According to the study published by Astute Analytica, the global predictive maintenance market is estimated to witness a rise in its revenue from US$ 4,560.7 Mn in 2021 to US$ 18,653.2 Mn by 2027. The market is registering a CAGR of 26.5% over the forecast period 2022-2027. The market is majorly driven by factors such as rising urbanization, rampant digitalization and increasing demand to decrease operation & maintenance cost. Predictive maintenance is a proactive maintenance technique that uses real-time asset data (collected through sensors), historical performance data, and advanced analytics to forecast when asset failure will occur. The result of predictive maintenance technology is that maintenance work can be scheduled and performed before an asset is expected to fail with minimal downtime. Predictive maintenance is used in various sectors like manufacturing, healthcare and transport on technology driven systems for operating.
Market Dynamics:
Drivers:
The growing impetus of the global predictive maintenance market is attributed to factors such as rising urbanization, rampant digitalization and increasing demand to decrease operation & maintenance cost. With rising urbanization, consumer preferences are also changing, people are more likely to rely on technology rather than any other human. Businesses prefer their operations to be conducted with zero error as most of the organization nowadays believe less time more work. Moreover, predictive maintenance relies on sensors to identify the need for maintenance. Not only are sensors more accurate than human senses, but they can detect internal wear that cannot be directly observed, is too dangerous for humans to inspect, or would otherwise require equipment to be shut down and opened up. Furthermore, predictive maintenance allow companies to trim operating costs as businesses can make operational predictions up to 20 times faster and more accurate than threshold-based surveillance systems. AI and IoT based predictive maintenance technologies help enterprises to predict equipment failures in advance.
Challenges:
To conduct predictive maintenance, employees must be trained to use monitoring equipment, interpret the data received from sensors, and analyze reports generated by predictive management software. This requires skilled workforce. Lack of skilled workforce is a challenge to the market growth.
Trends:
Solution providers equipped with AI and ML can collect and turn the wide amount of customer-related data into meaningful insights, as IoT generates a huge amount of data from connected devices. Further, the real-time inputs from sensors, actuators, and other control parameters would not only predict embryonic asset failures, but also help companies monitor in real-time and take prompt actions.
Regional Analysis of the Global Predictive Maintenance Market
Impact of COVID-19
Temporary shutdown of industries/ factories and numerous manufacturing units due to the pandemic affected the global predictive maintenance market negatively. However, with no ability to travel to client facilities to perform asset health analysis, service providers could not offer their services during the pandemic. However, post-covid times will witness an increase in adoption of predictive maintenance, as remote monitoring and diagnosis will be the key to enable predictive maintenance service during this time of social distancing. Also, increasing application of predictive analytics in the healthcare industry to better understand which patients are at risk, where resources are most needed, and where the disease is likely to spike next; will significantly contribute to the market growth.
Competitive Landscape
The key players in the global predictive maintenance market are IBM, SAP, SIEMENS, Microsoft, GE and Intel among others. Through extensive research, it is found that big players have adopted various competitive strategies such as mergers & acquisitions in order to have a grip of emerging market.
Strategies Adopted by Key Market Players
Segmentation Overview
The following are the different segments of the Global Predictive Maintenance Market:
By Component segment of the Global Predictive Maintenance Market is sub-segmented into:
By Deployment Mode segment of the Global Predictive Maintenance Market is sub-segmented into:
By Technology segment of the Global Predictive Maintenance Market is sub-segmented into:
By Organization Size segment of the Global Predictive Maintenance Market is sub-segmented into:
By Region segment of the Global Predictive Maintenance Market is sub-segmented into:
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