • 6.2.2. Process Digital Twin
    • 6.2.3. System Digital Twin

Chapter 7. Global Digital Twin in Oil & Gas Market, By Application

  • 7.1. Key Insights
  • 7.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 7.2.1. Drilling
    • 7.2.2. Emergency evacuation
    • 7.2.3. Pipelines
    • 7.2.4. Intelligent Oil fields
    • 7.2.5. Virtual Learning and Training
    • 7.2.6. Asset Monitoring and Maintenance
    • 7.2.7. Project Planning and lifecycle management
    • 7.2.8. Collaboration and knowledge sharing
    • 7.2.9. Offshore platforms and infrastructure
    • 7.2.10. Exploration and geological study

Chapter 8. Global Digital Twin in Oil & Gas Market, By Deployment

  • 8.1. Key Insights
  • 8.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 8.2.1. Cloud based
    • 8.2.2. On-Premises

Chapter 9. Global Digital Twin in Oil & Gas Market, By Enterprise Size

  • 9.1. Key Insights
  • 9.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 9.2.1. Large Enterprises
    • 9.2.2. Small and Medium-sized Enterprises (SMEs)

Chapter 10. Global Digital Twin in Oil & Gas Market Analysis, By Region

  • 10.1. Key Insights
  • 10.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 10.2.1. North America
      • 10.2.1.1. The U.S.
      • 10.2.1.2. Canada
      • 10.2.1.3. Mexico
    • 10.2.2. Europe
      • 10.2.2.1. Western Europe
        • 10.2.2.1.1. The UK
        • 10.2.2.1.2. Germany
        • 10.2.2.1.3. France
        • 10.2.2.1.4. Italy
        • 10.2.2.1.5. Spain
        • 10.2.2.1.6. Rest of Western Europe
      • 10.2.2.2. Eastern Europe
        • 10.2.2.2.1. Poland
        • 10.2.2.2.2. Russia
        • 10.2.2.2.3. Rest of Eastern Europe
    • 10.2.3. Asia Pacific
      • 10.2.3.1. China
      • 10.2.3.2. India
      • 10.2.3.3. Japan
      • 10.2.3.4. South Korea
      • 10.2.3.5. Australia & New Zealand
      • 10.2.3.6. ASEAN
      • 10.2.3.7. Rest of Asia Pacific
    • 10.2.4. Middle East & Africa (MEA)
      • 10.2.4.1. UAE
      • 10.2.4.2. Saudi Arabia
      • 10.2.4.3. South Africa
      • 10.2.4.4. Rest of MEA
    • 10.2.5. South America
      • 10.2.5.1. Brazil
      • 10.2.5.2. Argentina
      • 10.2.5.3. Rest of South America

Chapter 11. North America Digital Twin in Oil & Gas Market Analysis

  • 11.1. Key Insights
  • 11.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 11.2.1. By Type
    • 11.2.2. By Component
    • 11.2.3. By Application
    • 11.2.4. By Deployment
    • 11.2.5. By Enterprise Size
    • 11.2.6. By Country

Chapter 12. Europe Digital Twin in Oil & Gas Market Analysis

  • 12.1. Key Insights
  • 12.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 12.2.1. By Type
    • 12.2.2. By Component
    • 12.2.3. By Application
    • 12.2.4. By Deployment
    • 12.2.5. By Enterprise Size
    • 12.2.6. By Country

Chapter 13. Asia Pacific Digital Twin in Oil & Gas Market Analysis

  • 13.1. Key Insights
  • 13.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 13.2.1. By Type
    • 13.2.2. By Component
    • 13.2.3. By Application
    • 13.2.4. By Deployment
    • 13.2.5. By Enterprise Size
    • 13.2.6. By Country

Chapter 14. Middle East & Africa Digital Twin in Oil & Gas Market Analysis

  • 14.1. Key Insights
  • 14.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 14.2.1. By Type
    • 14.2.2. By Component
    • 14.2.3. By Application
    • 14.2.4. By Deployment
    • 14.2.5. By Enterprise Size
    • 14.2.6. By Country

Chapter 15. South America Digital Twin in Oil & Gas Market Analysis

  • 15.1. Key Insights
  • 15.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 15.2.1. By Type
    • 15.2.2. By Component
    • 15.2.3. By Application
    • 15.2.4. By Deployment
    • 15.2.5. By Enterprise Size
    • 15.2.6. By Country

Chapter 16. The UK Digital Twin in Oil & Gas Market Analysis

  • 16.1. Key Insights
  • 16.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 16.2.1. By Type
    • 16.2.2. By Component
    • 16.2.3. By Application
    • 16.2.4. By Deployment
    • 16.2.5. By Enterprise Size

Chapter 17. Germany Digital Twin in Oil & Gas Market Analysis

  • 17.1. Key Insights
  • 17.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 17.2.1. By Type
    • 17.2.2. By Component
    • 17.2.3. By Application
    • 17.2.4. By Deployment
    • 17.2.5. By Enterprise Size

Chapter 18. Italy Digital Twin in Oil & Gas Market Analysis

  • 18.1. Key Insights
  • 18.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 18.2.1. By Type
    • 18.2.2. By Component
    • 18.2.3. By Application
    • 18.2.4. By Deployment
    • 18.2.5. By Enterprise Size

Chapter 19. Spain Digital Twin in Oil & Gas Market Analysis

  • 19.1. Key Insights
  • 19.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 19.2.1. By Type
    • 19.2.2. By Component
    • 19.2.3. By Application
    • 19.2.4. By Deployment
    • 19.2.5. By Enterprise Size

Chapter 20. France Digital Twin in Oil & Gas Market Analysis

  • 20.1. Key Insights
  • 20.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 20.2.1. By Type
    • 20.2.2. By Component
    • 20.2.3. By Application
    • 20.2.4. By Deployment
    • 20.2.5. By Enterprise Size

Chapter 21. Poland Digital Twin in Oil & Gas Market Analysis

  • 21.1. Key Insights
  • 21.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 21.2.1. By Type
    • 21.2.2. By Component
    • 21.2.3. By Application
    • 21.2.4. By Deployment
    • 21.2.5. By Enterprise Size

Chapter 22. Russia Digital Twin in Oil & Gas Market Analysis

  • 22.1. Key Insights
  • 22.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 22.2.1. By Type
    • 22.2.2. By Component
    • 22.2.3. By Application
    • 22.2.4. By Deployment
    • 22.2.5. By Enterprise Size

Chapter 23. Company Profile (Company Overview, Financial Matrix, Key Product landscape, Key Personnel, Key Competitors, Contact Address, Measurement Methods, and Business Strategy Outlook)

  • 23.1. Ansys, Inc.
  • 23.2. General Electric
  • 23.3. IBM Corporation
  • 23.4. Microsoft Corporation
  • 23.5. Oracle Corporation
  • 23.6. PTC Inc.
  • 23.7. Robert Bosch GmbH
  • 23.8. SAP SE
  • 23.9. Siemens AG
  • 23.10. SWIM.AI
  • 23.11. Other prominent players