Predictive Maintenance Global Market Report 2024

The predictive maintenance market size has grown exponentially in recent years. It will grow from $7.36 billion in 2023 to $9.49 billion in 2024 at a compound annual growth rate (CAGR) of 28.8%. The growth observed in the historical period can be attributed to the reduction of equipment downtime, cost savings, and adherence to regulatory compliance and safety standards.

The predictive maintenance market size is expected to see exponential growth in the next few years. It will grow to $25.68 billion in 2028 at a compound annual growth rate (CAGR) of 28.3%. The anticipated growth in the forecast period can be credited to the integration with enterprise systems, the application of predictive analytics for complex systems, and an emphasis on proactive maintenance strategies. Noteworthy trends expected in the forecast period encompass the adoption of digital twin technology, cross-industry collaboration, advancements in sensor technologies, the implementation of advanced analytics and machine learning, as well as the utilization of cloud-based solutions.

The significant drive to reduce maintenance costs, equipment failure, and downtime is a key factor contributing to the growth of the predictive maintenance market. Equipment downtime, which refers to the period during which specific equipment is not operational due to unplanned failure, poses challenges to business operations. Frequent equipment failures and unplanned downtime of large equipment can disrupt production activities, lead to idle staff time, incur financial penalties, and more. A study published by Senseye Ltd., a UK-based provider of AI-driven predictive maintenance software, in June 2021 revealed that each production plant loses an estimated $172 million annually due to unplanned downtime. Consequently, manufacturing units are investing in advanced technologies to mitigate the potential adverse effects of machinery downtime in the plant. Therefore, the growing demand to reduce maintenance costs, equipment failure, and downtime is anticipated to drive the demand for predictive maintenance throughout the forecast period.

The increasing adoption of the Internet of Things (IoT) is expected to propel further growth in the predictive maintenance market. IoT is a networked system of interconnected computing devices, mechanical and electronic machinery with unique identities (UIDs), capable of transferring data without requiring human-to-human or human-to-computer interaction. IoT devices in predictive maintenance facilitate real-time monitoring of equipment, data collection for analysis, proactive identification of potential issues, reduction of downtime, and optimization of maintenance schedules to enhance operational efficiency. According to the State of IoT - Spring 2022 report by IoT Analytics in May 2022, there were 12.2 billion active endpoints in 2021, marking an 8% increase in the total number of IoT connections. The IoT industry is projected to experience an 18% increase to reach 14.4 billion active connections in 2022. Hence, the growing adoption of IoT serves as a driving force for the predictive maintenance market.

The increasing adoption of advanced technology emerges as a prominent trend in the predictive maintenance market. Leading companies in this sector are directing their efforts towards delivering technologically advanced solutions to meet the evolving demands of end customers, thereby solidifying their market positions. These companies are integrating next-generation technologies, including IoT, AI (artificial intelligence), ML (machine learning), thermography, and cloud computing, into their services to align with the growing market need for enhanced maintenance capabilities. An illustration of this trend is evident in the actions of Avanseus, a Singapore-based provider of predictive maintenance and intelligent monitoring solutions. In December 2021, Avanseus introduced an AI-based predictive maintenance solution within the operations of Bharti Airtel, a prominent Indian communications company. This solution leverages machine learning algorithms and artificial intelligence to proactively predict and prevent incidents in the network, supporting the pursuit of zero-fault, zero-touch networks.

Major players in the predictive maintenance market are intensifying their focus on introducing innovative solutions, such as the Asset Risk Predictor, to gain a competitive advantage. The Asset Risk Predictor employs advanced analytics to assess and forecast the risk of equipment failure, aiding industrial organizations in optimizing maintenance strategies and minimizing downtime. A case in point is the move by Rockwell Automation Inc., a US-based automation company, which, in September 2023, launched its inaugural artificial intelligence (AI) predictive maintenance software, Asset Risk Predictor. This software utilizes AI sensor data, machine recipes, and operational environments to predict asset health, enabling users to detect and address potential failures before they occur. The tool's capability to recognize signs of equipment failure allows it to predict breakdowns days in advance, facilitating quicker reactions by automatically generating work orders in the computerized maintenance management system (CMMS).

In June 2022, Siemens, a Germany-based technology company with a focus on industry, infrastructure, transport, and healthcare, acquired Senseye, a UK-based predictive maintenance solution provider, for an undisclosed amount. This acquisition positioned Senseye as a subsidiary of Siemens, bolstering Siemens' presence in the digital services portfolio.

Major companies operating in the predictive maintenance market report are Google LLC, Microsoft Corporation, Hitachi Ltd., Amazon Web Services Inc., Siemens AG, General Electric Company, International Business Machines Corporation, Cisco Systems Inc., Oracle Corporation, Schneider Electric SE, OPEX Group Ltd., SAP SE, Hewlett Packard Enterprise Company, SAS Institute Inc., Splunk Inc., Larsen & Toubro Infotech Limited, PTC Inc., TIBCO Software Inc., Fluke Corporation, Software AG, Banner Engineering Corporation, Altair Engineering Inc., C3.AI Inc., Axiomtek Co. Ltd., SparkCognition Inc., Uptake Technologies Inc., RapidMiner Inc., Dingo Inc., Factory Five Racing Inc., Senseye Ltd., Aspen Technology Inc., Dassault Systèmes SE, Rockwell Automation Inc., Honeywell International Inc.

North America was the largest region in the predictive maintenance market in 2023. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the predictive maintenance market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa. The countries covered in the predictive maintenance market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Russia, South Korea, UK, USA, Canada, Italy, Spain.

Predictive maintenance is categorized into solutions and services. Predictive maintenance solutions refer to custom-designed software or platforms for asset management, tailored to unique business requirements and operating on Internet of Things (IoT) technology. These solutions are deployed on both on-premise and cloud infrastructure, catering to stakeholders such as MRO, OEM/ODM, and technology integrators. They find applications in heavy machinery, small machinery, and various industries, including aerospace & defense, automotive & transportation, energy & utilities, healthcare, IT & telecommunication, manufacturing, oil & gas, and others.

The predictive maintenance market research report is one of a series of new reports that provides predictive maintenance market statistics, including the predictive maintenance industry global market size, regional shares, competitors with a predictive maintenance market share, detailed predictive maintenance market segments, market trends and opportunities, and any further data you may need to thrive in the predictive maintenance industry. This predictive maintenance market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenarios of the industry.

The predictive maintenance market includes revenues earned by entities by providing services such as monitoring equipment, failure mode, and condition. The market value includes the value of related goods sold by the service provider or included within the service offering. Only goods and services traded between entities or sold to end consumers are included.

The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD, unless otherwise specified).

The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.


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