New Study from StrategyR Highlights a $3.9 Billion Global Market for Predictive Maintenance for Manufacturing Industry by 2026
SAN FRANCISCO, Feb. 23, 2022 /PRNewswire/ -- A new market study published by Global Industry Analysts Inc., (GIA) the premier market research company, today released its report titled "Predictive Maintenance for Manufacturing Industry - Global Market Trajectory & Analytics". The report presents fresh perspectives on opportunities and challenges in a significantly transformed post COVID-19 marketplace.
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Edition: 9; Released: February 2022
Executive Pool: 6184
Companies: 115 - Players covered include eMaint Enterprises, LLC; General Electric Company; IBM Corporation; PTC, Inc.; Robert Bosch GmbH; Rockwell Automation, Inc.; SAS Institute, Inc.; Schneider Electric SA; Software AG and Others.
Coverage: All major geographies and key segments
Segments: Component (Software, Services); Deployment (On-Premise, Cloud-Based); Technology (Machine Learning, Deep Learning, Big Data & Analytics)
Geographies: World; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; and Rest of Europe); Asia-Pacific; Rest of World.
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ABSTRACT-
Global Predictive Maintenance for Manufacturing Industry Market to Reach US$3.9 Billion by the Year 2026
Predictive maintenance is proactive and is designed to increase reliability of machine and decrease their downtime. As competition in the manufacturing industry intensifies and the challenges for successful survival increase in magnitude, companies are focusing on improving their financial performance by scrutinizing closely the manufacturing reliability of their operations. Effective asset performance management is this regard represents an absolute necessity. Also strengthening the emphasis on asset management is the legislation of stringent workplace safety regulations. Occupational safety norms create the need for routine inspection of the condition of plant and manufacturing assets. As a subset of asset management, predictive maintenance (PdM) is forecast to benefit from the growing manufacturer investments in asset management systems and corporate wide implementation of asset management regimes. Maintenance is getting a notable makeover due to ongoing digital transformation, with the use of advanced data capturing and analytics tools leading to emergence of predictive maintenance. End-to-end integration of PdM with the entire lifecycle of the industrial plant is a key trend in vogue to enable the creation of more efficient workflows, elicit higher productivity and ensure better correlation of data among various sources.
Amid the COVID-19 crisis, the global market for Predictive Maintenance for Manufacturing Industry estimated at US$1.6 Billion in the year 2022, is projected to reach a revised size of US$3.9 Billion by 2026, growing at a CAGR of 21.4% over the analysis period. Software, one of the segments analyzed in the report, is projected to grow at a 19.6% CAGR to reach US$2.5 Billion by the end of the analysis period. After a thorough analysis of the business implications of the pandemic and its induced economic crisis, growth in the Services segment is readjusted to a revised 23.6% CAGR for the next 7-year period. This segment currently accounts for a 41.8% share of the global Predictive Maintenance for Manufacturing Industry market. Predictive maintenance software accesses the plant's big data to gain additional insights about the operating environment in the plant and other extraneous factors that influence machine operation. Maintenance and repair services are vital for the proper functioning of enterprise assets while being key to the continuity and effectiveness of business operations. The proliferating deployment of sensing systems and advanced digital technologies such as IoT, AI and Big Data will spur the momentum for predictive maintenance.
The U.S. Market is Estimated at $478.2 Million in 2022, While China is Forecast to Reach $634.8 Million by 2026
The Predictive Maintenance for Manufacturing Industry market in the U.S. is estimated at US$478.2 Million in the year 2022. The country currently accounts for a 29.2% share in the global market. China, the world's second largest economy, is forecast to reach an estimated market size of US$634.8 Million in the year 2026 trailing a CAGR of 26.4% through the analysis period. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at 17.9% and 17.5% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 18.2% CAGR while Rest of European market (as defined in the study) will reach US$417.6 Million by the end of the analysis period.
Prescriptive Maintenance Emerges Into the Spotlight as the Future of Asset Management
Prescriptive analytics revolves around seeing the future and shaping it to realize a desired outcome. For shaping the future, however, the ability to see future probabilities is necessary. Consequently, prescriptive analytics requires a synergetic working of the two aspects of analytics – predictive and prescriptive for it create the inherent value and deliver. The integration is also key to support widespread adoption given that descriptive analytics is already well-spread and predictive analytics is gaining footprint. If the two were to be sourced from separate systems, although unrealistic, and clubbed together to offer a prescriptive analytical environment, the ability to pinpoint any inaccuracy among the two would not just be difficult but cause significant disruption to business. Besides, any attempts to preempt such error-laced prescriptions in future would be equally daunting. As a result, predictive analytics is required to be a part of any prescriptive analytical system for effective deployment and for garnering error-free prescriptions.
Prescriptive analytics find use in equipment maintenance. Prescriptive analytics analyze various data sets to anticipating and preventing unplanned machinery downtime, optimizing field scheduling and enhancing maintenance planning. It helps in predicting when and why equipment such as electric submersible pumps would fail, and make suitable recommendations to prevent the failure from occurring. RxM provides the much needed solution to improve operation of plant assets. Unlike in predictive maintenance, AI and machine learning in RxM do more than just monitor, identify impending failure, and give simple fix-it solutions. RxM Also determines what actions to be taken based on the data collected and analyzed, Analytics is therefore a vital part of RxM and helps analyze data, make multiple recommendations such as recalibrating machines and shortening the time gap between maintenance schedules, and finally also provide insights as to why a particular problem occurred and what can be done to not just remedy the problem but also prevent it from occurring in the future. While predictive maintenance also provides valuable recommendations, the suggestions are limited to improvements needed to avoid unscheduled downtime and in most data cover only devices and data generated by machines. Predictive analytics, in short, do not have the ability to analyze other variables. For instance, while conventional machine condition monitoring technologies only predict impending failure, prescriptive condition monitoring provides information on the environmental and operating conditions preceding equipment failure so much so that equipment failure can be prevented or maintenance can be scheduled even before conventional condition monitoring identifies early signs of a possible failure. More
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Global Industry Analysts, Inc., (www.strategyr.com) is a renowned market research publisher the world`s only influencer driven market research company. Proudly serving more than 42,000 clients from 36 countries, GIA is recognized for accurate forecasting of markets and industries for over 33 years.
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