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The global predictive maintenance market size was USD 4.15 Billion in 2021 and is expected to register a revenue CAGR of 30.6% during the forecast period.

NEW YORK, NY, UNITED STATES, June 27, 2023/EINPresswire.com/ -- The global predictive maintenance market was valued at USD 4.15 billion in 2021 and is projected to achieve a compound annual growth rate (CAGR) of 30.6% during the forecast period. The increasing demand for maximizing asset availability and minimizing maintenance costs, driven by the rapid adoption of Internet of Things (IoT) and rising investments in predictive maintenance, are expected to fuel the growth of market revenue in the coming years. Predictive maintenance is a strategy that utilizes data analysis tools and procedures to identify abnormalities in operations and potential issues in processes and equipment, enabling timely repairs before encountering any errors. By leveraging historical and real-time data from various areas of a business, predictive maintenance predicts potential problems.

IoT, Artificial Intelligence (AI), and system integration play a crucial role in connecting, collaborating, exchanging, analyzing, and acting on data from different assets and systems. Enterprises now use AI and Machine Learning (ML) technologies to analyze IoT data with exceptional precision and speed, surpassing traditional business intelligence tools. With the advent of predictive maintenance, businesses can forecast equipment malfunctions up to 20 times faster and more accurately compared to threshold-based monitoring devices. These solutions collect data through predictive maintenance sensors, industrial controls, and business systems like Enterprise Asset Management (EAM) and Enterprise Resource Planning (ERP) software. These software applications interpret data and utilize it to identify areas that require improvement. Predictive maintenance and its associated sensors find applications in vibration analysis, oil analysis, thermal imaging, and equipment monitoring.

Nevertheless, unexpected downtime due to equipment breakdowns poses significant losses across various industries such as industrial manufacturing and oil & gas sectors. Pipeline leaks, for instance, are common in the oil & gas industry and can result in substantial financial penalties and fines, leading to significant resource losses for organizations. To address these operational challenges, some predictive maintenance solution providers are developing innovative technological advancements. One such example is Rayven, an IoT solutions provider specializing in predictive maintenance, which offers AI and IoT-based pipeline monitoring and leak detection solutions to help oil & gas companies save money and mitigate losses.

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Segments Covered in the Report

Based on the components outlook, the predictive maintenance market can be divided into two main categories: solutions and services. Under solutions, there are standalone solutions and integrated solutions. Standalone solutions refer to independent predictive maintenance tools, while integrated solutions involve the integration of predictive maintenance capabilities into existing systems.

In terms of services, the market includes professional services such as system integration, support and maintenance, consulting, and managed services. System integration services focus on integrating predictive maintenance solutions into the client's infrastructure. Support and maintenance services ensure the smooth operation of predictive maintenance systems. Consulting services provide expert guidance on implementing and optimizing predictive maintenance strategies, while managed services involve outsourcing the management of predictive maintenance processes to a third-party provider.

From a deployment perspective, predictive maintenance solutions can be deployed either on the cloud or on-premises. Cloud deployment offers options such as public cloud, private cloud, and hybrid cloud, providing flexibility and scalability for organizations. On-premises deployment, on the other hand, involves hosting predictive maintenance solutions within the organization's own infrastructure.

Regarding the technique outlook, predictive maintenance employs various techniques to identify and address potential issues. These techniques include power system assessment, infrared thermography, temperature monitoring, fluid analysis, circuit monitor analysis, and vibration monitoring. Each technique focuses on different aspects of equipment and process monitoring to ensure early detection of abnormalities.

The industry vertical outlook highlights the diverse sectors that benefit from predictive maintenance. These include government and defense, manufacturing, energy and utilities, transportation and logistics, healthcare and life sciences, and others. Each industry vertical can leverage predictive maintenance to enhance operational efficiency, reduce downtime, and optimize asset performance.

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Strategic development:

The predictive maintenance market is witnessing significant strategic developments aimed at enhancing its growth and addressing evolving industry needs. These strategic initiatives encompass various aspects such as technological advancements, partnerships, acquisitions, and expansions.

Technological advancements play a pivotal role in shaping the predictive maintenance landscape. Companies are actively investing in research and development to innovate new solutions and improve existing ones. For instance, advancements in artificial intelligence (AI), machine learning (ML), and Internet of Things (IoT) technologies are enabling more accurate and efficient predictive maintenance capabilities. These advancements empower businesses to leverage data analytics and predictive modeling for proactive maintenance and asset optimization.

Partnerships and collaborations are key strategic moves in the predictive maintenance market. By joining forces with technology providers, solution integrators, and industry players, companies can enhance their offerings and expand their market reach. Partnerships often involve combining domain expertise and technological capabilities to create comprehensive predictive maintenance solutions. Collaborative efforts enable the integration of complementary technologies and the provision of end-to-end solutions that cater to diverse customer needs.

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Competitive Landscape:

IBM Corporation, SAP, Siemens, Microsoft, General Electric, Intel Corporation, Robert Bosch GmbH, Cisco Systems, Inc., ABB, and SKF.

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