Process Data Analysis with AI

01
Description

Our AI-supported process data analysis helps you identify planned and actual values, weak points in production, bottlenecks, and quality deviations just in time. By creating a digital twin of your components, you can evaluate their condition in real time, automatically detect deviations, and analyze their causes. At the touch of a button, you can identify your production limits, the transitions from good to bad parts, and thus the parameters for 100 percent quality and 0 percent scrap rate.
Our strength lies in breaking down your data silos and providing professional data fusion. We link, analyze, and visualize your camera or sensor data, as well as your data from ERP, MES, and QMS systems and Excel, with your machine data from production.
AI is used to evaluate the data and derive statistical forecasts. The machine learning models run directly on the equipment, analyze data streams in real time, and immediately report anomalies to machine operators and process engineers. You receive clear warnings for preventive measures and can use Predictive Quality to evaluate your product quality in advance. The algorithms detect patterns that indicate future quality deviations, enabling precise, data-based predictions.
Thanks to the modular system, we can meet the different needs and requirements of a company. Our solution is tailor-made and can be customized as desired.
Enquire now without obligation!
02
Your benefits


Thanks to the modular system, we can meet the different needs and requirements of a company. Our solution is tailor-made and can be customized as desired.
AI immediately detects deviations and predicts quality risks at an early stage, allowing you to take proactive action.

Reduction of scrap and rework through early error detection
Deviations are detected before they cause consequential damage. This measurably reduces scrap, rework, and quality costs.

Stable processes through transparent manufacturing boundaries
Clear, data-based process limits make stable parameter windows visible and detect deviations at an early stage.

Higher product quality through data-driven decisions
Decisions are based on reliable process and quality data to ensure reproducible quality.

Elimination of data silos
We consolidate machine, IT, and test data in a central database so that causes and correlations become immediately apparent.

Flexibly adaptable to your systems and processes
The solution can be configured in a modular fashion and rolled out tailored to different plants, lines, and workflows.

Elimination of time-consuming manual evaluations through digital shop floor management
Automated evaluations replace Excel reports and manual analyses, enabling faster responses directly on the shop floor.
03
How it works

Data from relevant internal company systems such as ERP, MES, or QMS is automatically merged with machine parameters, alarms, measurement data, and optical inspection results. This creates a complete quality and process history. With the help of this data, every component that can be identified by unique part numbers or codes can be visually represented as a digital twin.
The visualization of process data in the front end is tailored precisely to your individual needs. With the help of continuous monitoring and the targeted use of various pre-trained machine learning models, including deep learning methods, trends, outliers, and complex patterns in process data are detected at an early stage. Deviations from the normal state are identified in real time, for example for anomaly detection, quality predictions, or process optimization.
Alerts and specific instructions for action are displayed directly on the HMI of your machines or remotely on your PC/tablet, enabling you to respond quickly. Models and threshold values can be continuously optimized based on new data and feedback to continuously improve detection quality.
All data remains within your company and is not stored redundantly. The solutions can be implemented on-premises on an internal company server or in a cloud of your choice.
Would you like to find the causes of errors more quickly and stabilize your production in the long term?
Enquire now without obligation!
04
FAQ

1. To what extent does your solution use AI?
Our AI primarily supports production in pattern recognition and the analysis of anomalies in production data. In addition, we use machine learning for quality predictions and process value forecasts in order to detect deviations at an early stage.
Another focus is process optimization, e.g., to determine stable and optimal process parameters for reproducible quality.
2. What algorithms do you use for anomaly detection and quality predictions?
Depending on the use case, we rely on proven machine learning methods such as Random
Forest, K-Means, and Support Vector Machines (SVM).
The specific algorithms used depend on the data situation, process dynamics, and target scenario (e.g., real-time anomaly detection vs. forecasting).
3. Where is the production data stored?
Depending on your requirements, data can be stored locally in your IT environment (on-premise) or in a cloud infrastructure.
This allows both security and compliance requirements as well as scaling requirements to be clearly mapped.
4. What is the maximum number of machines and systems that can be networked?
Our solution is scalable and tailored to your needs—from pilot projects with a few systems to the networking of entire lines, plants, or heterogeneous system landscapes.
5. Can Excel sources also be evaluated?
Yes, in addition to machine and system data, Excel sources can also be included in the analysis, such as manually maintained quality data, test reports, or shift reports.
6. Do you also supply the appropriate hardware?
Wir sind auf Softwareentwicklung spezialisiert und liefern keine Hardware selbst.
Stattdessen arbeiten wir mit Technologiepartnern zusammen und empfehlen Ihnen
passende Hardware, die auf Ihren Use Case abgestimmt ist.
Auf Wunsch unterstützen wir Sie außerdem bei der Auswahl, der Spezifikation und der Integration in Ihre bestehende Umgebung.

PROCESS
DATA
ANALYSIS

