The Predictive Maintenance Market size to grow from USD 4.2 billion in 2021 to USD 15.9 billion by 2026, at a Compound Annual Growth Rate (CAGR) of 30.6% during the forecast period. Various factors such as increasing spending on marketing and advertising activities by enterprises, increasing use of emerging technologies to gain valuable insights, growing need to reduce maintenance cost, equipment failure and downtime, and proliferation of customer channels are expected to drive the adoption of predictive maintenance technologies, and proliferation of customer channels are expected to drive the adoption of predictive maintenance technologies and services.
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Predictive maintenance is a strategy that employs data analysis tools and procedures to find operational irregularities and potential flaws in machinery and processes so that users can correct them before they break down.. This, in turn, enables enterprises to significantly reduce unplanned machine downtime and decide whether any particular asset needs maintenance. By determining the ideal time to do maintenance on a piece of equipment, predictive maintenance aims to maximise reliability while minimising costs and maintenance frequency. To assist their employees in making better choices regarding when maintenance should be carried out, maintenance managers use sensor data, predictive maintenance, artificial intelligence, and machine learning.
Hybrid Cloud segment to account for a higher CAGR during the forecast period
The hybrid cloud mode offers several economic advantages. Businesses cannot afford to simply put aside their significant investments in on-premises infrastructure and business models. Vendors have realized these challenges and are introducing hybrid solutions to enable companies with on-premises deployments to take advantage of the cloud and easily adopt cloud solutions. For instance, Microsoft has recognized the importance of hybrid cloud and cloud-enabled many of its on-premises server solutions. The company has simultaneously enabled many of its cloud services to work with on-premises solutions.
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Energy and Utilities vertical to have the higher CAGR during the forecast period
The sector is one of the largest verticals serving a huge customer base. The adoption of predictive maintenance solutions is rapidly changing the operational and performance model of the energy and utility industry. Adding predictive maintenance to the system helps to improve customer satisfaction, reduce maintenance costs, and improve safety and compliance systems. This investment of predictive maintenance in energy and utilities has a high ROI and other benefits. The need for new data sources, new programs, and efficient management of resources as well as increasing competition from alternative energy providers are the growth drivers for this market.
APAC to hold higher CAGR during the forecast period
The predictive maintenance market has been segmented into five major regions: North America, Europe, APAC, Latin America, and MEA. APAC is expected to grow at a good pace during the forecast period. The factors driving the growth of the predictive maintenance market in this area include a growing emphasis on minimising maintenance and downtime and rising awareness of predictive maintenance. But rising data security worries are impeding business expansion. Additionally, growing demand for better asset management is opening up new business prospects for market players. The need for manufacturers to figure their parameters of failure for machines and create an outline for their associated system would drive the adoption of predictive maintenance solutions and services in Asia Pacific. China, Japan, India, and Bangladesh have displayed ample growth opportunities in the predictive maintenance market. Other countries in the region, such as India, South Korea, Singapore, Hong Kong, Australia, and Malaysia, are exploring ways to integrate predictive maintenance solutions and services.
The major vendors in the global predictive maintenance market include Microsoft(US), IBM(US), SAP(Germany), SAS Institute (US), Software AG (Germany), TIBCO Software (US), HPE (US), Altair (US), Splunk (US), Oracle (US), Google (US), AWS (US), GE (US), Schneider Electric (France), Hitachi (Japan), and PTC (US).
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