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市场调查报告书
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1804253

预知保全的全球市场:各零件，各展开形态，各技术，各组织规模，各产业，各地区-市场规模，产业动态，机会分析，预测(2025年～2033年)

Global Predictive Maintenance Market: Component, Deployment Mode, Technology, Organization Size, Industry, Region-Market Size, Industry Dynamics, Opportunity Analysis and Forecast for 2025-2033
出版日期: | 出版商: Astute Analytica | 英文 223 Pages | 商品交期: 最快1-2个工作天内

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简介目录

预测性维护市场正经历前所未有的成长，这得益于各行各业对最大程度减少设备停机时间和提高营运效率的迫切需求。 2024 年，该市场规模为 89.6 亿美元，预计到 2033 年将达到 910.4 亿美元，2025 年至 2033 年的复合年增长率高达 29.4%。这种快速扩张主要得益于人工智慧 (AI) 和物联网 (IoT) 等先进技术的融合，这些技术使企业能够采用数据驱动的维护策略。

北美在预测性维护市场中占主导地位，这得益于其强大的工业基础、对尖端技术的早期采用以及对数位转型计划的大量投资。製造业、能源业和医疗保健业等关键产业正在采用预测性维护解决方案，以提高生产力并降低营运风险。该地区汇聚了众多领先的解决方案提供商，包括 IBM、微软、通用电气和 PTC 等全球巨头，这些公司的总部和研究实验室构建了创新生态系统。

值得关注的市场发展

在预测性维护市场，一些领先的参与者正在利用创新技术和策略性措施来塑造产业格局。思科系统公司、通用电气公司、SAP SE、施耐德电气 SE 和西门子等主要参与者已成为先进预测性维护解决方案开发和部署的关键参与者。这些产业领导者正在积极推行各种策略以巩固其市场地位，包括合作、併购以及与其他公司的联盟。

这项策略活动的一个显着例子发生在 2025 年 2 月，IBM 以 64 亿美元完成了对 HashiCorp 的收购。此次收购将 HashiCorp 的技术与 IBM 的 Red Hat 产品整合，显着增强了 IBM 的多云功能和预测性维护产品组合。此举巩固了 IBM 致力于利用云端基础架构提供可扩展、灵活的预测性维护解决方案的承诺。

C3 AI 于 2025 年 6 月获得美国空军快速维持办公室 (RAO) 价值 1300 万美元的任务订单，进一步凸显了市场的动态性。该合约旨在将 C3 AI 支援人工智慧的预测性维护系统扩展到更多飞机平台，显示在高度复杂和关键环境中，人们越来越依赖人工智慧来增强维护能力。此次扩展凸显了预测性维护技术在国防应用中的战略重要性，因为设备可靠性和任务准备就绪至关重要。

核心成长动力

2024 年，预测性维护市场将成为全球各产业的关键领域，主要驱动力是降低维护成本和最大程度减少非计画性停机的迫切需求。意外的设备故障可能导致昂贵的维修费用、生产停工和收入损失，造成毁灭性的财务影响。对于各行各业的利害关係人来说，这些风险凸显了采用预测性维护策略的重要性，这些策略能够及早发现潜在问题并及时介入。

新兴机会趋势

2024年，预测性维护市场将经历重大转型，这得益于数位孪生技术的广泛应用。数位孪生是实体资产的复杂虚拟复製品，使利害关係人能够在虚拟环境中模拟、监控和分析设备效能。这种创新方法使公司能够测试各种场景、预测潜在故障并优化维护计划，而无需将实际资产暴露于现实世界的风险和中断之中。

优化障碍

2024年，资料安全和品质问题将成为预测性维护市场面临的重大课题，直接影响这些系统对利害关係人的有效性和可靠性。预测性维护高度依赖工业设备中嵌入的物联网设备和感测器产生的大量资料的收集和分析。这些资料的准确性和完整性至关重要，任何外洩都可能严重损害系统检测潜在故障和准确预测维护需求的能力。对于采用预测性维护解决方案的组织而言，确保这些资料免受网路威胁并保持其品质是重中之重。

详细的市场区隔

按组件划分，整合式和独立式预测性维护解决方案共同主导预测性维护市场，占了超过 70% 的市场占有率。这些解决方案透过提供全面的分析并实现对设备健康状况的即时监控，从根本上改变了工业运作。这些系统将先进的资料处理技术与复杂的演算法结合，可以深入了解机器状况，预测潜在故障，并主动安排维护活动。

按技术划分，振动监测技术已成为预测性维护市场的基本组成部分，占了超过 22.6% 的市场占有率。这项技术因其能够在广泛的工业应用中检测设备故障的早期迹象而备受推崇。透过持续测量和分析机械发出的振动，振动监测系统可以识别通常先于机械故障出现的细微异常。

按行业划分，製造业在预测性维护市场中占主导地位，占了25.7%的绝对占有率。这一主导地位反映了製造业对维持持续营运和最大限度地减少代价高昂的设备停机时间的迫切需求。利用先进的预测性维护工具，製造商可以分析大量营运数据，侦测设备性能下降的早期迹象，并及时安排维护干预。

依部署类型划分，本地部署是预测性维护市场的主导方法，占超过63.6%的市场。这种偏好主要源自于对增强资料管理和严格安全要求日益增长的需求。各行各业的组织都将敏感业务资料的保护放在首位，这使得本地部署解决方案特别具有吸引力。

各市场区隔明细

各零件

  • 解决方案
    • 整合型
    • 独立的
  • 服务
    • 管理·服务
    • 专业·服务

各展开形态

  • 云端
    • 公共云端
    • 私有云端
    • 混合·云端
  • 内部部署

各技术

  • 震动监测
  • 电力试验
  • 油分析
  • 超音波检测器
  • 衝击脉衝
  • 热/红外线监测
  • 光学(相机)监测
  • ML资料库
  • 其他

各组织规模

  • 大企业
  • 中小企业

各产业

  • 航太·防卫
  • 製造业
  • 能源·公共产业
  • 石油、天然气
  • 运输
  • 仓库·物流
  • 医疗保健·生命科学
  • 汽车
  • 海运
  • 其他

各地区

  • 北美
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 英国
    • 德国
    • 法国
    • 义大利
    • 西班牙
    • 波兰
    • 俄罗斯
    • 其他
  • 亚太地区
    • 中国
    • 印度
    • 日本
    • 韩国
    • 澳洲·纽西兰
    • ASEAN
      • 马来西亚
      • 新加坡
      • 泰国
      • 印尼
      • 菲律宾
      • 越南
      • 其他
    • 其他地区
  • 中东·非洲
    • 阿拉伯联合大公国
    • 沙乌地阿拉伯
    • 南非
    • 其他
  • 南美
    • 阿根廷
    • 巴西
    • 其他

主要市场参与企业

  • Fujitsu Limited
  • Hitachi, Ltd.
  • Toshiba Corporation
  • Mitsubishi Electric Corporation
  • Google Llc
  • IBM Corporation
  • Microsoft Corporation
  • Oracle Corporation
  • SAP Se
  • Software Ag
  • Onyx Insight
  • Amazon Web Services, Inc.
  • SAS Institute
  • Hakunamatata Solutions
  • 其他

目录

第1章 调查架构

第2章 调查手法

第3章 摘要整理:全球预知保全市场

第4章 全球预知保全市场概要

  • 产业价值链分析
    • 科技开发商
    • 整合商
    • 服务供应商
    • 终端用户
  • 产业展望
    • 预知保全的ROI检测概要
  • 大环境分析
  • 波特的五力分析
  • 市场动态和趋势
  • COVID-19对市场成长趋势的影响评估
  • 市场成长与展望
  • 竞争仪表板

第5章 全球预知保全市场分析(各零件)

  • 重要的洞察
  • 市场规模与预测，2020年～2033年(100万美元)
    • 解决方案
    • 服务

第6章 全球预知保全市场分析(各展开形态)

  • 重要的洞察
  • 市场规模与预测，2020年～2033年(100万美元)
    • 云端
    • 内部部署

第7章 全球预知保全市场分析(各技术)

  • 重要的洞察
  • 市场规模与预测，2020年～2033年(100万美元)
    • 震动监视
    • 电力实验
    • 油分析
    • 超音波检测器
    • 休克脉衝
    • 热/红外线监测
    • 光学(相机)监视
    • ML资料库
    • 其他

第8章 全球预知保全市场分析(各组织规模)

  • 重要的洞察
  • 市场规模与预测，2020年～2033年(100万美元)
    • 大企业
    • 中小企业

第10章 全球预知保全市场分析(各产业)

  • 重要的洞察
  • 市场规模与预测，2020年～2033年(100万美元)
    • 航太·防卫
    • 製造业
    • 能源·公共产业
    • 石油、天然气
    • 交通机关
    • 仓库·物流
    • 医疗保健·生命科学
    • 汽车
    • 船舶
    • 其他

第11章 全球预知保全市场分析(各地区)

  • 重要的洞察
  • 市场规模与预测，2020年～2033年(100万美元)
    • 北美
    • 欧洲
    • 亚太地区
    • 中东·非洲
    • 南美

第12章 北美的预知保全市场分析

第13章 欧洲的预知保全市场分析

第14章 亚太地区的预知保全市场分析

第15章 中东·非洲的预知保全市场分析

第16章 南美的预知保全市场分析

第17章 企业简介

  • IBM
  • Microsoft Corporation
  • SAP
  • ABB Ltd.
  • Siemens AG
  • Schneider Electric
  • Hitachi, Ltd.
  • Oracle Corporation
  • Fujitsu Ltd.
  • GE
  • Rockwell Automation
  • Amazon Web Services, Inc.
  • PTC
  • Mitsubishi Electric Corporation
  • Robert Bosch
  • Honeywell
  • Onyx Insight
  • SAS Institute
  • Toshiba Corporation
  • Google Llc
  • Software Ag
  • Hakunamatata Solutions
  • 其他
简介目录
Product Code: AA0322172

In 2024, the predictive maintenance market is witnessing unparalleled growth fueled by the pressing demand to minimize equipment downtime and boost operational efficiency across various industries. Valued at US$ 8.96 billion in 2024, the market is projected to soar dramatically, reaching an estimated US$ 91.04 billion by 2033. This represents a remarkable compound annual growth rate (CAGR) of 29.4% over the forecast period from 2025 to 2033. The rapid expansion is largely attributed to the integration of advanced technologies such as artificial intelligence (AI) and the Internet of Things (IoT), which empower companies to adopt data-driven maintenance strategies.

North America maintains a dominant position in the predictive maintenance market, bolstered by its strong industrial base, early adoption of cutting-edge technologies, and significant investments in digital transformation initiatives. Key sectors such as manufacturing, energy, and healthcare have embraced predictive maintenance solutions to enhance productivity and reduce operational risks. The region serves as a hub for leading solution providers, including global giants like IBM, Microsoft, General Electric, and PTC, whose headquarters and research centers foster an ecosystem of innovation.

Noteworthy Market Developments

In the predictive maintenance market, several prominent players are shaping the industry landscape with their innovative technologies and strategic initiatives. Leading companies such as Cisco Systems, Inc., General Electric Company, SAP SE, Schneider Electric SE, and Siemens have established themselves as key contributors to the development and deployment of advanced predictive maintenance solutions. These industry leaders are actively pursuing various strategies to strengthen their market positions, including partnerships, mergers and acquisitions, and collaborations with other firms.

A notable example of such strategic activity occurred in February 2025 when IBM completed its acquisition of HashiCorp for US$ 6.4 billion. This acquisition significantly bolstered IBM's multicloud capabilities and predictive maintenance portfolio by integrating HashiCorp's technologies with IBM's Red Hat offerings. The move reinforced IBM's commitment to delivering scalable and flexible predictive maintenance solutions that leverage cloud infrastructure.

Further highlighting the dynamic nature of the market, in June 2025, C3 AI secured a US$ 13 million task order from the U.S. Air Force Rapid Sustainment Office. This contract is intended to expand C3 AI's AI-enabled predictive maintenance system across additional aircraft platforms, demonstrating the increasing reliance on artificial intelligence to enhance maintenance capabilities in highly complex and critical environments. The expansion underscores the strategic importance of predictive maintenance technologies in defense applications, where equipment reliability and mission readiness are paramount.

Core Growth Drivers

In 2024, the predictive maintenance market has become a critical area of focus for industries worldwide, primarily driven by the pressing need to reduce maintenance costs and minimize unplanned downtime. Unexpected equipment failures can have devastating financial consequences, leading to expensive repairs, production halts, and lost revenue. For stakeholders across sectors, these risks underscore the importance of adopting predictive maintenance strategies that enable early detection of potential issues and timely intervention.

Emerging Opportunity Trends

In 2024, the predictive maintenance market is undergoing a significant transformation driven by the widespread adoption of digital twin technology. Digital twins are sophisticated virtual replicas of physical assets that enable stakeholders to simulate, monitor, and analyze the performance of equipment in a virtual environment. This innovative approach allows companies to test various scenarios, predict potential failures, and optimize maintenance schedules without exposing actual assets to real-world risks or disruptions.

Barriers to Optimization

In 2024, data security and quality issues have emerged as significant challenges within the predictive maintenance market, directly impacting the effectiveness and reliability of these systems for stakeholders. Predictive maintenance heavily depends on the collection and analysis of vast amounts of data generated by IoT devices and sensors embedded in industrial equipment. The accuracy and integrity of this data are crucial, as any compromise can severely undermine the system's ability to detect potential failures and predict maintenance needs accurately. Ensuring robust protection of this data from cyber threats, as well as maintaining its quality, has become a top priority for organizations adopting predictive maintenance solutions.

Detailed Market Segmentation

By Component, integrated and standalone predictive maintenance solutions collectively dominate the predictive maintenance market, capturing over 70% of the market share. These solutions have fundamentally transformed industrial operations by providing comprehensive analytics and enabling real-time monitoring of equipment health. By combining advanced data processing with sophisticated algorithms, these systems allow organizations to gain deep insights into the condition of their machinery, predict potential failures, and schedule maintenance activities proactively.

By Technology, vibration monitoring technology has established itself as a fundamental component of the predictive maintenance market, commanding over 22.6% of the market share. This technology is highly valued for its exceptional ability to detect early signs of equipment faults across a wide range of industrial applications. By continuously measuring and analyzing the vibrations produced by machinery, vibration monitoring systems can identify subtle anomalies that often precede mechanical failures.

Based on industry, manufacturers are leading the predictive maintenance market, holding a commanding share of over 25.7%, largely due to their strategic adoption of advanced monitoring technologies. This dominance reflects the manufacturing sector's critical need to maintain continuous operations and minimize costly equipment downtime. By leveraging sophisticated predictive maintenance tools, manufacturers are able to analyze vast amounts of operational data, detect early signs of equipment degradation, and schedule timely maintenance interventions.

By Deployment, on-premise deployments have become the dominant approach in the predictive maintenance market, securing over 63.6% of the market share. This preference is primarily due to the growing need for enhanced data control and stringent security requirements. Organizations across various industries are increasingly prioritizing the protection of sensitive operational data, which makes on-premise solutions particularly attractive.

Segment Breakdown

By Component:

  • Solutions
    • Integrated
    • Standalone
  • Services
    • Managed Services
    • Professional Services

By Deployment Mode:

  • Cloud
    • Public Cloud
    • Private Cloud
    • Hybrid Cloud
  • On-Premises

By Technology:

  • Vibration Monitoring
  • Electrical Testing
  • Oil Analysis
  • Ultrasonic Detectors
  • Shock Pulse
  • Thermal/Infrared Monitoring
  • Optical (cameras) Monitoring
  • ML Database
  • Others

By Organization Size:

  • Large Enterprises
  • Small and Medium-Sized Enterprises

By Industry:

  • Aerospace and Defense
  • Manufacturing
  • Energy and Utilities
  • Oil & Gas
  • Transportation
  • Warehouse & Logistics
  • Healthcare and Lifesciences
  • Automotive
  • Marine/ Shipping
  • Others

By Region:

  • North America
    • The U.S.
    • Canada
    • Mexico
  • Europe
    • The UK
    • Germany
    • France
    • Italy
    • Spain
    • Poland
    • Russia
    • Rest of Europe
  • Asia Pacific
    • China
    • India
    • Japan
    • South Korea
    • Australia & New Zealand
    • ASEAN
      • Malaysia
      • Singapore
      • Thailand
      • Indonesia
      • Philippines
      • Vietnam
      • Rest of ASEAN
    • Rest of Asia Pacific
  • Middle East & Africa
    • UAE
    • Saudi Arabia
    • South Africa
    • Rest of MEA
  • South America
    • Argentina
    • Brazil
    • Rest of South America

Leading Market Participants

  • Fujitsu Limited
  • Hitachi, Ltd.
  • Toshiba Corporation
  • Mitsubishi Electric Corporation
  • Google Llc
  • IBM Corporation
  • Microsoft Corporation
  • Oracle Corporation
  • SAP Se
  • Software Ag
  • Onyx Insight
  • Amazon Web Services, Inc.
  • SAS Institute
  • Hakunamatata Solutions
  • 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 Predictive Maintenance Market

Chapter 4. Global Predictive Maintenance Market Overview

  • 4.1. Industry Value Chain Analysis
    • 4.1.1. Technology Developer
    • 4.1.2. Integrator
    • 4.1.3. Service Provider
    • 4.1.4. End User
  • 4.2. Industry Outlook
    • 4.2.1. Overview of Measuring the ROI of Predictive Maintenance
  • 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
  • 4.8. 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 Predictive Maintenance Market Analysis, By Component

  • 5.1. Key Insights
  • 5.2. Market Size and Forecast, 2020-2033 (US$ Mn)
    • 5.2.1. Solutions
      • 5.2.1.1. Integrated
      • 5.2.1.2. Standalone
    • 5.2.2. Services
      • 5.2.2.1. Managed Services
      • 5.2.2.2. Professional Services

Chapter 6. Global Predictive Maintenance Market Analysis, By Deployment Mode

  • 6.1. Key Insights
  • 6.2. Market Size and Forecast, 2020-2033 (US$ Mn)
    • 6.2.1. Cloud
      • 6.2.1.1. Public Cloud
      • 6.2.1.2. Private Cloud
      • 6.2.1.3. Hybrid Cloud
    • 6.2.5. On-Premises

Chapter 7. Global Predictive Maintenance Market Analysis, By Technology

  • 7.1. Key Insights
  • 7.2. Market Size and Forecast, 2020-2033 (US$ Mn)
    • 7.2.1. Vibration Monitoring
    • 7.2.2. Electrical Testing
    • 7.2.3. Oil Analysis
    • 7.2.4. Ultrasonic Detectors
    • 7.2.5. Shock Pulse
    • 7.2.6. Thermal/Infrared Monitoring
    • 7.2.7. Optical (cameras) Monitoring
    • 7.2.8. ML Database
    • 7.2.9. Others

Chapter 8. Global Predictive Maintenance Market Analysis, By Organization Size

  • 8.1. Key Insights
  • 8.2. Market Size and Forecast, 2020-2033 (US$ Mn)
    • 8.2.1. Large Enterprises
    • 8.2.2. Small and Medium-Sized Enterprises

Chapter 10. Global Predictive Maintenance Market Analysis, By Industry

  • 10.1. Key Insights
  • 10.2. Market Size and Forecast, 2020-2033 (US$ Mn)
    • 10.2.1. Aerospace and Defense
    • 10.2.2. Manufacturing
    • 10.2.3. Energy and Utilities
    • 10.2.4. Oil & Gas
    • 10.2.5. Transportation
    • 10.2.6. Warehouse & Logistics
    • 10.2.7. Healthcare and Lifesciences
    • 10.2.8. Automotive
    • 10.2.9. Marine/ Shipping
    • 10.2.10. Others

Chapter 11. Global Predictive Maintenance Market Analysis, By Region

  • 11.1. Key Insights
  • 11.2. Market Size and Forecast, 2020-2033 (US$ Mn)
    • 11.2.1. North America
      • 11.2.1.1. The U.S.
      • 11.2.1.2. Canada
      • 11.2.1.3. Mexico
    • 11.2.2. Europe
      • 11.2.2.1. The UK
      • 11.2.2.2. Germany
      • 11.2.2.3. France
      • 11.2.2.4. Spain
      • 11.2.2.5. Italy
      • 11.2.2.6. Russia
      • 11.2.2.7. Poland
      • 11.2.2.8. Rest of Europe
    • 11.2.3. Asia Pacific
      • 11.2.3.1. China
      • 11.2.3.2. India
      • 11.2.3.3. Japan
      • 11.2.3.4. South Korea
      • 11.2.3.5. Australia & New Zealand
      • 11.2.3.6. ASEAN
      • 11.2.3.7. Rest of Asia Pacific
    • 11.2.4. Middle East & Africa
      • 11.2.4.1. UAE
      • 11.2.4.2. Saudi Arabia
      • 11.2.4.3. South Africa
      • 11.2.4.4. Rest of MEA
    • 11.2.5. South America
      • 11.2.5.1. Argentina
      • 11.2.5.2. Brazil
      • 11.2.5.3. Rest of South America

Chapter 12. North America Predictive Maintenance Market Analysis

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

Chapter 13. Europe Predictive Maintenance Market Analysis

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

Chapter 14. Asia Pacific Predictive Maintenance Market Analysis

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

Chapter 15. Middle East & Africa Predictive Maintenance Market Analysis

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

Chapter 16. South America Predictive Maintenance Market Analysis

  • 16.1. Key Insights
  • 16.2. Market Size and Forecast, 2020-2033 (US$ Mn)
    • 16.2.1. By Component
    • 16.2.2. By Deployment Mode
    • 16.2.3. By Technology
    • 16.2.4. By Organization Size
    • 16.2.5. By Industry
    • 16.2.6. By Country

Chapter 17. Company Profile (Company Overview, Financial Matrix, Key Product landscape, Key Personnel, Key Competitors, Contact Address, and Business Strategy Outlook)

  • 17.1. IBM
  • 17.2. Microsoft Corporation
  • 17.3. SAP
  • 17.4. ABB Ltd.
  • 17.5. Siemens AG
  • 17.6. Schneider Electric
  • 17.7. Hitachi, Ltd.
  • 17.8. Oracle Corporation
  • 17.9. Fujitsu Ltd.
  • 17.10. GE
  • 17.11. Rockwell Automation
  • 17.12. Amazon Web Services, Inc.
  • 17.13. PTC
  • 17.14. Mitsubishi Electric Corporation
  • 17.15. Robert Bosch
  • 17.16. Honeywell
  • 17.17. Onyx Insight
  • 17.18. SAS Institute
  • 17.19. Toshiba Corporation
  • 17.20. Google Llc
  • 17.21. Software Ag
  • 17.22. Hakunamatata Solutions
  • 17.23. Other Prominent Players