Predictive Maintenance Market

Predictive Maintenance Market Analysis by Component (Solutions, and Services); by Deployment Mode (Cloud, and On-Premises); by Organization Size (Large Enterprises, and Small and Medium Sized Enterprises and by End User Vertical (Government and Defense, Automotive, Energy and Utilities, Transportation and Logistics, Healthcare and Life Sciences, Food and Beverage, Digital Industry, and Others) – Global Supply & Demand Analysis & Opportunity Outlook 2021-2031

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Report ID: 4675 | Published On: Feb 06, 2023

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Inflation and Looming Recession to Haunt Businesses:

In 2022 & 2023, market players expected to sail in rough waters; might incur losses due to huge gap in currency translation followed by contracting revenues, shrinking profit margins & cost pressure on logistics and supply chain. Further, U.S. economy is expected to grow merely by 3% in 2022. Purchasing power in the country is expected to fell nearly by 2.5%.
On the other hand, European countries to see the worst coming in the form of energy crisis especially in upcoming winters!! Right after COVID-19, inflation has started gripping the economies across the globe. Higher than anticipated inflation, especially in western world had raised concerns for national banks and financial institutions to control the economic loss and safeguard the interest of the businesses. Increased interest rates, strong USD inflated oil prices, looming prices for gas and energy resources due to Ukraine-Russia conflict, China economic slowdown (~4% in 2022) disrupting the production and global supply chain and other factors would impact each industry negatively.

In The News

  • Software AG launched ARIS, a new toolkit that assists the planning, execution, and evaluation of sustainability activities. This free solution centers processes around its clients' sustainability plans, aiding in their execution and making them transparent and verifiable.

  • Asystom announced its selection for the predictive maintenance project implemented by Northumbrian Water Limited. The organization aims at implementing predictive maintenance in order to prevent breakdowns, offer their customers with uninterrupted services, and lower operating costs.

Global Predictive Maintenance Market Definition

Predictive maintenance refers to the technique that uses condition-analyzing methods and data analytics tools to monitor the condition and performance of equipment of its piece. It detects any sort of abnormal patterns in the equipment and helps in forecasting the appropriate time of giving maintenance to the machine. It is mostly a data-driven procedure used for proactive maintenance of the machines. With the help of predictive maintenance, it gets easy to detect the problems in the equipment at an early stage so that it can get fixed before it faces any errors or completely breaks down.

Analyzing the data collected about the operation of machinery is done by predictive analytics and data science. Moreover, predictive maintenance systems often have the following component for their efficient working, data collection, data transformation, condition monitoring, asset life evaluation, a decision support system, and a human interface. There are various technologies that are included that have leveled up the strategies of predictive maintenance, it includes, corona detection, sound level metrics, oil analysis, vibration analysis, thermal imaging, and others.

There are many companies in the market that provide the service of predictive maintenance but the ultimate goal is to plan maintenance for when it will be both the most convenient and least expensive. Its ability of early detection and predictive maintenance, reduces the production lost in the company, minimized the expenditure on maintenance, and the cost of spare parts, and also increases the life of equipment.

Global Predictive Maintenance Market: Key Insights

Base Year


Forecast Year




Base Year Market Size (2021)

USD 5,261.4 Million

Forecast Year Market Size (2031)

USD 81,582.5 Million

Regional Scope

  • North America (U.S., and Canada)
  • Latin America (Mexico, Argentina, Rest of Latin America)
  • Asia-Pacific (Japan, China, India, Indonesia, Malaysia, Australia, Rest of Asia-Pacific)
  • Europe (U.K., Germany, France, Italy, Spain, Russia, NORDIC, Rest of Europe)
  • Middle East and Africa (Israel, GCC North Africa, South Africa, Rest of the Middle East and Africa)

Global Predictive Maintenance Market Size, Forecast, and Trend Highlights Over 2021 - 2031

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.

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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.

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Global Predictive Maintenance Market: Growth Drivers and Challenges

Growth Drivers

  • Rising Demand for Emerging Technologies

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.

  • Need to Reduce Maintenance Costs and Downtime

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.

  • Growing Adoption of IoT (Internet of Things) by the Company

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.

  • Massive Generation of Data

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.

  • Increased Prevalence of Digitization Among the Company

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.


  • Shortage of Expert Workers in the Market
  • Constant maintenance and Upgradation of the System
  • Escalating Challenges with Data Privacy - A significant amount of production and manufacturing data is gathered, transferred to, and stored in a database when using a predictive maintenance method. Moreover, the upgradation of industries to industry 4.0 generated a huge amount of data every hour. These industries are at higher risk of cyber-attacks, network breaches, and data theft. Therefore, the use of predictive maintenance can also lead to danger to the huge amounts of data, which in turn develops hesitancy among end users to adopt predictive maintenance.

Predictive Maintenance Market Segmentation

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

  • Solutions
    • Integrated
    • Standalone
  • Services
    • Professional Services
    • System Integration
    • Support and Maintenance
    • Consulting
    • Managed Services

         By Deployment Mode

  • Cloud
  • On-Premises

          By Organization Size

  • Large Enterprises
  • Small and Medium Sized Enterprises

          By End User Vertical

  • Government and Defense
  • Automotive
  • Energy and Utilities
  • Transportation and Logistics
  • Healthcare and Life Sciences
  • Food and Beverage
  • Digital Industry
  • Others

Global Predictive Maintenance Market Regional Synopsis

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.

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Top Featured Companies Dominating the Global Predictive Maintenance Market

    • Inc.
      • Company Overview
      • Business Strategy
      • Key Product Offerings
      • Financial Performance
      • Key Performance Indicators
      • Risk Analysis
      • Recent Development
      • Regional Presence
      • SWOT Analysis
    • 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
    • General Electric
    • SAP SE
    • Microsoft Corporation
    • IBM Corporation
    • Siemens AG
    • Hitachi Consulting Corporation
    • Fujitsu Limited

Key Questions Answered in the Report

1) What are the major factors driving the growth of the predictive maintenance market?

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.

2) What would be the CAGR of predictive maintenance market over the forecast period?

Ans: The market is anticipated to attain a CAGR of 31.9% over the forecast period, i.e., 2022 – 2031.

3) What are the challenges affecting the predictive maintenance market growth?

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.

4) Which region will provide more business opportunities for growth of predictive maintenance market in future?

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.

5) Who are the major players dominating the predictive maintenance market?

Ans: The major players in the market are 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.

6) How are the company profiles selected?

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.

7) What are the segments in the predictive maintenance market?

Ans: The market is segmented by component, deployment mode, organization size, end-use verticals, and by region.

8) Which segment captures the largest market size in the end-use verticals segment in the predictive maintenance market?

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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