Preventive vs. Predictive Maintenance: Which is More Effective for Reducing Downtime
Preventive maintenance and predictive maintenance are two popular maintenance strategies that organizations and OEMs use to reduce equipment downtime and optimize their operations.
The goal of both these strategies is to reduce machine downtime and get and get highest OEE (Overall Equipment Effectiveness) to minimize maintenance costs.
In the following blog post, we will try and understand the difference between preventive and predictive maintenance and deeply evaluate which methods are more effective in reducing downtime.
This is a simple and easy method that involves performing regular maintenance of machines in each set of time. This type of maintenance is based on time, usages, or specific events, such as the number of hours of operation, calendar dates, or the number of parts produced.
- In sectors like manufacturing, aviation, and healthcare where equipment failure can lead to significant downtime or loss of income, preventive maintenance is a common practice.
- Organizations can identify and fix equipment problems before they cause a breakdown or failure by conducting routine maintenance.
- Facilities Net conducted a survey that found that proactive maintenance reduces downtime by an average of 17.8% compared to reactive maintenance. In addition, compared to reactive maintenance, preventive maintenance can lower the cost of machinery repairs by as much as 40%.
Here are some interesting statistics:
- In 2020, 76% of companies in the manufacturing industry worldwide prioritized preventive maintenance. (Plant Engineering, 2020).
- Preventive maintenance is favored by 80% of maintenance personnel as part of a multi-faceted maintenance strategy (Plant Engineering)
- 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.
- 88% of manufacturing companies use preventive maintenance, 52% use run-to-failure, 40% apply preventive maintenance using analytics tools, and 22% reliability-centered maintenance (RCM) using operational data analysis.
Machine learning algorithms and other predictive technologies are used in predictive maintenance, a data-driven maintenance strategy, to identify possible equipment failures before they happen. This type of maintenance involves collecting data from sensors and other sources and using analytics to identify patterns and anomalies that indicate equipment problems.
- Predictive maintenance plays a key role in detecting and addressing machine issues before it goes into complete failure mode.
- In this way, companies can avoid accidents and can ensure the safety of their employees and customers.
- According to a report by Deloitte, Organizations that implement predictive maintenance can see up to a 40% reduction in maintenance costs, a 70% decrease in downtime, and a 25–30% increase in overall equipment effectiveness (OEE).
- In addition, predictive maintenance can reduce maintenance costs by up to 25%.
Some statistics that will blow your mind:
- Predictive maintenance can reduce machine downtime by 30%-50%, and increase machine life by 20%-40%
- The global predictive maintenance market will grow to $23.5 billion by 2024.
- According to the research by Forrester, 47% of global manufacturers use predictive maintenance technologies to reduce operational costs.
- In Spain, 60% of businesses had already invested, or planned investments, in predictive maintenance in 2018. In Germany, 54% had already invested in predictive maintenance and 80% planned to invest.
Which One Is Better?
To make a choice between predictive and preventive maintenance we must consider several factors:
- Type of equipment.
- The industry.
- Specific maintenance requirements.
- Reduce unnecessary maintenance costs.
In general, preventive maintenance is more effective for reducing downtime in equipment that has a predictable failure pattern or where the cost of failure is relatively low. Predictive maintenance is more effective for reducing downtime in equipment that has an unpredictable failure pattern or where the cost of failure is high.
According to a survey by Plant Engineering, 66% of respondents use a combination of preventive and predictive maintenance, while 17% use only preventive maintenance, and 15% use only predictive maintenance. The survey also found that organizations that use a combination of both strategies experience the lowest downtime and maintenance costs.
Conclusion: To minimize equipment downtime and maximize operations, preventive and predictive maintenance are both useful tactics.
Despite the more conventional nature of preventive maintenance, predictive maintenance is growing in acceptance as a result of developments in data analytics and machine learning.
Businesses that combine the two approaches are likely to achieve the best maintenance cost and downtime reduction outcomes.
Organizations can ensure that they are working at maximum efficiency and minimizing equipment downtime by selecting the appropriate maintenance approach for their equipment and industry.