How to segment your maintenance data to find efficiency gains

If you’re a maintenance leader, we’re willing to bet there are two KPIs that keep you up at night: unplanned downtime and overall maintenance costs. 

They’re important numbers, and they’re the metrics most maintenance teams’ success is judged by. But what are they really telling you, beyond a high level pass/fail reading of your overall maintenance operations?

In our experience, these metrics aren’t working hard enough to show the full picture of maintenance operations. Too many teams miss the opportunity to go deeper, but doing so can provide context and ignite action that changes the trajectory of the organization. 

In this article, we'll look at a few different ways you can sort, view, standardize, and segment unplanned downtime and maintenance cost data to uncover key insights and increase your impact on the business.

Key Takeaways

• Unplanned downtime and maintenance cost are important metrics, but they’re also surface-level. Segmenting each can help gain insights and spark action that benefits the business.
• Unplanned downtime can be segmented by asset, site, and failure code to learn more about exactly what’s causing and driving up downtime hours. 
• Maintenance cost can be segmented by asset, site, and line item to get to the bottom of—and eliminate—unnecessary cost drivers.
• Diving into data now will benefit your maintenance organization for years to come.

How to segment unplanned downtime data for better insights

Unplanned downtime is essential for any facility to track and report on. It’s the most telling indicator that a maintenance strategy is (or isn’t) doing what it’s supposed to. If unplanned downtime is up, it usually means that something larger needs to be fixed, especially as the cost of downtime continues to rise. 

But without diving past that topline metric, it’s difficult to know what that “something larger” is. This is why it’s important to look at unplanned downtime from a number of vantage points. 

Three ways to segment unplanned downtime data

By asset

Sorting and segmenting unplanned downtime metrics by asset or asset type can quickly reveal clear patterns. Knowing, for example, that mixers are breaking down more often than any other asset is a more valuable piece of information than, “downtime has increased slightly, on average.”

Once you’ve segmented downtime by asset, you can take a closer look at a few things, such as:

• The PM schedule for assets breaking down most often
• Throughput rates for assets or asset categories experiencing the most unplanned downtime
• Which technicians are servicing which assets

Each of these specific views provide clues. With those clues, you can form a hypothesis and make an action plan.

By site

When you segment unplanned downtime by site, a vastly different picture can start to emerge. All other things being equal, a higher downtime rate for one site over another usually points to culture, enablement, and training issues.

If one site is clearly lagging behind others, there are a number of questions you can ask to dig deeper:

• Does this site have fewer experienced frontline staff members compared to other sites?
• Does it have higher throughput targets than other sites? 
• How is PM compliance managed at this site?
• Are this site’s technicians rewarded for hitting their maintenance targets?

The answers to these questions can shine a light on where training and culture need work.

By failure code

Segmenting unplanned downtime by failure code allows you to get granular about what’s going wrong, allowing you to answer questions like:

• Is a specific part of a specific asset repeatedly failing? 
• Is one type of failure occurring in certain production conditions?
• Is a failure repeatedly occurring in between inspections/PMs meant to prevent that specific failure? 

Grouping and analyzing failure codes will highlight any holes in your maintenance and inspection approach pretty quickly. If one failure code emerges for a certain asset or set of circumstances, it’s a pretty safe bet that further action is needed.

How to turn unplanned downtime insights into action

Segmenting your downtime data in the right ways will lead you to the right corrective actions. Here are a few examples of actions you can spark:

• If segmenting by asset highlights a clear outlier, introduce a more mature maintenance strategy for that asset (or consider replacing it). 
• If site-by-site analysis tells you that one site is experiencing much more downtime than others, audit your maintenance program and maintenance culture at that site. Are technicians empowered to do their best work and find efficiencies? If not, how can you change that?
• If one failure code is showing up more often than any other, what can you implement to prevent that failure? The answer could be something as technical as installing a sensor or as simple as introducing a more frequent routine inspection.

How to segment maintenance costs data for better insights

Much like unplanned downtime, maintenance cost as a top line metric can tell you one thing: whether your overall maintenance costs are too high or not. 

It’s tough to turn that one insight into further action. So let’s look at the different ways you can segment and view your maintenance costs to gain valuable insights for your team and the organization. 

Three ways to segment maintenance costs

By asset

Sorting and analyzing maintenance costs by asset can tell you very quickly which assets are costing the organization—and your maintenance program—money. Once your maintenance cost data is grouped by asset, you can go further by looking at data such as:

• The age of the assets incurring the highest costs
• The PM schedule for high-cost assets
• The throughput of your highest-cost assets

When you can isolate costs by asset and then examine why the costs may be higher for certain assets, you can make a big difference quickly. 

By site

Similar to unplanned downtime, segmenting your maintenance costs by site can tell you whether there’s a training or process problem that needs to be addressed. If one site’s maintenance costs are largely different than others, you can start looking into:

• How parts inventory is managed for that site (are there unnecessarily high shipping costs associated with emergency orders for parts not on hand?)
• Planned maintenance percentage and PM compliance for one site compared to others

By line item

Sometimes the only way to understand what’s making your costs so high is by looking at each cost—or category of cost—in isolation. Grouping your costs this way will make it crystal clear what’s driving your topline metric up, whether it’s emergency parts shipping, loss of production hours, or overtime costs associated with labor hours. 

How to turn maintenance costs insights into action

Depending on which patterns emerge when you segment and analyze your maintenance costs, you can take a number of actions:

• Make a compelling case to replace an aging, problem asset
• Revisit the maintenance strategy for a certain asset or facility
• Overhaul a process that’s currently driving up maintenance costs

No matter what’s influencing your maintenance costs, understanding them at a close range can help you turn a money pit into a profit center over time and with the right corrective measures.  

For example, many maintenance teams have seen big increases in parts costs in recent years. If that's eating up a lot of your budget, one way to mitigate the impact is to audit your vendor list, find vendors that charge less, and set higher minimum stock levels so you can manage longer lead times for less expensive orders.

Putting maintenance data to work now will pay off for years to come

There are countless reasons to dig deeper into topline metrics. Perhaps one of the most compelling is that doing so allows you to highlight and celebrate your own team’s efforts over time. 

When you can clearly show exactly how your team is turning downtime into uptime and high maintenance costs into profit, you’re also clearly showing the value of maintenance to your organization’s bottom line. You’ll also be able to make data-backed recommendations that benefit everyone, whether it’s investing in new tools, training programs, or systems that will create more value where it’s needed.