Technology often gets all the credit when it comes to predictive maintenance. That’s unfair. Technology is important, but it’s only a small piece of a great predictive maintenance program.
The organizational culture has to be right. New processes need to be tested and refined. Data needs to be expertly managed.
In short, the journey to predictive maintenance is slow, but worth it if it’s done right.
This article is all about building a predictive maintenance program that will last. We explore the six pillars of a strong predictive maintenance program, how you can develop each area, and how to use them to achieve predictive maintenance.
A short refresher on predictive maintenance
Predictive maintenance (PdM) lives in the same family as preventive maintenance. They’re both proactive types of maintenance—work is done on an asset before something bad happens to it, not after a failure has shut it down.
The difference between preventive maintenance and predictive maintenance lies in the methods used, the amount of lead-time you have for a task, and the precision of scheduling. PdM uses condition-monitoring tools and techniques and asset information to track the real-time and historical equipment performance so you can anticipate failure before it happens.
Since predictive maintenance aims to give you an ideal window for proactive maintenance tasks, it can help minimize the time equipment is being maintained, the production hours lost to maintenance, and the cost of spare parts and supplies. We outline maintenance-strategies/”>where predictive maintenance fits in your overall maintenance strategy here.
The six pillars of a predictive maintenance program
A sturdy predictive maintenance program is built on six pillars: People, data, processes, tools and parts, equipment, and technology. If one pillar is not stable or is left to rot, your whole program can crumble.
People: Culture eats strategy for breakfast
The long journey to predictive maintenance always starts with people.
“In other words, culture eats strategy for breakfast.”
Every other pillar of a predictive maintenance program needs people to build and maintain it. Data needs interpreting. Technology needs setting up and managing. That’s why everyone in your organization should understand how PdM works, why it’s important and what they can do to make it successful.
Getting people at your facility onboard with the (many) changes that come with predictive maintenance is absolutely essential, but not always easy. This article from Software Advice offers some great tips on change management, getting buy-in from your maintenance team, and creating a great culture at your facility.
Technology is like a dash of salt in a predictive maintenance program—it ties the other ingredients together and makes them shine.
Data: The link between past, present and future
But with quantity also comes the need for quality.
“If you don’t have good information coming from the plant floor, it won’t matter how good your algorithms are, you won’t be able to make good decisions with it,” says Jared Evans, the chief operating officer at MAJiK Systems.
Data is the link between current asset performance and the future state of the asset. That’s why everything, from throughput to failure modes and beyond, must be constantly updated. These numbers also have to be accurate everywhere. If they’re different from system to system, it’ll throw your whole program into disarray.
Or as Jason puts it, “If you have bad data coming from your machines and software, it’s like the weatherperson telling you it’s sunny out when it’s actually raining. You’ll step into the rain and get soaked.”
Processes: A steady hand on the predictive maintenance ship
Simply put, your processes are the way you work your plans and does the things it needs to do every day to be successful. An effective predictive maintenance program helps make your whole operation predictable so it can maximize everything from working hours to asset performance.
Processes in a predictive maintenance program are people-driven and equipment-driven.
“You need to understand who is responsible for what, how frequently you review data and tasks, how you communicate, and how you plan, escalate, and complete tasks,” says Jason.
Tools and parts: Trusty sidekicks ready for the spotlight
Tools are the instruments used to measure the condition of assets, like infrared cameras, and the tools needed to inspect or repair equipment. Parts are the different components of equipment, but not just any old parts will do for predictive maintenance, as we’ll see below.
Equipment: Not all machines were made for predictive maintenance
Anyone who says reactive maintenance can be totally eliminated has never had their windshield cracked by a stray pebble. While this isn’t exactly an on-the-job example, the lesson still applies to the shop floor: You can’t anticipate everything.
It’s important to know which of your equipment allows you to anticipate failure on it when setting up a predictive maintenance program
Jason also recommends applying predictive maintenance to your most critical assets with the most observable failure modes because of the time and money needed to build a PdM program.
If you’re looking for information on choosing the best equipment for your predictive maintenance program, check out this starter pack of resources (PF track with balanced maintenance strategies, P-F curve, condition-based maintenance blogs)
Technology: The glue that keeps the other elements together
Technology is like a dash of salt in a predictive maintenance program—it ties the other ingredients together and makes them shine. It helps you manage, facilitate, and optimize the other pillars of predictive maintenance.
“Technology gives you an extra set of eyes,” says Bryan, “so you can collect real-time data without having someone on your team constantly looking at the information.”
This is a big job, one that can’t be done by a single piece of technology.
“You need to know what products are being run and when, the cost of all your activities, when maintenance was last done. The list goes on. You need several pieces of technology to capture all this data, store it, and make sense of it.”
There are lots of different technologies that can be used to manage a predictive maintenance program, from ERPs to MES systems and CMMS software. We explored the most common of these technologies here.
How to build a predictive maintenance program
Predictive maintenance: Part of a balanced strategy
The best way to think of predictive maintenance is like a bowl of cereal in an old TV commercial: It’s part of a balanced breakfast (or maintenance strategy). Predictive maintenance isn’t the only strategy to strive for. Instead, it should supplement your overall maintenance program.
“Predictive maintenance will never replace all other forms of maintenance,” says Jason.
“Creating a predictive maintenance program isn’t about making a checklist. You can’t just tick off a bunch of tasks, flip a few switches and be completely predictive. It’s a journey. It might take 10 years to go 10% predictive.”
A predictive maintenance program won’t solve all your problems. But there are some serious benefits to having one, like a more reliable operation that allows everyone at your organization to grow and be more efficient.
Taking advantage of those benefits relies on building on key maintenance fundamentals. When those fundamentals are strong, you’ll have a strategy that’ll weather any challenge thrown at it.