Manufacturing orgs are aiming to slash costs everywhere as the industry continues to face challenges. Maintenance costs are one such area—but there’s a fork in the road when it comes to maintenance approaches. Many asset management leaders are choosing between a preventive or predictive maintenance strategy.
Each approach shares a core philosophy of data-driven maintenance, which can help lower costs. However, they differ in a few important ways, especially when it comes to scheduling maintenance activities.
At a high level, preventive maintenance involves servicing equipment regularly, which can help organizations take a more proactive approach to avoiding breakdowns. This strategy can reduce the need for reactive maintenance, which typically results in higher costs and extended machine downtime.
Predictive maintenance or PdM, on the other hand, goes a step further. It uses real-time data to identify potential issues before they occur, allowing maintenance to be scheduled at the most optimal time. This proactive maintenance strategy helps companies optimize operations and manage maintenance costs more efficiently.
In this piece, we’ll examine the specifics to help you choose a proactive maintenance strategy that can reduce machine downtime throughout your facilities.
Preventive maintenance happens at regular intervals
As we noted above, preventive maintenance is a maintenance management strategy that aims to prevent machine failures by servicing machines before any signs of breakdown occur. The goal: less unplanned downtime and more stable operations—both of which ultimately benefit customers and your bottom line.
Preventive maintenance strategies typically fall into three buckets:
Usage-based maintenance: Maintenance occurs based on usage metrics (e.g., the number of hours in operation). For instance, you might check up on a robotic packaging arm after 1,000 hours of use to manage wear and tear.
Calendar-based maintenance: Regular maintenance tasks are scheduled at regular calendar intervals (e.g., weekly, monthly, or annually).
Condition-based maintenance: This approach relies on IoT sensors to monitor the real-time condition of your equipment. You can use this information to schedule maintenance based on specific triggers (e.g., a spike in temperature, noise, etc.) and identify potential issues before they escalate into full-blown machine failures.
No matter which preventive maintenance strategy you choose, a well-defined maintenance plan with planned downtime will help you mitigate the risk of costly breakdowns and keep your operation running smoothly. But to realize these benefits, you’ll need the right blend of…
Data. Collect both historical data (about equipment usage) and real-time data (about equipment conditions) to gauge your maintenance needs.
Technology. Use IoT devices and data products to gather and analyze the data above.
Training. Make sure your maintenance team knows how to interpret equipment data so it can recognize problems before they spiral.
Next, let’s take a look at predictive maintenance and what a cost-effective strategy might look like for your organization.
Predictive maintenance uses failure patterns to proactively schedule check-ups
The key difference between preventive and predictive maintenance lies in how they use equipment data. Instead of defining fixed intervals for maintenance scheduling, a predictive maintenance strategy identifies patterns that could signal potential machine failures—and schedules equipment check-ups to reduce the risk of a breakdown.
In a literal sense, predictive maintenance is a form of condition-based maintenance; after all, it relies on IoT sensors to gather data about the state of your equipment. But in a preventive context, condition-based maintenance is inherently reactive, responding to asset conditions in the moment. On the other hand, predictive maintenance uses equipment condition monitoring and data to identify when maintenance will be most crucial and inform scheduling accordingly.
Here’s an example to illustrate. Consider a facility that uses IoT sensors to continuously monitor the temperature, vibration, and pressure of a hydraulic press. Instead of scheduling maintenance after a sudden spike in temperature, a predictive maintenance strategy would identify trends in the asset’s condition.
If sensors detect that the temperature has been steadily rising over the past week and readings surpass a certain threshold, machine learning algorithms can predict that a particular piece of equipment may soon fail. The maintenance team can then schedule repairs at the optimal time to avoid an unexpected breakdown—and unnecessary maintenance work.
At the facility level, the benefits of predictive maintenance practices are significant. You can…
Reduce unplanned machine downtime. By anticipating potential machine failures, you can reduce the average number of unexpected breakdowns and keep your equipment up and running.
Improve the lifespan of each asset. Your maintenance team can address small issues before they escalate, which means your equipment will last longer overall.
Lower maintenance costs. With a predictive maintenance strategy, you only need to schedule maintenance exactly when it’s needed—which can reduce unnecessary spending.
Just like a preventive maintenance strategy, though, you’ll need three key ingredients:
Data: IoT sensors are crucial here to collect data about asset conditions. Historical data is also important, as it can help to separate trends from flukes.
Technology: Alongside IoT sensors, you’ll want data products with robust analytics and automation capabilities. This is what enables the predictive component—and allows you to optimize your maintenance scheduling.
Training: Make sure your maintenance team has a strong grasp on data analytics and can serve as a human counterbalance against any predictive maintenance errors.
The takeaway? By leveraging predictive maintenance, companies can optimize their maintenance schedules and improve asset performance. Next up: how to decide whether a predictive or preventive approach is best for your organization.
Comparative analysis: how to think about cost and timing factors
When evaluating predictive maintenance versus preventive maintenance, it’s important to consider both cost and time factors. Let’s take a look at each.
1. Cost
Unless you’re adopting a condition-based maintenance approach, a preventive maintenance program likely won’t require a significant upfront investment in IoT sensors, machine learning algorithms, and advanced maintenance software.
There are trade-offs, though. Preventive maintenance can sometimes lead to overly cautious scheduling that results in unnecessary maintenance. And if machine failures occur outside fixed maintenance windows, your equipment uptime could also suffer.
Predictive maintenance does require more spending up front on enabling technology. But it can also help you avoid unnecessary maintenance and lower maintenance costs over time.
2. Timing
Preventive maintenance generally occurs at regular intervals, providing predictability in planning maintenance activities. That can be helpful if you have limited maintenance staff on hand.
Predictive maintenance is more dynamic, though—and maintenance scheduling needs may change as asset condition trends evolve.
Which maintenance approach is best for your organization?
Struggling to choose between predictive and preventive maintenance? Alongside the factors above, make sure to evaluate…
Asset criticality. If your asset lifecycles are complex and critical to operations, a predictive maintenance strategy might be more beneficial. Conversely, for simpler assets with predictable failure triggers, preventive maintenance may suffice.
Organizational capacity. Implementing predictive maintenance requires an investment in technology and expertise in data analysis. Organizations must assess their data collection abilities, as well as their readiness to adopt AI, machine learning, and IoT technologies to support this approach. Smaller companies with limited resources may find it more practical to start with a preventive maintenance plan.
If your company has limited resources or relatively simple machinery, starting with a preventive maintenance strategy can provide a solid foundation for maintenance management. Gradually incorporating elements of predictive maintenance, such as condition-based maintenance techniques, can enhance your proactive approach over time.
For organizations managing extensive, complex assets, investing in a predictive maintenance strategy offers the best potential for long-term cost savings and operational efficiency. The use of automation, IoT (internet of things) sensors, and data analytics can optimize maintenance schedules and reduce unnecessary maintenance tasks.
Roadmap your path to better maintenance
We’ve given you a lot of food for thought as you evaluate predictive maintenance and preventive maintenance. The key takeaway here: the right maintenance approach should align with your operational goals, your team’s capabilities, and the specific needs of your equipment. No matter which maintenance approach you choose, you’ll likely see better uptime and asset performance.
These outcomes are crucial in today’s manufacturing environment. With fewer experienced workers available, the team you have will rely on your equipment to keep pace with customer demand. And you’ll want to minimize equipment failures so you can avoid costly delays – along with any ripple effects that follow. With a proactive maintenance strategy, you can more easily mitigate risk and adapt to industry-wide shifts.
Not sure how to translate your maintenance vision into reality? It helps to have a supportive partner. TXI can help you understand where you are and map where to go. To find out more, let’s chat – we’d love to start a conversation.
