INFOS – Digital Textile Passport
INFOS participated as a partner and co-author of the scientific article “Machine Learning-Based Data Quality Assessment for the Textile and Clothing Digital Product Passport”, recently published in the journal Applied Sciences (MDPI).
The study focuses on the application of Machine Learning techniques to ensure the quality and reliability of data in the context of the Digital Product Passport (DPP) in the textile industry.
INFOS's contribution
Marcelo Alves, head of INFOS's innovation area, is one of the authors of the article and made a decisive contribution to:
- Defining the technical requirements for integration between systems;
- Developing data validation APIs applicable to the DPP ecosystem;
- Adapting Machine Learning models to the context of the textile industry;
- Analysing the performance metrics of the models applied to anomaly detection.
This contribution reinforces INFOS's role as an agent of technological innovation in the textile sector, not only as a solutions provider but also as an active partner in applied research.
The importance of the study
The article responds to a central challenge in the industry: ensuring that the data used for traceability, sustainability and transparency has quality and consistency.
The DPP requires the integration of data from multiple actors, and the application of Machine Learning techniques makes it possible to detect and correct errors before they are incorporated into critical systems.
With this advance, the industry can gain efficiency, credibility and confidence in the process of digitalisation and transition to the circular economy.
Conclusion
INFOS's participation in this scientific work reflects its commitment to innovation in the textile sector and its ability to collaborate on high-impact research projects.