The Client

An international industrial company manufacturing components for the automotive and heavy industry sectors, with several production plants across Europe. The organization was developing Industry 4.0 initiatives and collecting data from production lines (IoT, sensors, MES, ERP), but the data was not fully utilized for predictive analysis or process optimization.

The problem

Operational data was being collected, but analysis was mainly retrospective. Maintenance teams reacted to failures instead of preventing them, and production planning relied on static assumptions.

The key challenges

• lack of ML expertise to build predictive models,
• no dedicated person responsible for transforming raw data into production-ready models,
• limited integration of data from multiple systems (MES, SCADA, ERP),
• pressure to reduce downtime and maintenance costs.

The company was not ready to build a full data science team but needed an expert capable of delivering measurable results quickly.

The solution

Within a staff augmentation model, we provided a Senior Machine Learning Engineer with experience in industrial projects and predictive maintenance.

The expert joined the client’s IT/OT team and was responsible for:

• analyzing and preparing data from sensors and production systems,
• building predictive models to detect anomalies and predict machine failures,
• implementing data pipelines within the client’s environment (Python, SQL),
• deploying the model in a production environment with performance monitoring,
• collaborating with the maintenance team to interpret model outputs.

The project was delivered iteratively – starting with a proof of concept on a selected production line and then scaling the solution to additional plants.

The conclusions

• 25% reduction in unplanned downtime on the pilot production line.
• Faster response to potential failures through an early warning system.
• Improved maintenance planning based on data rather than fixed schedules.
• Establishment of a foundation for further AI initiatives in production optimization.

The client achieved fast and measurable business results without building a separate data science department and reduced operational risk in critical production processes.