BEST-BEFORE develops an AI-based methodology for predictive analysis based on the degradation pattern for different durability properties over time. This will put the optimum best-before date for sustainable longevity in the clothing industry.

BEST-BEFORE contributes to:

1. reduced energy and material usage and emissions in the clothing development stage
2. reduced effluents, e.g. micro-plastics discharge over washing cycles
3. inputs for eco-design innovation in clothing industry

Expected results and effects

The most important results for BEST-BEFORE are: (i) clothing durability analysis based on risk analysis, (ii) AI-based method for predicting wear resistance and (iii) evaluation of technical feasibility. With this insight, BEST-BEFORE can replace the generic, resource-intensive product development in the clothing industry with the help of a predictive method. In addition, sewage contaminants of e.g. microplastics are reduced in the user phase by optimizing the service life and providing valuable knowledge for eco-design.

Planned approach and implementation

BEST-BEFORE is divided into five activities. Activity 1 determines the scope and the most important requirements in view of the clothing's deficiencies in durability and cause and effect. Activity 2 tests selected durability properties and assess their successive degradation. Activity 3 develops predictive methods based on collected data. Activity 4 evaluates the technical feasibility of being able to set requirements levels for different durability properties. Activity 5 is about overall project management.