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預測性維護市場的全球市場預測（截至 2030 年）：按組件、監控技術、組織規模、技術、最終用戶和區域進行分析

Predictive Maintenance Market Forecasts to 2030 - Global Analysis By Component, Monitoring Technique, Organization Size, Technology, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3個工作天內

價格

2023年全球預測性維護市場規模為103.4億美元，預計2030年將達710.5億美元，預測期內複合年成長率為31.7%。預測性維護市場包括使用先進的分析、機器學習演算法和物聯網感測器來提前預測設備故障、最佳化維護計劃並減少停機時間。透過分析歷史資料和即時感測器訊息，預測維護解決方案可以檢測表明潛在故障的模式和異常情況，並實現主動維護干預。這種方法使企業能夠避免代價高昂的非計劃性停機、最大限度地降低維護成本並延長資產的使用壽命。

根據世界銀行資料，2020年美國製造業付加遠超過23,370億美元。根據加拿大政府統計，製造業對GDP的貢獻約為1,740億加元，製造業每年的出口額約為3,540億加元。

資產績效管理需求不斷成長

APM 整合了資料分析、機器學習和物聯網感測器，以即時監控工業資產的運作狀況和效能。透過持續收集和分析資料，APM 系統可以在潛在設備故障或效率低下發生之前識別出指示潛在設備故障或效率低下的模式和異常情況。這種主動方法使組織能夠更有效地安排維護任務、最大限度地減少停機時間並降低整體營運成本。此外，隨著產業日益認知到最大化資產壽命和最佳化維護策略的重要性，APM 解決方案的採用不斷增加。

引進費用

預測性維護技術可以透過主動識別設備故障來顯著節省成本，但實施此類系統所需的初始投資可能對許多組織來說是令人望而卻步的。此成本不僅包括購買預測性維護軟體和硬體，還包括與資料收集、整合和人力資源培訓相關的成本。然而，用感測器和連接改造現有機器可能會進一步增加成本。

感測器技術的進步

感測器技術的進步透過實現更準確、更及時的設備健康狀況監控，正在徹底改變預測性維護市場。這些感測器配備物聯網連接、機器學習演算法和即時資料分析等功能，可連續監控溫度、振動和效能指標等各種參數。透過收集和分析這些資料，預測維修系統可以提前預測潛在的設備故障，防止代價高昂的停機並最大限度地提高營運效率。此外，這些感測器還可以深入了解使用模式和環境條件，從而實現更準確的維護計劃和資源分配。

環境和操作的可變性

隨著時間的推移，溫度波動、濕度水平和暴露於各種元素等環境因素會對設備性能產生不同的影響。同樣，由於不同的使用模式、不同的工作負載和維護實踐而導致的操作可變性進一步使預測性維護工作變得更加複雜。這些動態變數使得開發能夠準確預測設備故障和維護需求的穩健預測維護模型變得困難。此外，每個行業都有不同的營運環境，這增加了複雜性，需要針對行業量身定做的解決方案。

COVID-19 的影響

企業加速採用遠端監控和預測分析技術，以盡量減少身體接觸並確保在封鎖和社交距離期間的業務連續性。最佳化資產性能和防止製造、能源和運輸等關鍵行業的意外停機的需求推動了對預測性維護解決方案的需求激增。疫情造成的經濟放緩促使企業優先考慮成本效率和資產最佳化，進一步刺激預測性維護工具的採用，以簡化營運並充分利用資源。

預計腐蝕監測領域在預測期內將是最大的

預計腐蝕監測領域將成為預測期內最大的領域。腐蝕是許多行業的通用問題，如果不加以控制，可能會導致設備劣化、結構缺陷和代價高昂的故障。透過將腐蝕監測系統納入預測性維護策略，公司可以檢測腐蝕的早期徵兆並及時干預以防止進一步的損壞。這些系統利用各種技術，包括感測器、探頭和無損檢測方法，持續評估腐蝕程度並預測未來的劣化。

能源和公共產業領域預計在預測期內複合年成長率最高。

能源和公共產業領域預計在預測期內複合年成長率最高。發電廠、電網和公用事業涵蓋大量基礎設施和設備，最需要有效的維護實務。該領域的預測性維護涉及透過物聯網感測器持續監控設備狀況並分析大量資料以檢測異常並提前預測潛在故障。這種主動的方法不僅降低了維護成本，還提高了安全性和可靠性，確保為消費者提供不間斷的服務，同時最大限度地提高資源利用率並最大限度地減少對環境的影響。

比最大的地區

客戶通路的普及、對資產維護和營運成本的日益關注，以及人工智慧 (AI)、機器學習 (ML)、聲學監測和物聯網 (IoT) 等最尖端科技的日益採用，將推動北美進入預測期，預計在此期間佔據最大市場佔有率。此外，由於人們對預測指標及其重要性的認知不斷增強以及技術的早期採用，該地區的市場正在進一步成長。

複合年成長率最高的地區

預計歐洲在預測期內將出現獲利成長。歐盟能源效率和排放指令等法規的實施正在激勵企業採用預測性維護策略。因此，公司正在增加對預測性維護技術的投資，以遵守這些法規，同時提高營運績效。此外，政府舉措採用預測性維護解決方案提供津貼、補貼和稅收優惠，使各行業的公司更容易獲得這些技術，從而進一步推動市場成長。

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目錄

第1章執行摘要

第2章 前言

  • 概述
  • 相關利益者
  • 調查範圍
  • 調查方法
    • 資料探勘
    • 資料分析
    • 資料檢驗
    • 研究途徑
  • 研究資訊來源
    • 主要研究資訊來源
    • 二次研究資訊來源
    • 先決條件

第3章市場趨勢分析

  • 促進因素
  • 抑制因素
  • 機會
  • 威脅
  • 技術分析
  • 最終用戶分析
  • 新興市場
  • COVID-19 的影響

第4章波特五力分析

  • 供應商的議價能力
  • 買方議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭公司之間的敵對關係

第5章全球預測維護市場：按組成部分

  • 服務
    • 培訓和諮詢
    • 支援與維護
  • 解決方案
    • 獨立的
    • 融合的
  • 其他組件

第6章全球預測維護市場：透過監控技術

  • 腐蝕監測
  • 熱成像
  • 油液分析
  • 振動監測
  • 扭力監控
  • 其他監測技術

第7章全球預測維修市場：依組織規模

  • 中小企業
  • 主要企業

第8章全球預測維修市場：依技術分類

  • 人工智慧
  • 分析和資料管理
  • 其他技術

第9章全球預測維護市場：依最終用戶分類

  • 製造業
  • 資訊科技/通訊
  • 衛生保健
  • 能源/公共產業
  • 汽車/交通
  • 航太/國防
  • 其他最終用戶

第10章全球預測維護市場：按地區

  • 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
    • 其他歐洲國家
  • 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
    • 南美洲其他地區
  • 中東/非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東/非洲

第11章 主要進展

  • 協議、夥伴關係、協作和合資企業
  • 收購和合併
  • 新產品發布
  • 業務擴展
  • 其他關鍵策略

第12章 公司概況

  • Siemens
  • Schneider Electric SE
  • Rockwell Automation
  • Robert Bosch GmbH
  • Microsoft
  • IBM Corporation
  • Hitachi, Ltd
  • Honeywell International Inc
  • General Electric
  • Cisco Systems, Inc
  • Accenture plc
Product Code: SMRC26135

According to Stratistics MRC, the Global Predictive Maintenance Market is accounted for $10.34 billion in 2023 and is expected to reach $71.05 billion by 2030 growing at a CAGR of 31.7% during the forecast period. The Predictive Maintenance Market encompasses the use of advanced analytics, machine learning algorithms, and IoT sensors to predict equipment failures before they occur, thereby optimizing maintenance schedules and reducing downtime. By analyzing historical data and real-time sensor information, predictive maintenance solutions can detect patterns and anomalies indicative of potential breakdowns, enabling proactive maintenance interventions. This approach helps businesses avoid costly unplanned downtime, minimize maintenance costs, and extend the lifespan of their assets.

According to World Bank data, manufacturing value addition in 2020 in the US was well above USD 2,337 billion. According to Government of Canada statistics, the manufacturing sector's contribution to the GDP was nearly CAD 174 billion, and exports from the sector were approximated at CAD 354 billion per year.

Market Dynamics:

Driver:

Increasing demand for asset performance management

APM integrates data analytics, machine learning, and IoT sensors to monitor the health and performance of industrial assets in real-time. By continuously collecting and analyzing data, APM systems can identify patterns and anomalies that indicate potential equipment failures or inefficiencies before they occur. This proactive approach enables organizations to schedule maintenance tasks more efficiently, minimizing downtime and reducing overall operational costs. Furthermore, as industries increasingly recognize the importance of maximizing asset lifespan and optimizing maintenance strategies, the adoption of APM solutions continues to rise.

Restraint:

Cost of implementation

While predictive maintenance technology offers the potential for substantial cost savings by identifying equipment failures before they occur, the initial investment required to implement such systems can be prohibitive for many organizations. This cost encompasses not only the purchase of predictive maintenance software and hardware but also the expenses associated with data collection, integration, and personnel training. However, retrofitting existing machinery with sensors and connectivity features can further escalate costs.

Opportunity:

Advancements in sensor technologies

Advancements in sensor technologies are revolutionizing the predictive maintenance market by enabling more accurate and timely monitoring of equipment health. These sensors, equipped with capabilities like IoT connectivity, machine learning algorithms, and real-time data analysis, allow for continuous monitoring of various parameters such as temperature, vibration, and performance metrics. By collecting and analyzing this data, predictive maintenance systems can predict potential equipment failures before they occur, thus preventing costly downtime and maximizing operational efficiency. Additionally, these sensors provide insights into usage patterns and environmental conditions, allowing for more precise maintenance scheduling and resource allocation.

Threat:

Environmental and operational variability

Environmental factors such as temperature fluctuations, humidity levels, and exposure to various elements can impact equipment performance differently over time. Similarly, operational variability stemming from diverse usage patterns, workload fluctuations, and maintenance practices further complicates predictive maintenance efforts. These dynamic variables make it challenging to develop robust predictive maintenance models that can accurately anticipate equipment failures and maintenance needs. The diversity in operational environments across industries adds another layer of complexity, requiring tailored solutions for different sectors.

Covid-19 Impact:

It accelerated the adoption of remote monitoring and predictive analytics technologies as companies sought to minimize physical contact and ensure operational continuity amid lockdowns and social distancing measures. This surge in demand for predictive maintenance solutions was driven by the need to optimize asset performance and prevent unexpected downtime in critical industries such as manufacturing, energy, and transportation. The economic slowdown induced by the pandemic prompted businesses to prioritize cost efficiency and asset optimization, further driving the adoption of predictive maintenance tools to streamline operations and maximize resource utilization.

The Corrosion Monitoring segment is expected to be the largest during the forecast period

Corrosion Monitoring segment is expected to be the largest during the forecast period. Corrosion is a common issue in many industries, leading to equipment degradation, structural weakness, and ultimately, costly failures if left unchecked. By integrating corrosion monitoring systems into predictive maintenance strategies, businesses can detect early signs of corrosion, allowing for timely interventions to prevent further damage. These systems utilize various techniques such as sensors, probes, and non-destructive testing methods to continuously assess corrosion levels and predict future deterioration.

The Energy & Utilities segment is expected to have the highest CAGR during the forecast period

Energy & Utilities segment is expected to have the highest CAGR during the forecast period. With the vast infrastructure and equipment spread across power plants, grid networks, and utility facilities, the need for efficient maintenance practices is paramount. Predictive maintenance in this sector involves the continuous monitoring of equipment conditions through IoT sensors, analyzing vast amounts of data to detect anomalies and predict potential failures before they occur. This proactive approach not only reduces maintenance costs but also enhances safety and reliability, ensuring uninterrupted service delivery to consumers while maximizing resource utilization and minimizing environmental impact.

Region with largest share:

Due to the spread of customer channels, rising concerns over asset maintenance and operating costs, and the increasing adoption of cutting-edge technologies like artificial intelligence (AI), machine learning (ML), acoustic monitoring, and the Internet of Things (IoT), North America commanded the largest share of the market during the extrapolated period. Furthermore, the market in the region has grown even more as a result of growing awareness of predictive metrics, their significance, and early technological adoption.

Region with highest CAGR:

Europe region is projected to witness profitable growth over the forecast period. The implementation of regulations such as the European Union's directives on energy efficiency and emissions reduction is incentivizing companies to adopt predictive maintenance strategies. Consequently, companies are increasingly investing in predictive maintenance technologies to comply with these regulations while simultaneously improving their operational performance. Moreover, government initiatives offering grants, subsidies, or tax incentives for adopting predictive maintenance solutions further stimulate market growth by making these technologies more accessible to businesses across different sectors.

Key players in the market

Some of the key players in Predictive Maintenance market include Siemens, Schneider Electric SE, Rockwell Automation, Robert Bosch GmbH, Microsoft, IBM Corporation, Hitachi, Ltd, Honeywell International Inc, General Electric, Cisco Systems, Inc and Accenture plc.

Key Developments:

In July 2022, two companies in Houston announced they would develop a new predictive maintenance software. Shape Corporation, along with Radix Engineering and Software, collaborated to develop a tool that would enable companies that operate floating production units to implement their system to positively impact their cash flow and environment, and health impact.

In July 2022, Keolis and Stratio announced a partnership that would provide predictive maintenance solutions to Keolis' fleet. Keolis provides solutions to public transit systems, and Stratio develops computerized maintenance management systems; The Stratio Platform will enable real-time data to be made available to Keolis' engineers to ensure minimal downtime.

In July 2022, Valmet announced a new application that would enable better tracking of machinery. The application is part of Valmet Industrial Internet portfolio which offers predictive maintenance and root cause analysis solutions for various machines in the paper and pulp industry.

In March 2022, C3 AI announced that it had reached a phenomenal number of more than 10,000 machines of Shell Corporation under their predictive maintenance program. The program uses more than 3 million sensors and 11,000 ML models.

Components Covered:

  • Service
  • Solution
  • Other Components

Monitoring Techniques Covered:

  • Corrosion Monitoring
  • Thermography
  • Oil Analysis
  • Vibration Monitoring
  • Torque Monitoring
  • Other Monitoring Techniques

Organization Size Covered:

  • Small & Medium-sized Enterprises
  • Large Enterprises

Technologies Covered:

  • Artificial Intelligence
  • Analytics & Data Management
  • Other Technologies

End Users Covered:

  • Manufacturing
  • IT & Telecommunication
  • Healthcare
  • Energy & Utilities
  • Automotive & Transportation
  • Aerospace & Defense
  • 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 2021, 2022, 2023, 2026, and 2030
  • 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 Market, By Component

  • 5.1 Introduction
  • 5.2 Service
    • 5.2.1 Training & Consulting
    • 5.2.2 Support & Maintenance
  • 5.3 Solution
    • 5.3.1 Standalone
    • 5.3.2 Integrated
  • 5.4 Other Components

6 Global Predictive Maintenance Market, By Monitoring Technique

  • 6.1 Introduction
  • 6.2 Corrosion Monitoring
  • 6.3 Thermography
  • 6.4 Oil Analysis
  • 6.5 Vibration Monitoring
  • 6.6 Torque Monitoring
  • 6.7 Other Monitoring Techniques

7 Global Predictive Maintenance Market, By Organization Size

  • 7.1 Introduction
  • 7.2 Small & Medium-sized Enterprises
  • 7.3 Large Enterprises

8 Global Predictive Maintenance Market, By Technology

  • 8.1 Introduction
  • 8.2 Artificial Intelligence
  • 8.3 Analytics & Data Management
  • 8.4 Other Technologies

9 Global Predictive Maintenance Market, By End User

  • 9.1 Introduction
  • 9.2 Manufacturing
  • 9.3 IT & Telecommunication
  • 9.4 Healthcare
  • 9.5 Energy & Utilities
  • 9.6 Automotive & Transportation
  • 9.7 Aerospace & Defense
  • 9.8 Other End Users

10 Global Predictive Maintenance Market, By Geography

  • 10.1 Introduction
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
    • 10.2.3 Mexico
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 Italy
    • 10.3.4 France
    • 10.3.5 Spain
    • 10.3.6 Rest of Europe
  • 10.4 Asia Pacific
    • 10.4.1 Japan
    • 10.4.2 China
    • 10.4.3 India
    • 10.4.4 Australia
    • 10.4.5 New Zealand
    • 10.4.6 South Korea
    • 10.4.7 Rest of Asia Pacific
  • 10.5 South America
    • 10.5.1 Argentina
    • 10.5.2 Brazil
    • 10.5.3 Chile
    • 10.5.4 Rest of South America
  • 10.6 Middle East & Africa
    • 10.6.1 Saudi Arabia
    • 10.6.2 UAE
    • 10.6.3 Qatar
    • 10.6.4 South Africa
    • 10.6.5 Rest of Middle East & Africa

11 Key Developments

  • 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 11.2 Acquisitions & Mergers
  • 11.3 New Product Launch
  • 11.4 Expansions
  • 11.5 Other Key Strategies

12 Company Profiling

  • 12.1 Siemens
  • 12.2 Schneider Electric SE
  • 12.3 Rockwell Automation
  • 12.4 Robert Bosch GmbH
  • 12.5 Microsoft
  • 12.6 IBM Corporation
  • 12.7 Hitachi, Ltd
  • 12.8 Honeywell International Inc
  • 12.9 General Electric
  • 12.10 Cisco Systems, Inc
  • 12.11 Accenture plc

List of Tables

  • Table 1 Global Predictive Maintenance Market Outlook, By Region (2021-2030) ($MN)
  • Table 2 Global Predictive Maintenance Market Outlook, By Component (2021-2030) ($MN)
  • Table 3 Global Predictive Maintenance Market Outlook, By Service (2021-2030) ($MN)
  • Table 4 Global Predictive Maintenance Market Outlook, By Training & Consulting (2021-2030) ($MN)
  • Table 5 Global Predictive Maintenance Market Outlook, By Support & Maintenance (2021-2030) ($MN)
  • Table 6 Global Predictive Maintenance Market Outlook, By Solution (2021-2030) ($MN)
  • Table 7 Global Predictive Maintenance Market Outlook, By Standalone (2021-2030) ($MN)
  • Table 8 Global Predictive Maintenance Market Outlook, By Integrated (2021-2030) ($MN)
  • Table 9 Global Predictive Maintenance Market Outlook, By Other Components (2021-2030) ($MN)
  • Table 10 Global Predictive Maintenance Market Outlook, By Monitoring Technique (2021-2030) ($MN)
  • Table 11 Global Predictive Maintenance Market Outlook, By Corrosion Monitoring (2021-2030) ($MN)
  • Table 12 Global Predictive Maintenance Market Outlook, By Thermography (2021-2030) ($MN)
  • Table 13 Global Predictive Maintenance Market Outlook, By Oil Analysis (2021-2030) ($MN)
  • Table 14 Global Predictive Maintenance Market Outlook, By Vibration Monitoring (2021-2030) ($MN)
  • Table 15 Global Predictive Maintenance Market Outlook, By Torque Monitoring (2021-2030) ($MN)
  • Table 16 Global Predictive Maintenance Market Outlook, By Other Monitoring Techniques (2021-2030) ($MN)
  • Table 17 Global Predictive Maintenance Market Outlook, By Organization Size (2021-2030) ($MN)
  • Table 18 Global Predictive Maintenance Market Outlook, By Small & Medium-sized Enterprises (2021-2030) ($MN)
  • Table 19 Global Predictive Maintenance Market Outlook, By Large Enterprises (2021-2030) ($MN)
  • Table 20 Global Predictive Maintenance Market Outlook, By Technology (2021-2030) ($MN)
  • Table 21 Global Predictive Maintenance Market Outlook, By Artificial Intelligence (2021-2030) ($MN)
  • Table 22 Global Predictive Maintenance Market Outlook, By Analytics & Data Management (2021-2030) ($MN)
  • Table 23 Global Predictive Maintenance Market Outlook, By Other Technologies (2021-2030) ($MN)
  • Table 24 Global Predictive Maintenance Market Outlook, By End User (2021-2030) ($MN)
  • Table 25 Global Predictive Maintenance Market Outlook, By Manufacturing (2021-2030) ($MN)
  • Table 26 Global Predictive Maintenance Market Outlook, By IT & Telecommunication (2021-2030) ($MN)
  • Table 27 Global Predictive Maintenance Market Outlook, By Healthcare (2021-2030) ($MN)
  • Table 28 Global Predictive Maintenance Market Outlook, By Energy & Utilities (2021-2030) ($MN)
  • Table 29 Global Predictive Maintenance Market Outlook, By Automotive & Transportation (2021-2030) ($MN)
  • Table 30 Global Predictive Maintenance Market Outlook, By Aerospace & Defense (2021-2030) ($MN)
  • Table 31 Global Predictive Maintenance Market Outlook, By Other End Users (2021-2030) ($MN)

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