Built to provide research that’s more actionable and strategically valuable, The Business Research Company’s 2026 market reports include market attractiveness analysis, total addressable market sizing, company benchmarking matrices, interactive Excel dashboards, broader supply chain intelligence, emerging startup tracking, and in-depth product insights.
Operational Predictive Maintenance Market Forecast: What Value Will The Market Reach By 2030?
The operational predictive maintenance market has experienced substantial expansion in recent years. It is projected to increase from $9.19 billion in 2025 to $11.59 billion in 2026, demonstrating a compound annual growth rate (CAGR) of 26.1%. This historical growth can be attributed to factors such as widespread reactive maintenance practices, the considerable expenses linked to equipment downtime, the surge in industrial automation, the integration of sensor technologies, and an escalating demand for operational efficiency.
The operational predictive maintenance market size is anticipated to experience substantial growth in the coming years. It is projected to reach $29.41 billion by 2030, reflecting a compound annual growth rate (CAGR) of 26.2%. This expansion throughout the forecast period can be attributed to the increasing application of AI and machine learning for maintenance purposes, the widespread adoption of cloud-based predictive platforms, seamless integration with IoT-enabled devices, the growing demand for cost optimization in operations, and a focused effort to minimize unplanned downtime. Key trends anticipated during this period include the implementation of predictive analytics, maintenance solutions powered by machine learning, real-time monitoring of equipment, strategies for optimizing asset performance, and integration with various enterprise systems.
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Operational Predictive Maintenance Market Demand Drivers: What’s Powering Industry Growth?
The expanding presence of Internet of Things (IoT) devices is set to fuel the growth of the operational predictive maintenance market moving forward. IoT devices are defined as nonstandard computing hardware, such as sensors, actuators, or appliances, that connect wirelessly to a network to transmit data. Their proliferation is driven by widespread high-speed internet availability, increasing industrial automation and supply chain management, and enhanced data analytics capabilities. These devices are pivotal in operational predictive maintenance, facilitating real-time monitoring, data analytics, early issue detection, condition-based maintenance, predictive insights, and continuous improvement, thereby enabling organizations to optimize asset performance, lower costs, and boost operating efficiency. For instance, the GSM Association, a UK-based non-profit industry organization, anticipates global IoT connections will climb to 23.3 billion by 2025, up from 15.1 billion connections registered in 2021. Therefore, the rising number of IoT devices is a significant driver for the operational predictive maintenance market.
Operational Predictive Maintenance Market Segment Analysis Spotlighting Growth Areas
The operational predictive maintenance market covered in this report is segmented –
1) By Type: Software, Services
2) By Deployment Model: Cloud, On-Premise
3) By Technology: Machine Learning, Deep Learning, Big Data And Analytics
4) By End User: Public Sector, Automotive, Manufacturing, Healthcare, Energy And Utility, Transportation, Other End Users
Subsegments:
1) By Software: Predictive Analytics Software, Machine Learning Software, Data Integration Tools, Asset Management Software, Real-Time Monitoring Software
2) By Services: Implementation Services, Consulting Services, Training and Support Services, Maintenance and Upgrades, Managed Services
Operational Predictive Maintenance Market Innovation Trends: What Developments Are Reshaping The Industry?
Leading companies in the operational predictive maintenance market are concentrating on technological advancements, such as AI-driven analytics and real-time monitoring, to improve equipment reliability and efficiency. This helps businesses proactively address their maintenance needs and minimize operational disruptions. Machine learning systems analyze sensor data to identify patterns indicative of potential issues, enabling proactive maintenance to optimize performance and prevent failures. For instance, in June 2024, Hitachi Industrial Equipment Systems Co., Ltd., a Japan-based manufacturer and seller of industrial equipment and components, launched its ‘Predictive Diagnosis Service’ for air compressors, utilizing machine learning and remote monitoring to detect and prevent potential issues. This service combines real-time data with maintenance expertise to enhance operational efficiency, minimize downtime, and reduce environmental impact.
Operational Predictive Maintenance Market Leading Companies: Who Holds The Strongest Market Presence?
Major companies operating in the operational predictive maintenance market are Google LLC; Microsoft Corporation; Robert Bosch GmbH; Hitachi Ltd.; Amazon Web Services Inc.; The International Business Machines Corporation; General Electric Company; Schneider Electric SE; SAP SE; Svenska Kullagerfabriken AB; Rockwell Automation Inc.; SAS Institute Inc.; Micro Focus; Splunk Inc.; PTC Inc.; Software AG; TIBCO Software Inc.; C3.AI Inc; Softweb Solutions Inc; Fiix Software; Uptake Technologies Inc.; eMaint Enterprises LLC; Seebo Interactive Ltd.; Asystom; Ecolibrium Energy
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Operational Predictive Maintenance Market Regional Breakdown: Where Is Demand Concentrated?
North America was the largest region in the operational predictive maintenance market in 2025. The regions covered in the operational predictive maintenance market report are Asia-Pacific, South East Asia, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.
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Wasay has over a decade of experience in market research, data modelling, and analytics, with prior experience at GlobalData and Decision Tree Consulting Services. At The Business Research Company , he leads research operations across syndicated studies, customized consulting engagements, and the Global Market Model platform. His professional experience includes supporting organizations such as Boston Consulting Group, KPMG, and Ernst & Young. Wasay holds a degree in Electronics and Communications Engineering, postgraduate management qualifications from International Management Institute Belgium and Indian School of Business and Entrepreneurship, and completed the Integrated Program in Business Analytics from Indian Institute of Management Indore.
