HOMAG faced recurring unplanned downtime driven by reactive maintenance processes and limited visibility into early machine failure signals. We partnered with them to create a machine learning–based predictive maintenance proof of concept that analysed machine logs and aligned anomaly detection with CRM service data collected over a two‑year period.

The initiative validated early-stage detection performance (~53%), establishing a foundation for proactive maintenance, improved reliability insights, and more targeted service interventions, supporting improvements in Mean Time Between Failures (MTBF).

The proof-of-concept demonstrated measurable predictive capabilities, detecting 9 of 17 known failure-related events, corresponding to a detection rate of approximately 53%. This falls within a typical range observed in early-stage predictive maintenance initiatives (40–60%), confirming the approach's viability.

Using existing machine log data and CRM service ticket information as validation sources, the solution supports earlier identification of potential machine failures, supporting improvements in MTBF and reducing reliance on reactive service interventions. It also introduces a structured framework to evaluate detection performance using true positives, false positives, and missed events.

HOMAG’s maintenance processes were largely reactive, relying on service tickets raised after machine failures had already occurred. This limited the ability to anticipate issues, leading to unplanned downtime, increased service costs, and operational disruption for end customers.

Although machine data was available, it was not systematically used to detect early warning signals or correlated with service records to generate actionable insights. As a result, maintenance teams lacked the visibility needed to prioritise interventions effectively or prevent failures before they impacted operations.

With proven AI capabilities and a long-standing partnership, we teamed with HOMAG to develop a focused proof of concept that would validate whether existing machine and service data could support predictive maintenance. The approach combined data engineering and machine learning with operational validation, ensuring that detected anomalies were directly aligned with real maintenance events and business relevance.

The initiative introduced a machine learning–based approach to detect anomalies in machine behaviour ahead of potential failures, shifting maintenance from reactive response to early intervention.

Machine log and state data were processed and structured into a format suitable for analysis, enabling the identification of patterns associated with abnormal behaviour. These signals were then correlated with CRM service tickets to validate whether detected anomalies corresponded to real-world maintenance events.

This validation framework ensured that the solution remained grounded in operational reality. By clearly defining true positives, false positives, and missed events within predictive intervals, the team established a transparent method for measuring performance and refining the model.

As a result, maintenance teams gain earlier visibility into potential issues, allowing for more targeted and prioritised interventions. This reduces reliance on emergency call-outs and supports more efficient allocation of service resources. At the same time, the alignment between machine data and CRM systems strengthens the connection between technical insights and business processes.

Importantly, as a proof of concept, the solution demonstrates what is achievable while also defining clear boundaries. It validates detection capability but does not yet represent a fully optimised or production‑scale predictive maintenance implementation. Instead, it provides a tested foundation and proves the potential for further iteration and improvement.

The solution is currently a proof-of-concept with validated detection performance that supports further development and scaling. The results demonstrate that predictive maintenance based on existing machine and service data is feasible and can deliver operational value.

The approach is repeatable across other industrial equipment manufacturers and operators. By applying similar data preprocessing pipelines, anomaly detection models, and CRM-based validation frameworks, organisations can extend predictive maintenance capabilities across different machine fleets.

Next steps include refining detection accuracy, reducing false positives, and transitioning towards a production-ready system with higher detection rates, often observed in the 70–90% range in mature implementations.

This initiative marks a clear shift from reactive maintenance towards data-driven, proactive reliability in industrial machinery operations. By validating early-detection capabilities, it establishes a practical path to reduce downtime and improve service efficiency.

If you are considering how predictive maintenance can be applied to your equipment or operations, send us a message and let’s see how this approach can work for your plants.