Reliability-Centered Maintenance
The main goal of using CMMS is to increase control over your assets.
CMMS aids maintenance programs developing high-level goals for tracking costs, setting benchmarks and monitoring the bottom line each day. A successful CMMS supports methods used within a structured and reliable maintenance program.
This approach was established as a high-level strategy for balancing efficiency, safety and equipment care.
Assess your maintenance department, and ask – is it constantly operating in a reactive state?
Two Maintenance Approaches
There are two basic approaches to conducting maintenance:
- Reactive: Maintenance programs focus on repairs as the equipment breaks. These technicians might frequently feel overwhelmed by sudden failures, and the resulting downtime could halt a business’s entire production line. This “run-to-failure” mindset is most often used in newly established maintenance programs, and it’s classically inefficient and expensive.
- Proactive: Maintenance programs focus on scheduled repairs. These technicians begin each day conducting planned maintenance either through preventive or predictive methods. CMMS helps technicians have proactive mindsets with routine maintenance.
While complete proactive control may not be possible, a systematic approach to maintenance operations leads to minimal downtime and accountable staff.
The bottom line:
Being proactive with maintenance leads to continuous improvement to workflows, increased uptime and reduced spending on unnecessary repairs.
CMMS helps companies schedule maintenance duties on a clear and simple electronic platform, keeping everyone on the same page while measuring key performance indicators (KPI).
Preventive vs Predictive
Predictive and preventive maintenance are both proactive ways to conduct maintenance.
Preventive maintenance, or planned/scheduled maintenance, is the most common method of conducting routine maintenance. For all maintenance actions based on a calendar or routine system, technicians are conducting preventive maintenance such as changing the oil, replacing a part or ordering new equipment to prevent a break-down.
Predictive maintenance relies on conducting maintenance based on trends within equipment data. This technology is tied to condition-based monitoring systems for reading the output (condition) of an asset’s variables. For example: If a piece of equipment has had temperatures continually rise, an alert or work order for predictive maintenance will trigger before equipment failure. Predictive maintenance is based on predicting when an asset needs attention rather than simply replacing a part when it could have lasted longer.
As predictive technology develops, some vendors are applying various upgrades to their solutions, while others consider the technology unnecessary.
As with any cutting-edge product, predictive technology is more expensive and requires a more complicated implementation.
What do the experts have to say?
Due to the nature of a predictive system, Margeaux Girardin with Hippo CMMS explains: “There is also an element of analyzing data and ensuring that you are capturing all of your maintenance operations in the system, so this requires a true champion of the software and buy-in from all your users.”
A true “champion” is a system that’s been thoughtfully designed as an organization’s go-to for answering tough questions, and often refers to a business intelligence solution. Predictive technology goes hand-in-hand with these complexities, and gives CMMS high level applications.
But the success of any technology often comes down to your users. As Girardin points out: “A system is only as good as the people who use it and pouring more money into it increases the risk and could decrease your ROI.”
While a predictive system gives you the best-informed view of your assets, you must also consider your team’s new responsibilities. Between capturing data and defining new workflows, your investment is going toward a whole new maintenance system, not just a software product, and this will affect how the department operates.
So, why consider it?
Predictive technology ties into advancements and software features for obtaining data never before available, and could be what sets you aside from your competition.
If you’re set on finding a CMMS solution that’s able to track more than just preventive/planned maintenance, condition-based maintenance is the next step toward having a predictive system without going all in on the most intelligent technology.
Condition-based Maintenance
Condition-based monitoring is the first step in building a predictive CMMS system. This not only requires software capabilities, but hardware as well.
To read the current condition of equipment, there needs to be hardware in place for measuring the asset.
The sudden popularity of predictive maintenance is due to revolutionary advancements with hardware capabilities. The hardware measures set variables and the software interprets these readings within the CMMS.
Condition monitoring can interpret a range of readings such as with:
- Equipment temperatures through infrared imaging
- Lubricant viscosity and wear metals through oil analysis
- Pressure states for fluid and airflow
- Measuring cavitation and related wear damage
- Vibration frequencies for detecting structural looseness, misalignment, bearing issues, sheave run-out, and many electrical problems
Enhanced communication between equipment pieces through the Industrial Internet of Things (IIoT) have paved the way for CMMS to store insightful data.
Rona Palmer with eMaint described how this trend is playing out in the marketplace: “As the hardware becomes more affordable and the benefits understood, we see more clients incorporating predictive maintenance technologies.”
In the past, hardware products built for reading assets and communicating information across locations weren’t designed to be widely used. Now that those products have become less expensive and more popular, there is a greater demand for software solutions to incorporate those hardware sensors.
With predictive maintenance comes the related technology for interpreting asset readings. Edward 
Garibian with eRPortal recently described the significance of these features: “Alerts and triggers are in place for notifying staff of significant changes as they happen in order to act on them without delay. This function combined with mobility gives maintenance personnel the ability to react on the fly to problems before there’s any downtime.”
CMMS solutions primarily serve to store useful information. Depending on the complexity of the CMMS, reporting on the condition of your assets could end with measuring and saving the data for an expert to analyze.
As Garibian alludes, some advanced systems have functions that automatically notify staff to a needed action.
The depth of these triggered work orders are an example of the next maintenance trend. Whether the system produces a simple alert for staff to check on a piece of equipment, or a prescriptive work order detailing what’s wrong and what must be done, this leads to the next advancement for CMMS, maintenance departments and beyond.
The Ideal Approach
The ultimate goal of predictive technology is to produce an automatic work order specifying a maintenance action once the asset has shown a condition of imminent failure. This requires the system to have:
- Intelligent algorithms for reporting functions
- Stored historical data for determining trends
- A standard variable to compare with the asset’s normal vs. abnormal condition
- The required hardware integrated depending on what type of asset needs to be measured
Most CMMS solutions are able to compile data trends according to an asset’s history, but they may require a third party tool for analyzing and presenting the data without the help of an expert. These third party solutions often specialize in specific industries and technologies depending on the type of asset that needs measurement, and are “champions” in their specialty. This type of technology also relates to prescriptive analytics, a more refined model of knowing an outcome, and is often found within business intelligence solutions.
What most CMMS solutions focus on is providing support for these predictive methods. Jason Johnson with MPulse described his solution’s systematic approach to predictive maintenance: “For MPulse, producing a prediction is the job of analytical software or good old fashioned human experts. We’re an important part of the overall activity of predictive maintenance by:
- Collecting and storing predictive data
- Providing a means for alerting people to action
- Providing trend data that trained personnel can associate with previous failures in maintenance history.”
While most CMMS solutions aren’t able to give corrective action based on trending data, information is collected for making predictions and frequently connects with third party tools for industry-specific needs.
Why this matters:
The difference is having the solution offer a prescriptive work order versus providing a maintenance technician the data to decide what action to take.
Some solutions do, however, offer the capability to suggest what should be done and when, and uphold high standards for predictive features.
These methods are often tied to consulting services and analytically focused solutions partnered with CMMS.
For more on this, check out our interview with Fred Schenkelberg, Reliability Engineering and Managing Consultant with FMS Reliability and lecturer with the University of Maryland.
