Predictive Maintenance Market Size, Share & Industry Analysis
商品番号 : SMB-74467
| 出版社 | Fortune Business Insights |
| 出版年月 | 2025年3月 |
| ページ数 | 160 |
| 価格タイプ | シングルユーザライセンス |
| 価格 | USD 4,850 |
| 種別 | 英文調査報告書 |
世界の予知保全市場は 2024年に109億3,000万ドルと評価され、年平均成長率（CAGR）26.5％で成長し、2032年までに707億3,000万ドルに達するとFortune Business Insightsでは予測しています。製造、エネルギー、ヘルスケア、輸送などの業界では、運用コストを削減し、生産性を向上させるために予知保全ソリューションを採用しています。
予知保全は、AI、IoT、ビッグデータを活用して機器の故障を事前に予測することで、業界に革命をもたらしています。このアプローチにより、ダウンタイムが短縮され、効率が向上し、資産の寿命が延びます。企業がデジタル変革を優先するにつれて、予知保全市場は急成長を遂げています。
Fortune Business Insightsでは市場成長促進要因として下記を挙げています。
- Industry 4.0とデジタル化
- コスト削減と効率化
- AIと機械学習（ML）の進化
- IoTとコネクティビティの成長
「予知保全市場 : コンポーネント（ハードウェア、ソフトウェア）、展開別（オンプレミス、クラウドベース）、企業タイプ別（大企業、中小企業）、テクノロジー別（IoT、AI および機械学習、デジタルツイン、高度な分析）、用途別（状態監視、予測分析、リモート監視、資産追跡、保守スケジュール）、最終用途別（軍事および防衛、エネルギーおよび公共事業、製造、ヘルスケア、IT および通信、物流および輸送）、地域別の市場規模、シェア、業界分析および予測、2025-2032年 – Predictive Maintenance Market Size, Share & Industry Analysis, By Component (Hardware, Software), By Deployment (On-premise, Cloud-based), By Enterprise Type (Large Enterprises, Small & Mid-sized Enterprises), By Technology (IoT, AI & Machine Learning, Digital Twin, Advance Analytics), By Application (Condition Monitoring, Predictive Analytics, Remote Monitoring, Asset Tracking, and Maintenance Scheduling), By End-Use (Military & Defense, Energy & Utilities, Manufacturing, Healthcare, IT and Telecom, Logistics & Transportation), Regional Forecast, 2025 – 2032」は予知保全の世界市場を調査し、主要セグメント別に分析・予測したFortune Business Insightsの市場調査レポートです。
調査対象セグメント
- 構成要素
- ハードウェア
- ソフトウェア
- 統合型
- スタンドアロン
- 展開
- オンプレミス
- クラウドベース
- 企業タイプ
- 大企業
- 中小企業（SME）
- 技術
- IoT
- 人工知能と機械学習
- デジタルツイン
- 高度分析
- その他（モダンデータベース、ERPなど）
- 用途
- 状況監視
- 予測分析
- 遠隔監視
- 資産追跡
- 保全計画
- エンドユース
- 軍事＆防衛
- エネルギー＆公益事業
- 製造
- 医療
- IT＆電気通信
- 物流＆輸送
- その他（化学品、製紙、印刷、農業など）
- 地域
- 北米
- 米国
- カナダ
- メキシコ
- 南米
- ブラジル
- アルゼンチン
- その他の南米
- 欧州
- 英国
- ドイツ
- フランス
- イタリア
- スペイン
- ロシア
- ベネルクス諸国
- 北欧
- その他の欧州
- 中東＆アフリカ
- トルコ
- イスラエル
- GCC
- 北アフリカ
- 南アフリカ
- その他の中東＆アフリカ
- アジア太平洋地域
- 中国
- インド
- 日本
- 韓国
- ASEAN
- オセアニア
- その他のアジア太平洋地域
- 北米
調査対象企業
- IBM Corporation (米国)
- General Electric (米国)
- Siemens (ドイツ)
- C3.ai, Inc. (米国)
- PTC (米国)
- Rockwell Automation (米国)
- 日立製作所 (日本)
- UpKeep (米国)
- Augury Ltd. (米国)
- The Soothsayer (P-Dictor) (タイ)
Growth Factors of PREDICTIVE MAINTENANCE Market
Predictive maintenance is revolutionizing industries by leveraging AI, IoT, and big data to anticipate equipment failures before they occur. This approach reduces downtime, improves efficiency, and extends asset lifespan. As businesses prioritize digital transformation, the predictive maintenance market is experiencing exponential growth.
Market Overview
The global predictive maintenance market was valued at $10.93 billion in 2024 and is projected to reach $70.73 billion by 2032, growing at an impressive 26.5% CAGR. Industries such as manufacturing, energy, healthcare, and transportation are adopting predictive maintenance solutions to reduce operational costs and enhance productivity.
Key Growth Drivers
- Industry 4.0 and Digitalization
Smart factories and digital twins are driving the adoption of predictive maintenance. Companies are integrating IoT sensors, AI, and cloud computing to monitor equipment health in real time.
- Cost Savings and Efficiency
Traditional reactive maintenance leads to unexpected breakdowns and higher costs. Predictive maintenance enables proactive servicing, minimizing unplanned downtime and reducing expenses.
- AI and Machine Learning Advancements
AI-driven analytics can process vast amounts of sensor data, identifying patterns that indicate potential failures. This improves accuracy and reliability in maintenance predictions.
- IoT and Connectivity Growth
The rise of connected devices allows companies to collect real-time equipment data, facilitating remote monitoring and predictive analytics.
- Post-COVID Digital Acceleration
The pandemic pushed industries to invest in digital solutions. Many companies shifted toward automated, remote maintenance to ensure business continuity.
Challenges in Adoption
Despite rapid growth, challenges remain:
- Skilled Workforce Shortage: Implementing predictive maintenance requires expertise in AI, IoT, and data analytics.
- High Initial Investment: Small and medium enterprises (SMEs) may struggle with the cost of technology implementation.
- Data Security Concerns: Increased connectivity raises cybersecurity risks, requiring robust protection measures.
Regional Insights
- North America leads the market due to early AI and IoT adoption.
- Europe is investing in smart manufacturing, boosting demand.
- Asia-Pacific is experiencing rapid industrialization, making predictive maintenance crucial for manufacturing and logistics.
Key Players and Market Strategies
Major companies like IBM, General Electric, Siemens, and SAP are driving innovation through partnerships and AI-powered solutions. They are investing in cloud-based predictive maintenance platforms to cater to a wider range of industries.
Future Outlook
The predictive maintenance market is set to reshape industries by increasing automation, reducing operational risks, and improving cost efficiency. With continuous advancements in AI, IoT, and 5G, predictive maintenance will become a standard practice for businesses aiming to achieve maximum uptime and efficiency.
ATTRIBUTE DETAILS
- Study Period: 2019-2032
- Base Year : 2024
- Estimated Year : 2025
- Forecast Period : 2025-2032
- Historical Period : 2019-2023
- Growth Rate : CAGR of 26.5% from 2025 to 2032
- Unit : Value (USD Billion)
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),
Table of Contents
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, 2024
5. Global Predictive Maintenance Market Size Estimates and Forecasts, By Segments, 2019-2032
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, 2019-2032
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, 2019-2032
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, 2019-2032
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, 2019-2032
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, 2019-2032
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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