CC BY 4.0Mazzuto, GiovanniGiovanniMazzutoCiarapica, Filippo EmanueleFilippo EmanueleCiarapicaHellmich, Jan HendrikJan HendrikHellmichMoya-Ruiz, LauraLauraMoya-RuizFraile Gil, FranciscoFranciscoFraile Gil2024-11-142024-11-142024-08-28https://publica.fraunhofer.de/handle/publica/478930https://doi.org/10.24406/h-47893010.5281/zenodo.1338532410.24406/h-478930Market shifts and changing consumer demands highlight the challenges of traditional mass production techniques. This workshop proposes an Artificial Intelligence-integrated system with a multi-layer Digital Twin for optimized food production, adapting to product characteristics and facilitating real-time monitoring. Traceability services maintain product and process information, complemented by Digital Twin services projecting potential scenarios. Data-driven AI models optimize decision-making, from production layout adjustments to operational enhancements. Throughout it, human oversight is ensured using interactive dashboards, integrating technology with expertise. Implementation involves monitoring variables, managing model complexity, conducting analyses, applying knowledge effectively, interacting with stakeholders, and ensuring interoperability across functionalities.enDigitalizationDigital twinMachine learningOptimization algorithmsTraceabilityProduction line optimization600 Technik, Medizin, angewandte Wissenschaften::620 Ingenieurwissenschaftenpaper