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Every day, factories generate large amounts of production and machine data. Yet in many cases, this data remains unused. The effort required to analyze the data is virtually insurmountable, especially for small and medium-sized enterprises. This is because it requires specialized staff to compile the data from many different sources, painstakingly process it manually, and finally present it in charts and diagrams.<\/p>\n
\u201cAccessing and analyzing production and machine data must become easier,\u201d says Matthias Schneider of the IT Architectures for Production research team at the Fraunhofer Institute for Manufacturing Engineering and Automation IPA. This is precisely where the research project \u201cVisualization of Production Data as a Generative Interactive Extension\u201d (ViPGeniE) comes in. In collaboration with the three Baden-W\u00fcrttemberg-based companies ads-tec Industrial IT GmbH, Data Coffee GmbH, and Ulrich GmbH & Co. KG, the Fraunhofer IPA aims to make production and machine data more easily accessible through generative artificial intelligence.<\/p>\n
Just as many people today interact with generative artificial intelligence in their personal lives, employees will in the future be able to use chat to request specific data\u2014such as certain machine statuses or temperature trends. In response, they will receive not only text but also relevant charts that the AI generates specifically for its human interlocutor.<\/p>\n
Building on this, employees can ask the AI further questions. In a sense, a dialogue emerges between human and machine, during which correlations or the causes of errors become clear. This is made possible by the interplay of several technologies: Production and sensor data are continuously collected and described in a so-called digital twin\u2014a virtual representation of the production process. This provides the necessary contextual information so that the AI can interpret the data correctly.<\/p>\n
\u201cThis significantly lowers the barrier to using production and machine data,\u201d says Schneider, \u201cbecause specialized knowledge is no longer required to access and evaluate the data.\u201d It also eliminates the wait times involved in, for example, adapting existing static visualizations or creating new ones. Even if a company has its own data analysis specialists, they are usually not immediately available and can only respond to requests after a delay. Small and medium-sized enterprises across all industries would benefit particularly from ViPGeniE. This is because they usually cannot afford to employ data analysts at all. <\/p>\n
The researchers led by Schneider at Fraunhofer IPA are taking on several key tasks in the ViPGeniE project: They are developing the system architecture\u2014a flexible environment in which various language models can be used equally\u2014and digital twins that structure the data, thereby making analysis by generative AI possible in the first place.<\/p>\n
The project partners plan to present an initial demonstrator\u2014which shows how generative AI can visualize complex production and machine data from a wide variety of sources in a comprehensible way within seconds\u2014in the second quarter of 2027.<\/p>\n
Name:<\/b> Visualization of Production Data as a Generative Interactive Extension (ViPGeniE)
Partners: <\/b>ads-tec Industrial IT GmbH, Data Coffee GmbH, Fraunhofer IPA, Ulrich GmbH & Co. KG
Duration: <\/b>March 1, 2026 \u2013 February 29, 2028
Funding amount:<\/b> 647,624 euros from the Invest BW innovation funding program
Funding agency:<\/b> Baden-W\u00fcrttemberg Ministry of Economic Affairs, Crafts, and Tourism<\/p>\n
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