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시장보고서
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예지보전 시장 규모, 점유율, 성장 및 세계 산업 분석 : 유형별 및 용도별, 지역별 인사이트와 예측(2026-2034년)

Predictive Maintenance Market Size, Share, Growth and Global Industry Analysis By Type & Application, Regional Insights and Forecast to 2026-2034
발행일: | 리서치사: Fortune Business Insights Pvt. Ltd. | 페이지 정보: 영문 120 Pages | 배송안내 : 문의

    
    
    



※ 본 상품은 영문 자료로 한글과 영문 목차에 불일치하는 내용이 있을 경우 영문을 우선합니다. 정확한 검토를 위해 영문 목차를 참고해주시기 바랍니다.

예지보전 시장 성장 요인

세계 예지보전 시장은 2025년 136억 5,000만 달러로 평가되었고, 2026년에는 171억 1,000만 달러로 성장하고 2034년에는 973억 7,000만 달러에 달할 것으로 예측됩니다. 예측 기간 중 24.30%의 높은 CAGR을 나타낼 것으로 예측됩니다. 북미는 2025년 33.30%의 점유율로 시장을 선도하고 있으며, AI, IoT, 클라우드 기반 산업 솔루션의 조기 도입이 견인 요인으로 작용하고 있습니다. 예지보전(PdM)은 인더스트리 4.0에서 중요한 역할을 하며, 실시간 데이터, 분석, 인공지능을 활용하여 조직이 설비의 고장을 사전에 예측할 수 있게 해줍니다.

시장 개요

예지보전은 IoT 센서, AI, 머신러닝, 예측 분석, 디지털 트윈을 통합하여 설비 상태를 지속적으로 모니터링합니다. 센서에서 수집된 데이터는 엣지 또는 클라우드에서 분석되어 고장이 발생하기 전에 예측합니다. 이러한 접근 방식은 다운타임 감소, 자산 수명 연장, 유지보수 비용 최적화를 실현할 수 있습니다. 제조업, 에너지, 의료, IT 분야에서의 디지털 전환의 진전은 전 세계 시장 도입을 가속화하고 있습니다.

생성형 AI의 영향

생성형 AI의 통합은 모델 개발 자동화, 수리 전략 생성, 상황에 맞는 유지보수 가이던스 제공을 통해 예지보전을 혁신하고 있습니다. 생성형 AI는 대규모 데이터 사이언스 팀에 대한 의존도를 낮추면서 예측 정확도를 높입니다. 제조업에서는 생성형 AI를 활용한 예지보전 시스템으로 다운타임을 30% 줄이고, 유지보수 비용을 20% 절감하여 생산성을 크게 향상시키고 있습니다. 이러한 발전은 전 세계에서 차세대 예지보전 솔루션에 대한 수요를 강화하고 있습니다.

시장 동향

예측 유지보수 시장을 형성하는 주요 동향은 저렴하고 비용 효율적인 유지보수 솔루션에 대한 수요 증가입니다. 예지보전은 사후 대응형 유지보수 대비 최대 40%, 예방적 유지보수 대비 8-12%의 비용절감이 가능하며, 다운타임을 최대 50%까지 줄일 수 있습니다. IoT 기반 예지 시스템은 노동력, 예비 부품 및 자원의 효율적인 배분을 실현하여 비용에 민감한 기업에게 예지보전을 매우 매력적인 선택으로 만듭니다.

시장 성장 촉진요인

OEM 수준의 PdM 통합

OEM(Original Equipment Manufacturer)는 고장을 조기에 감지하고 안전성과 신뢰성을 향상시키기 위해 예측보전 기능을 장비에 직접 내장하고 있습니다. 자동차 제조업체와 기술 프로바이더간의 제휴로 도입이 가속화되고 있습니다. 2024년 9월, COMPREDICT가 르노 그룹과 제휴하여 가상 센서 기반 예측 유지보수를 도입합니다. 이를 통해 하드웨어 비용을 절감하고 유연성을 향상시킬 수 있었습니다.

시장 성장 억제요인

숙련된 인력 부족

AI 기반 IoT 및 분석 플랫폼을 관리할 수 있는 숙련된 전문가가 부족한 것이 큰 문제입니다. 머신러닝, 사이버 보안, 네트워크, 데이터 분석에 대한 전문 지식은 예지보전 도입에 필수적입니다. 이러한 기술 격차는 특히 신흥 시장에서 도입이 늦어질 수 있습니다.

시장 기회

인더스트리 4.0과 첨단 기술

인더스트리 4.0의 급속한 보급은 큰 성장 기회를 가져다 줍니다. AI, 머신러닝, IoT의 통합으로 고장 예측의 정확도가 향상되고 실시간 모니터링이 가능해집니다. 업계 조사에 따르면 제조업체의 72%가 인더스트리 4.0 기술을 도입하고 있으며, 예지보전은 그 중 가장 널리 도입된 용도 중 하나입니다.

세분화 분석

  • 구성요소별: 2024년에는 소프트웨어가 시장을 주도할 것으로 예상되며, 클라우드 기반 및 독립형 예지보전 플랫폼으로 인해 빠른 성장세를 이어갈 것으로 전망됩니다.
  • 도입 형태별: 데이터 보안의 필요성 때문에 2024년에는 On-Premise 솔루션이 주도했으나, 클라우드 기반 도입이 가장 높은 CAGR로 성장하고 있습니다.
  • 기업 규모별: 2024년에는 대기업이 시장을 주도했으나, 저렴한 SaaS 모델로 인해 중소기업의 도입이 빠르게 확대되고 있습니다.
  • 기술별: 2024년에는 IoT가 시장을 주도하는 가운데, AI와 머신러닝이 가장 빠르게 성장할 것으로 예측됩니다.
  • 용도별로는 2024년 상태 모니터링이 가장 큰 점유율을 차지했으며, 예측 분석이 가장 빠르게 성장할 것으로 예측됩니다.
  • 최종 용도별: 2024년에는 제조업이 주도적인 위치를 차지하고, 의료 및 에너지 분야가 그 뒤를 이었습니다.

지역별 인사이트

  • 북미: 2025년 45억 4,000만 달러 규모에 달할 것으로 예상되며, AI 및 클라우드 투자가 견인차 역할을 할 것으로 전망됩니다.
  • 아시아태평양: 인더스트리 4.0의 노력으로 인해 가장 높은 CAGR로 성장할 것으로 예측됩니다.
  • 유럽: AI를 통한 생산성 향상이 강력한 성장을 지원합니다.
  • 남미: 급속한 디지털 전환과 IT 예산 증가가 성장을 가속하고 있습니다.
  • 중동 및 아프리카: 스마트 인프라와 IoT를 활용한 유지보수 도입 확대가 예상됩니다.

목차

제1장 서론

제2장 개요

제3장 시장 역학

  • 거시 및 미시경제 지표
  • 촉진요인, 억제요인, 기회 및 동향
  • 생성형 AI의 영향

제4장 경쟁 구도

  • 주요 기업이 채택하는 비즈니스 전략
  • 주요 기업의 통합 SWOT 분석
  • 세계의 예지보전 시장의 주요 기업의 시장 점유율/순위(2025년)

제5장 세계의 예지보전 시장 추산·예측 : 부문별(2021-2034년)

  • 주요 조사 결과
  • 컴포넌트별
    • 하드웨어
    • 소프트웨어
      • 통합형
      • 스탠드얼론
  • 도입 형태별
    • 온프레미스
    • 클라우드 기반
  • 기업 유형별
    • 대기업
    • 중소기업(SME)
  • 테크놀러지별
    • IoT
    • 인공지능 및 기계학습
    • 디지털 트윈
    • 첨단 분석
    • 기타(최신 데이터베이스, ERP 등)
  • 애플리케이션별
    • 상태 감시
    • 예측 분석
    • 원격 감시
    • 자산 추적
    • 유지보수 스케줄링
  • 용도별
    • 군·방위
    • 에너지·유틸리티
    • 제조업
    • 의료
    • IT·통신
    • 물류·운송
    • 기타(화학, 종이·인쇄, 농업 등)
  • 지역별
    • 북미
    • 남미
    • 유럽
    • 중동 및 아프리카
    • 아시아태평양

제6장 북미의 예지보전 시장의 규모 추산·예측(부문별, 2021-2034년)

  • 국가별
    • 미국
    • 캐나다
    • 멕시코

제7장 남미의 예지보전 시장의 규모 추산·예측(부문별, 2021-2034년)

  • 국가별
    • 브라질
    • 아르헨티나
    • 기타 남미 국가

제8장 유럽의 예지보전 시장의 규모 추산·예측(부문별, 2021-2034년)

  • 국가별
    • 영국
    • 독일
    • 프랑스
    • 이탈리아
    • 스페인
    • 러시아
    • 베네룩스
    • 북유럽 국가
    • 기타 유럽

제9장 중동 및 아프리카의 예지보전 시장의 규모 추산·예측(부문별, 2021-2034년)

  • 국가별
    • 튀르키예
    • 이스라엘
    • GCC
    • 북아프리카
    • 남아프리카공화국
    • 기타 중동 및 아프리카

제10장 아시아태평양의 예지보전 시장의 규모 추산·예측(부문별, 2021-2034년)

  • 국가별
    • 중국
    • 인도
    • 일본
    • 한국
    • ASEAN
    • 오세아니아
    • 기타 아시아태평양

제11장 주요 10사의 기업 개요

  • IBM Corporation
  • General Electric
  • Siemens
  • C3.ai, Inc.
  • Rockwell Automation
  • PTC
  • Hitachi, Ltd.
  • UpKeep
  • Augury Ltd.
  • The Soothsayer(P-Dictor)
KSA 26.02.25

Growth Factors of predictive maintenance Market

The global predictive maintenance market was valued at USD 13.65 billion in 2025 and is projected to grow to USD 17.11 billion in 2026, reaching USD 97.37 billion by 2034, registering a strong CAGR of 24.30% during the forecast period. North America dominated the market with a 33.30% share in 2025, driven by early adoption of AI, IoT, and cloud-based industrial solutions. Predictive Maintenance (PdM) plays a critical role in Industry 4.0, enabling organizations to predict equipment failures in advance using real-time data, analytics, and artificial intelligence.

Market Overview

Predictive maintenance integrates IoT sensors, AI, machine learning, predictive analytics, and digital twins to continuously monitor equipment health. Data collected from sensors is analyzed at the edge or in the cloud to forecast failures before breakdowns occur. This approach reduces downtime, improves asset lifespan, and optimizes maintenance costs. Increasing digital transformation across manufacturing, energy, healthcare, and IT sectors is accelerating market adoption globally.

Impact of Generative AI

The integration of generative AI is transforming predictive maintenance by automating model development, generating repair strategies, and offering contextual maintenance guidance. Generative AI reduces reliance on large data science teams while improving prediction accuracy. In manufacturing, generative AI-driven PdM systems have resulted in 30% lower downtime and 20% reduced maintenance costs, significantly boosting productivity. This advancement is strengthening demand for next-generation PdM solutions worldwide.

Market Trends

A key trend shaping the predictive maintenance market is the growing demand for affordable and cost-efficient maintenance solutions. Predictive maintenance can reduce costs by up to 40% compared to reactive maintenance and 8-12% compared to preventive maintenance, while cutting downtime by up to 50%. IoT-based predictive systems enable efficient allocation of labor, spare parts, and resources, making PdM highly attractive for cost-conscious enterprises.

Market Growth Drivers

Integration of PdM at OEM Level

OEMs are embedding predictive maintenance directly into equipment to detect failures early and improve safety and reliability. Partnerships between automotive manufacturers and technology providers are accelerating adoption. In September 2024, COMPREDICT partnered with Renault Group to deploy virtual sensor-based predictive maintenance, reducing hardware costs and enhancing flexibility.

Market Restraints

Shortage of Skilled Workforce

A major challenge is the scarcity of skilled professionals capable of managing AI-driven IoT and analytics platforms. Expertise in machine learning, cybersecurity, networking, and data analytics is critical for PdM implementation. This skills gap may slow adoption, particularly in emerging markets.

Market Opportunities

Industry 4.0 and Advanced Technologies

The rapid adoption of Industry 4.0 presents significant growth opportunities. AI, ML, and IoT integration improves failure prediction accuracy and enables real-time monitoring. According to industry insights, 72% of manufacturers have adopted Industry 4.0 technologies, with predictive maintenance being one of the most widely implemented applications.

Segmentation Analysis

  • By Component: Software dominated the market in 2024 and continues to grow rapidly due to cloud-based and standalone PdM platforms.
  • By Deployment: On-premise solutions led in 2024 due to data security needs, while cloud-based deployments are growing at the highest CAGR.
  • By Enterprise Type: Large enterprises dominated in 2024, while SMEs are witnessing rapid adoption due to affordable SaaS models.
  • By Technology: IoT led the market in 2024, while AI and machine learning are expected to grow fastest.
  • By Application: Condition monitoring held the largest share in 2024; predictive analytics is projected to grow fastest.
  • By End-Use: Manufacturing dominated in 2024, followed by healthcare and energy sectors.

Regional Insights

  • North America: Valued at USD 4.54 billion in 2025, driven by AI and cloud investments.
  • Asia Pacific: Expected to grow at the highest CAGR due to Industry 4.0 initiatives.
  • Europe: Strong growth supported by AI-driven productivity gains.
  • South America: Rapid digital transformation and rising IT budgets fuel growth.
  • Middle East & Africa: Growing adoption of smart infrastructure and IoT-enabled maintenance.

Competitive Landscape

Major players include IBM, Siemens, General Electric, C3.ai, Rockwell Automation, SAP, Microsoft, ABB, Honeywell, and Schneider Electric. Companies focus on partnerships, acquisitions, and AI-driven product innovation to strengthen global presence.

Conclusion

The predictive maintenance market is set to expand from USD 13.65 billion in 2025 to USD 17.11 billion in 2026, reaching USD 97.37 billion by 2034, driven by Industry 4.0 adoption, AI and IoT integration, and increasing demand for cost-efficient maintenance solutions. While workforce skill shortages remain a challenge, advancements in generative AI, cloud platforms, and OEM-level integration will unlock significant growth opportunities. Predictive maintenance will remain a cornerstone of digital industrial transformation throughout the forecast period.

Segmentation By Component

  • Hardware
  • Software
    • Integrated
    • Standalone

By Deployment

  • On-premise
  • Cloud-based

By Enterprise Type

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

By Technology

  • IoT
  • Artificial Intelligence and Machine Learning
  • Digital Twin
  • Advance Analytics
  • Others (Modern Database, ERP, etc.)

By Application

  • Condition Monitoring
  • Predictive Analytics
  • Remote Monitoring
  • Asset Tracking
  • Maintenance Scheduling

By End-use

  • Military and Defense
  • Energy and Utilities
  • Manufacturing
  • Healthcare
  • IT and Telecom
  • Logistics and Transportation
  • Others (Chemicals, Paper and Printing and Agriculture, etc.)

By Region

  • North America (By Component, By Deployment, By Enterprise Type, By Technology, By Application, By End-Use, and By Country)
    • U.S.
    • Canada
    • Mexico
  • South America (By Component, By Deployment, By Enterprise Type, By Technology, By Application, By End-Use, and By Country)
    • Brazil
    • Argentina
    • Rest of South America
  • Europe (By Component, By Deployment, By Enterprise Type, By Technology, By Application, By End-Use, and By Country)
    • U.K.
    • Germany
    • France
    • Italy
    • Spain
    • Russia
    • Benelux
    • Nordics
    • Rest of Europe
  • Middle East & Africa (By Component, By Deployment, By Enterprise Type, By Technology, By Application, By End-Use, and By Country)
    • Turkey
    • Israel
    • GCC
    • North Africa
    • South Africa
    • Rest of Middle East & Africa
  • Asia Pacific (By Component, By Deployment, By Enterprise Type, By Technology, By Application, By End-Use, and By Country)
    • China
    • India
    • Japan
    • South Korea
    • ASEAN
    • Oceania
    • Rest of Asia Pacific

Companies Profiled in the Report IBM Corporation (U.S.), General Electric (U.S.), Siemens (Germany), C3.ai, Inc. (U.S.), PTC (U.S.), Rockwell Automation (U.S.), Hitachi Ltd. (Japan), UpKeep (U.S.), Augury Ltd. (U.S.), The Soothsayer (P-Dictor) (Thailand), etc.

Table of Content

1. Introduction

  • 1.1. Definition, By Segment
  • 1.2. Research Methodology/Approach
  • 1.3. Data Sources

2. Executive Summary

3. Market Dynamics

  • 3.1. Macro and Micro Economic Indicators
  • 3.2. Drivers, Restraints, Opportunities and Trends
  • 3.3. Impact of Generative AI

4. Competition Landscape

  • 4.1. Business Strategies Adopted by Key Players
  • 4.2. Consolidated SWOT Analysis of Key Players
  • 4.3. Global Predictive Maintenance Key Players Market Share/Ranking, 2025

5. Global Predictive Maintenance Market Size Estimates and Forecasts, By Segments, 2021-2034

  • 5.1. Key Findings
  • 5.2. By Component (USD)
    • 5.2.1. Hardware
    • 5.2.2. Software
      • 5.2.2.1. Integrated
      • 5.2.2.2. Standalone
  • 5.3. By Deployment (USD)
    • 5.3.1. On-premise
    • 5.3.2. Cloud-based
  • 5.4. By Enterprise Type (USD)
    • 5.4.1. Large Enterprises
    • 5.4.2. Small and Mid-sized Enterprises (SMEs)
  • 5.5. By Technology (USD)
    • 5.5.1. IoT
    • 5.5.2. Artificial Intelligence and Machine Learning
    • 5.5.3. Digital Twin
    • 5.5.4. Advance Analytics
    • 5.5.5. Others (Modern Database, ERP, etc.)
  • 5.6. By Application (USD)
    • 5.6.1. Condition Monitoring
    • 5.6.2. Predictive Analytics
    • 5.6.3. Remote Monitoring
    • 5.6.4. Asset Tracking
    • 5.6.5. Maintenance Scheduling
  • 5.7. By End-Use (USD)
    • 5.7.1. Military and Defense
    • 5.7.2. Energy and Utilities
    • 5.7.3. Manufacturing
    • 5.7.4. Healthcare
    • 5.7.5. IT and Telecom
    • 5.7.6. Logistics and Transportation
    • 5.7.7. Others (Chemicals, Paper and Printing, and Agriculture, etc.)
  • 5.8. By Region (USD)
    • 5.8.1. North America
    • 5.8.2. South America
    • 5.8.3. Europe
    • 5.8.4. Middle East & Africa
    • 5.8.5. Asia Pacific

6. North America Predictive Maintenance Market Size Estimates and Forecasts, By Segments, 2021-2034

  • 6.1. Key Findings
  • 6.2. By Component (USD)
    • 6.2.1. Hardware
    • 6.2.2. Software
      • 6.2.2.1. Integrated
      • 6.2.2.2. Standalone
  • 6.3. By Deployment (USD)
    • 6.3.1. On-premise
    • 6.3.2. Cloud-based
  • 6.4. By Enterprise Type (USD)
    • 6.4.1. Large Enterprises
    • 6.4.2. Small and Mid-sized Enterprises (SMEs)
  • 6.5. By Technology (USD)
    • 6.5.1. IoT
    • 6.5.2. Artificial Intelligence and Machine Learning
    • 6.5.3. Digital Twin
    • 6.5.4. Advance Analytics
    • 6.5.5. Others (Modern Database, ERP, etc.)
  • 6.6. By Application (USD)
    • 6.6.1. Condition Monitoring
    • 6.6.2. Predictive Analytics
    • 6.6.3. Remote Monitoring
    • 6.6.4. Asset Tracking
    • 6.6.5. Maintenance Scheduling
  • 6.7. By End-Use (USD)
    • 6.7.1. Military and Defense
    • 6.7.2. Energy and Utilities
    • 6.7.3. Manufacturing
    • 6.7.4. Healthcare
    • 6.7.5. IT and Telecom
    • 6.7.6. Logistics and Transportation
    • 6.7.7. Others (Chemicals, Paper and Printing, and Agriculture, etc.)
  • 6.8. By Country (USD)
    • 6.8.1. United States
    • 6.8.2. Canada
    • 6.8.3. Mexico

7. South America Predictive Maintenance Market Size Estimates and Forecasts, By Segments, 2021-2034

  • 7.1. Key Findings
  • 7.2. By Component (USD)
    • 7.2.1. Hardware
    • 7.2.2. Software
      • 7.2.2.1. Integrated
      • 7.2.2.2. Standalone
  • 7.3. By Deployment (USD)
    • 7.3.1. On-premise
    • 7.3.2. Cloud-based
  • 7.4. By Enterprise Type (USD)
    • 7.4.1. Large Enterprises
    • 7.4.2. Small and Mid-sized Enterprises (SMEs)
  • 7.5. By Technology (USD)
    • 7.5.1. IoT
    • 7.5.2. Artificial Intelligence and Machine Learning
    • 7.5.3. Digital Twin
    • 7.5.4. Advance Analytics
    • 7.5.5. Others (Modern Database, ERP, etc.)
  • 7.6. By Application (USD)
    • 7.6.1. Condition Monitoring
    • 7.6.2. Predictive Analytics
    • 7.6.3. Remote Monitoring
    • 7.6.4. Asset Tracking
    • 7.6.5. Maintenance Scheduling
  • 7.7. By End-Use (USD)
    • 7.7.1. Military and Defense
    • 7.7.2. Energy and Utilities
    • 7.7.3. Manufacturing
    • 7.7.4. Healthcare
    • 7.7.5. IT and Telecom
    • 7.7.6. Logistics and Transportation
    • 7.7.7. Others (Chemicals, Paper and Printing, and Agriculture, etc.)
  • 7.8. By Country (USD)
    • 7.8.1. Brazil
    • 7.8.2. Argentina
    • 7.8.3. Rest of South America

8. Europe Predictive Maintenance Market Size Estimates and Forecasts, By Segments, 2021-2034

  • 8.1. Key Findings
  • 8.2. By Component (USD)
    • 8.2.1. Hardware
    • 8.2.2. Software
      • 8.2.2.1. Integrated
      • 8.2.2.2. Standalone
  • 8.3. By Deployment (USD)
    • 8.3.1. On-premise
    • 8.3.2. Cloud-based
  • 8.4. By Enterprise Type (USD)
    • 8.4.1. Large Enterprises
    • 8.4.2. Small and Mid-sized Enterprises (SMEs)
  • 8.5. By Technology (USD)
    • 8.5.1. IoT
    • 8.5.2. Artificial Intelligence and Machine Learning
    • 8.5.3. Digital Twin
    • 8.5.4. Advance Analytics
    • 8.5.5. Others (Modern Database, ERP, etc.)
  • 8.6. By Application (USD)
    • 8.6.1. Condition Monitoring
    • 8.6.2. Predictive Analytics
    • 8.6.3. Remote Monitoring
    • 8.6.4. Asset Tracking
    • 8.6.5. Maintenance Scheduling
  • 8.7. By End-Use (USD)
    • 8.7.1. Military and Defense
    • 8.7.2. Energy and Utilities
    • 8.7.3. Manufacturing
    • 8.7.4. Healthcare
    • 8.7.5. IT and Telecom
    • 8.7.6. Logistics and Transportation
    • 8.7.7. Others (Chemicals, Paper and Printing, and Agriculture, etc.)
  • 8.8. By Country (USD)
    • 8.8.1. United Kingdom
    • 8.8.2. Germany
    • 8.8.3. France
    • 8.8.4. Italy
    • 8.8.5. Spain
    • 8.8.6. Russia
    • 8.8.7. Benelux
    • 8.8.8. Nordics
    • 8.8.9. Rest of Europe

9. Middle East & Africa Predictive Maintenance Market Size Estimates and Forecasts, By Segments, 2021-2034

  • 9.1. Key Findings
  • 9.2. By Component (USD)
    • 9.2.1. Hardware
    • 9.2.2. Software
      • 9.2.2.1. Integrated
      • 9.2.2.2. Standalone
  • 9.3. By Deployment (USD)
    • 9.3.1. On-premise
    • 9.3.2. Cloud-based
  • 9.4. By Enterprise Type (USD)
    • 9.4.1. Large Enterprises
    • 9.4.2. Small and Mid-sized Enterprises (SMEs)
  • 9.5. By Technology (USD)
    • 9.5.1. IoT
    • 9.5.2. Artificial Intelligence and Machine Learning
    • 9.5.3. Digital Twin
    • 9.5.4. Advance Analytics
    • 9.5.5. Others (Modern Database, ERP, etc.)
  • 9.6. By Application (USD)
    • 9.6.1. Condition Monitoring
    • 9.6.2. Predictive Analytics
    • 9.6.3. Remote Monitoring
    • 9.6.4. Asset Tracking
    • 9.6.5. Maintenance Scheduling
  • 9.7. By End-Use (USD)
    • 9.7.1. Military and Defense
    • 9.7.2. Energy and Utilities
    • 9.7.3. Manufacturing
    • 9.7.4. Healthcare
    • 9.7.5. IT and Telecom
    • 9.7.6. Logistics and Transportation
    • 9.7.7. Others (Chemicals, Paper and Printing, and Agriculture, etc.)
  • 9.8. By Country (USD)
    • 9.8.1. Turkey
    • 9.8.2. Israel
    • 9.8.3. GCC
    • 9.8.4. North Africa
    • 9.8.5. South Africa
    • 9.8.6. Rest of MEA

10. Asia Pacific Predictive Maintenance Market Size Estimates and Forecasts, By Segments, 2021-2034

  • 10.1. Key Findings
  • 10.2. By Component (USD)
    • 10.2.1. Hardware
    • 10.2.2. Software
      • 10.2.2.1. Integrated
      • 10.2.2.2. Standalone
  • 10.3. By Deployment (USD)
    • 10.3.1. On-premise
    • 10.3.2. Cloud-based
  • 10.4. By Enterprise Type (USD)
    • 10.4.1. Large Enterprises
    • 10.4.2. Small and Mid-sized Enterprises (SMEs)
  • 10.5. By Technology (USD)
    • 10.5.1. IoT
    • 10.5.2. Artificial Intelligence and Machine Learning
    • 10.5.3. Digital Twin
    • 10.5.4. Advance Analytics
    • 10.5.5. Others (Modern Database, ERP, etc.)
  • 10.6. By Application (USD)
    • 10.6.1. Condition Monitoring
    • 10.6.2. Predictive Analytics
    • 10.6.3. Remote Monitoring
    • 10.6.4. Asset Tracking
    • 10.6.5. Maintenance Scheduling
  • 10.7. By End-Use (USD)
    • 10.7.1. Military and Defense
    • 10.7.2. Energy and Utilities
    • 10.7.3. Manufacturing
    • 10.7.4. Healthcare
    • 10.7.5. IT and Telecom
    • 10.7.6. Logistics and Transportation
    • 10.7.7. Others (Chemicals, Paper and Printing, and Agriculture, etc.)
  • 10.8. By Country (USD)
    • 10.8.1. China
    • 10.8.2. India
    • 10.8.3. Japan
    • 10.8.4. South Korea
    • 10.8.5. ASEAN
    • 10.8.6. Oceania
    • 10.8.7. Rest of Asia Pacific

11. Company Profiles for Top 10 Players (Based on data availability in public domain and/or on paid databases)

  • 11.1. IBM Corporation
    • 11.1.1. Overview
      • 11.1.1.1. Key Management
      • 11.1.1.2. Headquarters
      • 11.1.1.3. Offerings/Business Segments
    • 11.1.2. Key Details (Key details are consolidated data and not product/service specific)
      • 11.1.2.1. Employee Size
      • 11.1.2.2. Past and Current Revenue
      • 11.1.2.3. Geographical Share
      • 11.1.2.4. Business Segment Share
      • 11.1.2.5. Recent Developments
  • 11.2. General Electric
    • 11.2.1. Overview
      • 11.2.1.1. Key Management
      • 11.2.1.2. Headquarters
      • 11.2.1.3. Offerings/Business Segments
    • 11.2.2. Key Details (Key details are consolidated data and not product/service specific)
      • 11.2.2.1. Employee Size
      • 11.2.2.2. Past and Current Revenue
      • 11.2.2.3. Geographical Share
      • 11.2.2.4. Business Segment Share
      • 11.2.2.5. Recent Developments
  • 11.3. Siemens
    • 11.3.1. Overview
      • 11.3.1.1. Key Management
      • 11.3.1.2. Headquarters
      • 11.3.1.3. Offerings/Business Segments
    • 11.3.2. Key Details (Key details are consolidated data and not product/service specific)
      • 11.3.2.1. Employee Size
      • 11.3.2.2. Past and Current Revenue
      • 11.3.2.3. Geographical Share
      • 11.3.2.4. Business Segment Share
      • 11.3.2.5. Recent Developments
  • 11.4. C3.ai, Inc.
    • 11.4.1. Overview
      • 11.4.1.1. Key Management
      • 11.4.1.2. Headquarters
      • 11.4.1.3. Offerings/Business Segments
    • 11.4.2. Key Details (Key details are consolidated data and not product/service specific)
      • 11.4.2.1. Employee Size
      • 11.4.2.2. Past and Current Revenue
      • 11.4.2.3. Geographical Share
      • 11.4.2.4. Business Segment Share
      • 11.4.2.5. Recent Developments
  • 11.5. Rockwell Automation
    • 11.5.1. Overview
      • 11.5.1.1. Key Management
      • 11.5.1.2. Headquarters
      • 11.5.1.3. Offerings/Business Segments
    • 11.5.2. Key Details (Key details are consolidated data and not product/service specific)
      • 11.5.2.1. Employee Size
      • 11.5.2.2. Past and Current Revenue
      • 11.5.2.3. Geographical Share
      • 11.5.2.4. Business Segment Share
      • 11.5.2.5. Recent Developments
  • 11.6. PTC
    • 11.6.1. Overview
      • 11.6.1.1. Key Management
      • 11.6.1.2. Headquarters
      • 11.6.1.3. Offerings/Business Segments
    • 11.6.2. Key Details (Key details are consolidated data and not product/service specific)
      • 11.6.2.1. Employee Size
      • 11.6.2.2. Past and Current Revenue
      • 11.6.2.3. Geographical Share
      • 11.6.2.4. Business Segment Share
      • 11.6.2.5. Recent Developments
  • 11.7. Hitachi, Ltd.
    • 11.7.1. Overview
      • 11.7.1.1. Key Management
      • 11.7.1.2. Headquarters
      • 11.7.1.3. Offerings/Business Segments
    • 11.7.2. Key Details (Key details are consolidated data and not product/service specific)
      • 11.7.2.1. Employee Size
      • 11.7.2.2. Past and Current Revenue
      • 11.7.2.3. Geographical Share
      • 11.7.2.4. Business Segment Share
      • 11.7.2.5. Recent Developments
  • 11.8. UpKeep
    • 11.8.1. Overview
      • 11.8.1.1. Key Management
      • 11.8.1.2. Headquarters
      • 11.8.1.3. Offerings/Business Segments
    • 11.8.2. Key Details (Key details are consolidated data and not product/service specific)
      • 11.8.2.1. Employee Size
      • 11.8.2.2. Past and Current Revenue
      • 11.8.2.3. Geographical Share
      • 11.8.2.4. Business Segment Share
      • 11.8.2.5. Recent Developments
  • 11.9. Augury Ltd.
    • 11.9.1. Overview
      • 11.9.1.1. Key Management
      • 11.9.1.2. Headquarters
      • 11.9.1.3. Offerings/Business Segments
    • 11.9.2. Key Details (Key details are consolidated data and not product/service specific)
      • 11.9.2.1. Employee Size
      • 11.9.2.2. Past and Current Revenue
      • 11.9.2.3. Geographical Share
      • 11.9.2.4. Business Segment Share
      • 11.9.2.5. Recent Developments
  • 11.10. The Soothsayer (P-Dictor)
    • 11.10.1. Overview
      • 11.10.1.1. Key Management
      • 11.10.1.2. Headquarters
      • 11.10.1.3. Offerings/Business Segments
    • 11.10.2. Key Details (Key details are consolidated data and not product/service specific)
      • 11.10.2.1. Employee Size
      • 11.10.2.2. Past and Current Revenue
      • 11.10.2.3. Geographical Share
      • 11.10.2.4. Business Segment Share
      • 11.10.2.5. Recent Developments
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