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     273  0 Kommentare Global Operational Predictive Maintenance Market Will Procure US$ 3.1 Billion Value by 2024, End - Persistence Market Research

    NEW YORK, February 21, 2017 /PRNewswire/ --

    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.

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    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. 

    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.

    A sample of this report is available upon request@ http://www.persistencemarketresearch.com/samples/10394

    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. On-premise deployment will lose out to cloud-based deployment, but, will continue to be the dominant mode of deploying operational predictive maintenance. 

    View Report Table of Contents, Figures, and Tables, Here

    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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    Global Operational Predictive Maintenance Market Will Procure US$ 3.1 Billion Value by 2024, End - Persistence Market Research NEW YORK, February 21, 2017 /PRNewswire/ - 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 …