Interacting Traceability Tool for R&D Prototyping. The Pirelli NEXT MIRS Case

This thesis focuses on the Original Equipment (OE) tyres tailored for the Prestige automotive segment within Pirelli's diverse portfolio of cutting-edge products. The research, conducted in collaboration with the R&D and smart manufacturing departments at the Settimo Torinese (TO) plant, aims to improve traceability tools for the innovative robotized tyre manufacturing technology, known as NEXT MIRS. Unlike traditional tyre production, NEXT MIRS employs a non-serialized process, where different tyres from the same specification can undergo distinct phases in parallel. The existing traceability system for this technology is transformed into an interactive solution that integrates the individual traceability of each phase. This involves extracting information from heterogeneous sources, storing the information indices into an SQL Server table and the details inside an Amazon S3 bucket to present everything through a web application dashboard, developed with backend Python and frontend Angular 15, using the required complements for the latter: HTML, CSS and Typescript that provide the system functionality. The interactivity for traceability addresses the dynamic nature of the production plan, allowing real-time adjustments. Moreover, the system facilitates the generation of reports and statistics for various phases of tyre production, even when the entire production flow for all analysed tyres has yet to be completed. This work not only delves into the intricacies of the NEXT MIRS technology but also contributes to the ongoing advancements in traceability, a critical aspect in ensuring precision and adaptability in the production of high-performance tyres for the prestigious automotive segment.