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1804253

預知保全的全球市場:各零件，各展開形態，各技術，各組織規模，各產業，各地區-市場規模，產業動態，機會分析，預測(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個工作天內

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簡介目錄

預測性維護市場正經歷前所未有的成長，這得益於各行各業對最大程度減少設備停機時間和提高營運效率的迫切需求。 2024 年，該市場規模為 89.6 億美元，預計到 2033 年將達到 910.4 億美元，2025 年至 2033 年的複合年增長率高達 29.4%。這種快速擴張主要得益於人工智慧 (AI) 和物聯網 (IoT) 等先進技術的融合，這些技術使企業能夠採用數據驅動的維護策略。

北美在預測性維護市場中佔主導地位，這得益於其強大的工業基礎、對尖端技術的早期採用以及對數位轉型計劃的大量投資。製造業、能源業和醫療保健業等關鍵產業正在採用預測性維護解決方案，以提高生產力並降低營運風險。該地區匯聚了眾多領先的解決方案提供商，包括 IBM、微軟、通用電氣和 PTC 等全球巨頭，這些公司的總部和研究實驗室構建了創新生態系統。

值得關注的市場發展

在預測性維護市場，一些領先的參與者正在利用創新技術和策略性措施來塑造產業格局。思科系統公司、通用電氣公司、SAP SE、施耐德電氣 SE 和西門子等主要參與者已成為先進預測性維護解決方案開發和部署的關鍵參與者。這些產業領導者正在積極推行各種策略以鞏固其市場地位，包括合作、併購以及與其他公司的聯盟。

這項策略活動的一個顯著例子發生在 2025 年 2 月，IBM 以 64 億美元完成了對 HashiCorp 的收購。此次收購將 HashiCorp 的技術與 IBM 的 Red Hat 產品整合，顯著增強了 IBM 的多雲功能和預測性維護產品組合。此舉鞏固了 IBM 致力於利用雲端基礎架構提供可擴展、靈活的預測性維護解決方案的承諾。

C3 AI 於 2025 年 6 月獲得美國空軍快速維持辦公室 (RAO) 價值 1300 萬美元的任務訂單，進一步凸顯了市場的動態性。該合約旨在將 C3 AI 支援人工智慧的預測性維護系統擴展到更多飛機平台，顯示在高度複雜和關鍵環境中，人們越來越依賴人工智慧來增強維護能力。此次擴展凸顯了預測性維護技術在國防應用中的戰略重要性，因為設備可靠性和任務準備就緒至關重要。

核心成長動力

2024 年，預測性維護市場將成為全球各產業的關鍵領域，主要驅動力是降低維護成本和最大程度減少非計畫性停機的迫切需求。意外的設備故障可能導致昂貴的維修費用、生產停工和收入損失，造成毀滅性的財務影響。對於各行各業的利害關係人來說，這些風險凸顯了採用預測性維護策略的重要性，這些策略能夠及早發現潛在問題並及時介入。

新興機會趨勢

2024年，預測性維護市場將經歷重大轉型，這得益於數位孿生技術的廣泛應用。數位孿生是實體資產的複雜虛擬複製品，使利害關係人能夠在虛擬環境中模擬、監控和分析設備效能。這種創新方法使公司能夠測試各種場景、預測潛在故障並優化維護計劃，而無需將實際資產暴露於現實世界的風險和中斷之中。

優化障礙

2024年，資料安全和品質問題將成為預測性維護市場面臨的重大課題，直接影響這些系統對利害關係人的有效性和可靠性。預測性維護高度依賴工業設備中嵌入的物聯網設備和感測器產生的大量資料的收集和分析。這些資料的準確性和完整性至關重要，任何外洩都可能嚴重損害系統檢測潛在故障和準確預測維護需求的能力。對於採用預測性維護解決方案的組織而言，確保這些資料免受網路威脅並保持其品質是重中之重。

詳細的市場區隔

按組件劃分，整合式和獨立式預測性維護解決方案共同主導預測性維護市場，佔了超過 70% 的市場佔有率。這些解決方案透過提供全面的分析並實現對設備健康狀況的即時監控，從根本上改變了工業運作。這些系統將先進的資料處理技術與複雜的演算法結合，可以深入了解機器狀況，預測潛在故障，並主動安排維護活動。

按技術劃分，振動監測技術已成為預測性維護市場的基本組成部分，佔了超過 22.6% 的市場佔有率。這項技術因其能夠在廣泛的工業應用中檢測設備故障的早期跡象而備受推崇。透過持續測量和分析機械發出的振動，振動監測系統可以識別通常先於機械故障出現的細微異常。

按行業劃分，製造業在預測性維護市場中佔主導地位，佔了25.7%的絕對佔有率。這一主導地位反映了製造業對維持持續營運和最大限度地減少代價高昂的設備停機時間的迫切需求。利用先進的預測性維護工具，製造商可以分析大量營運數據，偵測設備性能下降的早期跡象，並及時安排維護幹預。

依部署類型劃分，本地部署是預測性維護市場的主導方法，佔超過63.6%的市場。這種偏好主要源自於對增強資料管理和嚴格安全要求日益增長的需求。各行各業的組織都將敏感業務資料的保護放在首位，這使得本地部署解決方案特別具有吸引力。

各市場區隔明細

各零件

  • 解決方案
    • 整合型
    • 獨立的
  • 服務
    • 管理·服務
    • 專業·服務

各展開形態

  • 雲端
    • 公共雲端
    • 私有雲端
    • 混合·雲端
  • 內部部署

各技術

  • 震動監測
  • 電力試驗
  • 油分析
  • 超音波檢測器
  • 衝擊脈衝
  • 熱/紅外線監測
  • 光學(相機)監測
  • ML資料庫
  • 其他

各組織規模

  • 大企業
  • 中小企業

各產業

  • 航太·防衛
  • 製造業
  • 能源·公共產業
  • 石油、天然氣
  • 運輸
  • 倉庫·物流
  • 醫療保健·生命科學
  • 汽車
  • 海運
  • 其他

各地區

  • 北美
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 波蘭
    • 俄羅斯
    • 其他
  • 亞太地區
    • 中國
    • 印度
    • 日本
    • 韓國
    • 澳洲·紐西蘭
    • ASEAN
      • 馬來西亞
      • 新加坡
      • 泰國
      • 印尼
      • 菲律賓
      • 越南
      • 其他
    • 其他地區
  • 中東·非洲
    • 阿拉伯聯合大公國
    • 沙烏地阿拉伯
    • 南非
    • 其他
  • 南美
    • 阿根廷
    • 巴西
    • 其他

主要市場參與企業

  • 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

目錄

第1章 調查架構

第2章 調查手法

第3章 摘要整理:全球預知保全市場

第4章 全球預知保全市場概要

  • 產業價值鏈分析
    • 科技開發商
    • 整合商
    • 服務供應商
    • 終端用戶
  • 產業展望
    • 預知保全的ROI檢測概要
  • 大環境分析
  • 波特的五力分析
  • 市場動態和趨勢
  • COVID-19對市場成長趨勢的影響評估
  • 市場成長與展望
  • 競爭儀表板

第5章 全球預知保全市場分析(各零件)

  • 重要的洞察
  • 市場規模與預測，2020年～2033年(100萬美元)
    • 解決方案
    • 服務

第6章 全球預知保全市場分析(各展開形態)

  • 重要的洞察
  • 市場規模與預測，2020年～2033年(100萬美元)
    • 雲端
    • 內部部署

第7章 全球預知保全市場分析(各技術)

  • 重要的洞察
  • 市場規模與預測，2020年～2033年(100萬美元)
    • 震動監視
    • 電力實驗
    • 油分析
    • 超音波檢測器
    • 休克脈衝
    • 熱/紅外線監測
    • 光學(相機)監視
    • ML資料庫
    • 其他

第8章 全球預知保全市場分析(各組織規模)

  • 重要的洞察
  • 市場規模與預測，2020年～2033年(100萬美元)
    • 大企業
    • 中小企業

第10章 全球預知保全市場分析(各產業)

  • 重要的洞察
  • 市場規模與預測，2020年～2033年(100萬美元)
    • 航太·防衛
    • 製造業
    • 能源·公共產業
    • 石油、天然氣
    • 交通機關
    • 倉庫·物流
    • 醫療保健·生命科學
    • 汽車
    • 船舶
    • 其他

第11章 全球預知保全市場分析(各地區)

  • 重要的洞察
  • 市場規模與預測，2020年～2033年(100萬美元)
    • 北美
    • 歐洲
    • 亞太地區
    • 中東·非洲
    • 南美

第12章 北美的預知保全市場分析

第13章 歐洲的預知保全市場分析

第14章 亞太地區的預知保全市場分析

第15章 中東·非洲的預知保全市場分析

第16章 南美的預知保全市場分析

第17章 企業簡介

  • 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
簡介目錄
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