Data and Error Analysis

Data and error analysis in the MES enables the collection, evaluation and use of production data to identify and correct errors and optimise processes. Real-time analysis, pattern recognition and statistical methods can be used to locate error sources and implement targeted measures to increase efficiency and product quality.

Opportunities and Challenges in Production

Data and error analysis helps optimise production processes and identify error sources early on. One challenge lies in processing large amounts of data from various sources to gain meaningful insights. Real-time analysis, AI-supported methods and statistical procedures are essential for detecting anomalies and deriving appropriate measures. Furthermore, integration into existing systems requires seamless networking of machines, sensors and IT structures. The goal is to increase quality, minimise waste and maximise efficiency.

Benefits of Systematic Data and Error Analysis

Systematic data and error analysis improves production through more precise error detection, reduced downtime and higher quality. Real-time analytics enable rapid responses to deviations, while AI-supported processes support long-term optimisation. Companies benefit from lower scrap rates, more efficient resource utilisation and improved overall equipment effectiveness (OEE). Furthermore, transparent data analysis facilitates continuous process improvement and contributes to predictive maintenance, thus minimising unplanned downtime.

Functions and Features

Early error detection
Root cause analysis
AI-supported analytics
Dashboards and reports
Downtime and scrap analysis
OEE per product/order
Processing time analysis
Setup time evaluations
Machine status overview
Correlation analysis
Seamless MES integration

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“Only those who understand their data can minimise errors and improve processes in a targeted manner.”

Martin Stimpfl CEO

Efficiency in Production as a Driver of Sustainability

Success Story

Efficiency in Production as a Driver of Sustainability

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