Plenty of things can go wrong in maintenance management. Hundreds of assets contain hundreds more components that can grind, wear down or dislodge—causing a failure that can cost thousands of dollars in machine downtime.
To avoid these situations, managers develop preventive maintenance (PM) schedules that help decrease downtime by performing maintenance before a problem occurs. According to a study from Plant Engineering magazine, 87 percent of maintenance managers use preventive maintenance.
If you are a manager who performs preventive maintenance, ask yourself: Is your schedule working? Is your level of reactive maintenance and unscheduled downtime satisfactory? If the answer is no, your company could benefit from PM optimization.
Many consulting companies offer optimization services, and each approach the process a little differently. Alternatively, if there is someone on your team with the proper experience, you can task them with managing PM optimization at your company.
We spoke with Ed Stanek, president of reliability solutions for engineering consulting service Predictive Service, about exactly what PM optimization is, how to determine if a company needs it and the three steps that lead to a more strategic and efficient preventive maintenance plan.
What Is Preventive Maintenance Optimization?
A maintenance manager uses historical knowledge about machinery or other assets to create a PM schedule, which is designed to keep assets running as long as possible. Using a computerized maintenance management system (CMMS), a manager can store this information and analyze it, using reporting tools, to inform their maintenance schedule. The goal is to identify when a machine is most likely to break down, and perform maintenance right before that moment.
When done effectively, this decreases labor costs, keeps assets running longer and, in many cases, increases profits. But when done ineffectively, companies can remain blind to areas for improvement. Unfortunately, ineffective PM schedules are relatively widespread.
“I haven’t met one customer who had a satisfactory PM program,” Stanek says. “While they’re doing all [this PM], they’re still having an unacceptable level of breakdowns.”
According to Stanek, a PM optimization process includes three basic steps:
- Choosing what assets should be tracked and monitored.
- Identifying the most effective maintenance plan for each type of machine and failure, eliminating redundant work and adding missing work.
- Implementing the new maintenance plan.
A CMMS takes a preventive maintenance plan and automates it, reducing the chances of missing scheduled work and having a machine fail. Of course, as with many software tools, if the information entered is flawed, the system will produce flawed results. After PM optimization, companies learn what maintenance procedures are most effective and appropriate for each situation. With this information entered into a CMMS, businesses can see significant improvements.
Companies can determine whether they require PM optimization by analyzing several things, such as overall maintenance costs, how many work orders are backlogged, how often assets break down and how much each hour of downtime costs the company. In fact, in our recent report about how a modern CMMS can increase machine uptime, we offer a tool you can use to calculate the average cost of machine downtime.
If downtime costs are negatively impacting your business, a PM optimization process can help reduce scheduled downtime by an average of 35 percent, Stanek says.
“The approach is to question every task and [find] a better task, remove what we don’t want, clean up [or] make a better version of what we do want, and then add what’s missing,” he says.
Step 1: Asset Selection
Before optimization can occur, it’s important to decide exactly which assets and machinery should be entered into the system. It seems logical to start with the most critical machines: those that would shut down the entire operation if they failed.
However, Stanek says it can be more impactful to start with the most numerous machines: those that make up the majority of your assets. Part of the PM optimization process, as we’ll see below, is eliminating work that makes no significant positive impact; it’s likely that, during this process, you’ll reduce labor hours by eliminating unnecessary maintenance from the machines your company has the most of.
“That’s freeing up people to work on all the things they can’t get to,” Stanek says.
Another part of this step includes analyzing assets to identify components, and using that analysis to rethink the entire maintenance strategy. Stanek insists that machines don’t fail, components do: In other words, the crux of any asset failure is the failure of a particular part within that machine.
He uses the example of a brake system on a vehicle. Stanek’s methodology includes identifying each component of the system by following the transfer of power: from the pedal, to the linkage, to the master cylinder and, finally, to where the caliper meets the pad.
With this approach, maintenance managers can see how likely each component is to fail, then develop PM to address that failure before it occurs (in the next step of the process). So, for example, by determining how an AC motor—a component used in many different pieces of machinery—will fail and creating a plan to prevent this, the maintenance department will have a strategy for any asset in which AC motors are used.
“Component-based strategy is what we’re after,” Stanek says. “So when you build an asset library with that in mind, you know what needs to be done for chains, sprockets, bearings—all those common things we’re responsible for maintaining.”
Below, we’ve built a simple tool to calculate the risk priority number (RPN) for assets. The tool will generate a RPN score; the higher the number, the more impactful that failure is to your overall operations. This can help you prioritize failure types and assets as you go through the PM optimization process.
Simply click on the chart below to begin using it. You can enter a ranking between one and 10 for the following three aspects:
- The likelihood of a failure (10 being “extremely likely”)
- How difficult that failure is to detect (10 being “very difficult to detect”)
- The severity or impact of such a failure (10 being “extremely severe”)
Step 2: Optimization
Now comes the actual optimization, which involves three steps:
- Removing redundant tasks
- Cleaning up usable tasks
- Adding tasks that were missing from the original plan
Stanek compares the process to cleaning out a garage: You must first remove everything before deciding what to put back in.
⇒ Task removal. In the first step, you determined what assets to prioritize for maintenance. With that information, the team performing optimization can start to remove PM tasks that don’t align with those priorities, are redundant or don’t actually prevent a failure.
Removing these tasks frees up capacity for maintenance teams to perform more important work in a strategic way, instead of simply relying on a PM schedule that doesn’t take into account the actual condition of a machine.
“People are changing parts on a machine because it’s October, not because they’re worn out,” Stanek says. “That’s like going to the doctor and saying I need a heart transplant because it’s October.”
⇒ Task cleanup. The remaining tasks are put through the ringer: Each is evaluated to see if the work described will prevent a failure, if the work is to be performed by the most appropriate employee and if the work is done in the most accurate way.
For example, Stanek notes, a PM schedule might say “check all the drive belts.” But that simple directive isn’t descriptive enough.
“What is he checking for? Two people might go check that and come back with different answers,” he says. “So what we want to do is push the PM to become more of a precision, data-driven exercise to measure the deflection or the wear of the belt—anything that can be measured.”
The goal here is to take pre-existing PMs that serve a specific purpose and add enough details that will lead to a measurable, consistent result, no matter which technician is assigned to the job. Following the optimization process, a company can enter these PMs into a CMMS so each user receives consistent instructions for the same task.
However, Jason Johnson of CMMS solution provider MPulse notes that too much information in a work order can also have an adverse effect on maintenance. Instead, work orders should contain only the information necessary for an employee to complete a task effectively.
For example, the MPulse screen below, meant for technicians, displays the work order type, priority and important dates—in other words, only the essentials.
And the work order form, sent to a worker’s mobile device through email, contains details about the asset as well as clear instructions for the task.
Going further, CMMS users can implement condition-based monitoring devices to constantly stream data into the system, so that work orders are automatically generated when a machine deviates from the optimal operating conditions.
⇒ Task addition. Finally, once the unnecessary PM tasks are removed and usable ones are optimized, it’s time to add in tasks for maintenance issues that were previously unaddressed.
“We’re going to look at everything that’s failing on the equipment around the PM [task] to see what we’re not doing,” Stanek says. “That’s really what optimization is: Fixing what we own, but also adding what we’re not inspecting right now.”
Step 3: Implementation
The previous steps establish the new PM program, but maintenance managers still need a tool to enact the program and begin building a database of historical maintenance. Fortunately, Stanek says, the optimization process is CMMS-neutral: You need a CMMS to manage the PM program, but not necessarily to optimize it.
“In fact, if you’ve got chaos and you plug in a CMMS, you’ve just got automated chaos,” he says. “So, our job involves everything inside and around the CMMS.”
Whichever CMMS your company chooses to use, a PM optimization team (or your designated optimization employee) can develop the work methodology around it. In addition to entering the optimized PM program into the software, employees must be trained to use the CMMS in a way that maintains the program’s benefits. For example, Predictive Service offers e-learning training services, including help with topics such as managing backlogs, scheduling best practices and work execution.
Following the PMO process, Stanek says his clients see various improvements in key performance indicators. These improvements include averages of:
- 15 percent elimination of tasks that provide no value
- 80 percent increase in objective, data-driven PM actions
- 40 percent reduction of maintenance labor hours
- 50 percent increase in required PM task coverage
- 35 percent reduction in scheduled downtime hours
With a newly optimized PM program and a comprehensive CMMS, companies are able to manage maintenance with more efficiency—which translates into minimal machine downtime and increased profits.
