{"id":11085,"date":"2026-02-05T16:20:32","date_gmt":"2026-02-05T15:20:32","guid":{"rendered":"https:\/\/eurocc.nscc.sk\/?p=11085"},"modified":"2026-02-05T17:58:27","modified_gmt":"2026-02-05T16:58:27","slug":"ked-vyrobna-linka-vie-co-sa-stane-o-10-minut","status":"publish","type":"post","link":"https:\/\/eurocc.nscc.sk\/en\/ked-vyrobna-linka-vie-co-sa-stane-o-10-minut\/","title":{"rendered":"When a production line knows what will happen in 10 minutes<\/strong>"},"content":{"rendered":"
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Success story: When a production line knows what will happen in 10 minutes<\/strong><\/p>\n\n\n\n

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Every disruption on a production line creates stress. Machines stop, people wait, production slows down, and decisions must be made under pressure. In the food industry\u2014especially in the production of filled pasta products, where the process follows a strictly sequential set of technological steps\u2014one unexpected issue at the end of the line can bring the entire production flow to a halt.<\/p>\n\n\n\n

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But what if the production line could warn in advance that a problem will occur in a few minutes? Or help decide, already during a shift, whether it still makes sense to plan packaging later the same day? These were exactly the questions that stood at the beginning of a research collaboration that brought together industrial data, artificial intelligence, and supercomputing power.<\/p>\n\n\n\n

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The research was carried out by an international team of experts in artificial intelligence and industrial analytics from both academia and the private sector. The project involved the company Prounion<\/strong> a.s.<\/strong> in cooperation with Constantine the Philosopher University in Nitra,<\/strong> as well as additional academic partners from the Czech Republic and Hungary.<\/p>\n\n\n\n

Challenge<\/strong><\/p>\n\n\n\n

Modern production lines generate enormous volumes of data\u2014from machine states and operating speeds to temperatures and production counts. Despite this, key operational decisions are still often made based on experience and intuition.<\/p>\n\n\n\n

The researchers focused on a real production line for filled pasta products, where the product passes through a fixed sequence of machines\u2014from raw material preparation, through forming and filling, to thermal processing and packaging. They identified two decisions with a critical impact on production efficiency:<\/p>\n\n\n\n