Press release
Operational Predictive Maintenance Market : Insights into the Competitive Scenario of the Market
Minimizing asset downtime continues to be one of the top operational priority for tech companies. Global syndicates are adopting measures for operational predictive maintenance in order to avert the mix up arising from integrating multiple technologies. As corporations spanned across the globe begin integrating big data and IoT for improving operational efficiency, the need for operational predictive maintenance systems is converging into a necessity.Persistence Market Research recently conducted a study on how the global market for operational predictive maintenance, which is presently valued at nearly US$ 600 million, will soar till the end of 2024. According to the key findings compiled in this study, the global operational predictive maintenance market is expected to attain a staggering 23.2% CAGR and reach US$ 3,158.3 million value by the end of 2024.
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Apropos the report, the demand for operational predictive maintenance in North America will remain the highest, compared to other regions. Companies in the US and Canada will account for sales of operational predictive maintenance systems worth over US$ 1,200 million by the end of 2024. However, Asia-Pacific’s operational predictive maintenance market will be rising at an exponential CAGR of 27.3%. The demand for such maintenance systems will also be high in Latin America, while Europe and the Middle East & Africa will show moderate growth in terms of revenues.
This 167 page report gives readers a comprehensive overview of the Operational Predictive Maintenance Market. Browse through Table. Browse through 54 figures and 24 data tables to unlock the hidden opportunities in this market.
Germany-based Robert Bosch GmbH and Software AG are known for providing advanced operational predictive maintenance systems and services across the globe. The global market for operational predictive maintenance is also witnessing participation for US tech companies such as SAS Institute Inc., PTC Inc., General Electric Company, IBM Corporation, eMaint Enterprises LLC and Rockwell Automation, Inc. Based in France, Schneider Electric SE is also a prominent provider for predictive asset analytics systems in the world.
The report further estimates that global demand for operational predictive maintenance software will remain higher in comparison with services. By the end of 2024, nearly three-fourth of global operational predictive maintenance revenues will accounted by software sales. When it comes to deployment of operational predictive maintenance systems, cloud-based deployment is projected to gain traction by nearly doubling its global revenue share through 2024
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https://www.persistencemarketresearch.com/samples/10394
Manufacturing plants will be deemed as the largest end-users of operational predictive maintenance. In 2016, operational predictive maintenance systems deployed by manufacturing industries brought in more than US$ 160 million in revenues. In due course of forecast period, revenues accounted by manufacturing industries as end-users will have surge at the CAGR of 22.4%. Fastest revenue growth attained by end-user, however, will be credited to public sector companies around the globe. Sales of operational predictive maintenance to public sector companies will soar at more than 25% CAGR. Likewise, the demand for operational predictive maintenance will also be considerably high in transportation, automotive, healthcare, and energy & utility industries.
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Persistence Market Research (PMR) is a third-platform research firm. Our research model is a unique collaboration of data analytics and market research methodology to help businesses achieve optimal performance.
To support companies in overcoming complex business challenges, we follow a multi-disciplinary approach. At PMR, we unite various data streams from multi-dimensional sources. By deploying real-time data collection, big data, and customer experience analytics, we deliver business intelligence for organizations of all sizes.
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