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8min March 22, 2022 8min Mar 22, 2022
March 22, 2022 8 minute read

The difference between preventative vs. predictive maintenance technology

Manufacturing maintenance teams aim to prevent downtime. While this seems like a straightforward directive, preventing downtime is quite complicated given the size and breadth of the machines, systems, and processes involved. 

This is why a comprehensive maintenance strategy is crucial for today’s manufacturers. 

Maintenance strategies are not always viewed as an advanced business function requiring significant consideration, development, and investment. This is at odds with the importance of preventing downtime. Your maintenance strategy determines how your assets and production capabilities will be cared for going forward. It’s your insurance against costly unplanned downtime events

This gives rise to the question: 

What type of maintenance strategy and technology is most effective in today’s manufacturing landscape: preventative or predictive?

As manufacturing continues to adopt Industry 4.0 technologies and capabilities (hello, Artificial Intelligence and Machine Learning), answering this question has a significant impact. Both preventative and predictive maintenance strategies involve scheduled maintenance and both impact production time. The challenge is determining which strategy best supports the overarching “prevent downtime” mission.

predictive maintenance on machine in manufacturing

We know many manufacturers are struggling with this decision. To help get a clearer picture of how these maintenance approaches work in the manufacturing landscape, we’ve broken them down and provided some pros and cons. Take a look.

Preventative maintenance strategy and technology

Historically, preventative maintenance has been the go-to strategy for manufacturers. Preventative maintenance requires teams to perform maintenance based on a predetermined schedule/cadence, regardless of whether there are any indications of an issue. Using this strategy, the goal is to have teams perform maintenance work before anything can go wrong. 

We liken this approach to getting your car’s oil changed. You get your car’s oil changed every 5,000 miles even though the car is running fine and nothing seems wrong. It’s simply time to get it done. 

Preventative maintenance is a sound maintenance strategy. It allows downtime to be planned and scheduled. This isn’t to say there isn’t some technique or strategy behind setting these schedules. For example, usage-based maintenance means that you’re using historical data and known best practices for sticking to a predetermined routine. You’re trying to operate a well-oiled machine—sometimes literally—for as long as you can.

This maintenance is not avoiding machine breaks, because some pieces will follow the expected usage, repair, breakdown, etc. Preventative maintenance is finding a balance between completing too much maintenance, too much downtime, or having too many breaks. Sometimes, you make the best decision for production because there is no good decision in that balance.

predictive maintenance strategy in manufacturing

Preventative maintenance is also predictable and relatively cheap to implement. It makes it possible for teams to prevent issues and monitor machine health. Like getting your car’s oil changed regularly, it does the job.

However, in today’s fast-paced environment, preventative maintenance presents some efficiency hurdles that can have serious implications. For instance:

It relies on best practices and historical—not live—data. Work is done based on past repair information and industry guidelines. In most cases, teams focus on machines when they’re performing scheduled work (or when they break down unexpectedly).

It occurs when there aren’t any issues. Teams may be wasting time, effort, or materials if they are on a cyclical schedule. Performing unnecessary maintenance can be costly and inhibit new, optimized maintenance schedules based on performance vs. timing.

It requires complicated logistics. From scheduling personnel to tracking needed inventory to massive record-keeping, preventative maintenance is a logistic three-ring circus. While identifying when you can perform maintenance can feel reassuring, it’s hard to monitor if you’re really timing your work well. Stopping the line at any time is hard for business, even if it’s necessary.

The biggest drawback to this maintenance methodology is that it’s based on habits. A plant manager or other management lead determines the timing, the machines, and the work to be performed. It’s difficult to know what information to use when setting different maintenance schedules. Should teams use historical data? Past records? The calendar? The equipment’s manual?

predictive maintenance on machine in manufacturing

As with car maintenance, preventative maintenance is effective in terms of making sure that machines are, more or less, kept in working order the majority of the time. However, the analog nature of preventative maintenance should be considered when evaluating your maintenance strategy. Will this strategy allow your business to keep pace with the rate of business? Is it the best use of your maintenance teams’ skills, time, and resources? 

Predictive maintenance strategy and technology

Predictive maintenance aligns with the Industry 4.0 landscape. Predictive maintenance technology uses data to inform when teams perform maintenance, moving this work to an as-needed basis. It relies on big data analysis to determine the key indicators for maintenance instead of relying on an arbitrary schedule.

For this reason, a data-driven predictive maintenance strategy has enormous benefits:

Less production interruption. Teams perform maintenance when there’s a clear indicator to perform maintenance. Production capacity may be greater given the ability to perform service as needed instead of proactively.

Focused, meaningful downtime. Teams have data showing them exactly where the problem is, making identifying and performing maintenance more efficient.

Less wasted inventory, time, and resources. Having a clear understanding of the maintenance you need allows for more controlled inventory (parts, lubricants, etc.). You’re fixing what’s about to break vs. fixing what might break.

Better machine health. Determining maintenance plans using performance data, leads to optimal machine usage and uptime. By adapting your maintenance plan to the equipments’ actual need, you increase your ability to run at peak performance.

Predictive maintenance is akin to taking your car to the shop because you get an alert that you’re approaching the ideal mileage for your current tire treads. You are optimizing your repair schedule around usage, data, and schedule.

You have data (in this analogy, maybe an email alert from your service company) that indicates your tires need maintenance. You know you need to rotate or replace your tires soon, but you can schedule a time that works best for you. The data gives you a warning, you address it, and voila you continue on your way.

Teams can perform maintenance before there’s a serious breakdown but without performing unnecessary preventative maintenance. 

predictive maintenance and analytics

One reason that many manufacturers still lack a predictive maintenance strategy is that these initiatives rely on advanced data analytics. Teams need to be able to collect and make sense of the machine ecosystem in order to identify as-needed maintenance. 

IIoT platforms are designed to facilitate this process. IIoT systems collate siloed data from a variety of systems, equipment, and other “smart” devices. The resulting information can then be used to optimize business results and inform predictive maintenance strategies.

For example, Braincube’s IIoT platform helps teams build a better predictive maintenance strategy via advanced self-service technology, including:

  • Plug-and-play Edge Apps to automate condition monitoring plant-wide.
  • Advanced Cloud Apps make it easier for teams to leverage Big Data to learn from historical maintenance events and better predict future events. 
  • Self-service analytics democratize Machine Learning and AI discoveries across the organization, equipping process experts with the technical tools to become Citizen Data Scientists.
  • Braincube’s Frequency Analysis node tracks machine vibrations and can trigger alerts when abnormalities are detected. 
  • Our Condition Monitoring App Package provides teams with off-the-shelf solutions to monitor the plant floor remotely. This provides instant visibility into real-time performance, enabling teams to focus on the most important tasks at hand. 

Given the pace that which manufacturing is becoming data-driven, adopting robust predictive maintenance technology ensures maintenance teams are staying in step. It also gives these teams space to bring their own data-driven insights, innovations, and creativity to this critical business activity. 


Both preventative and predictive maintenance methodologies are relevant in today’s manufacturing environment. However, more plants are undergoing digital transformation. Advanced computing technologies (e.g. AI and automated condition monitoring) are playing a larger role. 

Aligning your maintenance team’s capabilities to these Industry 4.0 technologies helps them achieve their prevent-downtime mission more effectively and efficiently. This means improving their access to advanced self-service analytics and condition monitoring tools. 

Preventing downtime is an enormous responsibility. Empower your maintenance teams with a strategy that promotes success. 

Predictive Maintenance: techniques and technologies for Industry 4.0

Learn why predictive maintenance is a better way of working and the technology that can bring this capability to your plants.

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