USD 4.4 billion
Report ID: SQMIG45E2137 | Region: Global | Published Date: May, 2023 | Pages: 157 | Tables: 59 | Figures: 77
Global Predictive Maintenance Market size was valued at USD 4.4 billion in 2021 and is poised to grow from USD 5.77 billion in 2022 to USD 38.63 billion by 2030, at a CAGR of 31.2% during the forecast period (2023-2030).
The Global Predictive Maintenance Market is a rapidly evolving sector at the intersection of technology and industrial maintenance. It uses advanced data analytics, machine learning, and IoT (Internet of Things) technologies to predict when equipment or machinery will likely fail. By analyzing real-time data and historical patterns, predictive maintenance enables companies to preemptively address maintenance issues, reducing downtime, and cutting operational costs. Several factors drive the market's growth, As industries increasingly rely on complex machinery, minimizing downtime is essential to maintain productivity and reduce costs, The availability of large volumes of data and advancements in data analytics have made predictive maintenance more accessible and effective, Integrating IoT devices into industrial equipment facilitates real-time data collection and analysis.
Key players in this market include IBM, Microsoft, SAP, and General Electric. They offer comprehensive solutions that enable businesses to monitor the condition of their assets and predict when maintenance is needed. The global predictive maintenance market is expected to continue its growth trajectory, with industries such as manufacturing, energy, and transportation recognizing the potential benefits of cost savings, improved reliability, and enhanced overall operational efficiency.
Global Market Size
USD 4.4 billion
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Global Predictive Maintenance Market is segmented on the basis of component, development mode, organization size and region. By component, the market is segmented into solution and services. By development mode, the market is segmented into on-premises and cloud. By organization size, the market is segmented into large enterprises and small & medium sized enterprises. By techniques analysis, the market is segmented into power system assessments, infrared thermography, temperature monitor, fluid analysis, circuit monitor analysis, and vibration monitoring. By region, the market is segmented into North America, Europe, Asia Pacific, Middle East and Africa, and Latin America.
Predictive Maintenance Market Analysis by Deployment Mode
A growing number of people are becoming concerned about their data's privacy when using cloud infrastructure, which is why on-premise deployment held the greatest market share in the past and is predicted to remain during the forecast period. In order to efficiently run their internal and external software solutions, most organizations prefer to have their own servers and data centers, which raises the demand for on-premises solutions.
Because cost reduction is one of the main benefits of cloud-based predictive maintenance management, the cloud segment is anticipated to experience rapid revenue growth over the course of the forecast period. Since the system lacks a physical data storage structure, this is significant. These systems require less money for hardware and routine maintenance, and data is accessible with ease. It makes it possible for management personnel and authorized consultants to quickly access the same tools and data wherever they are needed. Demand for cloud-based deployment is anticipated to increase because it makes algorithm sharing and improvement easier. Additionally, it increases the workers' accountability and improves their understanding of the actual and desired performance of the currently operational equipment.
Predictive Maintenance Market Analysis by Techniques Analysis
Throughout the anticipated period, the infrared thermography segment is anticipated to post consistent revenue growth. Manufacturers can profit from thermographic testing in a number of ways, especially when it's incorporated into a thorough maintenance plan. Thermographic testing helps to prevent expensive unplanned downtime and has an impact on the operational bottom line by identifying issues before the machines affect production and other operations by anticipating and scheduling repairs at periodic intervals when productivity will be least impacted.
During the forecast period, the vibration monitoring segment is expected to experience a moderate growth rate in revenue. Vibration monitoring is currently one of the most widely used preventive maintenance methods. It is particularly effective in identifying the early stages of equipment wear and potential failures or deterioration in processes. With appropriate equipment, vibration analysis can also provide insight into the root cause of an imminent problem, which is particularly important in equipment with multiple moving parts. The most critical predictions that can be made through vibration monitoring include vibration, temperature, voltage, current monitoring, and oil analysis. Vibration monitoring is particularly useful for wind turbines with primary rotor speeds ranging from 5 to 30 Revolutions Per Minute (RPM).
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North America held the largest revenue share in the global predictive maintenance market in 2022, thanks to the region's abundance of well-established end-use industries and high company awareness of the benefits of adopting newer, more cutting-edge technologies that help businesses save money and become more efficient. In order to keep up with the maintenance requirements of its massive fleet of fighters, bombers, tankers, transports, and helicopters. Estimates for the Air Force alone reach 5,400 aircraft.
Throughout the forecast period, market revenue growth in Europe is anticipated to be consistent. Some of the key reasons fueling the revenue growth of the predictive maintenance market in Europe include increasing emphasis on reducing maintenance frequencies and downtime as well as rising awareness of the benefits of predictive maintenance. For instance, a number of European military defense organizations use predictive maintenance to guarantee high spare component availability, which is essential to fleet maintenance. This aided in the analysis of logistical challenges, financial risk, and economic conditions, streamlining the supply chain for more rapid and affordable aircraft repairs.
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Predictive Maintenance Market Drivers
Increasing demand for Automated and Digitized Maintenance Processes to bring Lucrative Opportunities
Predictive Maintenance Market Restraints
Resistance to Change might Pose as a Threat
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The global predictive maintenance market's competitive environment is moderately fragmented, with a number of important businesses active on both a global and regional scale. Major corporations are working on R&D projects, conducting tests, and forming alliances to create and market more original and inventive products and devices.
Predictive Maintenance Market Top Player’s Company Profiles
Predictive Maintenance Market Recent Developments
In July 2022, Valmet introduced a novel application aimed at enhancing machinery tracking. This application is part of Valmet Industrial Internet's array of offerings, which focus on delivering solutions for preemptive maintenance and in-depth root cause analysis for various machinery within the paper and pulp industry.
In March 2022, C3 AI reported a significant milestone, with their predictive maintenance program now overseeing more than 10,000 machines within the Shell Corporation. This extensive program harnesses over 3 million sensors and utilizes 11,000 machine learning models, demonstrating its substantial scope and impact.
In January 2022, Amazon unveiled an asset tracking solution tailored for the pharmaceutical and biotech sectors. This innovative offering, known as Aizon Asset Health, leverages Aizon's GxP-compliant AI Software as a Service (SaaS) Platform.
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According to our analyses, the market is also being driven by the growing demand for cloud-based predictive maintenance solutions, which provide greater flexibility and scalability, and enable organizations to access real-time data from anywhere. In addition, the integration of predictive maintenance solutions with enterprise asset management (EAM) systems is also driving the market, as it enables organizations to gain a more comprehensive view of maintenance activities and optimize their maintenance schedules. The global predictive maintenance market is expected to continue to grow in the coming years, driven by the increasing demand for Industry 4.0 technologies, cloud-based solutions, and the integration of predictive maintenance with EAM systems. The market is also being shaped by the increasing use of AR and VR technologies in predictive maintenance, the expansion of the market into new industries, and the growing focus on sustainability and environmental benefits.
|Market size value in 2021||USD 4.4 billion|
|Market size value in 2030||USD 38.63 billion|
|Forecast Unit (Value)||USD Billion|
|Regions covered||North America (US, Canada), Europe (Germany, France, United Kingdom, Italy, Spain, Rest of Europe), Asia Pacific (China, India, Japan, Rest of Asia-Pacific), Latin America (Brazil, Rest of Latin America), Middle East & Africa (South Africa, GCC Countries, Rest of MEA)|
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Parent Market Analysis
KEY MARKET INSIGHTS
MARKET DYNAMICS & OUTLOOK
Market Size by Region
KEY COMPANY PROFILES
For the Global Predictive Maintenance Market, our research methodology involved a mixture of primary and secondary data sources. Key steps involved in the research process are listed below:
1. Information Procurement: This stage involved the procurement of Market data or related information via primary and secondary sources. The various secondary sources used included various company websites, annual reports, trade databases, and paid databases such as Hoover's, Bloomberg Business, Factiva, and Avention. Our team did 45 primary interactions Globally which included several stakeholders such as manufacturers, customers, key opinion leaders, etc. Overall, information procurement was one of the most extensive stages in our research process.
2. Information Analysis: This step involved triangulation of data through bottom-up and top-down approaches to estimate and validate the total size and future estimate of the Global Predictive Maintenance Market.
3. Report Formulation: The final step entailed the placement of data points in appropriate Market spaces in an attempt to deduce viable conclusions.
4. Validation & Publishing: Validation is the most important step in the process. Validation & re-validation via an intricately designed process helped us finalize data points to be used for final calculations. The final Market estimates and forecasts were then aligned and sent to our panel of industry experts for validation of data. Once the validation was done the report was sent to our Quality Assurance team to ensure adherence to style guides, consistency & design.
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