Unplanned Downtime Costs Demanding Condition Based Maintenance is expected to Drive Growth of the Predictive Maintenance Market
Companies across various industry verticals including manufacturing, healthcare, logistics and transportation are shifting from traditional BI (Business Intelligence) tools and are leveraging new-age technologies such as machine learning and artificial intelligence in a bid to achieve higher precision, speed and accuracy to carry out analysis of data. Emergence of predictive maintenance has enabled companies to make crucial operational predictions at a fast rate with higher accuracy and precision as compared to threshold based monitoring tools. Across various industry verticals, for instance oil and gas and industrial manufacturing, unplanned downtime due to equipment breakdown can result in higher cost to company resulting in higher cost of maintenance. Against this backdrop, technology companies have developed advanced analytics based on AI and IoT that facilitate predictive maintenance applications, using a set of technology and machine parameters, to detect equipment breakdown before it occurs. This can decrease or nullify downtime problems resulting in enhanced functionality and equipment life and a better ROI. Growing awareness amongst industrial customers regarding ability of condition based maintenance or predictive maintenance to control costs incurred by unplanned downtime, the predictive maintenance market is expected to gain significant traction in the coming years.
Real Time Condition Monitoring to Influence Predictive Maintenance Market Growth
Demand for enhanced management of assets is becoming highly crucial across every industry vertical. This has resulted in an increasing need for solutions offering real time condition monitoring using IoT (Internet of Things). With IoT networks integrating all assets of an enterprise in a live ecosystem, the transmission of data and its analysis on a real time basis is giving rise to live monitoring of asset condition. In addition, with data collated by machine learning and AI can provide meaningful real time insights, in turn enhancing the operational functionality of predictive maintenance. Moreover, various facets of service delivery, including but not limited to quality assessment, quality management and quality control without human intervention is possible with real time inputs provided by actuators, sensors and other controlling tools. With real time condition monitoring, embryonic failures of assets can be controlled. In this backdrop, real time predictive maintenance is expected to gain significant traction in the coming years as companies can take prompt decisions using live and continuous monitoring of assets. This aspect is expected to provide potential growth avenues in turn influencing growth of the predictive maintenance market.
Deployment of Predictive Maintenance via Cloud Likely to Witness an Upswing
Vendors of predictive maintenance provide cloud-based solutions. Various perceived benefits of cloud such as cost effectiveness, ease in data generation and data storage, enhanced management and scalability are expected significantly push the demand for cloud-based deployment of predictive maintenance solutions, in turn influencing the growth of the predictive maintenance market.
Predictive Maintenance Market to Gain High Traction in Asia Pacific
The predictive maintenance market is expected to witness significant growth rate across emerging economies in Asia Pacific region. This is mainly attributed towards growing investments by public and private sectors in technology to enhance maintenance solutions, translating in a higher demand for predictive maintenance. Moreover, manufacturing sector in the region is expected to be a lucrative platform of growth for predictive maintenance market as the sector has gained significant traction since past years. Providers of predictive maintenance solutions can focus on business expansion in the Asia Pacific region and enhance their market footprint.
Predictive Maintenance Competitive Scenario
Key companies in the predictive maintenance market are focusing on adopting growth strategies such as alliances and partnerships, new product launches and mergers and acquisitions to increase their market share. For instance, in 2020, PTC improved ThingWorx IoT platform in a bid to speed up industrial IoT deployment across entire value chain of enterprises. Upgraded ThingWorx platform will deliver expanded and new features that will support companies to create, scale up, implement and customize their solutions. In 2019, NXP Semiconductors and Microsoft collaborated to launch a novel Anomaly Detection Solution for Azure IoT users. This solution covers predictive maintenance for presence detection, rotating components along with intrusion detection to prevent failures, reduce downtime and enhance productivity alongside ensuring system safety. Same year, TIBCO Software acquired SnappyData. The acquisition will favor TIBCO by complementing to its TIBCO Connected Intelligence solution. This will provide a data fabric that will offer unified analysis which improves data science, data management and streaming for several use cases that require volume, agility and speed. Likewise, in February 2019, a new IoT solutions portfolio was launched by IBM that is a combination of advanced analytics and AI. This portfolio was built for asset-intensive companies such as MARTA (Metropolitan Atlanta Rapid Transit Authority) to enhance maintenance strategies. In 2021, Aizon launched Aizon Asset Health that helps biotech companies and pharmaceutical manufacturers enhance performance of equipment and assets using real time alerting and monitoring.
Few of the key players in the predictive maintenance market include IBM, SAP, Microsoft, TIBCO Software, PTC and Softweb Solutions.
The Global Predictive Maintenance Market is Segmented as Below:
By Organization Size
By Industry Vertical
Key Elements Included In The Study: Global Predictive Maintenance Market
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