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市场调查报告书
商品编码
1489349

预测性维护市场的全球市场预测（截至 2030 年）：按组件、监控技术、组织规模、技术、最终用户和区域进行分析

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个工作天内

价格

2023年全球预测性维护市场规模为103.4亿美元，预计2030年将达710.5亿美元，预测期内复合年增长率为31.7%。预测性维护市场包括使用先进的分析、机器学习演算法和物联网感测器来提前预测设备故障、优化维护计划并减少停机时间。透过分析历史资料和即时感测器讯息，预测维护解决方案可以检测表明潜在故障的模式和异常情况，并实现主动维护干预。这种方法使企业能够避免代价高昂的非计划性停机、最大限度地降低维护成本并延长资产的使用寿命。

根据世界银行资料，2020年美国製造业付加远超过23,370亿美元。根据加拿大政府统计，製造业对GDP的贡献约为1,740亿加元，製造业每年的出口额约为3,540亿加元。

资产绩效管理需求不断成长

APM 整合了资料分析、机器学习和物联网感测器，以即时监控工业资产的运作状况和效能。透过持续收集和分析资料，APM 系统可以在潜在设备故障或效率低下发生之前识别出指示潜在设备故障或效率低下的模式和异常情况。这种主动方法使组织能够更有效地安排维护任务、最大限度地减少停机时间并降低整体营运成本。此外，随着产业日益认识到最大化资产寿命和优化维护策略的重要性，APM 解决方案的采用不断增加。

引进费用

预测性维护技术可以透过主动识别设备故障来显着节省成本，但实施此类系统所需的初始投资可能对许多组织来说是令人望而却步的。此成本不仅包括购买预测性维护软体和硬件，还包括与资料收集、整合和人力资源培训相关的成本。然而，用感测器和连接改造现有机器可能会进一步增加成本。

感测器技术的进步

感测器技术的进步透过实现更准确、更及时的设备健康状况监控，正在彻底改变预测性维护市场。这些感测器配备物联网连接、机器学习演算法和即时资料分析等功能，可连续监控温度、振动和效能指标等各种参数。透过收集和分析这些资料，预测维修系统可以提前预测潜在的设备故障，防止代价高昂的停机并最大限度地提高营运效率。此外，这些感测器还可以深入了解使用模式和环境条件，从而实现更准确的维护计划和资源分配。

环境和操作的可变性

随着时间的推移，温度波动、湿度水平和暴露于各种元素等环境因素会对设备性能产生不同的影响。同样，由于不同的使用模式、不同的工作负载和维护实践而导致的操作可变性进一步使预测性维护工作变得更加复杂。这些动态变数使得开发能够准确预测设备故障和维护需求的稳健预测维护模型变得困难。此外，每个行业都有不同的营运环境，这增加了复杂性，需要针对行业量身定制的解决方案。

COVID-19 的影响

企业加速采用远端监控和预测分析技术，以尽量减少身体接触并确保在封锁和社交距离期间的业务连续性。优化资产性能和防止製造、能源和运输等关键行业的意外停机的需求推动了对预测性维护解决方案的需求激增。疫情造成的经济放缓促使企业优先考虑成本效率和资产优化，进一步刺激预测性维护工具的采用，以简化营运并充分利用资源。

预计腐蚀监测领域在预测期内将是最大的

预计腐蚀监测领域将成为预测期内最大的领域。腐蚀是许多行业的通用问题，如果不加以控制，可能会导致设备劣化、结构缺陷和代价高昂的故障。透过将腐蚀监测系统纳入预测性维护策略，公司可以检测腐蚀的早期征兆并及时干预以防止进一步的损坏。这些系统利用各种技术，包括感测器、探头和无损检测方法，持续评估腐蚀程度并预测未来的劣化。

能源和公共产业领域预计在预测期内复合年增长率最高。

能源和公共产业领域预计在预测期内复合年增长率最高。发电厂、电网和公用事业涵盖大量基础设施和设备，最需要有效的维护实务。该领域的预测性维护涉及透过物联网感测器持续监控设备状况并分析大量资料以检测异常并提前预测潜在故障。这种主动的方法不仅降低了维护成本，还提高了安全性和可靠性，确保为消费者提供不间断的服务，同时最大限度地提高资源利用率并最大限度地减少对环境的影响。

比最大的地区

客户通路的普及、对资产维护和营运成本的日益关注，以及人工智慧 (AI)、机器学习 (ML)、声学监测和物联网 (IoT) 等最尖端科技的日益采用，将推动北美进入预测期，预计在此期间占据最大市场份额。此外，由于人们对预测指标及其重要性的认识不断增强以及技术的早期采用，该地区的市场正在进一步增长。

复合年增长率最高的地区

预计欧洲在预测期内将出现获利成长。欧盟能源效率和排放指令等法规的实施正在激励企业采用预测性维护策略。因此，公司正在增加对预测性维护技术的投资，以遵守这些法规，同时提高营运绩效。此外，政府倡议采用预测性维护解决方案提供津贴、补贴和税收优惠，使各行业的公司更容易获得这些技术，从而进一步推动市场成长。

提供免费客製化：

订阅此报告的客户可以存取以下免费自订选项之一：

  • 公司简介
    • 其他市场公司的综合分析（最多 3 家公司）
    • 主要企业SWOT分析（最多3家企业）
  • 区域分割
    • 根据客户兴趣对主要国家的市场估计、预测和复合年增长率（註：基于可行性检查）
  • 竞争基准化分析
    • 根据产品系列、地理分布和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 前言

  • 概述
  • 相关利益者
  • 调查范围
  • 调查方法
    • 资料探勘
    • 资料分析
    • 资料检验
    • 研究途径
  • 研究资讯来源
    • 主要研究资讯来源
    • 二次研究资讯来源
    • 先决条件

第三章市场趋势分析

  • 促进因素
  • 抑制因素
  • 机会
  • 威胁
  • 技术分析
  • 最终用户分析
  • 新兴市场
  • COVID-19 的影响

第4章波特五力分析

  • 供应商的议价能力
  • 买方议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争公司之间的敌对关係

第五章全球预测维护市场：按组成部分

  • 服务
    • 培训和咨询
    • 支援与维护
  • 解决方案
    • 独立的
    • 融合的
  • 其他组件

第六章全球预测维护市场：透过监控技术

  • 腐蚀监测
  • 热成像
  • 油液分析
  • 振动监测
  • 扭力监控
  • 其他监测技术

第七章全球预测维修市场：依组织规模

  • 中小企业
  • 大公司

第八章全球预测维修市场：依技术分类

  • 人工智慧
  • 分析和资料管理
  • 其他技术

第九章全球预测维护市场：依最终用户分类

  • 製造业
  • 资讯科技/通讯
  • 卫生保健
  • 能源/公共产业
  • 汽车/交通
  • 航太/国防
  • 其他最终用户

第十章全球预测维护市场：按地区

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲国家
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东/非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东/非洲

第十一章 主要进展

  • 协议、伙伴关係、协作和合资企业
  • 收购和合併
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第十二章 公司概况

  • 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.