Data Processing

Introduction

Data Processing is the collection, transformation, and analysis of information to support decisions, control processes, and improve efficiency. In manufacturing, it forms the backbone of Industry 4.0, connecting machines, sensors, and software systems such as MES, BDE, and OEE.

Modern factories rely on real-time data processing to identify inefficiencies, prevent downtime, and optimize resource use.

Definition and Phases

Data processing typically follows four key steps:

1. Data collection – Capturing machine, process, and operator data via sensors, MES, or BDE systems.
2. Data preparation – Cleaning, validating, and standardizing information for analysis.
3. Data analysis – Identifying trends and anomalies using analytics or OEE tools.
4. Action and visualization – Displaying results in dashboards and triggering automated workflows.

Role in Smart Manufacturing

Data processing enables connected production environments to operate efficiently and predictively.
It supports:

• Transparency across all process levels
• Efficiency improvement through OEE-based performance tracking
• Quality control via real-time data validation
• Predictive maintenance based on sensor analytics
• Sustainability through precise energy and material monitoring

Integration with MES, BDE, and OEE

• MES collects and contextualizes production data in real time.
• BDE provides detailed operational inputs from machines and workers.
• OEE aggregates data into key performance metrics for productivity analysis.

This integration transforms raw machine data into actionable insights that drive continuous improvement.

Benefits

• Accurate decision-making based on live data

• Faster response to deviations

• Reliable, standardized data quality

• Improved productivity through targeted optimization

• Foundation for AI, simulation, and digital twins

Example

A manufacturing company used MES-based data processing to analyze unplanned stops. By correlating BDE machine logs with OEE losses, it identified setup issues as the main cause. Adjustments in scheduling reduced downtime by 22 % and raised OEE by 12 points.

Conclusion

Data Processing turns raw production data into business value. By combining MES, BDE, and OEE, manufacturers achieve transparency, efficiency, and continuous improvement — the pillars of a truly smart factory.