Description
The global predictive maintenance market size is expected to grow from USD 5,443.1 million in 2020 to USD 31,965.5 million by 2027, at a CAGR of 28.8% from 2021 to 2027. Predictive maintenance facilitates multiple advantages such as reduction in downtime, prolonging equipment life, improvement in plant safety, optimizing spare parts inventory, reduction of maintenance costs, better yield rate, optimization of maintenance schedule, and enhancement of asset availability.
An increase in the need to improve the uptime of an asset and reduce cost, growth in predictive maintenance investments, owing to IoT adoption, and rise in demand to extend the lifetime of aging assets drive the growth of the global predictive maintenance market. Further, increase in need to gain insights from the adoption of new technologies boosts the development of the predictive maintenance market.
The rise in need of improving the equipment’s uptime, reducing costs & quality risks, and extending the life of assets are the factors that majorly drive the growth of the predictive maintenance market. In addition, the rise in investment in prediction maintenance due to the adoption of IoT also drives market growth. The use of machine learning for predictive maintenance 4.0 offerings are expected to offer lucrative opportunities for this market during the forecast period.
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Global Predictive Maintenance Market Dynamics
Drivers: The need to improve the uptime of equipment and maintenance cost reduction
Companies need to reduce unexpected downtime by identifying problems before they cause a complete system failure. Major breakdowns are expensive due to lost operating time and secondary financial losses. For instance, unplanned downtime costs industrial manufacturers about $50 billion every year. In addition, the large or complex machinery has an even more significant maintenance impact on production and runtime costs. Poor maintenance strategies can reduce a plant’s overall productive capacity by 5-20%. Even if a minor defect in the system is not caught early, it can cause unexpected and costly downtime. Companies also intend to reduce planned manual inspections that would boost their productivity and production.
Furthermore, after using predictive maintenance, manufacturers can demonstrate the improved uptime and dropped maintenance costs and offer a measure of predictability to the customers. Thus, predictive maintenance results in an increased purchase price, thus making way for a strategic competitive edge that attracts the companies to adopt these solutions.
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Restraints: Lack of skilled workforce and professional workers
The modern equipment, technologies, and concepts inside industrial manufacturing result in a shift in the worker skillset essential to accomplish the transformation in maintenance processes. This has led to one of the significant challenges in the current manufacturing environment -that is the “skills gap.” Further, many workers are retiring who have experience on the legacy machine tool and factory floor. Their in-depth knowledge of the functionality of equipment and associated problem-solving is irreplaceable. There is a lack of incoming young workforce who have the technology-based skills required to complement the expertise of retiring and still-employed legacy workers.
Opportunities: Integration of predictive maintenance with IIoT and use of machine learning
Manufacturers are adopting machine learning-based predictive maintenance. It depends on a large amount of historical or test data, along with tailored machine-learning algorithms, to test different scenarios and predict the errors in the system. Then it generates the alerts accordingly. When properly designed and implemented, a machine learning algorithm will learn the specific data’s behavior and identify deviation in real-time. A machine monitoring system will comprise input about diverse temperatures, engine speed, and others. The system can then predict the time of the breakdown.
When predictive maintenance is coupled with the IIoT, it can catch equipment failures in advance. Due to the emergence of ‘Industry 4.0’ in the manufacturing landscape, companies are keen to adopt IIoT to achieve better insights into their operations. Predictive maintenance relies on sensors for gathering and analyzing data from various sources, such as a CMMS and critical equipment sensors. By means of this data, the IIoT can provide innovative prediction models and analytical tools to predict catastrophes and handle them proactively.
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Scope of the Report
The global predictive maintenance market research report has been segmented into component, deployment, technique, stakeholder, industry, and region.
By Component Outlook (Revenue, 2017-2027, USD Billion)
- Solution
- Service
By Deployment Outlook (Revenue, 2017-2027, USD Billion)
- Cloud
- On-premise
By Technique Outlook (Revenue, 2017-2027, USD Billion)
- Vibration Monitoring
- Electrical Testing
- Oil Analysis
- Ultrasonic Leak Detectors
- Shock Pulse
- Infrared
- Others
By Stakeholder Outlook (Revenue, 2017-2027, USD Billion)
- MRO
- OEM/ODM
- Technology Integrators
By Industry Outlook (Revenue, 2017-2027, USD Billion)
- Manufacturing
- Energy & utilities
- Aerospace & Defense
- Transportation & Logistics
- Government
- Healthcare
- Other
By Region Outlook (Revenue, 2017-2027, USD Billion)
- North America (US, Canada, Mexico)
- South America (Brazil, Argentina, Colombia, Peru, Rest of Latin America)
- Europe (Germany, Italy, France, UK, Spain, Poland, Russia, Slovenia, Slovakia, Hungary, Czech Republic, Belgium, the Netherlands, Norway, Sweden, Denmark, Rest of Europe)
- Asia Pacific (China, Japan, India, South Korea, Indonesia, Malaysia, Thailand, Vietnam, Myanmar, Cambodia, the Philippines, Singapore, Australia & New Zealand, Rest of Asia Pacific)
- The Middle East & Africa (Saudi Arabia, UAE, South Africa, Northern Africa, Rest of MEA)
The solution segment of the predictive maintenance market is projected to account for the largest share by component
Based on components, the predictive maintenance market is segmented into solution and service. The service segment is sub-segmented into professional service and managed service. The solution segment accounted for 71.35% of the global predictive maintenance market in 2020 and grew at a CAGR of 27.6%. The predictive maintenance solution generally comprises components such as sensor network, gateway hardware and software, data processing algorithms, and web or mobile interface. Companies are looking for an all-inclusive development solution that integrates hardware sensor boards, and software environments created precisely for predictive maintenance requirements. Using such integrated solutions, developers can instantly evaluate predictive maintenance, rapidly develop their applications, and use cloud-based machine learning resources to generate more sophisticated predictive maintenance capabilities. Thus, this is expected to drive the demand for integrated predictive maintenance solutions. Moreover, a rise in demand for advanced and industry vertical specific predictive maintenance capabilities is anticipated to boost the standalone predictive maintenance solutions market.
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Asia Pacific accounts for the highest CAGR during the forecast period in the predictive maintenance market.
Based on the regions, the global predictive maintenance market has been segmented across North America, Asia-Pacific, Europe, South America, and the Middle East & Africa. Asia Pacific is the fastest-growing region and is expected to register a CAGR of 30.3 during the forecast period 2021-2027, having a market revenue share of 26.27% in 2020. In Asia-Pacific, four main countries, namely, China, India, Japan, and the Rest of Asia-Pacific, are analyzed. China and Japan accounted for a significant share of this market in 2020, India and South Korea are expected to experience rapid growth during the forecast period.
A growing focus on trying innovative solutions for achieving optimized output for maintenance of assets is expected to influence the growth of the predictive maintenance market in Asia-Pacific. For instance, one of the Japanese utility companies is implementing AI for predictive maintenance. AI and big data analytics will aid this utility provider in carrying out the maintenance of its infrastructure. This helps the company in anticipating any failure to its infrastructure and avoids downtime. It can use all kinds of data such as weather, sensor, and temperature to analyze the risk of breakdowns. Moreover, many local governments in the region are also expanding their capabilities by adopting predictive maintenance solutions. For instance, the Electrical and Mechanical Services Department (EMSD) of Hong Kong implemented predictive maintenance solutions along with other IoT-based technologies in various other government departments of the state.
Key Market Players
The predictive maintenance market is mildly concentrated in nature with large numbers of global players operating in the market such as IBM Corporations, Microsoft, SAP SE, General Electric, Schneider Electric, Hitachi, PTC, Software AG, SAS, Engineering Consultants Group, Inc., Expert Microsystems, Inc., SparkCognition, C3 IoT, Uptake Technologies Inc., Fiix Inc., Operational Excellence (Opex) Group Ltd, TIBCO Software Inc., Asystom, and Sigma Industrial Precision. The following chart depicts the positioning of leading companies in the market based on two parameters, namely, competitive strength and geographical presence.
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