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한글목차
예지보전 시장 규모는 2025년 142억 8,000만 달러에서 2026년부터 2034년까지 CAGR 26.62%로 성장하여 2034년에는 1,194억 9,000만 달러에 달할 것으로 예측됩니다.
산업 분야에서 운영 효율성 향상과 다운타임 감소를 위한 데이터 기반 전략의 도입이 증가함에 따라 예지보전 시장은 빠르게 성장할 것으로 예상됩니다. IoT 기기의 보급과 첨단 분석 기술의 등장으로 조직은 장비의 상태를 실시간으로 모니터링할 수 있게 되었으며, 잠재적인 고장이 발생하기 전에 조기에 감지할 수 있게 되었습니다. 이러한 예방적 접근 방식은 유지보수 비용을 최소화할 뿐만 아니라 중요 자산의 수명을 연장하기 때문에 제조, 에너지, 운송 등의 분야에서 예지보전 솔루션은 필수 불가결한 요소입니다. 기업들이 예지적 인사이트의 가치를 인식함에 따라 예지보전 기술에 대한 투자가 급증하고 시장 확대가 가속화될 것으로 예상됩니다.
또한, 인공지능(AI)과 머신러닝을 예측 유지보수 시스템에 통합하여 고장 감지 및 분석에 혁명을 일으키고 있습니다. 이러한 기술을 통해 조직은 산업 공정에서 생성되는 방대한 데이터를 분석하여 임박한 고장을 암시할 수 있는 패턴이나 이상을 식별할 수 있습니다. 유지보수 수요를 정확하게 예측할 수 있는 능력을 통해 기업은 유지보수 계획을 최적화하고 자원을 효율적으로 배분하며 운영 중단을 최소화할 수 있습니다. 산업계가 디지털 전환을 추진함에 따라 보다 스마트하고 효율적인 유지보수 솔루션에 대한 수요에 힘입어 예지보전 시장은 크게 성장할 것으로 예상됩니다.
또한, 규제 압력과 안전 기준 준수에 대한 요구도 예지보전 기술의 채택을 촉진하고 있습니다. 산업 분야에서는 안전과 신뢰성에 대한 노력을 입증하는 것이 점점 더 요구되고 있으며, 고도의 예지보전 시스템에 대한 투자가 진행되고 있습니다. 시장이 진화함에 따라, 실시간 데이터 분석과 머신러닝을 통합한 보다 정교한 솔루션 개발에 초점이 맞춰져 끊임없이 변화하는 산업 환경에서 조직이 더 높은 수준의 업무 효율성과 회복력을 달성할 수 있도록 지원할 것입니다.
목차
제1장 소개
제2장 주요 요약
제3장 시장 변수, 동향, 프레임워크
제4장 세계의 예지보전 시장 : 구성요소별
제5장 세계의 예지보전 시장 : 전개별
제6장 세계의 예지보전 시장 : 기업 유형별
제7장 세계의 예지보전 시장 : 기술별
제8장 세계의 예지보전 시장 : 용도별
제9장 세계의 예지보전 시장 : 최종사용별
제10장 세계의 예지보전 시장 : 지역별
제11장 경쟁 구도
제12장 기업 개요
KSM
영문 목차
영문목차
The Predictive Maintenance Market size is expected to reach USD 119.49 Billion in 2034 from USD 14.28 Billion (2025) growing at a CAGR of 26.62% during 2026-2034.
The Predictive Maintenance market is set to experience exponential growth as industries increasingly adopt data-driven strategies to enhance operational efficiency and reduce downtime. With the advent of IoT devices and advanced analytics, organizations can now monitor equipment health in real-time, enabling the early detection of potential failures before they disrupt operations. This proactive approach not only minimizes maintenance costs but also extends the lifespan of critical assets, making predictive maintenance solutions indispensable across sectors such as manufacturing, energy, and transportation. As businesses recognize the value of predictive insights, investments in predictive maintenance technologies are expected to surge, driving market expansion.
Furthermore, the integration of artificial intelligence and machine learning into predictive maintenance systems is revolutionizing fault detection and analysis. These technologies enable organizations to analyze vast amounts of data generated by industrial processes, identifying patterns and anomalies that may indicate impending failures. The ability to predict maintenance needs accurately allows companies to optimize their maintenance schedules, ensuring that resources are allocated efficiently and minimizing operational disruptions. As industries continue to embrace digital transformation, the Predictive Maintenance market is poised for substantial growth, fueled by the demand for smarter, more efficient maintenance solutions.
Additionally, regulatory pressures and the need for compliance with safety standards are propelling the adoption of predictive maintenance technologies. Industries are increasingly required to demonstrate their commitment to safety and reliability, prompting investments in advanced predictive maintenance systems. As the market evolves, the focus will shift towards developing more sophisticated solutions that incorporate real-time data analytics and machine learning, enabling organizations to achieve higher levels of operational excellence and resilience in an ever-changing industrial landscape.
Our reports are meticulously crafted to provide clients with comprehensive and actionable insights into various industries and markets. Each report encompasses several critical components to ensure a thorough understanding of the market landscape:
Market Overview: A detailed introduction to the market, including definitions, classifications, and an overview of the industry's current state.
Market Dynamics: In-depth analysis of key drivers, restraints, opportunities, and challenges influencing market growth. This section examines factors such as technological advancements, regulatory changes, and emerging trends.
Segmentation Analysis: Breakdown of the market into distinct segments based on criteria like product type, application, end-user, and geography. This analysis highlights the performance and potential of each segment.
Competitive Landscape: Comprehensive assessment of major market players, including their market share, product portfolio, strategic initiatives, and financial performance. This section provides insights into the competitive dynamics and key strategies adopted by leading companies.
Market Forecast: Projections of market size and growth trends over a specified period, based on historical data and current market conditions. This includes quantitative analyses and graphical representations to illustrate future market trajectories.
Regional Analysis: Evaluation of market performance across different geographical regions, identifying key markets and regional trends. This helps in understanding regional market dynamics and opportunities.
Emerging Trends and Opportunities: Identification of current and emerging market trends, technological innovations, and potential areas for investment. This section offers insights into future market developments and growth prospects.
MARKET SEGMENTATION
By Component
Hardware
Software
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
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
COMPANIES PROFILED
IBM Corporation, General Electric, Siemens, C3ai Inc, PTC, Rockwell Automation, Hitachi Ltd, UpKeep, Augury Ltd, The Soothsayer PDictor
We can customise the report as per your requriements
TABLE OF CONTENTS
Chapter 1. PREFACE
1.1. Market Segmentation & Scope
1.2. Market Definition
1.3. Information Procurement
1.3.1 Information Analysis
1.3.2 Market Formulation & Data Visualization
1.3.3 Data Validation & Publishing
1.4. Research Scope and Assumptions
1.4.1 List of Data Sources
Chapter 2. EXECUTIVE SUMMARY
2.1. Market Snapshot
2.2. Segmental Outlook
2.3. Competitive Outlook
Chapter 3. MARKET VARIABLES, TRENDS, FRAMEWORK
3.1. Market Lineage Outlook
3.2. Penetration & Growth Prospect Mapping
3.3. Value Chain Analysis
3.4. Regulatory Framework
3.4.1 Standards & Compliance
3.4.2 Regulatory Impact Analysis
3.5. Market Dynamics
3.5.1 Market Drivers
3.5.2 Market Restraints
3.5.3 Market Opportunities
3.5.4 Market Challenges
3.6. Porter's Five Forces Analysis
3.7. PESTLE Analysis
Chapter 4. GLOBAL PREDICTIVE MAINTENANCE MARKET: BY COMPONENT 2022-2034 (USD MN)
4.1. Market Analysis, Insights and Forecast Component
4.2. Hardware Estimates and Forecasts By Regions 2022-2034 (USD MN)
4.3. Software Estimates and Forecasts By Regions 2022-2034 (USD MN)
Chapter 5. GLOBAL PREDICTIVE MAINTENANCE MARKET: BY DEPLOYMENT 2022-2034 (USD MN)
5.1. Market Analysis, Insights and Forecast Deployment
5.2. On-Premise Estimates and Forecasts By Regions 2022-2034 (USD MN)
5.3. Cloud-Based Estimates and Forecasts By Regions 2022-2034 (USD MN)
Chapter 6. GLOBAL PREDICTIVE MAINTENANCE MARKET: BY ENTERPRISE TYPE 2022-2034 (USD MN)
6.1. Market Analysis, Insights and Forecast Enterprise Type
6.2. Large Enterprises Estimates and Forecasts By Regions 2022-2034 (USD MN)
6.3. Small And Mid-Sized Enterprises (SMEs) Estimates and Forecasts By Regions 2022-2034 (USD MN)
Chapter 7. GLOBAL PREDICTIVE MAINTENANCE MARKET: BY TECHNOLOGY 2022-2034 (USD MN)
7.1. Market Analysis, Insights and Forecast Technology
7.2. IoT Estimates and Forecasts By Regions 2022-2034 (USD MN)
7.3. Artificial Intelligence And Machine Learning Estimates and Forecasts By Regions 2022-2034 (USD MN)
7.4. Digital Twin Estimates and Forecasts By Regions 2022-2034 (USD MN)
7.5. Advance Analytics Estimates and Forecasts By Regions 2022-2034 (USD MN)
7.6. Others Estimates and Forecasts By Regions 2022-2034 (USD MN)
Chapter 8. GLOBAL PREDICTIVE MAINTENANCE MARKET: BY APPLICATION 2022-2034 (USD MN)
8.1. Market Analysis, Insights and Forecast Application
8.2. Condition Monitoring Estimates and Forecasts By Regions 2022-2034 (USD MN)
8.3. Predictive Analytics Estimates and Forecasts By Regions 2022-2034 (USD MN)
8.4. Remote Monitoring Estimates and Forecasts By Regions 2022-2034 (USD MN)
8.5. Asset Tracking Estimates and Forecasts By Regions 2022-2034 (USD MN)
8.6. Maintenance Scheduling Estimates and Forecasts By Regions 2022-2034 (USD MN)
Chapter 9. GLOBAL PREDICTIVE MAINTENANCE MARKET: BY END-USE 2022-2034 (USD MN)
9.1. Market Analysis, Insights and Forecast End-use
9.2. Military And Defense Estimates and Forecasts By Regions 2022-2034 (USD MN)
9.3. Energy And Utilities Estimates and Forecasts By Regions 2022-2034 (USD MN)
9.4. Manufacturing Estimates and Forecasts By Regions 2022-2034 (USD MN)
9.5. Healthcare Estimates and Forecasts By Regions 2022-2034 (USD MN)
9.6. IT And Telecom Estimates and Forecasts By Regions 2022-2034 (USD MN)
9.7. Logistics And Transportation Estimates and Forecasts By Regions 2022-2034 (USD MN)
9.8. Others Estimates and Forecasts By Regions 2022-2034 (USD MN)
Chapter 10. GLOBAL PREDICTIVE MAINTENANCE MARKET: BY REGION 2022-2034(USD MN)
10.1. Regional Outlook
10.2. North America Market Analysis, Insights and Forecast, 2022-2034 (USD MN)
10.2.1 By Component
10.2.2 By Deployment
10.2.3 By Enterprise Type
10.2.4 By Technology
10.2.5 By Application
10.2.6 By End-use
10.2.7 United States
10.2.8 Canada
10.2.9 Mexico
10.3. Europe Market Analysis, Insights and Forecast, 2022-2034 (USD MN)
10.3.1 By Component
10.3.2 By Deployment
10.3.3 By Enterprise Type
10.3.4 By Technology
10.3.5 By Application
10.3.6 By End-use
10.3.7 United Kingdom
10.3.8 France
10.3.9 Germany
10.3.10 Italy
10.3.11 Russia
10.3.12 Rest Of Europe
10.4. Asia-Pacific Market Analysis, Insights and Forecast, 2022-2034 (USD MN)
10.4.1 By Component
10.4.2 By Deployment
10.4.3 By Enterprise Type
10.4.4 By Technology
10.4.5 By Application
10.4.6 By End-use
10.4.7 India
10.4.8 Japan
10.4.9 South Korea
10.4.10 Australia
10.4.11 South East Asia
10.4.12 Rest Of Asia Pacific
10.5. Latin America Market Analysis, Insights and Forecast, 2022-2034 (USD MN)
10.5.1 By Component
10.5.2 By Deployment
10.5.3 By Enterprise Type
10.5.4 By Technology
10.5.5 By Application
10.5.6 By End-use
10.5.7 Brazil
10.5.8 Argentina
10.5.9 Peru
10.5.10 Chile
10.5.11 South East Asia
10.5.12 Rest of Latin America
10.6. Middle East & Africa Market Analysis, Insights and Forecast, 2022-2034 (USD MN)
10.6.1 By Component
10.6.2 By Deployment
10.6.3 By Enterprise Type
10.6.4 By Technology
10.6.5 By Application
10.6.6 By End-use
10.6.7 Saudi Arabia
10.6.8 UAE
10.6.9 Israel
10.6.10 South Africa
10.6.11 Rest of the Middle East And Africa
Chapter 11. COMPETITIVE LANDSCAPE
11.1. Recent Developments
11.2. Company Categorization
11.3. Supply Chain & Channel Partners (based on availability)
11.4. Market Share & Positioning Analysis (based on availability)
11.5. Vendor Landscape (based on availability)
11.6. Strategy Mapping
Chapter 12. COMPANY PROFILES OF GLOBAL PREDICTIVE MAINTENANCE INDUSTRY
