Predictive Maintenance Statistics 2024 – Everything You Need to Know

Are you looking to add Predictive Maintenance to your arsenal of tools? Maybe for your business or personal use only, whatever it is – it’s always a good idea to know more about the most important Predictive Maintenance statistics of 2024.

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How much of an impact will Predictive Maintenance have on your day-to-day? or the day-to-day of your business? Should you invest in Predictive Maintenance? We will answer all your Predictive Maintenance related questions here.

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Best Predictive Maintenance Statistics

☰ Use “CTRL+F” to quickly find statistics. There are total 101 Predictive Maintenance Statistics on this page 🙂

Predictive Maintenance Usage Statistics

  • In the same survey, it was shown that usage of predictive maintenance had risen from 47% to 51%, and that running equipment to the point of failure had dropped from 61% to 57%. [0]

Predictive Maintenance Market Statistics

  • The global market for enterprise asset management software is valued at $4 billion, and it’s expected to grow at a CAGR of 11% per year. [0]
  • The global maintenance, repair, and operations market’s estimated value in 2020 was $616.01 billion. [1]
  • Moreover, the global CMMS software market is estimated to reach $1.26 billion by the end of 2026 at a CAGR of 9.8% from 2020 to 2026. [1]
  • According to the consulting group NMSC , the global predictive maintenance market is expected to considerably increase in size between 2020 and 2030. [2]

Predictive Maintenance Software Statistics

  • The global market for enterprise asset management software is valued at $4 billion, and it’s expected to grow at a CAGR of 11% per year. [0]
  • Perhaps coincidentally, about 80% of all CMMS users don’t use all the functions offered by the software. [0]
  • For one company in 2017, data visualization software reduced production hours by 320 hours while also increasing production by 15%. [0]
  • Moreover, the global CMMS software market is estimated to reach $1.26 billion by the end of 2026 at a CAGR of 9.8% from 2020 to 2026. [1]
  • Around 33% of respondents say new software is difficult or very difficult to adopt for their teams. [1]
  • In the same survey, it was shown that usage of predictive maintenance had risen from 47% to 51%, and that running equipment to the point of failure had dropped from 61% to 57%. [0]
  • Roughly 10% of industrial equipment ever actually wears out, meaning a very large portion of mechanical failures are avoidable. [0]
  • Historically, total productive maintenance has been shown to increase plant capacity by over 10% and productivity by 50%, but over half of all attempts to implement TPM result in failure. [0]
  • According to maintenance professionals, the leading causes of unscheduled equipment downtime are aging equipment (34%), mechanical failure (20%), operator error (11%), lack of time for maintenance (9%), and poor equipment design (8%). [1]
  • 50% reduction in downtime due to equipment failures Asset failures are costly and stressful. [3]
  • Big data can provide insight to oil and gas companies, this way equipment failures and the optimal lifetime of the system and components can be analyzed and predicted.[16]. [4]

Predictive Maintenance Adoption Statistics

  • The top challenges encountered by maintenance teams have to do with hiring, onboarding, and retaining people (48%), streamlining processes (27%), and successful technology adoption (25%). [1]

Predictive Maintenance Latest Statistics

  • Maintenance costs are estimated to range between15% and 40%of total production costs . [0]
  • Predictive maintenanceis highly cost effective, saving roughly8% to 12%over preventive maintenance, and up to 40% over reactive maintenance. [0]
  • Between 2004 and 2008, the U.S. spent about 57% of its transportation infrastructure budget on new construction projects. [0]
  • The 43% leftover was spent on maintaining the 98.7% of roads remaining. [0]
  • The U.S. has over 47,000 bridges that are considered to be in urgent need of repairs, while 38% of all America’s bridges need some form of repair work. [0]
  • As of 2018, about 53% of facilities use a CMMS to monitor their maintenance. [0]
  • Additionally, 55% use spreadsheets and schedules, and 44% still use paper. [0]
  • In 2017, 78% of companies who used a CMMS to manage their assets reported seeing improvements in equipment life. [0]
  • Up to 80% of all attempted CMMS implementations fail. [0]
  • According to Plant Engineering’s2018 maintenance survey, preventive maintenance is favored by 80% of maintenance personnel. [0]
  • The overall use of predictive maintenance rose from 47% in 2017 to 51% in 2018, though preventive maintenance is still preferred by 80% of maintenance personnel. [0]
  • 80% of manufacturing plants use preventive maintenance, and over half use predictive maintenance with analytical tools. [0]
  • In 2012, the U.S. Bureau of Labor Statistics estimated that the nation would be short 10 million workers over the course of the following six years. [0]
  • Predictive analytics yields a tenfold return on investment, and it results in a savings of 30% to 40%. [0]
  • The number of facilities who considered this a challenge was li 49%. [0]
  • 79% of businesses see predictive maintenance as the main application of industrial data analytics. [0]
  • Out of an average working day, only24.5% of the average maintenance worker’s timeis spent performing productive tasks. [0]
  • Anestimated 20.9% of wasted timeresults from travelling to different areas in the facility, with an additional 19.8% resulting from waiting for instructions. [0]
  • Poor maintenance strategies can reduce a company’s production capacity byas much as 20%. [0]
  • Roughly82% of companiesexperienced at least one instance of unplanned downtime in the last three years. [0]
  • The average age of industrial assets in the United States is about20 years, according to the Bureau of Economic Analysis. [0]
  • Aging equipment is theleading cause of unplanned downtimesat 50%, with lack of time coming in at 14%, according to a 2016 survey by Plant Engineering. [0]
  • About 44% of all unscheduled equipment downtimes result from aging equipment, making it the leading cause of unscheduled downtime. [0]
  • Unplanned downtimes cost an estimated $50 billion every year. [0]
  • Overall, downtime costs most factories somewhere between 5% and 20% of their productive capacity. [0]
  • Up to 30% of all manufacturing deaths are related to a maintenance activity. [0]
  • Companies can save between 12% and 18% by using preventive maintenance over reactive, and each dollar spent on PM saves an average of $5 later on. [0]
  • Factories throughout the U.S. are estimated to be using about $40 billion worth of outdated equipment. [0]
  • As of 2017, about 53% of companies adopted big data analytics, which includes predictive maintenance and IIoT technologies. [0]
  • By 2024, IIoT technologies are predicted to help create nearly $800 billion in economic value. [0]
  • Roughly 30% of all manufacturers have difficulty understanding IoT. [0]
  • Worldwide spending on IoT technology is estimated to reach $1.2 trillion in 2024 with a CAGR of 13.6%. [0]
  • The number of connected devices in use worldwide is estimated to be over 17 billion, with 7 billion IoT devices. [0]
  • The job outlook for maintenance professionals is projected to grow at about 8% over the next ten years. [0]
  • Operator error is blamed for 18% of unscheduled equipment downtime 39% of facilities still use paper records for maintenance reports. [5]
  • An estimated 20.9% of wasted time for maintenance workers is due to traveling to different areas in a factory, with an additional 19.8% as a result of waiting for instructions. [5]
  • Scheduled maintenance takes on average about 19 hours a week, with 31% reporting more than 30 hours a week and 14% spending between 20 and 29 hours. [5]
  • Job growth for industrial machinery mechanics, machinery maintenance workers and millwrights is projected to be 5% from 2018. [5]
  • Unplanned downtime costs industrial manufacturers an estimated $50 billion annually. [5]
  • Preventive maintenance is favored by 80% of maintenance personnel as part of a multi faceted maintenance strategy. [5]
  • Predictive maintenance can reduce machine downtime by 30%50%, and increase machine life by 20%. [5]
  • Staples gained customer insight by analyzing behavior, providing a complete picture of their customers, and realizing a 137% ROI. [6]
  • Lenovo is just one manufacturer that has used predictive analytics to better understand warranty claims – an initiative that led to a 10 to 15 percent reduction in warranty costs. [6]
  • Read the complete Orlando Magic story Roughly 90 percent of all data is unstructured. [6]
  • It is projected to register a value of $701.3 billion by 2026, growing at a CAGR of about 2.19%. [1]
  • In 2020, 46.91% of surveyed companies in North America reported that they spent 21% to 40% of their operating budget on the cleaning and maintenance of their equipment and supplies. [1]
  • Meanwhile, 35.67% spent 1% to 20% of their budget. [1]
  • On the other hand, 2.25% of companies spent more than 80% of their operating budget on equipment maintenance alone. [1]
  • Meanwhile, 18% of companies spend 40 hours or more per week on maintenance. [1]
  • Around 66% of maintenance teams say their budget is either staying the same or increasing in 2021. [1]
  • Meanwhile, 60% of manufacturing companies still performed reactive maintenance. [1]
  • In 2020, the majority of facility management companies (90.61%). [1]
  • On the other hand, 51% sought the services of distributors and vendors, while 24.86% opted for online training providers. [1]
  • Only 16.02% enlisted a thirdparty, industry. [1]
  • In all, 66% of maintenance professionals from various industries say they use CMMS to track their maintenance program, a 24% increase from 2019. [1]
  • 74% of CMMS users believe that this tool improves productivity, while 58% consider it cost. [1]
  • Furthermore, 57% of users see its ease of use as an advantage. [1]
  • Moreover, 51% of CMMS users think that the tool helps reduce downtime. [1]
  • Moreover, 32% of maintenance executives believe that IoT will help them better understand machine health, while 27% think it can help them better predict and prevent equipment shutdowns. [1]
  • Another 25% believe IoT will change the way their maintenance personnel work and interact with all levels of their operation. [1]
  • It is estimated that industrial IoT will generate around $800 billion in economic value by 2024. [1]
  • In relation to this, the global discrete manufacturing industry was already projected to spend $119 billion in IoT in 2019, while the process manufacturing industry was estimated to spend $78 billion. [1]
  • As such, 47% of manufacturing companies and teams still use in house spreadsheets for maintenance schedules, while 46% utilize clipboards and paper records of maintenance rounds. [1]
  • About 30% of maintenance professionals say they do not use handheld or mobile devices for maintenance tasks, and that they have no plans of using them in the future. [1]
  • 25% of maintenance leaders think IoT will have no impact on their operations. [1]
  • The real estate and leasing industry hired the most maintenance and repair workers in the US in 2019 at 22%. [1]
  • It is followed by manufacturing (13%), government (12%), educational services (8%), and healthcare and social assistance (8%). [1]
  • The employment rate for general maintenance and repair workers in the US is expected to grow by 4% come 2029. [1]
  • The average annual salary of maintenance workers in the state is $52,913, with the highest 10% earning around $73,000 and the lowest 10% receiving about $38,000 per year. [1]
  • The latest US jobs data also shows an unemployment rate of 2.3% among maintenance and repair workers. [1]
  • An additional 26 percent predicted savings of 25 percent or more. [7]
  • The study also revealed that most healthcare executives belong to organizations that are either now using predictive analytics or planning to do so within the next five years. [7]
  • An impressive 93 percent of healthcare executives stated that predictive analytics is important to their business’ future. [7]
  • According to the results, 24 percent of companies said they planned on implementing predictive maintenance within the next one to two years. [8]
  • Percentage of U.S. companies where predictive maintenance is being applied as of 2016 CharacteristicPercentage of respondents Exclusive Premium statistic. [8]
  • Available to download in PNG, PDF, XLS format 33% off until Jun 30th. [8]
  • According to Fero Labs, manufacturing companies discard 98% of all the data they can collect because they do not have the operational analytics capabilities to integrate that data into their operations. [3]
  • Naturally, we would expect hype around these impressive values and according to GE, even back in 2014, big data analytics was one of top 3 priorities for >84% of CxOs in factory settings. [3]
  • PwC report, predictive maintenance in manufacturing could improve uptime by 9% reduce costs by 12% reduce safety, health, environmental & quality risks by 14% extend the lifetime of aging assets by 20%. [3]
  • 3 5% increased machine useful life Since predictive maintenance reduces machine breakdowns and ensures operation in optimum settings, it can improve machine/robot useful life. [3]
  • 10 40% reduction in maintenance costs Since planned maintenance is based on a schedule, there will be cases when maintenance tasks will be performed when they are not needed. [3]
  • 1020% reduced waste Sub optimal operation that is not detected, can result in wasteful production. [3]
  • AIMultiple informs ~1M businesses including 55% of Fortune 500 every month. [3]
  • Available to download in PNG, PDF, XLS format 33% off until Jun 30th. [2]

I know you want to use Predictive Maintenance Software, thus we made this list of best Predictive Maintenance Software. We also wrote about how to learn Predictive Maintenance Software and how to install Predictive Maintenance Software. Recently we wrote how to uninstall Predictive Maintenance Software for newbie users. Don’t forgot to check latest Predictive Maintenance statistics of 2024.


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How Useful is Predictive Maintenance

One of the key advantages of predictive maintenance is its ability to shift maintenance strategies from reactive to proactive. Traditional maintenance practices often rely on a predefined schedule or waiting for a piece of equipment to fail before taking action. This reactive approach can lead to costly downtime, reduced productivity, and increased risk of equipment damage. Predictive maintenance, on the other hand, uses data-driven insights to identify early signs of potential equipment failure, enabling maintenance teams to intervene before a breakdown occurs. By staying one step ahead of potential issues, businesses can significantly extend the lifespan of their assets and ensure uninterrupted operations.

Furthermore, predictive maintenance can also play a crucial role in optimizing maintenance schedules and resource allocation. By accurately predicting when a piece of equipment is likely to fail, companies can plan maintenance activities more efficiently, scheduling repairs during planned downtime or low-demand periods. This not only minimizes disruptions to operations but also helps to avoid unnecessary maintenance tasks that can result in wasted time and resources. By focusing resources on the most critical maintenance tasks, organizations can maximize the value of their maintenance programs while reducing costs and increasing overall operational efficiency.

In addition to its operational benefits, predictive maintenance offers significant financial advantages for businesses. By proactively addressing maintenance issues before they escalate into major problems, companies can avoid expensive repairs, replacement costs, and lost revenue due to downtime. In fact, studies have shown that predictive maintenance can help reduce maintenance costs by up to 30% and increase equipment uptime by as much as 20%. With compelling financial incentives and a clear return on investment, it is not surprising that more and more organizations are embracing predictive maintenance as a key strategy to optimize their asset management practices.

One of the most exciting aspects of predictive maintenance is its potential to leverage emerging technologies to continuously improve predictive capabilities. As artificial intelligence, machine learning, and big data analytics continue to advance, predictive maintenance algorithms are becoming increasingly sophisticated and accurate, enabling more precise predictions and actionable insights. This constant evolution of predictive maintenance technology promises to drive further efficiency gains, cost savings, and operational improvements for organizations across a wide range of industries.

Ultimately, the growing popularity of predictive maintenance underscores its undeniable value as a game-changing strategy for modern businesses. By enabling companies to transition from reactive to proactive maintenance practices, optimize resource allocation, reduce costs, and improve operational efficiency, predictive maintenance has the potential to transform the way organizations manage their assets, boosting productivity, profitability, and competitiveness in an increasingly competitive marketplace. As businesses continue to adopt and refine predictive maintenance strategies, it is clear that the future of asset management lies in harnessing the power of data-driven insights to drive intelligent decision-making and drive sustainable growth.

In Conclusion

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