Data‑driven production optimisation at Rockfon

In many industrial production environments, performance losses are not always visible. In industrial automation, these losses often remain hidden when production data is incomplete or subjective.

Output fluctuates between shifts, downtime appears without a clear explanation, and improvement initiatives often rely on experience, assumptions or manual reporting rather than objective data.

Rockfon, part of the ROCKWOOL Group, faced exactly this challenge at its Wijnegem production site. One of its production lines showed highly inconsistent output: some shifts ran smoothly, while others were affected by frequent and seemingly unexplained stoppages. Manual downtime registration provided only partial and subjective insights, making it difficult to identify the real root causes.

To move from assumptions to facts, Rockfon needed objective, reliable production data.

Across industrial environments, performance losses are often underestimated when production behaviour is not measured objectively.

A large share of efficiency losses is caused by short stops, speed variations and minor disturbances that remain invisible in manual reporting. As a result, improvement actions are often based on assumptions rather than facts.

Establishing objective, real‑time production insight is therefore a critical first step towards stabilising output and enabling sustainable performance improvement, as illustrated by Rockfon’s case.

*The image above is representative of a previous automated packaging line project developed by ACE Belgium for Rockfon. The OEE optimisation described in this article was implemented on a different, relay‑controlled line.*

The challenge: inconsistent output and invisible downtime

Rockfon produces metal profiles for acoustic ceiling systems using roll forming technology. Large coils of strip material are shaped through guide rollers, cut to length and then transferred to the packaging process.

Although the line was mechanically capable of stable output, its performance varied significantly between shifts. The reasons remained unclear:

downtime events were inconsistently reported
data was subjective and incomplete
some production disturbances were too short or too subtle to be noticed
improvement actions were based largely on experience rather than measurement

In addition, Rockfon faced a risk common to many manufacturers: the gradual loss of production knowledge as experienced operators approach retirement. Operators with decades of experience intuitively knew when lubrication was needed or when a component was likely to cause trouble, but this knowledge was hard to document, transfer or use structurally.

Rockfon’s objective was clear:

to transform intuition and experience into objective, measurable production insight.

We desperately needed objective data to identify the cause of the outage

Niels Hermans

Process Engineer, Rockfon

Turning production behaviour into data

ACE project team at Rockfon, pictured in front of another production line for Rockfon.

To address this challenge, Rockfon turned to ACE Belgium. Building on a long‑standing collaboration, including earlier full automation projects for Rockfon’s packaging lines, ACE proposed a pragmatic, data‑driven approach focused on low‑threshold digitalisation.

Instead of introducing a complex MES solution or major mechanical changes, the goal was to first make the existing production behaviour measurable.

Together with Siemens, ACE designed a solution based on an Industrial Edge Controller. Even though the production line was relay‑controlled and did not originally include a PLC, objective data capture was still possible.

A PLC was added purely as a data collector. It gathered electrical signals from sensors on the machine and transmitted them to the Industrial Edge Controller, an industrial PC with its own operating system, capable of running Siemens and third‑party applications.

Using Siemens’ Industrial Edge platform, Rockfon was able to log and visualise objective production data in real time, creating immediate insight into machine behaviour and performance.

*The image shows the ACE project team at Rockfon, pictured in front of another production line from a previous packaging automation project.*

With the Industrial Edge Controller, we can capture data even on a production line without an existing PLC

Koen Aerts

Senior Project Leader, ACE Belgium

Fast results through objective insight

The impact of this approach was visible almost immediately.

Within just one day of implementation:

Overall Equipment Effectiveness (OEE) increased from 60% to 80%
Production output improved by 33%
The main causes of downtime became transparent and actionable

One of the first insights clearly illustrated the value of data. When the cutting dies moved forward, they had to activate twelve small switches simultaneously. Two of these switches were misaligned, causing unnecessary line stops. Without objective data, identifying this issue would have been extremely difficult.

Thanks to real‑time data, Rockfon could pinpoint the problem quickly, eliminate the unnecessary downtime and stabilise production output.

This data‑driven application was also featured in the Siemens Best Application Contest 2025–2026, highlighting its real‑world industrial relevance.

The line’s output has increased dramatically, and this is just the beginning

Bart Verlinden

Head of Engineering, Rockfon

Supporting operators, not monitoring them

Beyond performance improvements, the data‑driven approach also supported operators on the shop floor. With better visibility into what was happening on the line, operators were no longer constantly reacting to downtime.

Importantly, the system was not perceived as a monitoring tool, but as practical support, helping reduce frustration and enabling faster, more effective interventions when issues occurred.

By making production behaviour visible, Rockfon also took an important step towards preserving operational knowledge by translating years of experience into data that can be shared and reused.

A step towards the digital factory

This pilot project builds further on Rockfon’s broader automation journey, which previously included the full automation of packaging lines developed and installed by ACE Belgium.

With reliable production data now available, Rockfon is laying the foundation for further improvements:

predictive maintenance through additional sensor data
standardised reporting and comparison across production lines
more accurate cost analysis and budgeting
improved planning and resource utilisation

What started as a focused data pilot has become a key step towards a future‑ready, digitally supported production environment.

Engineering insight instead of assumptions

In many manufacturing environments, performance issues are addressed through assumptions, experience or isolated observations. This approach often limits improvement to symptoms rather than root causes.

The Rockfon case illustrates how objective production data can shift this dynamic. By making production behaviour measurable, manufacturers gain a structured basis for diagnosing issues, stabilising output and improving efficiency, even on existing, traditional production lines within industrial automation.

Data‑driven engineering enables better decisions, more predictable operations and a scalable foundation for OEE optimisation and long‑term manufacturing efficiency.

Objective data creates clarity.

Clarity supports better desicion-making.

Better decisions enable sustainable performance.

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