Growing Adoption Of Conditioning Monitoring Is Expected To Ensure Robust Growth In Predictive Maintenance Market
According to a new market research report published by Brisk Insights “Global Predictive Maintenance Market (Component (Solution, Services); Deployment(Cloud, On-Premise); Technique( Vibration monitoring, Electrical insulation, Oil analysis, Ultrasonic leak detectors, Performance testing, Others); End-User (Manufacturing, Energy and Utilities, Aerospace & Defense, Healthcare, Automotive, Government, Transportation, others)) – Growth, Future Prospects and Competitive Analysis,2019 - 2027”,the overall Predictive Maintenance market worldwide was valued at US$ 2.9Bn in 2018 and is set to grow with a CAGR of 28.5% during the forecast period.
The global Predictive Maintenance market is highly driven by the rapid adoption of conditioning monitoring techniques for maintaining the machines. The conditioning monitoring enables the operators to keep the track on the functioning of the machine by various techniques such as vibration monitoring, electrical insulation, oil analysis, ultrasonic leak detectors, performance testing, and others. The rising demand for predictive maintenance among the vertical is due to various advantages such as it reduction in cost for equipment, as the repair made prior to failure resulting in minimizing the cost for the equipment.
Secondly, the unscheduled downtime is minimized as the operators can judge the functionality of the machine which also results in saving of cost and time, increasing the production time. Moreover, the rising utilization of IoT and cloud-based system is also contributing in the growth of the predictive maintenance market. The deployment of cloud with the conditioning monitoring systems and devices has enabled the operators and the users to analyze the condition of the machines from anywhere across the globe, moreover, the techniques also enable the user to analyze functionality and performance of the machine. In 2018 November, Software AG launched a next-generation open and enterprise-grade cloud platform. This platform was made for deploying, testing, managing various enterprise, and IoT applications.
Based on End User, the manufacturing segments dominate the market with a market share of 28.6% in 2018. The high demand from the manufacturing industries for conditioning monitoring is a major factor for driving the predictive maintenance market. The ability of predictive maintenance to predict the machine failure allows the manufacturing industries to analyze the functionality of the machine allowing the user to repair the machine part which is affected to fail during the production.
Asia-Pacific is considered to be the fastest-growing region due to the presence of various manufacturing units, growing demand for automobile, transportation, and consumer electronics industries. Moreover, the presence of various key and local vendors in the region such as Ecolibrium Energy, Hitachi and others also contribute in the growth of the market. Moreover, the increasing rules and regulation for safety measurement in the region is a major factor for driving the predictive maintenance market in Asia-Pacific region. However, North America is the dominating region with a market share of 33% in 2018, which is followed by Europe. Both The North America and Europe has various automobile, manufacturing, oil and gas and other industries which are driving the market.
Some of the major companies profiled for predictive maintenance market are IBM Corporation, SAP SE, General Electric, Software AG, Robert Bosch, PTC Inc., Rockwell Automation, Warwick Analytics, Schneider Electric Se, and RapidMiner.
Browse the full report at https://www.briskinsights.com/report/predictive-maintenance-market
The Global Predictive Maintenance Market is Segmented into:
By Component Segment (2017–2027; US$ Bn)
By Technique Segment (2017–2027; US$ Bn)
By Deployment Segment (2017–2027; US$ Bn)
By End-User Segment (2017–2027; US$ Bn)
By Geography Segment(2017–2027; US$ Bn)
*Complete segmentation list is on report page