세계의 예지보전 시장 : 컴포넌트별, 배포 형태별, 기술별, 조직 규모별, 산업별, 지역별 - 시장 규모, 산업 역학, 기회 분석, 예측(2025-2033년)

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