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10 insanely useful metrics to improve your maintenance analysis

Maintenance analysis has changed a lot over the last decade or so. New tools and technology have increased our ability to collect and interpret data. It’s enabled us to make informed decisions that wouldn’t have been possible 10 years ago.

But if our understanding of maintenance analysis has changed, why do we still rely on the same handful of metrics we did 40 or 50 years ago?

Metrics like overall equipment effectiveness (OEE) and mean time to repair (MTTR) dominate almost every list of go-to industry measurements. But experts agree that they’re flawed. Not only are these traditional metrics prone to bias and inaccuracy, but they also often don’t have a purpose. And when data doesn’t have a purpose, you can’t use it to make key decisions, like whether to hire an extra technician or increase the frequency of a task.

That’s why we’ve put together 10 useful metrics you won’t see on any other list and some tips for how to use them to improve your maintenance program.

10 maintenance metrics for better maintenance analysis

#1 – Time spent supporting production

What is it?: The total time that the maintenance team spends on production-focused activities. Usually measured weekly, monthly, or quarterly.

How can you use it?: Everyone has to pitch in to complete a big order once in a while. But when once in a while turns into every day, maintenance suffers. This metric helps you catch an unhealthy backlog before it happens and reallocate resources to prevent it. It also helps you advocate for a higher headcount on your team or an increased training budget to help production staff learn minor maintenance tasks.

#2 – Follow-up work created after inspections

What is it?: The number of corrective work orders created from routine inspections. Usually measured monthly, quarterly, or annually.

How can you use it?: There are many different ways you can use this metric for maintenance analysis. You can sort it by machine, shift, or site to get insights into how your assets or team are performing. But the most useful is by task.

It’s a good sign when regular preventive maintenance includes follow-up repairs. It means your schedule is accurate and that you’re preventing bigger problems. It allows you to flag common repairs and build processes to make them more efficient. For example, you can create parts kits for quicker access.

If the failed inspection percentage is low, you can increase preventive maintenance intervals. This will reduce the amount of time and money spent on tasks without increasing risk.

#3 – Cost of follow-up maintenance vs expected cost of total failure

What is it?: A comparison between the cost of corrective maintenance (i.e. labor and parts) and the cost of asset failure if maintenance is not done (i.e. lost production, labor, and parts).

How can you use it?: Use this type of maintenance analysis to plan your maintenance strategy. For example, if regular inspections cost you more than failure, you can likely go with a run-to-failure approach for an asset over a preventive one.

You can also use this metric to prioritize tasks and backlog, and figure out how to allocate your budget.

How to decide if you should invest in regular PMs on an asset

#4 – Cost by maintenance type

What is it?: The total cost of maintenance (i.e. labor and parts) by maintenance type (ie. preventive, emergency, follow-up). Usually measured monthly, quarterly, and/or annually.

How can you use it?: Higher costs are usually the result of broken processes. This view allows you to find out which processes need work so you can increase efficiency.

For example, are work orders unclear and leading to increased repair times and labor costs? Try clarifying instructions.

Are you bringing outside contractors in to do emergency repairs? You could invest in more training for your team or hire a specialist.

#5 – Clean start-ups after maintenance

What is it?: The number of times a production line starts without stoppages or waste after completed maintenance. This is measured monthly, quarterly, and annually.

How can you use it?: Include this metric in your maintenance analysis to draw a direct line between your team’s work and increased output.

If clean start-ups are low, it gives you another chance to spot problems in your processes. For example, you might find that the specs for a production line may be out of date. This will lead technicians to rebuild components incorrectly and the line to stall. Updating the specs is a simple tweak that could lead to higher output.

#6 – Size of backlog

What is it?: The total number of hours of overdue and scheduled maintenance tasks. Track this metric weekly and monthly.

How can you use it?: This metric can be a godsend when it comes to getting your team some much-needed relief. Quantify the gap between available labor hours and your total backlog hours. You might find that the amount of backlog far outpaces how much your team can do. Use that to make a case for more budget to spend on extra overtime, hiring another technician, or bringing in more contractors.

#7 – Top 10 assets by downtime

What is it?: This is your heavy hitters list—the equipment that breaks down most often or takes the longest to repair. Keep tabs on these assets weekly, monthly, and quarterly.

How can you use it?: This metric keeps your biggest problems visible. You might raise an eyebrow at that, but highly visible problems get solved the fastest. This kind of maintenance analysis can help you prioritize your problem-solving efforts, make decisions quickly, and measure their impact.

For example, if you know asset A is at the top of your downtime list, you can start by isolating the reason why. Is it because repairs take longer on that asset? Is work being delayed? Does that piece of equipment break down again and again?

The answer to these questions will give you an idea of how to prevent failure in the future. You might get rid of obsolete parts that keep breaking. Or put an extra technician on a job. Or clarify how much lubrication should be used on a bearing. If all else fails, conducting this type of maintenance analysis helps justify a capital expenditure on new equipment.

#8 – Planned maintenance percentage (last 90 days)

What is it?: The ratio of planned maintenance to all other types of maintenance over the last 90 days.

How can you use it?: This is a measure of progress. Going from reactive to planned maintenance doesn’t happen overnight. The time frame allows you to make a clear connection between action and results. You can draw a line between what happened and its impact on your end goals.

For example, if your percentage has dropped, you can look at what happened in the last 90 days to cause that drop. That could be a massive, unexpected breakdown. Or an increase in production support during the busy season. If you want to increase the percentage, try creating a better work request process to uncover problems earlier. Or shorten inspection intervals on assets with the highest instances of unexpected downtime.

#9 – Wrench time (last 90 days)

What is it?: The amount of time technicians spend working on a piece of equipment as part of the total time it takes to complete a job. This is usually measured by job or as a weekly, monthly, and quarterly average.

How can you use it?: Wrench time is a common tool for maintenance analysis, but it’s often used the wrong way. Technicians usually (and unfairly) get the blame for low-wrench time. It leads to wrench time inflation as technicians fudge the numbers to avoid trouble.

Low wrench time usually has its roots in broken processes, not the ability of the technician. That leads to bigger backlogs, more reactive maintenance, and avoidable labor costs.

To use wrench time in your maintenance analysis, start with the jobs that have the lowest scores. Review these jobs step-by-step with technicians. Work together to find out where unclear or incomplete processes cause delays. You’ll spot bottlenecks easier when breaking the task down into smaller pieces. The result is more value for your team’s time and money.

Common reasons wrench time is low and how to fix them

#10 – Health and safety work orders completed

What is it?: The number of work orders completed for health and safety or compliance purposes. This is usually tracked monthly, quarterly, and annually.

How can you use it?: Some metrics are quantitative. Others are qualitative. This one is the latter. And it’s essential for measuring the performance of your maintenance team and the impact it has on your business. A safe workplace keeps accidents low, and productivity and morale high. Passing audits and remaining compliant is crucial to staff safety and avoiding fines.

Three big goals you can accomplish by combining these metrics

All the metrics mentioned above are powerful in their own right. But when combined, they supercharge your maintenance analysis and help you achieve three common goals:

Get a bigger budget and more time for maintenance

Metrics to combine:

  • Cost by maintenance type
  • Clean start-ups after maintenance
  • Top 10 assets by downtime

Getting more money and time for maintenance means winning over whoever divvies up the budget, and whoever leads production. The quickest way to get them on board is to align your plan with their goals. The three metrics above will help you get there.

First, highlight the cost-benefit of preventive maintenance. Regular preventive maintenance might seem expensive. But just one instance of emergency maintenance can cost up to $250,000. If you’re tracking cost by maintenance type, you can highlight how much the company is losing with reactive maintenance, and how much it can save you by investing in preventive maintenance.

Next, it’s time to sway the production team. Use clean start-ups after maintenance to show production that you have their best interests in mind. It emphasizes what is good for maintenance is often good for production.

No one is going to give you more resources without a plan. Your list of bad actors is a blueprint for how you’re going to make the most of your extra time and money. It quantifies the problem and makes it very clear where you’ll focus your efforts.

Get your maintenance team to buy into change

Metrics to combine:

  • Planned maintenance percentage (90 days)
  • Wrench time (last 90 days)
  • Follow-up work created after inspections

Change sucks. And that makes it hard for your team to get on board with a new system or process. The best way to change the mind of naysayers is to show them how your plan is eliminating their biggest pains. Tracking the metrics above is one way to do this.

These data points give you a chance to compare how you operated before a change (i.e. lots of reactive maintenance and frustration over guesswork) and what you’ve accomplished since implementing a new system or process. Seeing the pay-off first-hand makes it easier to convert any critics and expand your project, whether it’s setting up a CMMS or allowing machine operators to do routine maintenance.

Build a preventive maintenance program that would make most other companies jealous

Metrics to combine:

  • Cost by maintenance type
  • Follow-up work created after inspections
  • Cost of follow-up maintenance vs expected cost of total failure

The best preventive maintenance programs don’t have the most PMs. Instead, they have the most efficient PMs. That means doing the right work at the right time. These metrics will help you achieve this balance.

Measuring cost by maintenance type helps you allocate resources to preventive tasks and gauge the efficiency of your PMs. You can track if cost-cutting strategies are working and make sure they’re not leading to reactive costs down the line.

Keeping tabs on follow-up work is one way to optimize PM frequencies. If an inspection isn’t leading to corrective work, you can increase inspection intervals. That means you can use fewer labor hours and parts, and spend that money and time elsewhere. Similarly, comparing the costs of corrective maintenance and total failure ensures you’re not spending money on proactive tasks that aren’t worth it.

The best maintenance analysis is constantly evolving

The best maintenance metrics have a purpose. They are collected and used consistently. They guide decisions and inform you on how to run your maintenance program on a daily basis. This is the backbone of successful maintenance analysis.

On the flip side, all maintenance analysis is a work in progress. Revisit your metrics on a regular basis to make sure they’re still relevant to your goals and the way your maintenance team works. Some of the metrics listed above might work for you now, but you might find others are more effective in six months. Or maybe five years.

Lastly, the best maintenance analysis incorporates data that other departments find useful. If you can connect the metrics above to solve the challenges of other business units, you’ll be well on your way to creating a world-class maintenance program.

Source: https://www.fiixsoftware.com/blog/10-metrics-for-better-maintenance-analysis/

Where bad maintenance data comes from and how you can fix it

Not all maintenance data is created equal

Data: It’s the backbone of any maintenance program. It’s what you use to measure success. It tells you what assets need more attention and how that will impact your schedule. It’s what helps you survive maintenance audits unscathed. In short, data is the language that helps you tell the story of your maintenance team.

But not all data is created equal. And it could be that yours is failing to say what it needs to. Jason Afara, a Senior Solutions Engineer at Fiix, experienced this when he was a maintenance manager.

“We had more technicians than we did CMMS licenses, so we had people logging in after they had already completed a work order, just trying to fill in all the details they could remember,” he says. “We were always trying to catch up, and that impacted our credibility.”

The cost of bad maintenance data

That’s just it—when your data is off, it’s harder to go to bat for your team. It’s not as easy to justify buying a new piece of equipment, trade production time for maintenance or make a new hire if the data isn’t there to support that request.

It can impact your team on a day-to-day basis as well. For example, a technician might wait until the end of the day to log completed work. This gap in time could lead them to misremember how long it took them to do a job. Maybe they round down. No big deal, right? Except it is.

That one mistake could cause a domino effect. The next time you go to schedule that job, you plan less time for it. Now the technician is rushing to complete the work, increasing risk for both them and the machine. You’ll also lowball the cost of labor hours in your budget, putting you in a tricky situation with your finances.

Bad data and its consequences

Let’s dive into where your data can go wrong, and how you can audit it to start steering things in the right direction.

Where bad maintenance data begins

Bad data is often born from the best intentions. That makes it hard to spot. But there will always be a silver lining to go along with these issues—you have a data-driven culture. You know the numbers are key and the insight you get from them is even more valuable. That’s the most important ingredient for finding and eliminating bad data.

Here are two aspects of maintenance programs that most often contribute to bad or incomplete data.

Trying to boil the ocean

A lot of maintenance teams try to do too much, too soon with their data. Having the ability to track things is great, but if you don’t have a well-thought-out plan in place for what you’re going to measure—and why—you’ll run into problems.

It’s an easy trap to fall into. The advent of IIoT technology, like sensors that track every second of an asset’s behaviour, has introduced seemingly infinite ways to capture data. The trouble for maintenance managers doesn’t come from having too much data, but from not knowing how to pull out the data that matters.

Brandon De Melo, a Customer Success Manager at Fiix, puts it this way, “Let’s say you have a sensor that’s pulling machine data. That’s great, but you can’t stop there. You have to consider all the things that factor into that data, like downtime or other external factors that could affect it.”

Not thinking critically about metrics

Every maintenance team is held to certain KPIs—but are they the right ones? As Stuart Fergusson, Fiix’s Director of Solutions Engineering, points out, it can be easy to get caught in a cycle of tracking a number like labour hours simply because it’s the metric that comes from your boss (or their boss).

It’s important to take a critical lens to maintenance metrics and really think about whether they should be measured.

“At the end of the day, you need to be measuring the metrics that support your department,” says Fergusson. “Not enough people understand why they’re measuring what they’re measuring.”

Where bad maintenance data lives

We know what contributes to bad data, but where does it show up? Bad data is really good at blending in with clean data, so it’s not always obvious. But knowing the telltale signs of inaccurate information will help you spot it without pouring over dozens of reports. Here are the most common places where you can find bad maintenance data.

In your storeroom

Bad data can lurk alongside bearings and motors on the shelves of your storeroom. There are a few ways this can happen.

Firstly, it’s easy to have an out-of-date inventory count if you have obsolete parts sitting on shelves. If you don’t check in on your inventory to make sure it matches up with what’s actually available, you’ll run into problems when you have to pay for a part you weren’t expecting.

And then there’s the danger of fudging the numbers to make the bottom line look better.

“Let’s say it’s near the end of the month and you have to replace a $3,000 part,” says Afara.

“Some maintenance managers will say, ‘You know what? Let’s just wait for that repair so it actually hits our books next month.’ It turns into a bit of a game.” This hesitation can negatively impact the whole business if what’s in the books is valued over what’s actually needed to improve production.”

In your preventive maintenance schedule

Every maintenance team has their regular PMs—but how many of them are actually necessary?

“Maintenance can get really emotional really quickly,” says Afara. “You’ll have what’s called an emotional PM, where the team is doing a regular check just because there was a failure six plant managers ago and no one’s changed it.”

When maintenance teams inherit PMs, it’s easy not to question it, but it’s easy to see how things can snowball and tell an inaccurate story of which work actually needs to be done.

In your work order and asset histories

It doesn’t take much for data to go haywire when documenting work. Attention tends to go to the wrong places when a plant’s priorities are out of sorts.

“What commonly happens is, there’s such a focus on technician time,” says Afara. “A message comes from the top that every minute needs to be accounted for, and the result is that technicians are just making up time on work orders to show that they’ve done the eight hours they’ve been asked to.”

As we touched on earlier, the root problem here is a lack of specific planning. You’re worrying about the metric at the expense of strategy, which results in data that doesn’t tell the truth and can’t be used to drive real change.

In your reports

Every data set has its spikes and dips. The important part is how you’re making sense of the fluctuations that show up in your maintenance reports.

“Do you actually have anything in place to explain why, for example, a drop can happen in September and then happen again in January?” says De Melo.

Without critical analysis or an understanding of what contributed to an anomaly in the data, tracking those fluctuations is useless. You need to understand what happened before you can begin to understand what you could have done differently.

How to audit maintenance data

Now that we have a clearer picture of where maintenance data can go wrong, how can you start fixing it?

The answer will be different for each team, but the right place to start is wherever you’re having a problem with no way to explain why you’re having it.

“Let’s say you can’t figure out why you have so much unplanned downtime, and looking at the data isn’t helping you at all,” says De Melo.

“In this scenario, you’d want to talk to the production manager and start asking questions like, ‘How is this being tracked? Is there a system in place?’ There will always be a process of tracking down the right information, but you can’t just sit there and just twiddle your thumbs, hoping that the answer is going to come to you.”

In terms of creating a data audit checklist, again, your best bet is to approach it from a strategic perspective.

“Sit with some key stakeholders, like plant managers and technicians, and do some brainstorming around what you want to improve and understand better,” says De Melo.

“Once you know what you’re looking for, you can build a checklist that makes sense.”

The best maintenance data is data with a purpose

Taking a critical and thoughtful approach to auditing your maintenance data ensures that everything you’re tracking and analyzing is being examined for a reason. This helps you understand how each piece of data is connected. Then you can make actual improvements to your maintenance program instead of making smaller, less impactful changes around the margins.

“If you really understand your maintenance activity, everything else is just going to flow in behind it,” says Fergusson.

“Your plant leadership may not understand maintenance backlog or OT, but when you tell them that delaying a maintenance window is going to cost another $250,000 in our plant maintenance budget because of X, Y, Z, and you have the right data to back it up, they’ll listen.”

When all is said and done, the data is the easy part.

“If you have the culture and the metrics and the right people and processes in place to track everything, and you just don’t have the actual data, no problem. You can get that up and running in a week,” says Fergusson.

“More often, though, it’s the opposite. You have all the data, it’s all flowing somewhere, and everybody’s looking at different pieces of it, but none of it’s building to a true story.”

source: https://www.fiixsoftware.com/blog/how-to-improve-your-maintenance-data/