Fraunhofer Institute for Production Technology IPT
Fraunhofer Institute for Production Technology IPT

Achieving efficiency through data-driven production

Production processes generate a wealth of data – but only those who collect, analyze, and use this data in a structured manner can derive real added value from it. Whether predictive maintenance, digital twins, AI-supported quality control, or adaptive production control: the key lies in the professional handling of production data. 

However, companies are at very different starting points when it comes to digitized production and have different goals and ideas about it.

Different starting points – individual goals

Many companies are often still at the very beginning of their journey toward digitized production. The first step is to determine what data is already available and what data can still be obtained. Other companies have already taken the first steps and tapped into initial data sources, but they do not know how to combine and process this data in a meaningful way. Even companies that already use AI models or sophisticated data collection and processing can make further optimizations. Examples include ensuring long-term data quality and further developing the infrastructure.

We support companies in developing individual and practical concepts that are innovation-driven and specifically tailored to their specific starting situation.

Already implemented for industry

How we support you

Collect data

Wir analysieren Ihre Maschinendaten und Sensorik, decken Lücken auf und erschließen ungenutzte Potenziale. So gewinnen Sie Klarheit über den digitalen Reifegrad Ihrer Produktion.

Data storage

We link and synchronize different sources using a sophisticated data architecture. This allows you to use production data in a targeted manner and creates the basis for networked analyses and applications.

Orchestrate data

By analyzing existing information, you gain valuable insights, identify potential for optimization, and evaluate possible applications – the first step toward more efficient processes.

Ensuring data quality

We identify and clean up erroneous data and prepare it reliably. This creates a solid basis for AI models and optimization approaches with minimal risk of error.

Process data

We develop clear key figures and visualizations that facilitate decision-making. Together, we derive measures and improve your production control in the long term.

Understanding data

Our seminars strengthen your employees' understanding of data, impart practical know-how, and anchor data literacy in a sustainable manner – for independent optimization and data-driven processes.

Already implemented for industry

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AI-based anomaly detection to support final machine testing

Dokumentation und Reporting

Challenge

A machine manufacturer faced a challenge: malfunctions in fully assembled machines needed to be detected early in the final test. The test program ran through various process scenarios, while employees had to watch out for irregularities. However, due to the large number of sensors and parameters, this monitoring was very complex and heavily dependent on the experience of the employees. As a result, anomalies often went undetected, which later led to product defects at the customer's site.

Our solution

We developed a prototypical approach to data-based quality control. Selected machine sensors provided structured data that was specifically processed and linked to an AI model for anomaly detection. The model analyzed the behavior of the machine during the test run and provided precise information about detected deviations from the normal state – including a timestamp and specific details.

Results

  • Reliable detection of anomalies in the final inspection
  • Relief for employees through automated support
  • Prototypical verification of the detection of deviations based on sensor data

Automated image data analysis boosts quality in electronics manufacturing

Risiken feststellen

Challenge

At one electronics component manufacturer, product quality control was previously done manually through visual inspection. This process was not only time-consuming but also prone to errors—especially when employees were busy and tired.

Our solution

We developed a software demonstrator that automatically analyzes image data and visually marks potential defects. To improve model quality, image data was specifically processed and supplemented with data augmentation. A key requirement was to ensure high data quality to enable robust defect detection.

Results

  • Reliable detection of defective components through automated image analysis
  • Less manual inspection effort, reduced error rate
  • Increased customer satisfaction through better product quality

Product traceability through automated data collection in food production

Erfassung und Sicherung

Challenge

The EU requires complete traceability of food products. For many small and medium-sized enterprises (SMEs), however, this involves high costs and a great deal of digitization effort. Particularly in the case of sensitive or deformable products, many processing steps must be carried out manually by employees. This often means that automation is lacking, which makes complete and error-free documentation difficult. In addition, important production data is often not recorded systematically, which further complicates implementation.

Our solution

A data-based traceability system was designed and implemented as a software demonstrator. Through targeted sensor integration and a structured database in accordance with industry standards, product-specific information was automatically recorded and documented throughout the production process. The solution enabled tracking without additional effort on the part of employees.

Results

  • Automated traceability without additional burden on employees
  • Increased transparency in the production process
  • Reduction of manual errors through standardized data collection

AI-based analysis of cell growth through image-based data evaluation

Risiken verstehen

Challenge

Observing stem cell growth over long periods of time placed high demands on manual evaluation. Changes are often barely visible to the naked eye. In addition, fluctuating lighting conditions made image-based analysis more difficult. Incorrect or delayed detection leads to inefficient use of resources in expensive cell cultures.

Our solution

Using AI-supported image analysis, a software demonstrator was developed that automatically evaluates cell growth. This was based on structured image data that was continuously recorded, processed, and used to improve the model. The goal was to achieve reliable, objective detection of relevant growth phases.

Results

  • Automated detection of cell growth based on visual data
  • Targeted control of the further cultivation of valuable cell lines
  • More efficient use of resources and improved basis for decision-making

Context-sensitive instructions using a Digital Twin

Challenge

Production instructions are often outdated, costly to maintain, and not specifically tailored to the product or situation in question. As a result, new employees require long training periods, which means that errors in application are not uncommon.

Our solution

A prototype digital twin was developed that automatically generates context-sensitive instructions based on structured data—for example, from production, machines, or text modules. These instructions are provided in a situation-specific manner and directly support employees in performing their tasks. 

Results

  • Reduced training time and lower error rate
  • Validated software prototype for context-sensitive assistance
  • More flexible and efficient support for changing tasks

Where are you on the path to digitalized production?

We will meet you exactly where you are and work with you to develop a customized solution that will move your company forward step by step.

Start

Your company currently collects little or no production data. You are still in the early stages and first need to clarify what data is already available and what additional data you should collect.

Build

Your company already collects data from individual sources. However, you still lack a clear idea of how to connect this data and make it usable for further analysis.

Optimize

Your company has established data collection and processing systems or already uses AI models. Now it's time to ensure long-term data quality, continuously expand your infrastructure, and exploit further optimization potential.

 

Ready for data-driven production

Contact Dennis Grunert for a non-binding initial consultation.

 

Send request

Your advantages

  • You collect relevant data in a targeted manner. 
  • You recognize correlations and understand the causes.
  • You make data-based decisions.
  • You benefit