The predictive maintenance market size is projected to reach US$ 59.81 billion by 2031 from US$ 6.76 billion in 2023. The market is expected to register a CAGR of 27.4% during 2023–2031. The investment in predictive maintenance solutions to reduce costs and downtime is likely to remain a key trend in the market.
In recent decades, the high demand to reduce increasingly expensive energy costs and shift toward a sustainable future have made energy audits important. As energy expenditure is a major concern for industries, companies are continuously checking energy consumption and significantly aiming to minimize it.
Predictive maintenance improves equipment performance by merging Internet of Things sensors, artificial intelligence, and data science. It entails employing cloud-enabled technology to monitor and estimate maintenance requirements based on asset status, as well as identifying abnormalities that might lead to unexpected breakdowns. As a consequence, manufacturers profit significantly from forecasting equipment maintenance requirements in order to save costs and increase uptime. The core approach used to monitor industrial assets in real time is Condition Based Monitoring. Advanced IIoT sensors record complicated machine health data such as vibration, acoustics, temperature, and RPM, among others. The collected data is then evaluated to identify any variances in asset performance or problems that would be hard to detect with traditional equipment.
Organizations now face an increasing desire to reduce maintenance expenses, equipment failures, and downtimes. Predictive maintenance is an excellent method for addressing these difficulties. Predictive maintenance systems use data analytics and machine learning to identify possible flaws in equipment before they occur, allowing for proactive maintenance interventions. Predicting equipment breakdowns early allows firms to arrange repair during planned downtime, reducing interruptions to operations and maximizing equipment uptime.
AI-based predictive maintenance has a wide range of applications in manufacturing. It employs powerful machine learning algorithms to evaluate huge amounts of data collected during production, providing key insights into achieving manufacturing excellence. In addition, machine learning algorithms utilize massive amounts of historical data to simulate various situations and predict what will go wrong and when. Advanced artificial intelligence algorithms recognize a machine's typical data behavior and use it as a baseline to detect and warn of variations in real-time.
Key segments that contributed to the derivation of the predictive maintenance market analysis are component, deployment type, technique and industry.
The geographic scope of the predictive maintenance market report is mainly divided into five regions: North America, Asia Pacific, Europe, Middle East & Africa, and South & Central America.
North America dominated the market in 2023. The use of modern technologies such as Machine Learning (ML), acoustic monitoring, Artificial Intelligence (AI), and the Internet of Things (IoT), as well as the expansion of customer channels and growing concerns about asset maintenance and operating expenses, have all contributed to growth. Furthermore, the adoption of IoT-connected devices in consumer electronics and M2M applications, rising demand for connected automobiles in the automotive industry, and a growing desire for innovative consumer electronics are propelling market expansion.
The regional trends and factors influencing the Predictive Maintenance Market throughout the forecast period have been thoroughly explained by the analysts at Insight Partners. This section also discusses Predictive Maintenance Market segments and geography across North America, Europe, Asia Pacific, Middle East and Africa, and South and Central America.
| Report Attribute | Details |
|---|---|
| Market size in 2023 | US$ 6.76 Billion |
| Market Size by 2031 | US$ 59.81 Billion |
| Global CAGR (2023 - 2031) | 27.4% |
| Historical Data | 2021-2022 |
| Forecast period | 2024-2031 |
| Segments Covered |
By Component
|
| Regions and Countries Covered | North America
|
| Market leaders and key company profiles |
The Predictive Maintenance Market market is growing rapidly, driven by increasing end-user demand due to factors such as evolving consumer preferences, technological advancements, and greater awareness of the product's benefits. As demand rises, businesses are expanding their offerings, innovating to meet consumer needs, and capitalizing on emerging trends, which further fuels market growth.
Market players density refers to the distribution of firms or companies operating within a particular market or industry. It indicates how many competitors (market players) are present in a given market space relative to its size or total market value.
Major Companies operating in the Predictive Maintenance Market are:
Disclaimer: The companies listed above are not ranked in any particular order.
The predictive maintenance market is evaluated by gathering qualitative and quantitative data post primary and secondary research, which includes important corporate publications, association data, and databases. A few of the developments in the predictive maintenance market are listed below:
The “Predictive Maintenance Market Size and Forecast (2021–2031)” report provides a detailed analysis of the market covering below areas:
The high demand to reduce increasingly expensive energy costs and shift toward a sustainable future is driving the market.
North America dominated the predictive maintenance market in 2023.
The investment in predictive maintenance solutions to reduce costs and downtime is likely to remain a key trend in the market.
General Electric Co., Hitachi Ltd., IBM Corporation, Microsoft Corporation, and PTC, Inc. are among the leading players in the predictive maintenance market.
The estimated value of the Predictive Maintenance market is expected to reach US$ 59.81 billion by 2031.
The market is expected to grow at a CAGR of 27.4% over the forecast period.