New York, Aug. 23, 2022 (GLOBE NEWSWIRE) -- The Insight Partners published latest research study on “Predictive Maintenance Market Forecast to 2028 – COVID-19 Impact and Global Analysis – by Component (Solutions and Services), Deployment Type (Cloud and On-Premise), Technique (Vibration Monitoring, Electrical Testing, Oil Analysis, Ultrasonic Leak Detectors, Shock Pulse, Infrared and Others), Industry (Manufacturing, Energy & Utilities, Aerospace & Defense, Transportation & Logistics, Oil & Gas, and Others), and Geography; Shutter Type (Visible AGS, Non-Visible AGS) and Geography” The global predictive maintenance market growth is driven by the increasing number of initiatives for integrating artificial intelligence for predictive maintenance of critical assets into oil & gas applications.
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Market Size Value in | US$ 5.31 Billion in 2021 |
Market Size Value by | US$ 26.58 Billion by 2028 |
Growth rate | CAGR of 27.4% from 2022 to 2028 |
Forecast Period | 2022-2028 |
Base Year | 2021 |
No. of Pages | 221 |
No. Tables | 116 |
No. of Charts & Figures | 96 |
Historical data available | Yes |
Segments covered | Component, Deployment Type, Technique, and Industry |
Regional scope | North America; Europe; Asia Pacific; Latin America; MEA |
Country scope | US, UK, Canada, Germany, France, Italy, Australia, Russia, China, Japan, South Korea, Saudi Arabia, Brazil, Argentina |
Report coverage | Revenue forecast, company ranking, competitive landscape, growth factors, and trends |
Predictive Maintenance Market: Competitive Landscape and Key Developments
General Electric Co.; Hitachi Ltd.; IBM Corporation; Microsoft Corporation; PTC, Inc.; SAS Institute Inc.; Schneider Electric SE; Software AG; Syncron AB; and TOSL Engineering Ltd. are among the key predictive maintenance market players profiled during this study. In addition, several other important market players were studied and analyzed during the course of this market study to get a holistic view of the market and its ecosystem.
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In April, 2022, Wärtsilä Corporation signed an Optimized Maintenance Agreement (OMA) covering two LNG carrier vessels technically managed and operated by the Japanese shipping group MMS Co., Ltd. The agreement is expected to deliver maximized engine uptime and long-term cost predictability while optimizing maintenance for the highest efficiency and lowest carbon footprint. Wärtsilä's predictive maintenance solution, Expert Insight, is also included in the agreement.
In July, 2021, Schneider Electric implemented predictive maintenance for Nestlé's Dubai South factory. This solution is expected to aid Nestlé's operations team monitor power management, electrical loads, and temperature settings from the factory's connected assets, so they can address issues proactively, avoid unplanned downtime and mitigate safety risks.
Many predictive maintenance market players are providing differentiated solutions that are strongly competing in pricing and offerings, which is anticipated to drive the market growth over the forecast period. In addition, the increasing number of initiatives for integrating artificial intelligence for predictive maintenance of critical assets into oil & gas applications are expected to boost the market further.
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The predictive maintenance market is broadly segmented into 5 major regions—North America, Europe, Asia Pacific (APAC), Middle-East and Africa (MEA), and South America (SAM). Europe is pioneering digital transformation. The region has several large enterprises focusing on increasing efficiency through advanced technologies while reducing expenditure on reactive maintenance and losses from unforeseen equipment breakdown. Decision makers in such enterprises are approaching various predictive maintenance service providers to manage their manufacturing plants better, increase usage of advanced technologies, and increase productivity, among others. Such trends are driving the predictive maintenance market growth. Also, several key technology-based startups, coupled with many drone-based predictive maintenance service providers, are providing great growth opportunities for the predictive maintenance market in the region.
North America accounted for the largest market share in terms of revenue. This technologically advanced region includes key economies, such as the US, Canada, and Mexico. Technological advancements have led to a highly competitive market in the region. The growing uptake of advanced technologies, such as machine learning, the Internet of Things (IoT), acoustic monitoring, temperature technology, vibration analysis, motor circuit analysis, and others, is accelerating the growth of the predictive maintenance market.
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Predictive maintenance allows them to optimize maintenance tasks in real-time, maximizing their equipment's useful life while avoiding operational disruption. For organizations producing products on a mass scale, predictive maintenance is an impressive way to reduce defects in products and thus eliminate waste. For those, who produce parts and machinery, predictive maintenance is commonly used to set the technology for monitoring and inspecting the condition of the moving apparatus and motors.
Productivity, power, state of health, and wear within are all effectively monitored. The growing need to maintain production equipment, such as industrial robots, machinery, elevators, and pumps, to reduce overall downtimes is driving the uptake of predictive maintenance solutions and services in the manufacturing industry. Also, the surge in automation in the manufacturing sector, coupled with Industry 4.0, is expected to drive demand for these solutions to protect high-end equipment from damage. Such factors are strongly boosting the predictive maintenance market growth.
Predictive Maintenance Market Analysis: Industry Overview
Based on industry, the predictive maintenance market is segmented into manufacturing, energy & utilities, aerospace & defense, transportation & logistics, oil & gas, and others. The manufacturing industry is among the largest adopters of predictive maintenance. Traditionally, maintenance professionals used to combine both quantitative and qualitative techniques to predict impending failures and mitigate downtime in their manufacturing plants.
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