Data-driven recipe optimisation based on unified digital twins and shared prediction models

The importance of cross-process multivariate data analysis for improving products and processes is continuously increasing. Artificial intelligence and machine learning offer new possibilities to represent complex cause-effect relationships in models and to use them for optimisation. For consistent and scalable usage, unified data structures and representations of products, processes and resources are required in order to be able to use larger data populations as well as deploy these models in different application contexts. The paper presents an approach of shared prediction models for recipe optimisation based on unified digital twins in the beverage industry. For this purpose, a central generic data model was created, which is the basis for unified digital twins and thus the integration of physical and digital entities, as well as the foundation for cross-process data analysis.