The predictive maintenance market in MEA is expected to grow from US$ 580. 02 million in 2019 to US$ 1,723. 02 million by 2027; it is estimated to grow at a CAGR of 14. 7% from 2020 to 2027. Improved asset management for every vertical is increasingly needed.
New York, Oct. 28, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Middle East and Africa Predictive Maintenance Market Forecast to 2027 - COVID-19 Impact and Regional Analysis by Component, Deployment Type, Technique, and Industry" - https://www.reportlinker.com/p05978876/?utm_source=GNW
Solution providers equipped with machine learning (ML) and artificial intelligence (AI) can collect and make meaningful insights into the vast amount of customer-related data.
Internet of things (IoT) generates a massive amount of data from connected devices.Moreover, to optimize service delivery, such as quality assessment and predictive maintenance, AI can also be integrated with IoT devices without any human intervention.
Inputs from actuators, sensors, and other control parameters in real-time would not only predict embryonic asset failures but also help companies monitor and take prompt action in real-time, which is further driving the demand for predictive maintenance. The requirement of improved asset management is among the other factors expected to positively influence the demand for predictive maintenance.
The technology industry is one of the victims of COVID-19, and since the start of 2020, this industry has been reflecting the declining trend.With the imposition of lockdown across the MEA region, the trades have been witnessing shattering experience due to the unavailability of retailers, suppliers, online and authorized sales representatives in the market.
The region is projected to register a swift decline in their supply of predictive maintenance components and have halted their manufacturing activities and subsequently disrupting the importing of parts and equipment.
Based on technique, the vibration monitoring segment led the MEA predictive maintenance market in 2019.Vibration analysis, which is primarily used for high-speed rotational equipment, allows a technician to monitor the vibrations of a machine using a handheld analyzer or real-time sensors built into the equipment.
A machine that operates in peak condition exhibits a particular pattern of vibration.When components such as bearings and shafts begin to wear and fail, the machine generates another vibration pattern.
A trained technician can easily compare the readings against the known failure modes by proactively monitoring the equipment to determine its location.Misalignment, unbalanced components, bent shafts, loose mechanical parts, and motor problems are among the issues that can be detected with the vibration analysis.
Many organizations provide in-depth training to certify individuals as vibration analysts, which is fueling the growth of the MEA predictive maintenance market.
The overall MEA predictive maintenance market size has been derived using both primary and secondary sources.To begin the research process, exhaustive secondary research has been conducted using internal and external sources to obtain qualitative and quantitative information related to the market.
The process also serves the purpose of obtaining overview and forecast for the MEA predictive maintenance market with respect to all the segments pertaining to the region.Also, multiple primary interviews have been conducted with industry participants and commentators to validate the data, as well as to gain more analytical insights into the topic.
The participants who typically take part in such a process include industry experts, such as VPs, business development managers, market intelligence managers, and national sales managers, along with external consultants, such as valuation experts, research analysts, and key opinion leaders specializing in the MEA predictive maintenance market. Major players operating in the MEA predictive maintenance market include Hitachi, Ltd.; IBM Corporation; Microsoft Corporation; TIBCO Software Inc.; Schneider Electric SE; SAS Institute Inc.; and General Electric Company.
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