Global Predictive Maintenance Market, in the year 2019 was $4331.56 million and is expected to reach $31965.49 million in the year 2027 at a 28.8% of CAGR during the year 2020 to 2027

Preventive Maintenance is a proactive maintenance strategy that observes the performance and condition of an asset in actual time to forecast when an asset requires maintenance before it breaks down, and is also known as condition-based maintenance. The use of a combination of sensors, machine learning, the Internet of things (IoT), modeling, and data analytics will decide whether there are any warning signs for oncoming failure.

 This information allows to schedule maintenance whether the machinery or equipment needs it, rather than guessing for maintenance. Predictive maintenance if utilized correctly and frequently can help in avoiding expensive repairs and downtime of equipment’s and it could save thousands of dollars of the business.

The Global Predictive Maintenance Market, in the year 2019 was $4331.56 million and is expected to reach $31965.49 million in the year 2027 at a 28.8% of CAGR during the year 2020 to 2027.

The main objective of predictive maintenance is to track the performance of equipment and condition, to minimize the chance of failure under normal working conditions. The goal of predictive maintenance is to foresee failure and then use corrective maintenance to prevent the failure.

 To forecast the requirement of maintenance, the traditional system depends on the historical data like previous breakdown and efficiency of equipment or simply a periodic maintenance schedule.

Whereas, modern predictive maintenance systems gather data in real-time by continuously tracking equipment activities and using artificial intelligence techniques and advanced neural networks for determining and ringing alarms when potential equipment failure is detected.

Global Predictive Maintenance Market is further sub-divided according to components, testing type, deployment mode, technique, and industry vertical. Further, these sectors are segmented in different modes, which are responsible for the growth of the predictive maintenance market.

Global Predictive Maintenance Market-by components

• Solution 
• Services
• Hardware

Global Predictive Maintenance Market- by Testing type

• Electrical Insulation
• Infrared thermography 
• Vibration monitoring 

Global Predictive Maintenance Market-by Deployment mode

• On-premise
• Cloud

Global Predictive Maintenance Market-by Technique

• Advanced 
• Traditional

Global Predictive Maintenance Market- by Industry Vertical

• Health care
• Manufacturing
• Energy and utility
• Aerospace and Defense
• Transportation
• Automotive

Global Predictive Maintenance Market Drivers-

• Increasing investment in the predictive maintenance
• Growth in the Adoption of Real-Time Streaming Analytics Technology
• Increase in lot connectivity
• Increase in the demand for Automotive.

Key players of the Global Predictive Maintenance-

• Oracle Corporation (US)
• Axiomtek Co. Ltd (Taiwan)
• XMPro (US)
• Microsoft Corporation (US)
• Hitachi Ltd (Japan)
• IBM Corporation (US)
• RapidMiner (US)
• SAP SE (Germany)
• C3 IoT (US)
• Software AG (Germany)
• Comtrade (Ireland)

The market research gives an in-depth analysis of the various sectors for the overall growth in the market. It also includes an overview of leading companies creating a successful growth strategy, along with the recent developments in past and present, taking into consideration the growth of the predictive maintenance market.

Covid-19 impact on Global Predictive Maintenance Market-

Covid-19 had a severe impact on all the elements of technology. Due to the slow hardware supply and the reduction in the manufacturing capacity, there is a decrease in the growth of IT infrastructure. For a short period, the business providing services and solution are expected to slow down. Though the adoption of analytics, AI, security solutions, and collaborative applications are predicted to grow in the upcoming years.

However, businesses are in search of technologies to overcome this pandemic. Business intelligence professionals, Analytics professionals, AI, and ML have been invited after looking at their expertise to help executives in making decisions on how to acknowledge the new business challenges and build strategies according to the situations.