| A02138 | Pages: 428 | Mar 2021 | 19464 Views | ||
| Author(s) : Vishwa Gaul | Tables: 202 | Charts: 97 |
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Pandemic disrupted the entire world and affected many industries.
Get detailed COVID-19 impact analysis on the Predictive Maintenance Market
Request Now !The global predictive maintenance market size was valued at $4,331.56 million in 2019, and is projected to reach $31,965.49 million by 2027, growing at a CAGR of 28.8% from 2020 to 2027.
During COVID-19 pandemic, manufacturing & industrial sector was significantly affected and even the IT expenditure was declined by 3 to 4%. This in turn has also affected the predictive maintenance market. However, demand for PdM solutions from healthcare & energy and utilities sector is expected to increase during the forecast period.s
Predictive maintenance is an equipment performance and condition monitoring plan that reduces the risk of a failure under normal operating conditions. The aim is to anticipate a failure and then try to avoid it by corrective maintenance. Traditional systems rely on historical data about equipment performance and previous breakdowns or simply establish periodic maintenance schedules whether or not they are required to predict the need for maintenance. Modern predictive maintenance solutions on the other hand constantly monitor equipment behavior to collect data in real time and use advance neural network and artificial techniques to decide and raise alert when a possible equipment failure is bound to happen.
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Increase in need to boost asset uptime and minimize maintenance costs, rise in investments in predictive maintenance, owing to IoT adoption, and increase in need to extend the lifespan of ageing industrial machinery are the major factors that propel the predictive maintenance market growth. Furthermore, demand for predictive maintenance is on the rise, owing to increase in need to obtain insights from implementation of new technologies. However, market development is hampered by implementation challenges and data protection issues. Furthermore, adoption of advanced technologies such as machine learning, predictive maintenance integration with IIoT, and need for remote monitoring and asset management in the aftermath of the COVID-19 pandemic are expected to further drive the predictive maintenance market.
By deployment type, the on-premise segment dominated the overall predictive maintenance market size in 2019, and is expected to maintain its dominance during the forecast period. This is attributed to its modular sensors and easier deployments in pre-existing equipment. However, cloud-based predictive maintenance solutions are expected to exhibit highest growth rate during the forecast period, owing to direct IT control, remote accessibility, internal data delivery & handling, faster data processing using advance predictive analytics, efficient resource utilization, and cost-effectiveness.
By industry vertical, the manufacturing segment dominated the global predictive maintenance market share in 2019. Manufacturing equipment maintenance, such as machinery, pumps, elevators, and industrial robots, face a number of challenges, which is why predictive maintenance has become more common in the industry. However, the healthcare sector is expected to rise at the fastest pace in the future, owing to the fact that predictive maintenance of healthcare equipment such as X-ray, MR, tomography, and mammography is one of the most important considerations for hospitals looking to improve decision-making capabilities and improve operational efficiencies.
According to an article published in March 2021 by the news magazine Manufacturing Global, 98% of businesses report that a single hour of interruption affects their productivity, costing them more than $100,000. However, predictive maintenance solutions have the potential to minimize unplanned downtime and prolong life of machinery. Owing to this, adoption of predictive maintenance solutions have increased in recent years and is expected to grow further in the future as industries become more IoT friendly.
Lack of employees and personnel, coupled with global supply chain disruption as well as high demand for various goods during the COVID-19 pandemic encouraged companies to take extra care of their manufacturing equipment and machinery to increase output. This resulted in a surge in demand for predictive maintenance solutions across the globe.
Many companies have started to use smart sensors, advanced artificial intelligence systems, and other Industry Internet of Things (IIoT) solutions to track health and efficiency of vital machinery used in their
manufacturing process to avoid costly production downtimes.
Predictive maintenance solutions have allowed businesses to compensate for limited availability of workers during the COVID-19 pandemic as they can handle periodic monitoring, simple machinery troubleshooting, and other tasks.
The report focuses on growth prospects, restraints, and global predictive maintenance market trends. Moreover, the study includes Porter’s five forces analysis of the industry to understand impact of various factors such as bargaining power of suppliers, competitive intensity of competitors, threat of new entrants, threat of substitutes, and bargaining power of buyers on the growth of the given market.
The global predictive maintenance market is segmented on the basis of component, deployment, technique, stakeholder, industry vertical, and region. By component, it is bifurcated into solution and service. According to deployment, it is classified into cloud and on-premise. Further, by technique, it is divided into vibration monitoring, electrical testing, oil analysis, ultrasonic leak detectors, shock pulse, infrared, and others. By stakeholder, it is classified into MRO, OEM/ODM, and technology integrators. On the basis of industry vertical, it is classified into manufacturing, energy & utilities, aerospace & defense, transportation & logistics, government, and others. Region wise, it is analyzed across North America, Europe, Asia-Pacific, and LAMEA.
The key players operating in the global predictive maintenance market analysis include IBM Corporations; Microsoft; SAP SE; General Electric; Schneider Electric; Hitachi; PTC; Software AG; SAS; Engineering Consultants Group, Inc.; Expert Microsystems, Inc.; SparkCognition; C3.Ai; Uptake Technologies Inc.; Fiix Inc.; Operational Excellence (Opex) Group Ltd, TIBCO Software Inc.; Asystom; Reliability Solutions Sp. zo.o. and Sigma Industrial Precision.
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Rise in demand for increased asset uptime and lowering maintenance costs; increase in investments in predictive maintenance in industries as a result of IoT adoption; and the need to prolong lifetime of ageing industrial machinery are the major factors that propel the market growth. Furthermore, there is a rise in need for predictive maintenance as a result of a growing need to gain insights from implementation of new technologies. However, implementation problems and data security concerns hinder the market growth. Furthermore, in the aftermath of the COVID-19 pandemic, emerging technologies such as machine learning, predictive maintenance integration with IIoT, and need for remote monitoring and asset management are expected to further boost predictive maintenance market growth.
Manufacturers are adopting machine learning based predictive maintenance. It depends on 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 typical 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.
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When predictive maintenance is coupled with the IIoT, it can catch equipment failures in advance. Due to 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 which will predict catastrophes and handle them proactively.
Huge amount of data can be transmitted due to the affordability of bandwidth and storage, to offer a complete visibility over assets in a single plant and entire production network. For instance, Lubricants & filters in hydraulic valve manufacturing require IoT gateways and sensors, the oil quality can be monitored without manual assistance and maintained a constant grade.
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Ticket allocation system, cycle time monitoring, cutting fluids monitoring, cooling systems and other industries are adopting IIoT predictive maintenance to reduce the machine downtime. Thus, the integration of predictive maintenance and IIoT is expected to offer lucrative opportunities for this predictive maintenance market during the forecast period.
The real-time processing of underlying data makes it possible to make forecasts that form the basis for needs-based maintenance and consequently the reduction of downtimes. Dedicated condition monitoring systems are constructed with intelligent monitoring nodes, which apply adaptive as well as static rules to real-time condition data to provide immediate & local alerts. In addition, local nodes communicate with centralized web portal to permit the staff to analyze real-time data while it is happening. In addition, the interpretation of sensor data requires a combination of real-time analysis and an in-memory database to achieve a higher access speed to the data compared to hard disk drives, which fosters the adoption of real time condition monitoring. Oil & gas companies present significant opportunity to increase efficiency and reduce operational costs through better asset tracking and real-time condition monitoring.
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By adopting real-time condition monitoring of critical equipment, companies can have a strong confidence in the reliability of their equipment. It will alert immediately for emerging problems with its intelligent, on-site nodes that, even during internet outages, can alert you to immediate potential problems. Due to these significant benefits, the adoption of real-time condition monitoring is expected to offer better opportunities for predictive maintenance industry.
The COVID-19 pandemic has made the world adopt to remote working condition, owing to social distancing and self-isolation. Industries suffered during the pandemic from the lack of on-site workers, which decreased the productivity.
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Predictive maintenance solutions allowed real time monitoring of assets, with the help of advance sensors deployed on components that can detect and predict equipment failure. These solutions allowed remote monitoring of functional status equipment, which can be beneficial for times when only limited number of employees are allowed to work on-site.
Moreover, various organizations, such as research groups, academic institutions, hospitals, and consulting firms have developed a range of statistical models, predictive analytics, and forecasting exercises with the goal to assist health systems in making COVID-19 strategic decisions. These trends post pandemic have helped predictive maintenance solutions to be popularized for healthcare equipment diagnosis, remote, and constant monitoring.
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Key Benefits For Stakeholders
Key Market Segments
Key Market Players
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The workers in the manufacturing and fabrication industries embrace changing technologies, such as workflow automation, particularly in post-pandemic, when automation has become a necessity rather than a desirable feature. IIoT predictive maintenance offers new insights to activate effective maintenance events, hence, its adoption is anticipated to be easier. Furthermore, increase in need to reduce equipment downtime and improve overall life of crucial industrial equipment are some of the major factors that drive the predictive maintenance market growth. Moreover, vibration monitoring, electrical testing, and oil analysis are expected to be the major segments, in terms of revenue in the predictive maintenance market. Furthermore, the manufacturing sector has been greatly impacted by COVID-19. As the industry strives to resurrect its operations in these troubling times, it must move to analytics-driven processes to streamline operations as efficiently as possible. Even after recent economic slowdowns, the manufacturing industry is anticipated to be a leading segment in the global predictive maintenance market in the coming years.
Even after the global economic downturn following the COVID-19 pandemic, major corporations are still trusting IoT based solutions to improve their productivity. According to a report published in the Guardian, 47% of companies are planning to increase their IoT expenditures in coming years. Further, the market comprises several international and regional players. The global players focus on increasing their presence in many regions. This increases competition, in terms of features, quality, and price for local vendors. Market players are adopting various business strategies to enhance their product offerings, business expansion, and increase their market penetration. For instance, the predictive maintenance market strengthened it base in the healthcare asset management market during the COVID-19 pandemic.
The US based solutions provider, Accurent, a PdM solutions provider, introduced a new predictive maintenance application for ventilators in hospitals for free of cost during the pandemic.
Moreover, in January 2019, Hitachi Ltd., announced that it will strengthen its wind power generator maintenance services and expand its core products of wind power generation solution business. The move is a part of Hitachi’s efforts to strengthen its renewable energy business, including collaborative and community-based creation-oriented energy projects including storage batteries, solar power, and other elements.
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