A core element for Industry 4.0 is the digital twin: a virtual model of a product or process created with data collected by sensors that enables simulations or real-time analyses of the status of production. The use of digital twins seems beneficial in food processing for various reasons. To ensure the supply of food, production processes must allow a high flexibility and adaptivity. Furthermore, product quality is influenced by different quality levels of input materials. Especially in case of seasonal fluctuations of this raw material quality, an adjustment of parameters in the production process is essential. Introducing new products that are related to existing ones is also a challenge in food manufacturing. These introduction processes could be simplified by a digital twin of already existing products.

However, digital twins of food products have additional specific requirements compared to digital twins of material goods. Due to the variability of raw materials, these cannot be based only on the processing steps, but must also take into account the chemical, physical, or (micro)biological properties of food.

We have the vision to create a digital food twin that can be used to track the current state of production at any time. While Industry 4.0 approaches often focus on the analysis of machine data, this project aims at a product-related data analysis (e.g., the effects of pressure exerted by machines). With the help of machine learning (ML) and artificial intelligence methods, the digital twin will be generated from production data and other data sources (e.g., scientific models, process data, or raw material data) to ensure the traceability of the current production and the food status, but also to enable the simulation of the variability of the food in the processing process.