How DNP Turned Machine Data into Action to Increase Asset Availability by 17%

Key results

• Increased asset availability by 17% on key production equipment
• Eliminated paper-based downtime tracking and end-of-shift reporting
• Reduced time to submit maintenance requests to three clicks
• Started to move from calendar-based PMs to usage-based and predictive maintenance/li>

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DNP is the world's largest industrial printing manufacturer that counts major North American retailers, like Walgreens, among its customers. At that scale, an idle machine doesn't just slow down a shift—it ripples through the supply chain.

Yet for years, the company was running maintenance and operations largely on paper and gut feel.

"We didn't have much visibility into downtime during a shift," says Mauro Francisco, a Senior Process Engineer with 15 years of experience in manufacturing.

By then, it was too late to act. Supervisors were documenting work from memory and making decisions based on old information. There was no way to know if PMs ran long, whether a machine was showing signs of failure, or which assets caused the most lost production.

"We didn't have data to understand our current situation," Mauro says. "We asked ourselves, ‘Where do we start?’"

That question sparked a search for a better solution, which led DNP’s maintenance team to partner with MaintainX and MachineMetrics.

Challenges before MaintainX and MachineMetrics

No real-time visibility into machine performance

Downtime events were captured too late, if at all. Operators recorded issues by hand and supervisors reviewed those notes at shift end. By the time the information got to engineering, there were lots of gaps and any hope of filling them had disappeared. There was no way to spot the signs of downtime mid-shift or assess trends through time to create better maintenance practices.

Maintenance requests were slow, inconsistent, and unstructured

Maintenance requests were often submitted through paper work orders, but this process was informal and wasn’t always followed. The result was duplicated requests, information gaps, and no clear record of which machine had which issue, when, or how it was resolved. 

“It was very hard to understand what area of the machine was having the most issues," says Mauro.

PMs were tied to calendars, not to machine condition

Preventive maintenance ran on a time-based schedule, but without any connection to actual machine usage, the team had no way to know whether a PM was running ahead, behind, or on schedule. Components were serviced by date instead of what the machine was telling them.

No shared visibility between operations and maintenance leadership

Engineering, maintenance, and production were each working with fragments of the same picture. There were limited shared insights and a lack of visibility across all departments. Because of this decisions on where to focus maintenance effort were made partly with basic data, and partly on instinct and routine.

IT connectivity was uncharted territory

DNP's machines were not connected to any data collection layer. Integrating OPC UA protocols, establishing network infrastructure on the production floor, and getting IT and engineering aligned on a shared architecture was new territory for the team, which limited how quickly they could move.

Why MaintainX and MachineMetrics

Mauro and the team evaluated multiple platforms before landing on MachineMetrics as the machine intelligence layer and MaintainX as the maintenance execution system. The decision came down to a few stand-out capabilities:

The operator experience was simple, yet powerful

Operators and technicians (those closest to the machines) needed to actually use the system for it to be effective and bridge the gap between data and action. That meant the interface had to be fast, clear, and require little training. Both MaintainX and MachineMetrics had intuitive and interactive designs that made them the obvious choice for DNP. 

"We could see the data in real time,” says Mauro, “and it was an easy system to navigate."

Integration between MaintainX and MachineMetrics was real and workable

Connecting MaintainX and MachineMetrics didn't require custom development or a dedicated engineering sprint. Using an API key and asset mapping between the two platforms, the team was able to link machine events to maintenance workflows directly in a few days. 

"There was no custom code needed," as Graham Immerman, CRO at MachineMetrics, put it. "You drop in your secret key, map your assets, and build your workflows from there."

The meter capability unlocked usage-based thinking

One of the features that moved the needle for Mauro was MaintainX's meter functionality, which, once connected to MachineMetrics runtime data, allowed DNP to track machine usage against maintenance intervals. 

"Meters were the most important capabilities we set up in the beginning," he says. "And MaintainX and MachineMetrics allowed us to make it happen.”

The platform made it easy to achieve alignment across departments

DNP needed partners that would help them build alignment across operations, maintenance, production, and IT. MaintainX and MachineMetrics did just that by understanding the requirements and workflows of each group to ensure the transition was both painless and useful for each business unit. 

The foundation supported where DNP wanted to go

Mauro and team had predictive maintenance in mind from the beginning, and the company’s platforms needed to be capable of supporting that future state. The API architecture between MachineMetrics and MaintainX, combined with MaintainX's work order and meter infrastructure, gave DNP a concrete path forward toward this vision.

Results

Operators can request maintenance in three clicks

The integration between MachineMetrics and MaintainX changed how operators flag equipment issues and submit work requests. Instead of filling out paper forms or navigating a separate system, operators create maintenance requests directly from the MachineMetrics dashboard. Three clicks later, a work request is sent to maintenance. MaintainX automatically generates and schedules work orders based on requests, so the maintenance team can respond to problems faster than ever.

"Before, it was hard to get consistent work requests, prioritize them, and track them," Mauro says. "Now the operator just goes through three steps and the technician gets the request right away."

This closed the gap between the floor and the maintenance team and removed one of the biggest sources of incomplete work order data.

Real-time OEE data unlocked better decision-making

With MachineMetrics feeding live utilization and downtime data into a shared dashboard, visible on a shop floor monitor, DNP established a weekly meeting cadence built around actual machine performance. Teams review OEE trends, downtime categories, and recurring issues together. This has contributed to increasing machine availability by 17% on key production year-over-year, driving a significant increase in overall equipment effectiveness (OEE).

"We can now see weekly data and track our OEE," Mauro says. "That's something we didn't have before. We're using that data to give better reasons for solving machine issues."

Troubleshooting and response times fell sharply

Operators can categorize the reason for downtime in real time instead of waiting until the end of a shift. That data feeds directly into the weekly review and allows the team to act before the shift ends rather than reconstruct what happened after the fact. This has led to a drop in unplanned downtime.

"We can act before the end of the shift," Mauro says. "And we have meetings where we can understand what downtime belongs to each machine and decide where to take action, or take a lesson learned from a machine that's doing well."

A shared data layer replaced siloed, anecdotal reporting

Before MachineMetrics and MaintainX, engineering, maintenance, and production were all working off a different version of events. Now they share a common view. MaintainX holds the maintenance history while MachineMetrics holds the machine performance data. Together, they tell a complete story that leadership can review, act on, and learn from.

"The VP of Operations team, the engineering team, maintenance, production—everyone is now working from the same data," Mauro says. "That visibility is what changed how we make decisions."

Looking ahead

Mauro's near-term focus is completing the move to automated work order creation using machine data. Once the team has enough runtime data to establish reliable maintenance intervals, work orders will generate without human triggers or calendar-watching.

DNP also plans to expand to MachineMetrics’ full MES capabilities and MaintainX to additional machines and other facilities.

The long-term roadmap includes alarm auto-categorization, predictive maintenance modeling, and tighter integration between machine condition data and MaintainX workflows. The data foundation is in place. The next step is letting that data run more of the decision-making automatically.

"The future," Mauro says, "is combining predictive maintenance with automatic work orders, alarm categorization, and expanding to other facilities. We can expand this a lot."