60 MILLIONS FROM HORIZON 2020

In early 2019, Sintef, Cody and ten other European companies applied for support from the EU's Horizon 2020 program to develop the future solution for "fast moving consumer goods" (FMCG). The EU has now approved the application and supports the project with almost 6 million euros. FMCG means that the consumer can tailor a product to their preferences via a digital platform, and that the product is then produced automatically in a machine / factory. The project addresses the entire value chain from the customer's wishes and requirements for the product, generation and evaluation of recipes, production, delivery and finally feedback from the customer.

The production must be planned and tested digitally based on, among other things, the customer's requirements, legal requirements and production possibilities and limitations, as well as any chemical possibilities and limitations. A digital twin of the production process will be created and machine learning will be used to improve the process on the basis of feedback from the production and from the customer. Transparent data flow must be ensured with blockchain technology.

TITAAN

Cody has been asked to be a partner in the project based on our expertise as a machine builder and our data management software TITAAN. Our task is to design two production machines that will demonstrate the process; one for the production of liquid detergent and one for the production of detergent in powder form. TITAAN will handle the interface between the machines and the rest of the system.

PARTNERS

• SINTEF (Norway) Coordinator

• CODY AS (Norway)

• Centre for Process Innovation Limited (UK)

• Procter & Gamble Company (UK)

• VTT (Finland)

• Technology Centre of Metal-mechanical and Transport (Spain)

• IRIS Technology Group (Spain)

• Digital Catapult Centre (UK)

• PICOM (France)

• RDIUP (France)

• Stelar Security Technology Law Research UG (Germany)

• Effective Decisions (Romania)

• Dynamic & Security Computations (Spain)

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 870148.