Miriana Trevisiol
Design and Implementation of a Generative AI-Driven Data-to-Cost System: An Aerospace Case Study.
Rel. Alessandro Simeone, Yuchen Fan. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Gestionale (Engineering And Management), 2026
Abstract
Nowadays, the manufacturing industry, specifically Small and Medium Enterprises (SMEs), still faces significant challenges associated with data management within shop-floor environments. Through a preliminary state-of-the-art literature review, it was possible to understand current technologies, identify the discrepancy between theoretical models and practical industry scenarios, and establish the methodological foundation of this study. In the current transition toward Industry 5.0, a core challenge for numerous enterprises remains the systematic acquisition and management of real-time production data. This data infrastructure allows enterprises to identify the exact drivers of profitability and inefficiency. To facilitate data collection and ensure adaptation to real-world operational constraints, this research is based on a real case study conducted at M.R.M S.r.l, an Italian manufacturing company that operates in Aerospace and Automotive sectors, with prototype or serial sheet metal parts produced using cold forming processes.
The thesis focuses exclusively on data taken from the aerospace sector, though the methodology could also be applied to the automotive industry
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