Van Geloven's transformative shift to predictive maintenance drives efficiency and addresses labour shortages

Van Geloven, a McCain company, has successfully transitioned from reactive to predictive maintenance through a comprehensive digital overhaul, resulting in significant operational improvements and a strategic response to industry workforce challenges.

Van Geloven, a McCain company in the Netherlands, exemplifies a successful transition from reactive to predictive maintenance through a comprehensive, data-driven digital transformation that has fundamentally reshaped its ...

operations and culture across multiple sites in the Netherlands and Belgium. This shift, led by Nico Castelijn, Director of Engineering & Maintenance Appetisers CE, demonstrates how embracing new technologies, clear role definitions, and empowered teams can dramatically reduce downtime while addressing industry-wide challenges such as a skilled labour shortage.

Upon joining Van Geloven five years ago, Castelijn found maintenance teams operating largely as “firefighters,” whose primary function was reactive repair rather than proactive prevention. These teams, while effective in fixing issues quickly, lacked a mindset geared toward preventing breakdowns before they occurred—a common challenge in manufacturing operations. Drawing on his extensive experience across various industries, including the pharmaceutical sector where preventive maintenance is critical, Castelijn initiated a long-term transformation focused on predictive maintenance, beginning with structural and cultural foundations.

Critical to this transformation was the clear definition and standardisation of maintenance roles—maintenance managers, planners, and reliability engineers—across all sites. This created alignment and paved the way for a culture shift from firefighting to foresight. Castelijn emphasises that technology alone could not drive change; building trust, education, and fostering internal champions who modelled new behaviours were equally vital. The company actively promotes peer learning across its sites through regular cross-site meetings and hands-on visits, encouraging sharing of insights and collaborative problem-solving. This approach helped embed accountability and pride in owning maintenance data and outcomes.

Central to Van Geloven’s operational metamorphosis has been the integration of Power BI dashboards into daily routines. Every morning, maintenance and production teams review real-time data from the previous 24 hours—including equipment failures, technical downtime, and maintenance performance—creating a transparent and collaborative environment. This routine fosters root cause analysis and breaks down siloed communication, replacing reactive blame culture with proactive learning. Importantly, the company enforces unified definitions of downtime to ensure consistent cross-functional understanding and accountability.

Furthermore, Van Geloven has integrated these Power BI dashboards with its Computerised Maintenance Management System (CMMS) and Overall Equipment Effectiveness (OEE) system to provide a holistic view of maintenance activities and production losses. This integration enables real-time comparison, informed decision-making, and optimisation of resources to reduce unplanned stops. To support technicians on the ground, mobile tools with barcode and QR scanning facilitate immediate logging of work orders and failure data, minimising administrative burdens and increasing data accuracy.

A pioneering aspect of Van Geloven’s strategy is its gradual incorporation of artificial intelligence. Since 2025, a pilot AI module within the CMMS at its Tilburg site has been assisting technicians by quickly retrieving information from past incidents and manuals. This conversational AI acts as a knowledge assistant rather than a replacement for human expertise, particularly benefiting less experienced or independent workers by reducing time spent searching for information. Encouraged by the pilot’s success, Van Geloven plans to extend AI adoption to other sites over the next two years.

Industry-wide, such AI-driven predictive maintenance is gaining traction as a means to reduce downtime, enhance productivity, and improve decision-making. Reports from organisations like Rockwell Automation highlight how condition-based monitoring and machine learning enable early identification of potential failures, leading to targeted, timely interventions that prevent costly breakdowns. AI tools in maintenance, including conversational interfaces and advanced data analytics, are increasingly viewed as essential supplements to skilled technicians, whose availability is waning.

Addressing this skilled worker shortage is a significant challenge for Van Geloven and many European firms. The Netherlands, similar to other countries, faces a decline in technically trained workers with practical industrial experience. Van Geloven counterbalances this by adopting a flexible workforce model—60% permanent staff combined with 40% freelancers—and recruiting internationally, notably from South Africa, where technicians often possess multiple certifications and strong discipline. Castelijn emphasises that long-term solutions will involve creative uses of AI alongside strategic global hiring to sustain operational excellence amid rising technician scarcity.

Van Geloven’s maintenance transformation is closely measured through a set of key performance indicators (KPIs) that guide continuous improvement. These include technical downtime percentage, planned versus unplanned maintenance ratio, mean time to repair (MTTR), mean time between repairs (MTBR), maintenance cost breakdowns, internal versus external labour usage, and schedule adherence. By benchmarking these KPIs across sites, Van Geloven captures what works best and refines its maintenance strategy accordingly. Castelijn highlights that these KPIs foster a culture of accountability and elevate maintenance from a support function to a central driver of operational performance.

The results speak for themselves: some Van Geloven sites have reduced technical downtime from 20% to below 4%, achieving world-class standards in maintenance efficiency. Teams now lead their own improvement initiatives, supported by AI-enhanced workflows and a deeply ingrained culture of predictive maintenance. Castelijn warns other maintenance managers that such transformation is neither quick nor easy, typically requiring three to four years of persistent effort, strong leadership, and high-quality data collection. “If your CMMS data is poor, AI won’t help you. Garbage in, garbage out,” he advises.

Van Geloven’s journey exemplifies the broader industrial trend towards using AI, digital dashboards, and integrated data systems to transform maintenance from reactive firefighting to predictive forecasting. As demonstrated by similar case studies worldwide—such as Perth County Ingredients’ cloud-based CMMS integration and Furnas’ energy sector AI-driven solutions—embracing these technologies is essential for safeguarding productivity, reducing costs, and maintaining competitive advantage in an era defined by rapid technological change and workforce challenges.

In essence, Van Geloven’s experience confirms that smart, predictive maintenance is not merely a technological upgrade but a strategic imperative underpinned by cultural change, data discipline, and innovative workforce management. This approach ensures companies remain agile and efficient in a challenging global market where operational downtime can no longer be tolerated. As Castelijn concludes, “Predictive maintenance isn’t just smart—it’s survival.”

Source: Noah Wire Services