When Nico Castelijn, Director of Engineering & Maintenance Appetisers CE, joined Van Geloven five years ago, he encountered a familiar scene from many manufacturing companies: decentralised, reactive maintenance teams focused on "putting out fires" instead of avoiding them.

“Maintenance teams were proud firefighters,” he recalls. “They’d fix breakdowns successfully but weren’t necessarily thinking about how to stop them from happening again, perhaps even the next day.”

Drawing on over 20 years of experience in various industries, including the pharmaceutical industry—where traceability and preventive measures are crucial—Castelijn launched a long-term, data-driven transformation project across Van Geloven’s multiple sites, located in both the Netherlands and Belgium.

Building the Foundation:Role Clarity and Structure

One of the first steps in Van Geloven’s journey toward a more predictive maintenance model wasn’t technical—it was structural.

“Before you talk about dashboards, you have to define roles,” he says. “What does a maintenance manager do? A planner? A reliability engineer? We created those profiles and aligned them across sites.”

With consistent role expectations and a clear job structure in place, the foundation was laid for a culture shift—from reactive repair to proactive optimisation.

Transforming the maintenance culture across multiple sites required time and leadership, Castelijn notes. Some teams adjusted in six months; others took over a year. One key strategy was leveraging internal peer learning.

“Transforming the culture required more than new systems—it demanded trust, education, and internal champions, ones who brought the new way of working to colleagues.”

Castelijn encourages peer learning between sites. He urges his teams to visit one another, share experiences, and learn collectively as part of the company’s transformation journey.

He says that monthly cross-site meetings have become collaborative platforms at Van Geloven where technicians present real cases and share dashboard insights. Through this, ownership increases, and a proactive mindset takes hold.

“I tell our maintenance teams: take a company car, drive to another site, spend the day there. Show your work, share your problems, and learn from each other,” he explains. “That peer connection is what changes mindset.”

Data Becomes a Daily Habit

One of the most impactful breakthroughs in streamlining operations was the integration of Power BI dashboards into daily routines, says Castelijn.

“Every morning at 7 a.m. sharp, teams gather to review the past 24 hours of data—equipment breakdowns, performance metrics, and maintenance schedules—all displayed in real-time. This consistent, data-driven approach has replaced reactive, blame-oriented discussions with a culture of collaboration and root cause analysis.”

By making key operational insights instantly visible and accessible, the dashboards have not only improved transparency but also accelerated decision-making and problem-solving across departments.

Castelijn adds that the company uses unified definitions: if a breakdown causes two hours of lost production—even if only 30 minutes was repairing time—that whole time is logged as technical downtime. This clarity promotes cross-functional accountability and realistic planning.

Power BI and CMMS Integration: Turning Data into Action

Castelijn explains that in the past, each site used its separate maintenance system (CMMS), which wasn’t connected to the company’s SAP or finance systems. Because these systems didn’t communicate with each other, it wasn't easy to track trends or compare performance across sites.

Power BI dashboards, which were initially managed by an external provider, were brought in-house and linked to both the CMMS and the production efficiency system (OEE). This enabled the comparison of production losses and maintenance activities in real-time.

“The dashboards are part of the 7 a.m. routine now,” Castelijn says. Teams review the past 24 hours—failures, root causes, and upcoming PM tasks. It’s not about blame. It’s about learning and prioritising together.”

This integration helps teams plan downtime, optimise resources, and reduce unplanned stops. Maintenance and production data now share a common language.

Mobile Tools and Real-Time Logging

Castelijn emphasises that usability has been crucial in making digital tools successful. Today, technicians use mobile apps and QR/barcode scanners to log work orders, parts used, and failure data instantly.

“Technicians aren’t always keen on admin,” says Castelijn. “So, we made it easy for them. Now everything’s captured in real time—accurate, fast, and easy to share.”

In 2025, Van Geloven began piloting a new AI module in its CMMS at the company’s Tilburg site. The tool quickly answers technician queries by pulling data from past incidents and technical manuals.

“It’s not replacing our technicians—it’s empowering them,” Castelijn explains. “Especially for junior staff or independent workers, it reduces time wasted hunting for information.”

The pilot proved so successful that Tilburg became the first of McCain’s 58 global sites to adopt CMMS-integrated AI.

In his interview with Maintworld, Nico Castelijn shared his perspective on a growing challenge facing the maintenance industry: a shortage of skilled technicians.

Data-Driven KPIs Driving Change

To accelerate its shift from reactive to predictive maintenance, Van Geloven has made data a central part of its strategy. At the heart of this approach is a set of carefully chosen key performance indicators (KPIs) that guide day-to-day operations, long-term planning, and cross-site comparisons. These KPIs provide a clear view of what’s working, what needs attention, and where resources should be focused.

The main KPIs tracked include:

• Technical downtime percentage – Measures how much production time is lost due to technical failures.

• Planned vs. unplanned maintenance ratio – Highlights the balance between proactive and reactive work, aiming to reduce unexpected breakdowns.

• Mean Time to Repair (MTTR) and Mean Time Between Repairs (MTBR) – Indicators of equipment reliability and repair efficiency.

• Maintenance cost breakdowns – Tracks spending on preventive versus reactive maintenance, helping to optimise budget allocation.

• Internal vs. external labour usage – Provides insight into workforce efficiency and the use of third-party contractors.

• Schedule adherence – Measures how closely planned maintenance activities follow the schedule, supporting operational discipline.

By closely monitoring these metrics, Van Geloven can benchmark progress across its various sites, identify best practices, and continuously refine its maintenance strategy.

“More than just numbers, these KPIs help create a culture of accountability and continuous improvement—ensuring maintenance is not just a support function, but a key driver of operational performance,” Castelijn says.

A Model for Maintenance Transformation

Today, Van Geloven’s facilities are approaching world-class standards in maintenance. In some locations, technical downtime has dropped from 20% to below 4%, thanks to a shift toward predictive maintenance, root cause analysis, and empowered teams.

Maintenance crews are now developing and leading their own improvement initiatives, while AI has become an integral part of the daily workflow—assisting with planning, decision-making, and efficiency.

“This kind of transformation doesn’t happen in six months,” says Castelijn. “It takes three to four years, strong leadership, and a deep commitment to collecting and maintaining quality data,” he advises other maintenance managers.

His most significant piece of advice?

“If your CMMS data is poor, AI won’t help you. Garbage in, garbage out.”

In today’s competitive global market, where agility and efficiency are non-negotiable, predictive, data-driven maintenance is no longer a nice-to-have—it’s a necessity.

“Companies that don’t embrace this direction will fall behind,” Castelijn warns.

“Predictive maintenance isn’t just smart—it’s survival.”

Van Geloven’s journey proves that with the right vision, sustained commitment, and a supportive culture, even traditional manufacturing operations can lead the way in digital transformation.

Key Takeaways for Maintenance Leaders

• Downtime Reduction: Cut from 20% to under 4% in two years through focused root cause analysis.

• Agile Teams: 60% internal staff, 40% freelancers to maintain flexibility.

• AI support: AI integrated with the CMMS can save time and assist less experienced staff in making better decisions. Currently, at Van Geloven, AI is still in the testing and deployment phase only at the Tilburg site, but it is expected to be rolled out more broadly across other locations in the upcoming two years.

• KPI Discipline: Focus on MTTR (Mean Time To Repair), MTBF (Mean Time Between Failures), technical downtime %, planned vs. unplanned ratio, and schedule adherence.

• Change Timeline: True digital and cultural transformation takes 3–4 years of consistent effort.

A key step in Van Geloven’s digital transformation has been the development and internal hosting of Power BI dashboards, which are now fully integrated with both the company’s Computerised Maintenance Management System (CMMS) and

Overall Equipment Effectiveness (OEE) system. This integration enables real-time tracking and analysis of maintenance activities alongside production losses, allowing for more informed decision-making at all levels.

Text: NINA GARLO-MELKAS Photos VAN GELOVEN