Operational predictive maintenance software retrieve multiple data sources in real time to predict quality issues or asset failure. Adoption of these software solutions facilitate organizations to prevent downtime and reduce maintenance costs. Operational predictive software solutions detect failure patterns and minor anomalies to determine the assets and operational processes that are at the greatest risk of failure. Deployment of operation predictive maintenance software boosts equipment uptime and enhance supply chain processes and quality. One of the major factors for the increasing usage of these software solutions is their ability to accurately predict asset failure, enabling enterprises to take the asset out of production ensuing efficient supply chain.
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The operational predictive maintenance market has been experiencing massive growth in the recent years due to rise in demand for transforming maintenance operations and reducing asset downtime. Moreover, increasing demand for big data and Internet of Things (IoT) and rising focus of organizations on reducing operational cost is further expected to fuel the growth of operational predictive maintenance market during the forecast period. However, lack of training for operators and lack of trust in predictive maintenance technology is hindering the market growth. Increasing demand for real time steaming analytics and increasing demand from small and medium enterprises (SMEs) is expected to create huge opportunities for the companies operating in operational predictive maintenance market.
Operational Predictive Maintenance Market can be segmented on the basis of component as follows:
The global operational predictive maintenance market is segmented on the basis of components, deployment type, application, and geography. Based on the component, the global operational predictive maintenance market is classified into solutions and services. Further, services segment is further categorized into system integration, training and support, and consulting. Based on the deployment type, the global operational predictive maintenance market is further segmented into cloud-based and on-premise. Among these, cloud based operational predictive maintenance solutions market is expected to show swiftest growth enabling enterprises to reduce their dependence on data mining specialists, data integration and IT. In terms of application, the market is segmented automotive, energy and utilities, healthcare, manufacturing facilities, government and defense and transportation and logistics. Among these, manufacturing facilities are expected to hold the major market share for operational predictive maintenance market due to high deployment rate by manufacturers to reduce the maintenance cost consequently increasing the profitability. On the basis of geography, the global operational predictive maintenance market is segmented into North America, Europe, Asia Pacific, Latin America, and the Middle East and Africa. Among these, North America is expected to lead the operational predictive maintenance market in 2016. The growing big data market and high adoption of IoT are contributing to the growth for operational predictive maintenance market in the North America region. Moreover, heavy investments made by countries such as China, Japan, Korea, and India in Asia-Pacific to enhance the efficiency of production assets is further expected to offer sufficient growth opportunities for the operational predictive maintenance market in this region.
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Some of the leading companies operating in the global operational predictive maintenance market which are transmuting the market with technology innovation are IBM Corporation, SAS Institute Inc., Software AG, General Electric, Robert Bosch GmbH, Rockwell Automation, Inc., PTC, Inc., Schneider Electric, Svenska Kullagerfabriken AB, and Emaint Enterprises, LLC.
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