{"id":183285,"date":"2021-06-15T09:47:38","date_gmt":"2021-06-15T13:47:38","guid":{"rendered":"https:\/\/fiix.wpengine.com\/?p=183285"},"modified":"2022-01-20T15:32:02","modified_gmt":"2022-01-20T19:32:02","slug":"how-to-improve-your-maintenance-data","status":"publish","type":"post","link":"https:\/\/fiixsoftware.com\/blog\/how-to-improve-your-maintenance-data\/","title":{"rendered":"Where bad maintenance data comes from and how you can fix it"},"content":{"rendered":"

Not all maintenance data is created equal<\/h2>\n

Data: It\u2019s the backbone of any maintenance program. It\u2019s what you use to measure success. It tells you what assets need more attention and how that will impact your schedule. It\u2019s what helps you survive maintenance audits<\/a> unscathed. In short, data is the language that helps you tell the story of your maintenance team.<\/p>\n

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.<\/p>\n

\u201cWe had more technicians than we did CMMS software<\/a> 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,\u201d he says. \u201cWe were always trying to catch up, and that impacted our credibility.\u201d<\/p>\n

The cost of bad maintenance data<\/h2>\n

That\u2019s just it\u2014when your data<\/a> is off, it\u2019s harder to go to bat for your team. It\u2019s 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\u2019t there to support that request. <\/p>\n

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. <\/p>\n

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\u2019ll also lowball the cost of labor hours in your budget, putting you in a tricky situation with your finances.<\/p>\n

\"Bad<\/figure>\n

Let\u2019s dive into where your data can go wrong, and how you can audit it to start steering things in the right direction. <\/p>\n

Where bad maintenance data begins <\/h2>\n

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\u2014you have a data-driven culture. You know the numbers are key and the insight you get from them is even more valuable. That\u2019s the most important ingredient for finding and eliminating bad data. <\/p>\n

Here are two aspects of maintenance programs that most often contribute to bad or incomplete data. <\/p>\n

Trying to boil the ocean<\/h3>\n

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\u2019t have a well-thought-out plan in place for what you\u2019re going to measure\u2014and why\u2014you\u2019ll run into problems. <\/p>\n

It\u2019s an easy trap to fall into. The advent of IIoT technology, like sensors that track every second of an asset\u2019s behaviour, has introduced seemingly infinite ways to capture data. The trouble for maintenance managers doesn\u2019t come from having too much data, but from not knowing how to pull out the data that matters. <\/p>\n

Brandon De Melo, a Customer Success Manager at Fiix, puts it this way, \u201cLet\u2019s say you have a sensor that\u2019s pulling machine data. That\u2019s great, but you can\u2019t stop there. You have to consider all the things that factor into that data, like downtime or other external factors that could affect it.\u201d <\/p>\n

Not thinking critically about metrics<\/h3>\n

Every maintenance team is held to certain KPIs\u2014but are they the right ones? As Stuart Fergusson, Fiix\u2019s 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\u2019s the metric that comes from your boss (or their boss). <\/p>\n

It\u2019s important to take a critical lens to maintenance metrics and really think about whether they should be measured. <\/p>\n

\u201cAt the end of the day, you need to be measuring the metrics that support your department,\u201d says Fergusson. \u201cNot enough people understand why they\u2019re measuring what they\u2019re measuring.\u201d<\/p>\n

Get your guide to common maintenance metrics and the best way to use them<\/i><\/a><\/p>\n

Where bad maintenance data lives<\/h2>\n

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\u2019s 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.<\/p>\n

In your storeroom<\/h3>\n

Bad data can lurk alongside bearings and motors on the shelves of your storeroom. There are a few ways this can happen. <\/p>\n

Firstly, it\u2019s easy to have an out-of-date inventory count if you have obsolete parts sitting on shelves. If you don\u2019t check in on your inventory to make sure it matches up with what\u2019s actually available, you\u2019ll run into problems when you have to pay for a part you weren\u2019t expecting. <\/p>\n

And then there\u2019s the danger of fudging the numbers to make the bottom line look better.<\/p>\n

\u201cLet\u2019s say it\u2019s near the end of the month and you have to replace a $3,000 part,\u201d says Afara. <\/p>\n

\u201cSome maintenance managers will say, \u2018You know what? Let’s just wait for that repair so it actually hits our books next month.\u2019 It turns into a bit of a game.\u201d This hesitation can negatively impact the whole business if what\u2019s in the books is valued over what\u2019s actually needed to improve production.\u201d<\/p>\n

Want to do more accurate cycle counts? This free template can help<\/i><\/a><\/p>\n

In your preventive maintenance schedule<\/h3>\n

Every maintenance team has their regular PMs\u2014but how many of them are actually necessary? <\/p>\n

\u201cMaintenance can get really emotional really quickly,\u201d says Afara. \u201cYou’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.\u201d <\/p>\n

When maintenance teams inherit PMs, it\u2019s easy not to question it, but it\u2019s easy to see how things can snowball and tell an inaccurate story of which work actually needs to be done.<\/p>\n

In your work order and asset histories<\/h3>\n

It doesn\u2019t take much for data to go haywire when documenting work. Attention tends to go to the wrong places when a plant\u2019s priorities are out of sorts. <\/p>\n

\u201cWhat commonly happens is, there’s such a focus on technician time,\u201d says Afara. \u201cA 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\u2019ve done the eight hours they\u2019ve been asked to.\u201d <\/p>\n

As we touched on earlier, the root problem here is a lack of specific planning. You\u2019re worrying about the metric at the expense of strategy, which results in data that doesn\u2019t tell the truth and can\u2019t be used to drive real change.<\/p>\n

In your reports<\/h3>\n

Every data set has its spikes and dips. The important part is how you\u2019re making sense of the fluctuations that show up in your maintenance reports<\/a>. <\/p>\n

\u201cDo you actually have anything in place to explain why, for example, a drop can happen in September and then happen again in January?\u201d says De Melo. <\/p>\n

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.<\/p>\n

The best root cause analyses start with this template. Download it for free<\/i><\/a><\/p>\n

How to audit maintenance data <\/h2>\n

Now that we have a clearer picture of where maintenance data can go wrong, how can you start fixing it? <\/p>\n

The answer will be different for each team, but the right place to start is wherever you\u2019re having a problem with no way to explain why you\u2019re having it. <\/p>\n

\u201cLet\u2019s say you can\u2019t figure out why you have so much unplanned downtime, and looking at the data isn\u2019t helping you at all,\u201d says De Melo. <\/p>\n

\u201cIn this scenario, you\u2019d want to talk to the production manager and start asking questions like, \u2018How is this being tracked? Is there a system in place?\u2019 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.\u201d<\/p>\n

In terms of creating a data audit checklist, again, your best bet is to approach it from a strategic perspective. <\/p>\n

\u201cSit with some key stakeholders, like plant managers and technicians, and do some brainstorming around what you want to improve and understand better,\u201d says De Melo. <\/p>\n

\u201cOnce you know what you\u2019re looking for, you can build a checklist that makes sense.\u201d <\/p>\n

The best maintenance data is data with a purpose<\/h2>\n

Taking a critical and thoughtful approach to auditing your maintenance data ensures that everything you\u2019re 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.<\/p>\n

\u201cIf you really understand your maintenance activity, everything else is just going to flow in behind it,\u201d says Fergusson. <\/p>\n

\u201cYour plant leadership may not understand maintenance backlog<\/a> 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\u2019ll listen.\u201d <\/p>\n

When all is said and done, the data is the easy part. <\/p>\n

\u201cIf 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,\u201d says Fergusson. <\/p>\n

\u201cMore 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.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"

Not all maintenance data is created equal Data: It\u2019s the backbone of any maintenance program. It\u2019s what you use to measure success. It tells you what assets need more attention and how that will impact your schedule. It\u2019s 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. \u201cWe had more technicians than we did CMMS software 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,\u201d he says. \u201cWe were always trying to catch up, and that impacted our credibility.\u201d The cost of bad maintenance data That\u2019s just it\u2014when your data is off, it\u2019s harder to go to bat for your team. It\u2019s 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\u2019t there to support that request. […]<\/p>\n","protected":false},"author":31,"featured_media":183289,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2,146],"tags":[],"coauthors":[],"class_list":["post-183285","post","type-post","status-publish","format-standard","hentry","category-blog","category-maintenance-metrics"],"yoast_head":"\nBad Maintenance Data: How To Fix And Prevent It<\/title>\n<meta name=\"description\" content=\"Learn all about where bad maintenance data comes from and the steps you can take to find, fix, and prevent inaccurate maintenance data at your organization.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fiixsoftware.com\/blog\/how-to-improve-your-maintenance-data\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta 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