Turning maintenance data into manufacturing profit: Making the business case for reliability

According to Jason Bolte, maintenance and reliability center of excellence leader at Ardent Mills, the real problem isn’t the data you don’t have, it’s how teams think about and use the data they already have. Bolte did a presentation at the 2025 SMRP conference to show reliability leaders, they may already have the data they need.

“Oftentimes, we take a look at what we think we have available, and we get disappointed...,” Bolte said. “But there’s a lot of data we can pick from.”

The data you might not know you have: Leading and lagging indicators

Valuable numbers exist across the organization, even if they don’t live neatly inside a CMMS. To connect day-to-day maintenance activity with financial outcomes, Bolte relies on leading and lagging indicators. With a better understanding of leading and lagging indicators for your business, it reveals more data choices beyond traditional maintenance metrics.

Lagging indicators—like downtime cost, contractor spend, or maintenance cost per unit—are what leadership ultimately cares about. However, they’re the result of upstream behaviors. Leading indicators, such as training progress, planning quality, spare parts discipline, are the levers teams can control.

Not every organization has great work order data. Where else can you go to start? Ardent Mills focused on four key areas: advancing technician skills, executing MRO best practices, better maintenance workflow, and better PM and PdM completion.

With better technician skills, the business saves money by reducing downtime with less time spent learning on the job. With better planning and scheduling efficiency, the plant increases work order throughput. It could reduce emergency procurement costs by having the right parts in stock and on the right shelf. With proper PM and PdM execution, downtime is reduced by identifying defects before failure happens.

The leading and lagging indicators help develop data to show the financial impact on the business. For example, the leading metric around technician skills is the percentage of techs that are assessed or the percentage of techs with individual development plans (IDP). They can track the skills added per technician and skill level advances, against the lagging indicators (contractor spend, increased OEE, and reduced equipment downtime.)

Another data example for executing MRO best practices and spare parts management: The leading metrics are MRO parts consumption count and restocking orders, tracking the percentage of parts expedited, and a positive lagging indicator should indicate lowered expediting costs and a reduction in downtime. Tracking whether parts were consumed through work orders and whether restocking processes were followed created visibility into how the system was actually being used.

Planning and scheduling effectiveness provide another clear link between operational discipline and financial performance. Metrics like scheduled hours, planned work orders, and work order backlog helped determine whether teams were working proactively or reactively.

“If I get more work done in a given day with X amount of employees, what is the nature of that work? Is it proactive, or is it reactive?” Bolte asked. “Everyone knows that if we proactively fix things, it's much less expensive than reacting, being on our heels.”

For maintenance workflow execution, leading metrics are the percentage of technician hours scheduled and the number of work orders planned. Tracking the number of work orders completed and work order backlog should indicate a financial impact, hopefully reduced downtime and increased OEE.

Finally, looking at a total cost of ownership analysis on assets, the leading metrics are the percentage of critical assets on PdM and the percentage of assets with a bill of materials, then tracking the percentage of reactive work and the number of MRO stockouts, against the lagging indicators, cost per unit produced and OEE.

Maintenance metrics on their own may not win funding. But when those same activities are translated into profit impact or risk reduction, they become relevant to decision-makers in finance and operations.

Reliability teams can tap into other data sources to build a more complete picture of business performance. Other datasets that you may have in-house already:

• maintenance overtime (from payroll data)
• maintenance employee turnover (from HR data)
• maintenance spend (from financial data)
• maintenance rework (track manually)
• maintenance skills (track manually)
• % of inactive stock
• training hours as a % of total hours
• mean time to repair or replace.

Warning: Metrics can backfire

While metrics are essential, Bolte cautioned that they can easily drive unintended behavior if designed poorly. Targets that are too rigid or simplistic can lead teams to manipulate inputs rather than improve outcomes.

“Be careful what you measure because oftentimes metrics will drive the wrong behavior,” he said. Metrics should inform decisions, not distort them.

Even something as straightforward as schedule compliance can become distorted if incentives are misaligned. “A good example around maintenance workflow would be setting the goal for scheduled workloads completed at 100%. What kind of behavior do you think that could drive?” Bolte asked. Teams could underload schedules or schedule every job at eight hours. “We can work the book, so to speak, to make that goal happen,” he said. A more realistic goal would encourage better and more honest participation, especially if outcomes are tied to employee bonuses or other compensation.

Start where you are—and refine

Ultimately, Bolte’s message is about getting started and gaining momentum. “Don't get hung up on finding perfect data,” he said. Knowing the cost or even an estimation of an hour of downtime is a good place to start. “If that doesn't exist in your company, that work that you put in is probably going to set the bar,” Bolte said. With attention on the right numbers, the metrics can be revalidated and improved over time and realigned as needed. As teams validate assumptions and improve data quality, their financial models become more accurate and more credible.

Maintenance and reliability leaders that learn to connect the right data to meaningful financial outcomes can position reliability not as a cost center, but as a driver of business performance.

“My advice for others would be to identify the stakeholders and learn their language. That would be your finance team, could be risk management, team leadership. Learn how to connect reliability to your business,” Bolte said.