Operational Predictive Maintenance Market in 2024: Top Companies, Revenue Trends, and Growth Projections by 2033

The operational predictive maintenance market size has grown exponentially in recent years. It will grow from $5.78 billion in 2023 to $7.31 billion in 2024 at a compound annual growth rate (CAGR) of 26.5%. The growth in the historic period can be attributed to cost savings from reduced downtime and maintenance costs, improved asset reliability and performance, enhanced safety and risk mitigation, regulatory compliance requirements, growing awareness of predictive maintenance benefits.

The operational predictive maintenance market size is expected to see exponential growth in the next few years. It will grow to $18.62 billion in 2028 at a compound annual growth rate (CAGR) of 26.3%. The growth in the forecast period can be attributed to expansion into new industries and applications, demand for proactive maintenance solutions, market penetration in emerging economies, predictive maintenance adoption, increased focus on sustainability and energy efficiency initiatives. Major trends in the forecast period include integration of IoT sensors and data analytics, adoption of machine learning algorithms, expansion of applications across industries, development of cloud-based platforms, integration with enterprise asset management systems.

Market Overview -
Operational predictive maintenance (OPM) refers to a proactive maintenance strategy that utilizes data analytics, machine learning, and predictive modeling techniques to anticipate equipment failures or maintenance needs before they occur. The goal of OPM is to minimize downtime, reduce maintenance costs, and optimize the efficiency and reliability of equipment and processes.

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Increasing Number Of IoT Devices Propels Operational Predictive Maintenance Market
The increasing number of IoT (Internet of Things) devices is expected to propel the growth of the operational predictive maintenance market going forward. IoT devices refer to nonstandard computing hardware such as sensors, actuators, or appliances that connect wirelessly to a network and can transmit data. It arises because of the widespread availability of high-speed internet connectivity, increasing industrial automation and supply chain management, and data analytics capabilities. IoT devices play a critical role in operational predictive maintenance by enabling real-time monitoring, data analytics, early issue detection, condition-based maintenance, predictive insights, and continuous improvement, ultimately assisting organizations in optimizing asset performance, lowering costs, and increasing operating efficiency. For instance, in August 2022, according to Akamai Technologies Inc., a US-based internet company, IoT connections are expected to increase from 15.1 billion in 2021 to 23.3 billion IoT connections in 2025. Therefore, the increasing number of IoT devices drives the operational predictive maintenance market.

Competitive Landscape -
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

Revolutionizing Industrial Equipment Monitoring With AI-Based Predictive Maintenance Solutions
Major companies operating in the operational predictive maintenance market are developing innovative AI advancements, such as AI-based predictive maintenance solutions, to improve the accuracy, efficiency, and effectiveness of predictive maintenance. This solution uses AI (artificial intelligence) or ML (machine learning) technology to locally monitor the condition of industrial equipment without requiring an internet-based cloud connection. For instance, in May 2021, QuickLogic Corporation, a US-based developer of endpoint AI solutions, introduced an AI-based predictive maintenance solution utilizing the QuickLogic EOS S3 Platform and SensiML Analytics Toolkit. This solution integrates AI and ML technology to monitor manufacturing equipment, enabling the differentiation between normal and abnormal operations. It is a low-power, multi-core ARM Cortex System-on-Chip designed for mobile markets, including always-on voice applications, AI inferencing at the edge or endpoint, and general-purpose Internet of Things (IoT) applications. Additionally, this platform offers broad open-source software and hardware support, enabling developers to create innovative solutions quickly and efficiently.

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Key Segments -
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

Key highlights covered in the report -
1. Detailed market size forecast and historical data analysis
2. Key drivers influencing market growth
3. Identification of upcoming trends and potential opportunities in the market
4. Analysis of major players strategies, to understand competitive dynamics and market positioning
5. Evaluation of regional dynamics

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