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Predictive Maintenance Market Size USD 162.1 Billion By 2033, At A 32.2% CAGR, In-Depth Industry Insights
(MENAFN- EIN Presswire)
The global predictive maintenance industry was valued at $10.1 billion in 2023, and is estimated to reach $162.1 billion by 2033, growing at a CAGR of 32.2% from 2024 to 2033.
Major industry players such as SAP SE, Microsoft Corporation, IBM Corporation, ABB Ltd, Software AG, Schneider Electric SE, SAS Institute Inc., Amazon Web Services, Inc., Hitachi, Ltd., Google LLC
By region, Asia-Pacific held the highest market share in terms of revenue in 2023, accounting for three-fourths of the global predictive maintenance market, owing to widespread adoption of Industrial Internet of Things (IIoT) technologies. Manufacturing and industrial sectors in countries such as China, Japan, and South Korea, are integrating IIoT sensors to collect real-time data from machinery and equipment, facilitating continuous monitoring and early fault detection.
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By end user, the manufacturing segment held the highest market share in 2023, accounting for nearly two-fifths of the global predictive maintenance industry revenue and is expected to retain its dominance throughout the forecast period, owing to the integration of Industrial Internet of Things (IIoT) devices, which collect real-time data from machinery and equipment. These sensors monitor various parameters such as vibration, temperature, and pressure, providing continuous insights into the health of manufacturing assets.
However, the energy and utilities segment is projected to manifest the highest CAGR from 2024 to 2032, owing to an increase in deployment of IoT sensors across energy infrastructure, such as power plants, wind turbines, and grid systems. These sensors continuously collect data on equipment performance, environmental conditions, and operational parameters, providing real-time insights into the health of critical assets.
By technique, the vibration monitoring segment held the highest market share in 2023, accounting for more than one-third of the global predictive maintenance market revenue and is expected to retain its dominance throughout the forecast period, owing to the proliferation of wireless and IoT-enabled vibration sensors. These sensors provide real-time data and are easier to install and maintain compared to traditional wired sensors. They enable continuous monitoring of machinery without the need for extensive cabling, reducing setup time and costs.
However, the infrared segment is projected to manifest the highest CAGR from 2024 to 2033, owing to the enhanced sensitivity and accuracy of modern IR cameras. These advancements allow for more precise detection of thermal anomalies, enabling earlier identification of potential equipment failures. The resolution of IR cameras has improved, providing clearer and more detailed thermal images that help maintenance teams diagnose issues more effectively.
Buy this Complete Report (309 Pages PDF with Insights, Charts, Tables, and Figures) at:
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By deployment mode, the on-premise segment held the highest market share in 2023, accounting for more than one-third of the global predictive maintenance market revenue and is expected to retain its dominance throughout the forecast period, owing to increasing adoption in industries that require high data security and low latency, such as aerospace, defense, and critical infrastructure sectors. These systems offer robust control over data privacy and compliance with stringent regulatory standards, making them suitable for environments where data sensitivity is paramount.
However, the cloud segment is projected to manifest the highest CAGR from 2024 to 2033, owing to the widespread adoption of Internet of Things (IoT) devices and sensors that continuously collect data from machinery and equipment. This data is transmitted to cloud platforms, where it can be stored, processed, and analyzed in real-time, allowing for more accurate and timely predictions of equipment failures.
By component, the solution segment held the highest market share in 2023, accounting for more than one-third of the global predictive maintenance market revenue and is likely to retain its dominance throughout the forecast period, owing to rise in use of artificial intelligence (AI) and machine learning (ML) algorithms, allowing for more sophisticated analysis of data and better identification of patterns that precede equipment failures.
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However, the services segment is projected to manifest the highest CAGR from 2024 to 2033, owing to rise in adoption of AI and machine learning in predictive maintenance services, which allows for more precise and sophisticated analysis of data to predict equipment failures. Service providers are increasingly leveraging big data analytics to process vast amounts of information collected from sensors and IoT devices, enabling real-time monitoring and more accurate predictions.
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The report is valuable in highlighting business performance, operating segments, product portfolio, and strategic moves of market players.
WILMINGTON, DE, UNITED STATES, March 11, 2025 /EINPresswire / -- The global predictive maintenance market is experiencing growth due to rise in demand for increased asset uptime and lowering maintenance costs, increase in investments in predictive maintenance in industries as a result of IoT adoption, and advent of ML and AI. However, the implementation problems and data security concerns hinder market growth to some extent.The global predictive maintenance industry was valued at $10.1 billion in 2023, and is estimated to reach $162.1 billion by 2033, growing at a CAGR of 32.2% from 2024 to 2033.
Major industry players such as SAP SE, Microsoft Corporation, IBM Corporation, ABB Ltd, Software AG, Schneider Electric SE, SAS Institute Inc., Amazon Web Services, Inc., Hitachi, Ltd., Google LLC
By region, Asia-Pacific held the highest market share in terms of revenue in 2023, accounting for three-fourths of the global predictive maintenance market, owing to widespread adoption of Industrial Internet of Things (IIoT) technologies. Manufacturing and industrial sectors in countries such as China, Japan, and South Korea, are integrating IIoT sensors to collect real-time data from machinery and equipment, facilitating continuous monitoring and early fault detection.
Request Sample Pages:
By end user, the manufacturing segment held the highest market share in 2023, accounting for nearly two-fifths of the global predictive maintenance industry revenue and is expected to retain its dominance throughout the forecast period, owing to the integration of Industrial Internet of Things (IIoT) devices, which collect real-time data from machinery and equipment. These sensors monitor various parameters such as vibration, temperature, and pressure, providing continuous insights into the health of manufacturing assets.
However, the energy and utilities segment is projected to manifest the highest CAGR from 2024 to 2032, owing to an increase in deployment of IoT sensors across energy infrastructure, such as power plants, wind turbines, and grid systems. These sensors continuously collect data on equipment performance, environmental conditions, and operational parameters, providing real-time insights into the health of critical assets.
By technique, the vibration monitoring segment held the highest market share in 2023, accounting for more than one-third of the global predictive maintenance market revenue and is expected to retain its dominance throughout the forecast period, owing to the proliferation of wireless and IoT-enabled vibration sensors. These sensors provide real-time data and are easier to install and maintain compared to traditional wired sensors. They enable continuous monitoring of machinery without the need for extensive cabling, reducing setup time and costs.
However, the infrared segment is projected to manifest the highest CAGR from 2024 to 2033, owing to the enhanced sensitivity and accuracy of modern IR cameras. These advancements allow for more precise detection of thermal anomalies, enabling earlier identification of potential equipment failures. The resolution of IR cameras has improved, providing clearer and more detailed thermal images that help maintenance teams diagnose issues more effectively.
Buy this Complete Report (309 Pages PDF with Insights, Charts, Tables, and Figures) at:
/purchase-options
By deployment mode, the on-premise segment held the highest market share in 2023, accounting for more than one-third of the global predictive maintenance market revenue and is expected to retain its dominance throughout the forecast period, owing to increasing adoption in industries that require high data security and low latency, such as aerospace, defense, and critical infrastructure sectors. These systems offer robust control over data privacy and compliance with stringent regulatory standards, making them suitable for environments where data sensitivity is paramount.
However, the cloud segment is projected to manifest the highest CAGR from 2024 to 2033, owing to the widespread adoption of Internet of Things (IoT) devices and sensors that continuously collect data from machinery and equipment. This data is transmitted to cloud platforms, where it can be stored, processed, and analyzed in real-time, allowing for more accurate and timely predictions of equipment failures.
By component, the solution segment held the highest market share in 2023, accounting for more than one-third of the global predictive maintenance market revenue and is likely to retain its dominance throughout the forecast period, owing to rise in use of artificial intelligence (AI) and machine learning (ML) algorithms, allowing for more sophisticated analysis of data and better identification of patterns that precede equipment failures.
Enquiry Before Buying:
However, the services segment is projected to manifest the highest CAGR from 2024 to 2033, owing to rise in adoption of AI and machine learning in predictive maintenance services, which allows for more precise and sophisticated analysis of data to predict equipment failures. Service providers are increasingly leveraging big data analytics to process vast amounts of information collected from sensors and IoT devices, enabling real-time monitoring and more accurate predictions.
David Correa
Allied Market Research
+15038946022 ext.
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MENAFN provides the information “as is” without warranty of any kind. We do not accept any responsibility or liability for the accuracy, content, images, videos, licenses, completeness, legality, or reliability of the information contained in this article. If you have any complaints or copyright issues related to this article, kindly contact the provider above.
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