Learn about the global predictive maintenance market through The Business Research Company, which provides information on predictive maintenance market size, predictive maintenance market drivers and restraints, predictive maintenance market players, the COVID-19 impact on the predictive maintenance market, and more.

The global predictive maintenance market is expected to grow from $4.46 billion in 2021 to $5.86 billion in 2022 at a compound annual growth rate (CAGR) of 31.55%. The global predictive maintenance market size is expected to grow to $17.09 billion in 2026 at a compound annual growth rate (CAGR) of 30.67%.

The growing demand to reduce maintenance costs, equipment failure, and downtime is significantly contributing to the growth of the predictive maintenance market.

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The predictive maintenance market consists of sales of predictive maintenance by entities (organizations, sole traders, and partnerships) that refer to data-driven condition monitoring tools and techniques designed to analyze equipment conditions and predict maintenance requirements. It uses testing methods such as data acquisition, data transformation, asset health evaluation, prognostics, decision support systems, and a human interface layer that helps various industries to reduce maintenance and protect their machinery by connecting all the data sources combined with data analytics. The key element used in the process is the internet of things (IoT), which allows systems to work together to translate and analyze the recorded data.

Global Predictive Maintenance Market Segments Include:

By Component: Solutions, Service

By Deployment Mode: On-premises, Cloud

By Stakeholder: MRO, OEM/ODM, Technology Integrators

By Application: Heavy Machinery, Small Machinery, Others

By End User: Aerospace and Defense, Automotive and Transportation, Energy and Utilities, Healthcare, IT and Telecommunication, Manufacturing, Oil and Gas, Others

By Geography: The market is segmented into North America, South America, Asia-Pacific, Eastern Europe, Western Europe, Middle East and Africa. Among these regions, North America is the largest region in the global predictive maintenance market in 2021.

The adoption of advanced technology is a key trend gaining popularity in the predictive maintenance market. Major companies operating in the predictive maintenance market are focused on providing technologically advanced predictive maintenance solutions to meet the technical demands of end customers and strengthen their market position. These companies are implementing next-generation technologies into their services such as IoT, AI (artificial intelligence), ML (machine learning), thermography, cloud computing, and others to match the market demand for better maintenance.

TBRC’s Predictive Maintenance Market Report Covers:

Major Market Players: Microsoft, IBM, SAP, SAS Institute, Software AG, TIBCO Software, GE, Schneider Electric, PTC, C3 AI Inc, Uptake Technologies Inc, Asystom, Engineering Consultants Group Inc, HPE, Altair, Splunk, Oracle, Hitachi, AWS, Reliability Solutions Sp zoo, Expert Microsystems Inc, Fiix Inc, Operational Excellence (Opex) Group Ltd, Sigma Industrial Precision, SparkCognition, and Honeywell International Inc.

Regions: Asia-Pacific, China, Western Europe, Eastern Europe, North America, USA, South America, Middle East and Africa.

Countries: Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Russia, South Korea, UK, USA.

Time Series: Five years historic (2016-21) and ten years forecast (2022-2026-2031)

The Predictive Maintenance Global Market Report 2022  is one of a series of new reports from The Business Research Company that provides predictive maintenance market overviews, analyzes and forecasts market size, share, predictive maintenance market players, predictive maintenance market segments and geographies, leading competitor revenues, profiles and market shares.

TBRC’s predictive maintenance market report identifies top countries and segments for opportunities and strategies based on market trends and leading competitors’ approaches.

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