The global predictive maintenance market is estimated to garner a revenue of USD 81,582.5 Million by the end of 2031 by growing at a CAGR of 31.9% over the forecast period, i.e., 2022 – 2031. Further, the market generated a revenue of USD 5,261.4 Million in the year 2021. The growth of the market can be attributed to the growing need to reduce downtime and maintenance costs. Predictive maintenance predicts the best time for the maintenance of the equipment which also makes the maintenance procedure cost-effective, therefore it reduces the wastage of time and resources on the occasion of machine breakdown. Large factories lose 323 productivity hours annually on average. The average cost of lost sales, fines, downtime for employees, and restarting production lines is USD 532,000 per hour, or USD 172 million per facility yearly.
Get more information on this report:In addition to these, factors that are believed to fuel the market growth of the predictive maintenance market include digital transformation taking place in the organization is boosting the company’s progress along with the data consumption and production. The digital transformation of industries into industry 4.0, has introduced new predictive maintenance methods which are creating a new concept known as Maintenance 4.0. Organizations that have undergone digital transformation are anticipated to generate more than half of the global GDP by 2023. Moreover, every day, it is estimated that 1.145 trillion MB of data are produced. Around 300 billion emails were sent daily in 2022. Users sent about 650 million Tweets daily in 2022.
Base Year |
2021 |
Forecast Year |
2022-2031 |
CAGR |
31.9% |
Base Year Market Size (2021) |
USD 5,261.4 Million |
Forecast Year Market Size (2031) |
USD 81,582.5 Million |
Regional Scope |
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Growth Drivers
The rising adoption of technology for running the business efficiently is likely to increase the popularity of predictive maintenance technology. Around 65% of companies are looking forward to switching to various technologies and over 77%, who have already implemented the technology have expressed above-average satisfaction with it.
Predictive maintenance predicts the time of equipment failure and prevents its further failure, thus reducing the downtime and maintenance cost of the company. For industrial businesses, downtime often costs between USD 30,000 and USD 50,000 per hour. This indicates that downtime can reasonably cost the average business between USD 10 and USD 25 million a year.
Integration of IoT in predictive maintenance enables businesses to identify potential safety hazards, take action, and estimate problems before they have an impact on employees. In the next two years, starting from 2021, around 80% of businesses want to spend a considerable amount of money on at least one IoT project.
Predictive maintenance uses the data-driven technique by using real-time data for finding any sort of anomalies in machines. Global data production, collection, transfer, and usage are all expected to rise sharply, reaching 64.2 zettabytes in 2020. Global data generation is anticipated to increase to more than 180 zettabytes over the following five years, up until 2025.
Digital transformation is expected to boost predictive maintenance, by enhancing precision and blowing off downtime dramatically. Around 90% of all businesses have already embraced a digital-first company strategy or have plans to do so, while 55% of startups have done so.
Challenges
The global predictive maintenance market is segmented and analyzed for demand and supply by end-use vertical into government and defense, automotive, energy and utilities, transportation and logistics, healthcare and life sciences, food and beverage, digital industry, and others. Out of all types of end-use verticals, the energy and utilities segment is estimated to gain the largest market size of USD 14,110.3 million by the end of 2031, by growing at a CAGR of 32.6% over the projected time frame. Moreover, in 2021, the segment collected a revenue of USD 857.3 million. The growth of the segment can be attributed to the increasing need for minimizing productivity loss and maintenance costs. Predictive maintenance detects the possible issues in the equipment, which improves its productivity. Unplanned downtime costs the average oil and gas complex 32 hours of lost output each month, or USD 220,000 per hour. At each site, that comes to an annual total of USD 84 million.
The global predictive maintenance market is also segmented and analyzed for demand and supply by component into solutions and services. Amongst these two segments, the solutions segment is expected to garner a significant market revenue by growing at a CAGR of 31.6% over the forecast period. Moreover, the segment garnered a modest revenue in 2021. Furthermore, the solutions segment is categorized into integrated, and standalone. Growing need for a system, that combines all the technologies, to provide more integrated solutions. Such solutions are imperative in vast sectors, such as healthcare, food & beverage, and manufacturing. For instance, higher adoption of electronic medical records in the healthcare sector is expected to create huge demand for predictive maintenance solutions that integrated entire healthcare systems. During the period of the pandemic, nearly 73% of hospitals maintained electronic medical records in Japan. Moreover, in the United States, around 90% of office-based physicians use any one of the electronic health record (HER) systems. Furthermore, the growing need for solutions that have the ability to solve different issues in the industry is also expected to boost the growth of the segment. For instance, supply chain management needs predictive maintenance solutions for better safety & security, integration of the different regions, and most importantly enhances customer experience. On receiving better customer experience, around 89% of customers are believes to come back to the company make another purchase.
Our in-depth analysis of the global predictive maintenance market includes the following segments:
By Component |
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By Deployment Mode |
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By Organization Size |
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By End User Vertical |
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The Asia Pacific predictive maintenance market, amongst the market in all the other regions, is projected to hold the largest market revenue of USD 23,985.2 million by the end of 2031. The region’s market is to grow at the highest CAGR of 35.4% over the forecast period. The market in Asia Pacific garnered a revenue of USD 1,183.8 million in 2021. The growth of the market can be attributed majorly to the rapid penetration of digitization in the company. Around 70% of major corporations and middle-market businesses in the APAC region have a digital transformation strategy in place, with Taiwan leading the pack with 95%. This represents an increase from 2020 when 57% of APAC businesses had a digital strategy. Furthermore, the rise in the downtime occurring in the industry, which in turn leads to production loss is also expected to boost the market growth. Disruption in the supply chain management had increased the average downtime days by approximately 95% in the energy industries of the Asia Pacific region.
In 2023, market players might incur losses due to huge gap in currency translation followed by contracting revenues, shrinking profit margins & cost pressure on logistics and supply chain.
Controlling Inflation has become the first priority for global economies from last quarter of 2022 and to be followed in 2023. With skewed economic situations, rise in interest rate by governments to control spending and inflation, spiked oil and gas prices, high inflation, geo-political issues including U.S. & China trade war, Russia-Ukraine conflict to intensify the global economic issues.
The interest rates in the U.S. may be less sensitive in 2023 as compared to 2022; sigh of relief for businesses. Positive business sentiments, healthy business balance sheets, growth in construction spending (private construction value in 2022 stood at $1,429.2 billion, 11.7 percent (±1.0 percent) above the $1,279.5 billion spent in 2021, Residential construction in 2022 was $899.1 billion, up by 13.3 percent (±2.1 percent) from $793.7 billion in 2021, non-residential construction touched $530.1 billion, 9.1 percent (±1.0 percent) above the $485.8 billion in 2021.) showcases minimal impact of recession in the country.
Similarly, spiked spending in the European and major Asia economics including, India, China & Japan to showcase less impact on the global demand.
Ans: Increasing digital transformation, rising need for technologies, rising devices connected to IoT and need to curb the downtime are the few factors which are to boost the market growth.
Ans: The market is anticipated to attain a CAGR of 31.9% over the forecast period, i.e., 2022 – 2031.
Ans: Risk to data privacy, regular maintenance of predictive maintenance system and lack of skilled professionals are estimated to be the growth hindering factors for the market expansion.
Ans: The market in the Asia Pacific region is projected to hold the largest market share by the end of 2031 and provide more business opportunities in the future.
Ans: The major players in the market are C3.ai. Inc., Software AG, PTC, Asystom, Uptake Technologies Inc., TIBCO Software Inc., Wavelabs, OMRON Corporation, SIGMA Industrial Precision, DINGO Software Pty. Ltd., Operational excellence group limited (OPEX) Group Ltd., Fiix Inc., Ecolibirium Inc., Softweb Solutions Inc., S A S Institute Inc., Schneider Electric, GE Group, SAP SE, Microsoft Corporation, IBM Corporation, Siemens AG, Hitachi Consulting Corporation, and Fujitsu Limited.
Ans: The company profiles are selected based on the revenues generated from the product segment, the geographical presence of the company which determines the revenue generating capacity as well as the new products being launched into the market by the company.
Ans: The market is segmented by component, deployment mode, organization size, end-use verticals, and by region.
Ans: The energy and utilities segment is anticipated to garner the largest market size by the end of 2031 and display significant growth opportunities.
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