$10.84 Billion, Predictive Maintenance Market is Emerging with 15.1% of CAGR by Integration with IIoT and Use of Machine Learning by 2027

Predictive maintenance is a technique that evaluates the condition of an enterprise's machines and equipment to prevent failures during operation. It uses predictive algorithms with sensor data to estimate the possibility of equipment failure. It also identifies the root cause of complex machinery problems and helps determine which part requires repair or replacement. This minimizes the downtime and maximizing the life of the equipment. Predictive maintenance is a process of monitoring equipment through the operation to identify any deterioration, which allows carrying out maintenance by reducing operating costs. It is commonly used in Industry 4.0 framework. The primary goal of predictive maintenance is to deliver the most precise maintenance planning in advance to avoid unforeseen failures.

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The adoption of predictive maintenance system can ensure numerous advantages such as potentially extended service life of the equipment or assets, increased plant safety, optimized handling of spare parts, and fewer breakdowns and outages. Predictive maintenance solutions are installed to monitor and detect faults or anomalies in equipment but are only engaged in critical failure possibilities. This helps deploy limited resources, maximize the uptime of the device or equipment, enhance quality and supply chain processes, and improve overall satisfaction for all involved stakeholders.

Hitachi, Ltd, Software AG,IBM Corporation, Microsoft Corporation, PTC Inc., Syncron AB, TIBCO Software Inc, Schneider Electric SE, SAS, and General Electric Company are some of the well-established companies present in the global predictive maintenance market.

Strategic Insights:
The players in the predictive maintenance market focus on strategies such as partnerships, business expansion, and new product launchesto maintain their positions in the market. A few developmentsby key players are listed below:

In 2020,Cyient joined Microsoft Azure got certified for the Internet of Things (loT), ensuring that the customers will get loT solutions quickly with hardware and software that has been pre-tested and verified to work with Microsoft Azure loT services.

In 2019, Siemens Digital Industries Software and SAS announced a new partnership to help companies create new IoT edge and cloud-enabled solutions by applying SAS and open source streaming analytics through Siemens' MindSphere. The merge of Siemens' MindSphere and SAS Analytics would help the companies to increase productivity and reduce operational risk through predictive/prescriptive maintenance and optimized asset performance management.

Growing Need to Reduce Maintenance Cost and Downtime

To analyze IoT data, companies leverage AI and ML technologies to achieve incredible precision, accuracy, and speed over traditional business intelligence tools. With the advent of predictive maintenance, businesses can make operational predictions up to 20 times faster and more accurate than threshold-based surveillance systems. Unplanned downtimes resulting from equipment breakdown can cause huge revenue loss in various industries, such as manufacturing and oil & gas. AI-based IoT solutions offer predictive maintenance applications for enterprises to predict equipment failures in advance. Predictive maintenance-based solutions help companies identify patterns for predicting equipment failure in constant data streams.

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