MDA - Machine data aquisition

MDA enables a continuous data flow in production.

Key to optimal data integration

Machine data aquisition (MDA) provides real-time metrics for proactive decision-making, automated reporting to improve operational transparency, and historical data analysis for continuous optimization. These measures increase efficiency and productivity in production, making it a vital lever in your manufacturing operations.

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Was versteht man eigentlich unter Maschinen-datenerfassung?

What is Machine data aquisition (MDA)?

MDA stands for the ideal bidirectional flow of information between machines, equipment, and the MES (Manufacturing Execution System), ensuring everyone—from machine operators to production managers and management—stays up to date.

In machine data collection, the variety of technical connections/interfaces is essential: OPC UA/DA, MT-Connect, web interfaces, potential-free contacts, and more. Only with these can key metrics such as piece counts, malfunctions, alarms, QA data, and process values be automatically collected, visualized, and monitored in real time.

How is machine data collected?

Overview of the Benefits of Machine data aquisition

Nutzen der Maschinendaten-erfassung im Überblick

Automatic data collection saves time while improving data quality

Online monitoring: quick response times to disruptions and process deviations

Automatic dispatch of messages and alarm notifications

Customizable web dashboards and comprehensive reporting

Online data for controlling and post-calculation

Reliable bottleneck analysis based on current downtime data

KPI generator for your production key performance indicators (KPI)

Wo findet Maschinendaten-erfassung Anwendung?

Where is machine data collection used?

Our MDA system continuously collects data from various machines and systems in the manufacturing environment. Machine Data Collection (MDA) is applied in discrete manufacturing sectors such as the automotive industry, aerospace, pharmaceuticals, food and beverage, electronics manufacturing, metal processing, and textile and apparel industries. It is used to monitor, optimize, and ensure the sustainable use of production processes.