That title is a great start, and the answer is easy. The four main maintenance strategies are run-to-failure, preventive, condition-based, and predictive.
But if that title were just a bit longer, you’d get an even more useful answer. Let’s change it to “How do I find the maintenance strategy or combination of strategies that works the best for my assets and equipment, cutting downtime and eliminating frustration and stress for me and the maintenance team?”
And here’s the answer.
What is a maintenance strategy?
Before anything else, it’s important to have a working definition of maintenance strategy. You need to understand the category before looking at what’s in it.
Reducing it to the essentials, a maintenance strategy is a framework we employ to keep things up and running for the longest stretches possible using the smallest number of resources. Here resources are things like time, money, and labor. It can be helpful to break resources into different types when trying to track and control them, but always remember that they’re often interconnected. Labor costs money, for example. And everyone knows, time is money.
If maintenance were just about keeping things up and running for as long as possible, we could just pour tons of resources into every situation and call it a day.
Need to keep your family station wagon on the road? If you’re not taking resources into account, hiring a NASCAR pit crew to hang out in your garage becomes a viable solution. Or you could just buy a new station wagon every time the old one gets to a quarter tank of gas. Those are extreme examples, but they highlight how important it is to always consider resources when looking for the best maintenance strategy.
So, what are the options, how do they compare to one another, and what are the pros and cons of each maintenance strategy?
What is run-to-failure maintenance?
It might be a bit surprising to see this one on the list of strategies; it’s often presented as what happens when you don’t have a plan at all. But it can be a viable strategy if you use it with the right assets. Generally, run-to-failure works well when assets are disposable or cheaper to replace than repair. The classic example of a disposable asset is the lightbulb. It makes sense to squeeze as much value out of each bulb as you can before throwing it away and then quickly and easily replacing it. It’s designed to be unrepairable.
Some items are not specifically designed to be disposable but are usually thrown out all the same instead of repaired. By the time yours breaks, a newer model is already available for just a bit more than what it would cost to repair the old one. For example, what would happen if the computer screen you’re using right now suddenly died? It’s likely it would get replaced, not repaired.
What are the pros and cons of run-to-failure maintenance?
The great thing about run-to-failure is that it doesn’t require you to invest a lot of time or energy leading up to a failure. You’re not scheduling any inspections or tasks, freeing up the team for other work. But that doesn’t mean there’s zero prep. For one, you need to make sure you have the required parts and materials in inventory. It’s fast and easy to change a light bulb, but that’s only if you already have a spare light bulb on hand. For assets and equipment where replacements require a specific set of steps, even if they’re straightforward, you need to document the process and then find a place to keep it both safe and accessible.
The tricky part is making sure you’re only using it for the right types of assets, which can count as a disadvantage. If you use it where you shouldn’t, you’re setting yourself up for costly headaches. Another problem is with the unpredictability. Even though you can replace the part quickly, you never know when you need to, reducing your ability to plan and schedule.
What is preventive maintenance?
This one is a personal favorite. Preventive maintenance means you schedule maintenance in advance to catch small issues before they become large problems. Generally, you’re scheduling a combination of inspections and tasks for your assets and equipment. So, that might mean inspecting for anything from leaks to loose cables. For tasks, you might be periodically cleaning filters or replacing lubricant.
When assets or equipment is still new, you tend to schedule inspections and tasks based on the manufacturers’ recommendations, but over time you fine-tune your program using historical work order data. Back to using another car example, when you first get a new car, you follow the recommended schedule for oil changes. But if over time you notice the engine needs more oil more often, you adjust.
What are the pros and cons of preventive maintenance?
The big benefit of preventive maintenance is finding and fixing small issues before they have a chance to become big, budget-busting problems. The higher up on the P-F curve you can catch something, the easier and likely cheaper it is to fix. If during an inspection you notice a small oil leak, the repairs are going to be a lot easier than having to deal with a seized engine.
You also have the advantage of knowing what work the team is doing and when they’re doing it. So, you scheduled that inspection that caught the oil leak when you knew the line was going to be down, ensuring as much as possible that maintenance doesn’t negatively affect production. And because you caught the issue early, you have time to schedule the repairs. The techs know ahead of time what you need them to do, which makes their lives more predictable, less stressful. And you have time to order all the necessary parts and materials, avoiding rush delivery costs.
But the disadvantage with preventive maintenance is the possibility that you’re doing too much maintenance. And yes, too much of a good thing can be bad. Remember, preventive maintenance is about making data-backed guesses about when your assets and equipment need adjustments and repairs. But it’s not an exact science. You’re not doing work based on an exact knowledge of an asset’s condition. Instead, you’re scheduling things based on good guesses.
So, what are the possible problems? One, you’re wasting resources. If the maintenance techs are changing out the air filters too often, you’re throwing away MRO that’s likely still perfectly good. Two, you’re running risks you don’t need to be. Your techs are professionals who work carefully, but even the best of us can make a mistake. And even small mistakes can cost you. Maybe the air filter was put in backwards. Or the tech failed to secure a latch on an asset after they closed out the work order.
What are condition-based and predictive maintenance?
Looking at these two together makes it easier to understand each one individually. In very broad strokes, they’re two ways of using the same data collected from an asset.
The old-school version is the visual inspection. Departments schedule walkthroughs where technicians look for small issues before they become large problems. For the new-school version, imagine you have a vibration sensor on a fan inside an HVAC system. As soon as the vibrations get too strong, the maintenance department is notified. Or, you have a thermal imaging camera monitoring a motor. As soon as it gets too hot, alarm bells go off in the maintenance department office. The asset is locked out and checked.
In both cases, the system triggers a maintenance work order when the asset falls out of a predetermined comfort zone. The fan is spinning too slowly or quickly. The motor is too hot or too cold. Condition-based maintenance is all about how the asset’s condition right now compares to a set standard.
Here you’re using the data to look for trends, and then basing your schedule on them. Instead of worrying about what the fan is doing right now, you’re interested in what it’s been doing for the last year. You’re not constantly comparing the current condition to a predetermined ideal. You’re looking at the historical data and using it to make predictions. Predictive maintenance is all about how the past is going to tell you what to do in the future.
What are the pros and cons of condition-based and predictive maintenance?
Because you’re connecting maintenance directly to the assets’ real-time condition, you know what to do and when to do it. It’s basically the application of that adage “If it ain’t broke, don’t fix it.”
But when looking at costs connected to getting started, condition-based and predictive maintenance are the most expensive options. Remember, you need to invest in new sensors to capture data, software to make sense of that data, and then special training for your techs to leverage the data into good decisions.
But money isn’t what matters, in the end. The real question is “What’s the return on your investment?” If you’re using these maintenance strategies on assets that don’t need them, you’re not getting much value at all. Predictive maintenance on a light bulb is possible, but it’s also ridiculous.
But what about on a large, expensive, critical asset? Here, preventing even a few major failures a year could cover the annual cost of the maintenance program.
How can you make sure you’re choosing the right maintenance strategy?
Now that you know the options, how can you choose the one that’s best for each of your assets? You need to weigh two equally important factors: strategy cost and asset criticality.
From least to most expensive, the maintenance strategies are:
Remember, the last two require larger upfront and ongoing investments. You need to buy sensors, have them properly installed, and then get the software to monitor them. You’re also likely looking at additional training for your current staff. For predictive maintenance, you might have to bring in new people to push the data through sophisticated algorithms.
Cost can’t be your only consideration. If it were, run-to-failure would always be the best option. And you know it’s not.
So, what is criticality? It’s the answer to the question “How bad would it be if this asset stopped working?” To start to think about the answer, we can look at categories of consequences and levels of severity. Remember, every industry is different, so you need to make your own industry-specific lists.
Let’s look at some general ones. When assets break down, there can be negative effects on:
- Maintenance costs
Once you have the categories, you can think about levels of severity. To make things easy, we’ll limit ourselves to three:
Now you need to match them up, drawing on historical data from both your facility and industry standards. Take production, for example. You might decide that moderate is a production loss of less than an hour, severe is one to four hours, and catastrophic is five or more hours of lost production time. For each category of consequences, define every level of severity.
Once you’ve done that, you’re ready to look at each of your assets and determine its criticality.
What is a simple example of maintenance strategies in action?
Let’s walk through a quick example, an ice cream plant. One of your big assets is the refrigeration system in your warehouse. It’s where you store all the finished products before shipping them to stores. What happens if that asset breaks down?
For production, you know that in the past, it took you a long time to fix the asset, between five and eight hours, which means you could lose everything in the warehouse. And you can’t run the line if you don’t have anywhere to store the new product. So, when the asset is down, production takes a catastrophic hit.
In terms of safety, it could be only moderate. No one is going to get hurt if the warehouse hits room temperature. But there are going to be giant puddles of ice cream, and slips, trips, and falls are a major cause of workplace accidents.
Compliance is also where you have a lot of trouble. Because of all the rules and regulations concerning food processing and handling, failing to maintain the right temperature could cost you extra visits from an inspector.
The last one, maintenance costs, you determine is only moderate. The maintenance department can do the work itself and already keeps all the necessary parts and materials onsite. Even with some overtime, it’s never cost you a lot in labor to get the asset back up and running.
Based on the above information, you’d likely want preventive, condition-based, or predictive maintenance. Breakdowns of this specific asset are costly enough that avoiding them is worth any added upfront expenses.
But what about the forklifts that are used to unload delivery trucks? What if one of them breaks down? Production loss is below moderate, as is safety. There are no real compliance issues. But forklifts tend to be expensive to repair down at the ice cream plant. You don’t keep a lot of spare parts onsite, and the maintenance department often has to bring in a vendor. Criticality is relatively low, but still high enough to warrant an investment in preventive maintenance.
Which is the most expensive maintenance strategy?
Here is the short, direct answer: the most expensive maintenance strategy is whichever you’re using when you should be using a different one. Any of the four can be the most expensive if it’s the wrong one for you and your assets.
Unfortunately, a lot of organizations want their maintenance teams to choose a strategy based on whatever is going to cost the least in the short term. But what they really should be looking at isn’t the simple, raw cost. They should be looking at the return on investment (ROI).
Because, in the end, it doesn’t matter how much a strategy costs you; it matters how much it saves you.
Before you can choose the right strategy, you need to establish criticality. And the way to do that is to start to think of categories of consequences and levels of severity. They are specific to your industry and facility, so you’ll need to collect some data. Luckily, a good EAM solution is a great way to easily and efficiently collect work order histories and crunch the data for metrics and KPIs.
There are many different maintenance strategies, but generally people break them up into four types. Run-to-failure maintenance is when you only fix things after they’re broken. It works well on assets that are cheap to carry in inventory and easy to replace. The classic example is light bulbs. Preventive maintenance is when you schedule inspections and tasks to help you find and fix small issues before they have a chance to become big problems. With condition-based and predictive, you need sensors and fancy software to tell you when there is or when there’s going to be an issue. For large, complex, expensive assets where even a little downtime is punishingly expensive, the upfront and ongoing costs make sense. Finding the right strategy for your assets involves looking at both cost and critically. Once you know how bad breakdowns can hurt you, you can plan and schedule just the right amount of maintenance.