Predictive Maintenance Market to Grow at 28.8% CAGR | Predictive Maintenance Pioneers: Leading the Fourth Industrial Revolution

According to a recent report published by Allied Market Research, titled, "Predictive maintenance market by component, technique, deployment type, stakeholder, and industry vertical: opportunity analysis and industry forecast, 2020-2027,"the global predictive maintenance market size was valued at $4,331.56 million in 2019, and is projected to reach $31,965.49 million by 2027, growing at a CAGR of 28.8% from 2020 to 2027.

On the basis of deployment model, the on-premise segment dominated the overall predictive maintenance market size in 2019, and is expected to maintain this dominance during the forecast period, owing to versatile sensors and ease of installation on pre-existing hardware However, cloud-based predictive maintenance solutions are expected to grow at the fastest rate during the forecast period, owing to direct IT control, remote accessibility, internal data distribution & handling, faster data processing using advanced predictive analytics, efficient resource usage, and cost-effectiveness.

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The ability to process data in real-time enables the generation of forecasts that serve as the foundation for predictive maintenance, leading to reduced downtime. Specialized condition monitoring systems incorporate intelligent monitoring nodes that utilize both adaptive and static rules to analyze real-time condition data, issuing immediate local alerts. Moreover, these local nodes establish communication with a centralized web portal, allowing personnel to analyze data in real-time as it occurs. Furthermore, effectively interpreting sensor data necessitates a combination of real-time analysis and an in-memory database, resulting in faster data access compared to traditional hard disk drives. This factor promotes the adoption of real-time condition monitoring. Oil and gas companies, in particular, have a substantial opportunity to enhance operational efficiency and reduce costs by implementing improved asset tracking and real-time condition monitoring practices.

The market is experiencing growth due to an increased demand for higher asset uptime and cost reduction in maintenance. This growth is being driven by investments in predictive maintenance, particularly in industries embracing IoT technology. Additionally, the desire to extend the lifespan of aging industrial machinery is contributing to market expansion. Furthermore, the growing necessity for predictive maintenance arises from the desire to extract insights from the adoption of new technologies. Nonetheless, challenges related to implementation and concerns about data security act as impediments to market growth. Moreover, in the post-COVID-19 landscape, emerging technologies such as machine learning, the integration of predictive maintenance with IIoT, and the demand for remote monitoring and asset management are expected to further propel the predictive maintenance market.

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Covid-19 Scenario:

➢ Businesses were encouraged to take special care of their production equipment and machinery to maximize productivity, owing to a shortage of staff, global supply chain instability, and high demand for various products.

➢ This, in turn, led to a rise in demand for predictive maintenance solutions across the globe, boosting predictive maintenance industry.

Based on industry vertical, the manufacturing segment dominated the global predictive maintenance market share in 2019, owing to rise in challenges in manufacturing equipment maintenance, such as machinery, pumps, elevators, and industrial robots, which is has led to rise in use of predictive maintenance solutions. However, the healthcare sector is expected to grow at the fastest rate in the future as predictive maintenance of medical systems such as X-ray, MR, tomography, and mammography is becoming one of the most important considerations for hospitals looking to improve decision-making capabilities and operational efficiencies have also increased, owing to the COVID-19 pandemic testing their limits.

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Moreover, to avoid expensive production downtimes, several businesses have started to use smart sensors, advanced artificial intelligence systems, and other Industry Internet of Things (IIoT) solutions to monitor health and efficiency of critical machinery used in their manufacturing processes. Since predictive maintenance solutions can manage frequent tracking, basic machinery troubleshooting, and other activities, companies have been able to compensate for reduced availability of staff during the COVID-19 pandemic.

Manufacturers are increasingly embracing machine learning-based predictive maintenance. This approach relies on extensive historical or test data and employs customized machine-learning algorithms to assess various scenarios and predict system errors, subsequently generating alerts as needed. A well-designed and implemented machine learning algorithm learns the typical behavior of data and identifies deviations in real-time. A machine monitoring system gathers data on various parameters, such as temperature and engine speed, enabling it to predict the timing of potential breakdowns.

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Leading Market Players

➢ IBM Corporations
➢ Microsoft
➢ SAP SE
➢ General Electric
➢ Schneider Electric
➢ Hitachi
➢ PTC
➢ Software AG
➢ SAS
➢ Engineering Consultants Group
➢ Expert Microsystems
➢ SparkCognition
➢ C3.Ai
➢ Uptake Technologies
➢ Fiix Inc.
➢ Operational Excellence (Opex) Group Ltd
➢ TIBCO Software Inc.
➢ Asystom
➢ Reliability Solutions Sp. Zo.o
➢ Sigma Industrial Precision.

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