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1904734

預測性維護自動化市場預測至2032年：按組件、部署類型、技術、最終用戶和地區分類的全球分析

Predictive Maintenance Automation Market Forecasts to 2032 - Global Analysis By Component (Software and Services), Deployment Mode, Technology, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的一項研究，預計到 2025 年，全球預測性維護自動化市場規模將達到 33.7 億美元，到 2032 年將達到 160.9 億美元，在預測期內的複合年成長率為 25.0%。

自動化預測性維護是指利用自動化系統、進階分析技術、感測器和人工智慧即時監測資產健康狀況，並在潛在故障發生前進行預測。透過持續收集和分析振動、溫度和壓力等運作數據，企業可以僅在必要時安排維護。這種方法能夠最大限度地減少非計劃性停機時間，延長資產使用壽命，降低維護成本，並提高工業和製造環境中的營運效率。

從被動模式轉向主動模式

自動化預測性維護使企業能夠利用感測器、分析和機器學習演算法即時監測資產健康狀況。透過識別磨損或故障的早期徵兆，企業可以在故障發生前規劃維護活動。這種轉變顯著減少了資產密集產業的非計劃性停機時間、維修成本和生產損失。製造商優先考慮營運連續性和效率，以在瞬息萬變的市場中保持競爭力。工業IoT平台的日益普及進一步加速了這一轉變。隨著數位化成熟度的提高，預防性維護模式正從可選項升級轉變為策略必需品。

實施初期成本較高

實現精準的預測分析需要部署感測器、邊緣設備、資料平台和進階分析工具。與現有傳統基礎設施的整合往往會增加複雜性和部署時間。對於中小企業而言，由於短期收入的不確定性，證明資本投資的合理性是一項挑戰。管理資料模型和解讀預測結果也需要專業人員，從而增加營運成本。網路安全措施和數據管理方面的投資進一步加重了整體財務負擔。這些高昂的初始成本可能會減緩技術的普及，尤其是在對成本敏感的行業。

與數位雙胞胎的整合

數位雙胞胎能夠創建實體資產的虛擬副本，從而實現持續的模擬和效能分析。當與預測維修系統結合使用時，企業可以在虛擬環境中檢驗故障場景和維護策略。這種整合能夠提高診斷準確性，並最佳化資產全生命週期的決策。製造業、能源和交通運輸等行業正擴大利用數位雙胞胎進行資產最佳化。實體系統和數位系統之間的即時同步能夠改善維護計劃和資源分配。隨著數位雙胞胎技術的應用日益廣泛，預測性維護自動化解決方案的價值提案將進一步提升。

資料隱私與主權

對資料隱私和主權的擔憂日益加劇，為預測性維護自動化市場帶來了挑戰。這些系統嚴重依賴從連網機器和工業網路中持續收集資料。敏感的運行資料通常儲存或處理在雲端環境中，引發了人們對未授權存取的擔憂。諸如GDPR和特定地區的資料本地化法律等法規結構增加了合規的複雜性。跨境資料傳輸的潛在限制阻礙了全球維護平台的擴充性。網路安全風險，包括勒索軟體和工業間諜活動，進一步加劇了最終用戶的擔憂。

新冠疫情的影響：

新冠疫情對預測性維護自動化技術的應用產生了重大影響。製造業營運中斷凸顯了人工被動維護模式的風險。旅行限制導致現場巡檢受限，企業更加依賴遠端監控和自動化診斷。許多企業加快了數位轉型步伐，以確保在封鎖期間資產的可視性。供應鏈中斷凸顯了在人力有限的情況下維持設備可靠性的重要性。疫情後的復甦策略優先考慮自動化，以提高營運韌性。因此，預測性維護解決方案在多個產業中得到了更廣泛的應用。

預計在預測期內，軟體領域將佔據最大的市場佔有率。

預計在預測期內，軟體領域將佔據最大的市場佔有率。此領域涵蓋分析平台、人工智慧演算法、狀態監測應用和資產管理儀錶板。軟體解決方案能夠對各種類型的設備進行即時資料處理和預測建模。機器學習和雲端運算的不斷進步正在提升預測的準確性和擴充性。由於軟體主導方案的柔軟性和易於整合，企業更傾向於選擇此類方案。訂閱模式也有助於降低最終用戶的長期擁有成本。

預計製造業板塊在預測期內將實現最高的複合年成長率。

預計製造業將在預測期內實現最高成長率。製造商依賴複雜的機械設備，計劃外停機會對生產效率和收入造成重大影響。預測維修系統有助於及早發現生產設備的故障。智慧工廠和工業4.0的舉措普及正在推動對自動化維護解決方案的需求。製造商正在利用數據驅動的洞察來提高資產利用率並最佳化維護計劃。與製造執行系統 (MES) 的整合進一步提高了營運效率。

佔比最大的地區：

由於先進工業自動化技術的廣泛應用，預計北美地區在預測期內將佔據最大的市場佔有率。主要解決方案供應商和技術創新者的強大實力也推動了市場成長。美國和加拿大的各行業正在大力投資人工智慧驅動的資產管理系統。政府所推行的智慧製造扶持政策進一步促進了相關技術的應用。人們對營運效率和成本最佳化的高度重視也增強了市場需求。

年複合成長率最高的地區：

預計亞太地區在預測期內將呈現最高的複合年成長率。快速的工業化進程和不斷擴大的製造地正在推動全部區域的需求成長。中國、印度、日本和韓國等國家正大力投資數位轉型計畫。工業IoT和智慧工廠理念的日益普及正在推動市場成長。各國政府正在推廣自動化，以提高生產力和全球競爭力。該地區製造業對資產最佳化意識的提高也進一步推動了自動化技術的應用。

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目錄

第1章執行摘要

第2章 前言

  • 摘要
  • 相關利益者
  • 調查範圍
  • 調查方法
  • 研究材料

第3章 市場趨勢分析

  • 促進要素
  • 抑制因素
  • 機會
  • 威脅
  • 技術分析
  • 終端用戶分析
  • 新興市場
  • 新冠疫情的感染疾病

第4章 波特五力分析

  • 供應商的議價能力
  • 買方的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

5. 全球預測性維護自動化市場（按組件分類）

  • 軟體
    • 預測性維護軟體
    • 資產績效管理 (APM) 軟體
    • 狀態監控軟體
  • 服務
    • 諮詢及實施服務
    • 支援和維護服務

6. 全球預測性維護自動化市場依部署類型分類

  • 本地部署
  • 雲

7. 全球預測性維護自動化市場（依技術分類）

  • 物聯網和感測器
  • 人工智慧（AI）和機器學習
  • 巨量資料分析
  • 數位雙胞胎
  • 邊緣運算
  • 其他技術

8. 全球預測性維護自動化市場（依最終用戶分類）

  • 製造業
  • 能源與公共產業
  • 運輸/物流
  • 車
  • 石油和天然氣
  • 航太/國防
  • 化學品和製藥
  • 其他最終用戶

9. 全球預測性維護自動化市場（按地區分類）

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美國家
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第10章：重大進展

  • 協議、夥伴關係、合作和合資企業
  • 併購
  • 新產品發布
  • 業務拓展
  • 其他關鍵策略

第11章 企業概況

  • IBM Corporation
  • TIBCO Software Inc.
  • Microsoft Corporation
  • Uptake Technologies Inc.
  • SAP SE
  • C3.ai, Inc.
  • Siemens AG
  • Oracle Corporation
  • General Electric Company
  • ABB Ltd.
  • Schneider Electric SE
  • PTC Inc.
  • Hitachi, Ltd.
  • Honeywell International Inc.
  • Rockwell Automation, Inc.
Product Code: SMRC33143

According to Stratistics MRC, the Global Predictive Maintenance Automation Market is accounted for $3.37 billion in 2025 and is expected to reach $16.09 billion by 2032 growing at a CAGR of 25.0% during the forecast period. Predictive Maintenance Automation refers to the use of automated systems, advanced analytics, sensors, and artificial intelligence to monitor equipment conditions in real time and predict potential failures before they occur. By continuously collecting and analyzing operational data such as vibration, temperature, and pressure, it enables organizations to schedule maintenance only when needed. This approach minimizes unplanned downtime, extends asset lifespan, reduces maintenance costs, and improves overall operational efficiency across industrial and manufacturing environments.

Market Dynamics:

Driver:

Shift from reactive to proactive models

Predictive maintenance automation enables organizations to monitor asset health in real time using sensors, analytics, and machine learning algorithms. By identifying early signs of wear or malfunction, companies can schedule maintenance activities before breakdowns occur. This shift significantly reduces unplanned downtime, repair costs, and production losses across asset-intensive sectors. Manufacturers are prioritizing operational continuity and efficiency to remain competitive in dynamic markets. The growing availability of industrial IoT platforms is further accelerating this transition. As digital maturity improves, proactive maintenance models are becoming a strategic necessity rather than an optional upgrade.

Restraint:

High upfront implementation costs

Companies must deploy sensors, edge devices, data platforms, and advanced analytics tools to enable accurate predictive insights. Integration with existing legacy infrastructure often increases complexity and implementation timelines. Small and medium-sized enterprises face challenges in justifying capital expenditure due to uncertain short-term returns. Skilled personnel are also required to manage data models and interpret predictive outputs, adding to operational costs. Cybersecurity and data management investments further elevate the overall financial burden. These high upfront expenses can delay adoption, particularly in cost-sensitive industries.

Opportunity:

Integration with digital twins

Digital twins create virtual replicas of physical assets, enabling continuous simulation and performance analysis. When combined with predictive maintenance systems, organizations can test failure scenarios and maintenance strategies in a virtual environment. This integration enhances diagnostic accuracy and improves decision-making across asset lifecycles. Industries such as manufacturing, energy, and transportation are increasingly leveraging digital twins for asset optimization. Real-time synchronization between physical and digital systems improves maintenance planning and resource allocation. As digital twin adoption expands, it is expected to amplify the value proposition of predictive maintenance automation solutions.

Threat:

Data privacy & sovereignty

Data privacy and sovereignty concerns pose a growing challenge for the predictive maintenance automation market. These systems rely heavily on continuous data collection from connected machines and industrial networks. Sensitive operational data is often stored or processed in cloud environments, raising concerns about unauthorized access. Regulatory frameworks such as GDPR and region-specific data localization laws add compliance complexity. Cross-border data transfers can be restricted, limiting the scalability of global maintenance platforms. Cybersecurity risks, including ransomware and industrial espionage, further heighten apprehension among end users.

Covid-19 Impact:

The COVID-19 pandemic significantly influenced the adoption dynamics of predictive maintenance automation. Disruptions to manufacturing operations highlighted the risks associated with manual and reactive maintenance models. Travel restrictions limited on-site inspections, increasing reliance on remote monitoring and automated diagnostics. Many organizations accelerated digital transformation initiatives to ensure asset visibility during lockdowns. Supply chain interruptions emphasized the importance of maintaining equipment reliability with limited workforce availability. Post-pandemic recovery strategies have prioritized automation to enhance operational resilience. As a result, predictive maintenance solutions gained stronger acceptance across multiple industries.

The software segment is expected to be the largest during the forecast period

The software segment is expected to account for the largest market share during the forecast period. This segment includes analytics platforms, AI algorithms, condition monitoring applications, and asset management dashboards. Software solutions enable real-time data processing and predictive modeling across diverse equipment types. Continuous advancements in machine learning and cloud computing are enhancing prediction accuracy and scalability. Organizations prefer software-driven solutions due to their flexibility and ease of integration. Subscription-based models are also reducing long-term ownership costs for end users.

The manufacturing segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the manufacturing segment is predicted to witness the highest growth rate. Manufacturers rely on complex machinery where unplanned downtime can significantly impact productivity and revenue. Predictive maintenance systems help identify early-stage faults in production equipment. The growing adoption of smart factories and Industry 4.0 initiatives is driving demand for automated maintenance solutions. Manufacturers are increasingly using data-driven insights to optimize asset utilization and maintenance schedules. Integration with manufacturing execution systems further enhances operational efficiency.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, due to adoption of advanced industrial automation technologies. Strong presence of major solution providers and technology innovators supports market growth. Industries across the U.S. and Canada are investing heavily in AI-driven asset management systems. Favorable government initiatives promoting smart manufacturing further boost adoption. High awareness of operational efficiency and cost optimization strengthens demand.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR. Rapid industrialization and expanding manufacturing bases are driving demand across the region. Countries such as China, India, Japan, and South Korea are investing heavily in digital transformation initiatives. Increasing adoption of industrial IoT and smart factory concepts is accelerating market growth. Governments are promoting automation to enhance productivity and global competitiveness. Rising awareness of asset optimization among regional manufacturers is further supporting adoption.

Key players in the market

Some of the key players in Predictive Maintenance Automation Market include IBM Corporation, TIBCO Software, Microsoft, Uptake Technologies, SAP SE, C3.ai, Inc., Siemens AG, Oracle Corporation, General Electric, ABB Ltd., Schneider Electric, PTC Inc., Hitachi, Ltd, Honeywell, and Rockwell Automation.

Key Developments:

In December 2025, IBM and Confluent, Inc. announced they have entered into a definitive agreement under which IBM will acquire all of the issued and outstanding common shares of Confluent for $31 per share, representing an enterprise value of $11 billion. Confluent provides a leading open-source enterprise data streaming platform that connects, processes and governs reusable and reliable data and events in real time, foundational for the deployment of AI.

In October 2025, Oracle announced the latest capabilities added to Oracle Database@AWS to better support mission-critical enterprise workloads in the cloud. In addition, customers can now procure Oracle Database@AWS through qualified AWS and Oracle channel partners. This gives customers the flexibility to procure Oracle Database@AWS through their trusted partners and continue to innovate, modernize, and solve complex business problems in the cloud.

Components Covered:

  • Software
  • Services

Deployment Modes Covered:

  • On-Premises
  • Cloud

Technologies Covered:

  • IoT & Sensors
  • Artificial Intelligence & Machine Learning
  • Big Data Analytics
  • Digital Twin
  • Edge Computing
  • Other Technologies

End Users Covered:

  • Manufacturing
  • Energy & Utilities
  • Transportation & Logistics
  • Automotive
  • Oil & Gas
  • Aerospace & Defense
  • Chemicals & Pharmaceuticals
  • Other End Users

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Technology Analysis
  • 3.7 End User Analysis
  • 3.8 Emerging Markets
  • 3.9 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global Predictive Maintenance Automation Market, By Component

  • 5.1 Introduction
  • 5.2 Software
    • 5.2.1 Predictive Analytics Software
    • 5.2.2 Asset Performance Management (APM) Software
    • 5.2.3 Condition Monitoring Software
  • 5.3 Services
    • 5.3.1 Consulting & Implementation Services
    • 5.3.2 Support & Maintenance Services

6 Global Predictive Maintenance Automation Market, By Deployment Mode

  • 6.1 Introduction
  • 6.2 On-Premises
  • 6.3 Cloud

7 Global Predictive Maintenance Automation Market, By Technology

  • 7.1 Introduction
  • 7.2 IoT & Sensors
  • 7.3 Artificial Intelligence & Machine Learning
  • 7.4 Big Data Analytics
  • 7.5 Digital Twin
  • 7.6 Edge Computing
  • 7.7 Other Technologies

8 Global Predictive Maintenance Automation Market, By End User

  • 8.1 Introduction
  • 8.2 Manufacturing
  • 8.3 Energy & Utilities
  • 8.4 Transportation & Logistics
  • 8.5 Automotive
  • 8.6 Oil & Gas
  • 8.7 Aerospace & Defense
  • 8.8 Chemicals & Pharmaceuticals
  • 8.9 Other End Users

9 Global Predictive Maintenance Automation Market, By Geography

  • 9.1 Introduction
  • 9.2 North America
    • 9.2.1 US
    • 9.2.2 Canada
    • 9.2.3 Mexico
  • 9.3 Europe
    • 9.3.1 Germany
    • 9.3.2 UK
    • 9.3.3 Italy
    • 9.3.4 France
    • 9.3.5 Spain
    • 9.3.6 Rest of Europe
  • 9.4 Asia Pacific
    • 9.4.1 Japan
    • 9.4.2 China
    • 9.4.3 India
    • 9.4.4 Australia
    • 9.4.5 New Zealand
    • 9.4.6 South Korea
    • 9.4.7 Rest of Asia Pacific
  • 9.5 South America
    • 9.5.1 Argentina
    • 9.5.2 Brazil
    • 9.5.3 Chile
    • 9.5.4 Rest of South America
  • 9.6 Middle East & Africa
    • 9.6.1 Saudi Arabia
    • 9.6.2 UAE
    • 9.6.3 Qatar
    • 9.6.4 South Africa
    • 9.6.5 Rest of Middle East & Africa

10 Key Developments

  • 10.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 10.2 Acquisitions & Mergers
  • 10.3 New Product Launch
  • 10.4 Expansions
  • 10.5 Other Key Strategies

11 Company Profiling

  • 11.1 IBM Corporation
  • 11.2 TIBCO Software Inc.
  • 11.3 Microsoft Corporation
  • 11.4 Uptake Technologies Inc.
  • 11.5 SAP SE
  • 11.6 C3.ai, Inc.
  • 11.7 Siemens AG
  • 11.8 Oracle Corporation
  • 11.9 General Electric Company
  • 11.10 ABB Ltd.
  • 11.11 Schneider Electric SE
  • 11.12 PTC Inc.
  • 11.13 Hitachi, Ltd.
  • 11.14 Honeywell International Inc.
  • 11.15 Rockwell Automation, Inc.

List of Tables

  • Table 1 Global Predictive Maintenance Automation Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global Predictive Maintenance Automation Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global Predictive Maintenance Automation Market Outlook, By Software (2024-2032) ($MN)
  • Table 4 Global Predictive Maintenance Automation Market Outlook, By Predictive Analytics Software (2024-2032) ($MN)
  • Table 5 Global Predictive Maintenance Automation Market Outlook, By Asset Performance Management (APM) Software (2024-2032) ($MN)
  • Table 6 Global Predictive Maintenance Automation Market Outlook, By Condition Monitoring Software (2024-2032) ($MN)
  • Table 7 Global Predictive Maintenance Automation Market Outlook, By Services (2024-2032) ($MN)
  • Table 8 Global Predictive Maintenance Automation Market Outlook, By Consulting & Implementation Services (2024-2032) ($MN)
  • Table 9 Global Predictive Maintenance Automation Market Outlook, By Support & Maintenance Services (2024-2032) ($MN)
  • Table 10 Global Predictive Maintenance Automation Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 11 Global Predictive Maintenance Automation Market Outlook, By On-Premises (2024-2032) ($MN)
  • Table 12 Global Predictive Maintenance Automation Market Outlook, By Cloud (2024-2032) ($MN)
  • Table 13 Global Predictive Maintenance Automation Market Outlook, By Technology (2024-2032) ($MN)
  • Table 14 Global Predictive Maintenance Automation Market Outlook, By IoT & Sensors (2024-2032) ($MN)
  • Table 15 Global Predictive Maintenance Automation Market Outlook, By Artificial Intelligence & Machine Learning (2024-2032) ($MN)
  • Table 16 Global Predictive Maintenance Automation Market Outlook, By Big Data Analytics (2024-2032) ($MN)
  • Table 17 Global Predictive Maintenance Automation Market Outlook, By Digital Twin (2024-2032) ($MN)
  • Table 18 Global Predictive Maintenance Automation Market Outlook, By Edge Computing (2024-2032) ($MN)
  • Table 19 Global Predictive Maintenance Automation Market Outlook, By Other Technologies (2024-2032) ($MN)
  • Table 20 Global Predictive Maintenance Automation Market Outlook, By End User (2024-2032) ($MN)
  • Table 21 Global Predictive Maintenance Automation Market Outlook, By Manufacturing (2024-2032) ($MN)
  • Table 22 Global Predictive Maintenance Automation Market Outlook, By Energy & Utilities (2024-2032) ($MN)
  • Table 23 Global Predictive Maintenance Automation Market Outlook, By Transportation & Logistics (2024-2032) ($MN)
  • Table 24 Global Predictive Maintenance Automation Market Outlook, By Automotive (2024-2032) ($MN)
  • Table 25 Global Predictive Maintenance Automation Market Outlook, By Oil & Gas (2024-2032) ($MN)
  • Table 26 Global Predictive Maintenance Automation Market Outlook, By Aerospace & Defense (2024-2032) ($MN)
  • Table 27 Global Predictive Maintenance Automation Market Outlook, By Chemicals & Pharmaceuticals (2024-2032) ($MN)
  • Table 28 Global Predictive Maintenance Automation Market Outlook, By Other End Users (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.