Predictive Maintenance Statistics 2024 – Everything You Need to Know

• 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.

Reference

1. upkeep – https://www.upkeep.com/learning/maintenance-statistics.
2. financesonline – https://financesonline.com/maintenance-statistics/.
3. statista – https://www.statista.com/statistics/748080/global-predictive-maintenance-market-size/.
4. aimultiple – https://research.aimultiple.com/predictive-maintenance/.
5. wikipedia – https://en.wikipedia.org/wiki/Predictive_maintenance.
6. parsable – https://parsable.com/blog/quality/12-manufacturing-maintenance-statistics-to-consider-when-planning-for-2020/.
7. sas – https://www.sas.com/en_us/insights/analytics/predictive-analytics.html.
8. cio – https://www.cio.com/article/228901/what-is-predictive-analytics-transforming-data-into-future-insights.html.
9. statista – https://www.statista.com/statistics/745196/us-companies-with-predictive-maintenance/.

How Useful is Predictive Maintenance

At its core, predictive maintenance is about staying one step ahead of potential problems with machinery or equipment. Instead of waiting for something to break down and then fixing it, predictive maintenance uses historical data and real-time monitoring to identify patterns and trends that may indicate an impending issue. This proactive approach has the potential to significantly reduce downtime, increase equipment lifespan, and improve overall operational efficiency.

One of the key advantages of predictive maintenance is its ability to turn maintenance into a more strategic function within an organization. Instead of simply reacting to problems as they arise, maintenance teams can plan and prioritize their efforts based on data-driven insights. This not only helps to optimize resource allocation but also minimizes the risk of costly unplanned downtime.

Furthermore, by using predictive maintenance, organizations can move away from traditional calendar-based maintenance schedules towards a more dynamic and intelligent approach. This means that maintenance activities are only carried out when necessary, rather than at predetermined intervals. As a result, organizations can optimize their maintenance budgets and achieve greater cost-effectiveness.

Another benefit of predictive maintenance is its ability to support better decision-making. By providing real-time insights into the health and performance of assets, predictive maintenance enables organizations to make informed decisions about when to replace, repair, or upgrade equipment. This can help to extend the lifespan of assets, reduce overall maintenance costs, and improve asset reliability.

However, while predictive maintenance offers many advantages, it is not without its challenges. Implementing a successful predictive maintenance program requires a significant investment in technology, data analytics capabilities, and training. Organizations must also be willing to embrace a culture of continuous improvement and data-driven decision-making in order to fully realize the benefits of predictive maintenance.

Moreover, predictive maintenance is not a one-size-fits-all solution. Different organizations have different maintenance needs, priorities, and resources. While predictive maintenance may be highly effective for some industries or assets, it may be less practical or cost-effective for others. Organizations must carefully assess their unique requirements and constraints before deciding whether or not to implement a predictive maintenance program.

In conclusion, predictive maintenance has the potential to revolutionize asset management and maintenance practices by enabling organizations to be more proactive, strategic, and cost-effective in their maintenance efforts. However, the success of predictive maintenance depends on a number of factors, including the organization’s willingness to invest in technology, data analytics, and training, as well as its ability to adapt to a more data-driven approach. By carefully considering these factors and assessing their unique needs, organizations can determine the usefulness of predictive maintenance in their specific context and chart a path towards more efficient and effective maintenance practices.

In Conclusion

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