Seattle, WA -- (SBWIRE) -- 02/24/2020 -- Operational predictive maintenance refers to a practice of determining the risk of failures in an operational process. In this, predicative analytics software and services access multiple data sources in real-time, in order to identify asset failure and quality issues in product development process. Moreover, predicative analytics software evaluates failure patterns and minor variances, in order to determine the risk of failures in operational process in order to reduce or avoid future losses. In other words, this software helps in improving device and equipment uptime. Operational predictive maintenance services are comprised of integration and implementation, consulting services, and training & support. These services optimize operational processes by supporting operational departments with the help of advanced analytics software in daily business process.

The global operational predictive maintenance market was estimated to account for US$ 1,138.4 Mn in terms of value by the end of 2019

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Market Dynamics- Drivers

Growing awareness regarding maintenance operation and reducing asset downtime is expected to drive growth of the global operational predictive maintenance market during the forecast period
In industrial facilities, managers are constantly focused on enhancing maintenance process at manufacturing plants and other operating ecosystems. Facility manager can prevent virtual downtime with predictive maintenance, especially when the equipment is not operating to its maximum potential Moreover, predictive maintenance system can control various types of data including temperature, energy use, output, run time, etc. to enhance decision making and operations at manufacturing plants or manufacturing units. This, in turn, is accelerating the adoption of operational predictive maintenance solutions at manufacturing facilities and thereby driving the market growth in the near future. For instance, according to Schneider Electric's study, an operational predictive maintenance system can facilitate significant operational benefits, connected devices, helping in manufacturing facilities through its network, and monitoring facilities in order to minimize the downtime of a manufacturing plant.

Growing implementation of new technologies is expected to propel the global operational predicative maintenance market over the forecast period

Technology-based ERP software solutions are developed for enterprises for predictive maintenance and to enhance their efficiency. Numerous energy-based manufacturing plants have implemented operational predictive maintenance solution. Moreover, many public sector and manufacturing companies are rapidly implementing new technologies, in order to predict failures and reduce losses related to it. These factors are expected to boost the market growth in the near future. Furthermore, advancements in technology have made predictive maintenance cost-effective and easily available for manufacturers. Since IoT is connected to every industry including logistics, transportation, utility, automotive, manufacturing, etc. many enterprises are increasingly adopting operational predictive maintenance solutions, in order to minimize downtime and optimize efficiency by allowing key vendors to invest in the global operational predictive maintenance market for innovation in services.

Market Opportunity

Advancement in cloud-based solution targeting new industries is expected to pose lucrative business opportunity for market players in the near future

Rapid adoption of cloud computing, Internet of Things, artificial intelligence, mobile devices, cognitive systems, and machine learning has provided real-time data processing, in order to provide efficient services in the shortest time possible anytime and anywhere. Moreover, there is significant demand for cloud-based analytical tools with many several key players already offering ready-to-run online services, which do not require installation. These factors are expected to present lucrative business opportunity for market players in the near future.

Growing adoption of operational predictive maintenance solutions by small and mid-sized enterprises (SMEs)

With the development of solutions with updated algorithms, the service providers are offering bundled services with solutions, which is more affordable for customers. Increasing number of key players has led to high availability of such solutions with bundled services. Moreover, SMEs are rapidly deploying such bundled services. Many market players are offering novel bundled solutions, in order to attract SMEs, which in turn, is presenting major business opportunities for market players in the region.

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Competitive Section

Key companies in the global operational predictive maintenance market are General Electric Company, IBM Corporation, eMaint Enterprises LLC, Software AG, Schneider Electric SE, SAS Institute Inc., Rockwell Automation Inc., PTC, Inc., and Robert Bosch GmbH.