Mean time to failure measures a part's reliability. It shows you how long, on average, a part lasts before it breaks and you have to replace it. On a basic level, it tells you about the quality of the parts you're buying. The longer they last, the more value you're getting from them.
But knowing, for example, how long your light bulbs tend to last can also tell you a lot about your overall maintenance operation, helping you fine-tune your inventory control and preventive maintenance scheduling.
First, let's start with some basic definitions.
Mean time to failure (MTTF) is the amount of time on average that a part can run before it breaks. Because you use this maintenance metric only for things that you can't repair, you can also think of MTTF as a part's average lifespan.
It's worth looking a bit more closely at the idea of something being "repairable." In many cases, the non-repairable part lives inside a repairable asset. For example, you can calculate the MTTF for a fan belt inside a forklift. When the fan belt breaks, you can't safely repair it, but once you replace it, the forklift works fine again.
And that's why MTTF can be so important: you're tracking the smaller, replaceable parts that keep your larger, repairable assets up and running. One fan belt might not be worth a lot to you, but the asset it lives inside certainly is critical to your operations. And that means the more you know about your fan belts, the better.
Mean time to failure is an average, so to calculate it, you need a group of identical parts, and you need to know how long each one of them lasted.
MTTF = total hours of operation divided by the total number of parts.
Take light bulbs, for example.
Start with five, all of them with matching:
We take the amount of time in hours each lasted (20, 22, 15, 20, 21), add them together (98), and then divide by the number of light bulbs, which is five.
The MTTR is 19.6 hours.
The advantage we have with light bulbs is that they don't tend to last for a long time, which means we don't have to wait a long time to get enough data to calculate their MTTF. But what about with other assets and parts, ones that do tend to last for a long time?
To get around this problem, we can test a larger number of parts over a shorter amount of time.
For example, if I want to know the MTTF of a wheel, I start by getting 100 of them and running them for three months. That gives me the first part of the equation, the number of operating hours. Three months multiplied by 100 wheels is 300 months total operating time.
But I don't want to divide that by the number of parts, which is 100. Instead, I need to find out how many of those wheels failed during the three months. Let's say it was two.
Three hundred divided by two is 150 months, which is about 12.5 years.
It's unlikely those wheels are going to last, on average, that long.
And that shows how MTTF steadily loses its reliability as parts last longer and longer. It also means you might need to rely more on manufacturers' numbers than ones you generate using your own data for certain parts. It might be tough to convince the head of the purchasing department that you need 100 wheels for an experiment.
Because when you can track MTTF, you can then predict roughly when things are going to break; you basically have a reliable crystal ball. Being able to see the future, even if imperfectly, sets you up for new ways to improve efficiencies and cut costs.
Knowing the MTTF for any given part gives you a good idea of how much real value you're getting for your money. Parts that last a long time might be more expensive up front, but they're likely worth the higher initial cost over the long haul. Something that's cheap but doesn't last as long is a poor investment.
It's the basic wisdom behind Captain Samuel Vimes "Boots" theory of socioeconomic unfairness, which explains why rich people are rich because they manage to spend less money.
A cheap pair of boots might cost only $10, but then they last only a short time, one or two seasons. Good boots cost $50, but they last a decade or more. You spend more money buying extra pairs of cheap boots over those same ten years, and in the end, your feet are still wet.
Not only do you now know what to buy, but you also know when you should buy it. Because MTTF tells you roughly when parts are going to break, you can line up your deliveries so that replacement parts arrive just in time, cutting your inventory carrying costs.
Take fan belts, for example. The problem with carrying them in inventory is they slowly degrade over time. If you want to slow down rubber deterioration, you need to store the belts in cold, dark, dry, and oxygen-free conditions. That's not impossible, but it's an added set of requirements you could avoid completely by simply ordering the fan belts so that they arrive just before you need them, pushing the associated costs and concerns of storage back onto the vendor.
For something like light bulbs, it's the same idea but with different advantages. Bulbs tend to have a long shelf-life, so you don't have to worry about them degrading or spoiling. But you do have to worry about finding room to store them and keep them organized. The fewer you can keep on hand while still meeting your maintenance needs, the less time techs waste rummaging through multiple boxes looking for the right size and wattage.
Directly related to the advantage of knowing when you're going to need parts is knowing the best times to schedule preventive maintenance inspections and tasks.
If you know your fan belts tend to die after X number of days of operation, you can set up inspections for one week before. For parts where it's more difficult to perform meaningful inspections, you can instead switch out the parts with fresh ones, confident that you've already squeezed as much value out of them as you safely can before they fail.
The absolute best time to implement a CMMS solution was last year. The second-best time is right now.
If you're ready to make the jump to a modern maintenance management, all you need to do is reach out to providers and get the conversation started. Once they have an idea of your current situation and challenges, they can clearly explain your options and how to move your CMMS project from the planning stages to full implementation.