Predictive Maintenance Market Size Worth $10.84 Billion, Globally, by 2027 at 15.1% CAGR - Exclusive Report by The Insight Partners

NEW YORK, Dec. 14, 2021 /PRNewswire/ -- According to the new market research report "Predictive Maintenance Market Forecast to 2027 - COVID-19 Impact and Global Analysis by Component (Solution, Services), Deployment Type (Cloud, On-premise), Technique (Vibration Monitoring, Electrical Testing, Oil Analysis, Ultrasonic Leak Detectors, Shock Pulse, Infrared, Others), Industry (Manufacturing, Energy & Utilities, Aerospace & Defense, Transportation & Logistics, Others), and Geography", published by The Insight Partners, the global predictive maintenance market was valued at US$ 3.55 billion in 2019 and is projected to reach US$ 10.84 billion by 2027; it is expected to grow at a CAGR of 15.1% during 2019–2027. Get the Latest COVID-19 Analysis on predictive maintenance market at https://www.theinsightpartners.com/covid-analysis-sample/TIPRE00007686

Predictive Maintenance Market: Competitive Landscape and Key Developments

Hitachi, Ltd.; Software AG; IBM Corporation; Microsoft Corporation; PTC Inc.; Syncron AB; TIBCO Software Inc.; Schneider Electric SE; SAS Institute Inc.; and General Electric Company are among the key players in the global predictive maintenance market. The leading companies are focusing on expanding and diversifying their market presence by acquiring new customer base, thereby tapping prevailing business opportunities.

In 2020, Cyient has been a Microsoft Azure Certified for the Internet of Things (loT) partner, guaranteeing that clients can rapidly acquire loT solutions using hardware and software that has been pre-tested and validated to operate with Microsoft Azure loT services.

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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 parts require repair or replacement. This allows for minimizing downtime and maximizing the life of the equipment. Predictive maintenance is a process for monitoring equipment through the operation to identify any deterioration, allowing for maintenance planning and reduced 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.

Furthermore, adopting predictive maintenance 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 equipment faults or anomalies. 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. The equipment is monitored using traditional and advanced techniques that allow the machinery to be planned for maintenance before a failure occurs. These techniques are equipped with vibration monitoring, electrical insulation, infrared thermography, temperature monitoring, ultrasonic leak detection, and oil analysis tools. Most countries adopt condition-monitoring predictive maintenance to assess the performance of an asset in real time. Advanced techniques, however, are used significantly in developed economies such as the US, a few Western European countries, and some developed economies in Asia Pacific and the Middle East. The critical element of an advanced process is the Internet of Things (IoT) technology, which enables various assets and systems to connect, work together, share, and analyze the data.

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Globally, the countries have witnessed an unprecedented rise in the number of COVID-19 confirmed cases resulting in large-scale lockdown across different regions. Moreover, the retailers and brands faced various short-term challenges while maintaining the supply chain activities through retailers, distributors, raw material suppliers, and the company's production during the lockdown across several countries. Furthermore, the rising number of cases continues to negatively influence the production, distribution, and supply chain for the production of predictive maintenance-based software and services across the globe.

Growing Need to Reduce Maintenance Cost and Downtime to Drive the Market During Forecast Period:

Companies leverage AI and ML technologies to analyze IoT data 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. Due to the growing awareness among manufacturing industry managers about maintenance operations and the need to reduce downtime, the demand for predictive maintenance solutions has significantly increased. Managers in manufacturing companies are continually improving equipment maintenance processes in their manufacturing facilities and finding methods that can reduce faulty operations and increase process benefits.

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Predictive Maintenance Market: Offering

Predictive maintenance is carried out with the help of data collection and pre-processing, early detection of faults, time to failure prediction, scheduling of support, and optimization of resources. Based on component, the predictive maintenance market is segmented into software and service. The software segment holds a larger market share in 2019; however, the service segment is anticipated to grow at a higher CAGR during the forecast period. Based on solution, the predictive maintenance market can be further categorized into integrated and standalone software solutions. The demand for integrated solutions is high owing to its increasing popularity and awareness from various clusters of several industries. Thus, the growing need for a single, multi-functional software makes integrated software more popular than standalone software. Standalone software lacks the scope of customization; however, due to its low-cost profile, standalone software is widely deployed in small and medium-sized enterprises.

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