BeverGreen

Short description – BeverGreen significantly improves the energy and resource efficiency of the beverage industry through networking and the cross-value-chain use of digital tools. To this end, green digital product and process twins are created to enable a carbon-neutral transformation—from the production process all the way through to reusable packaging logistics. The development and use of digital twins (DT) and machine learning (ML) to increase transparency and reduce emissions are demonstrated through exemplary application scenarios in the beverage industry. Building on the Da-Pro research project, the consortium—comprising family- and owner-managed companies in the German brewing industry, as well as solution and research partners in the fields of ML and DC—has been expanded to include the user Kontor N, two startups, and the research partners Technical University of Munich (TUM) and University of Applied Sciences Zwickau.

Objectives of all project partners

Project Duration:
May 1, 2023 to Apr 30, 2026

Project Management:
Prof. Dr. Christoph Laroque (WHZ)

Partner:
Bitburger Braugruppe GmbH, Augustiner-Bräu Wagner KG, Kontor N GmbH & Co. KG, TU München, Spicetech GmbH, RapidMiner GmbH, RIF e. V., PRECOGIT GmbH, daibe UG

Rising energy and raw material costs, limited resources, climate protection initiatives, and new resilience requirements for supply chains call for a paradigm shift—from individual manufacturing facilities to entire value-added networks. Digital technologies offer new opportunities to analyze energy and resource consumption as well as material cycles and to derive data-driven improvements. The BeverGreen research project builds on this foundation.

To this end, an assistance system is being developed to map existing data structures to application-specific ontologies that contain energy-related information and form the basis for green digital twins. It also serves as the foundation for integrating additional internal and external datasets (e.g., life-cycle assessment databases), such as those related to CO2 equivalents (CO2e). The core objective is the development of green digital twins in combination with machine learning (ML), which serve as beacons by identifying and implementing energy and resource savings in exemplary application scenarios, thereby contributing to the sustainable transformation of the beverage industry. The focus is on closed-loop production systems and circular value chains in the beverage and brewing industries.

The WHZ is collaborating with TUM to develop a simulation model of a cross-sector value-added network in the beverage and brewing industry as a tool for testing and evaluating solutions for the emerging digital twins (DT) and services. On the one hand, the simulation is intended to enable a comprehensive evaluation of processes and systems based on (historical) real-world data from the partners; on the other hand, it is intended to provide a tool for targeted data generation and analysis. Such a virtual instance of the beverage and brewing industry’s value chain network is also intended to serve as a tool for data generation and knowledge discovery, providing, for example, data to determine the product carbon footprint along the supply chain.

The use of simulation as a virtual test system proves beneficial because various scenarios can be tested in a risk-free, digital environment. Building on the targeted data generation and knowledge discovery in simulation data, the findings are incorporated into the emerging ontologies.

The researchers at WHZ are also contributing their expertise in the field of reverse simulation. The resulting simulation model is also intended to be used via the cross-sector value-added network to provide an IT service for coordinating the circular value chain. This ties in with a submodel of the resulting simulation model by examining the material flow based on the final process stages and targets of the value chain. Backward simulation specifies the reversal of the process logic—including implemented control and priority rules—and the backward execution. The IT service is intended to contribute to demand-driven circular and filling planning based on the green DZ and to unlock the underlying potential for improvement.

Prof. Dr. Christoph Laroque
Professor of Business Analytics
christoph.laroque[at]whz.de

Jenny Rüffer
Research Assistant
jenny.rueffer[at]whz.de