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Warranty 4.0 Project - Big Data Analytics applied to the automotive warranty sector

Giulia Ioannone

Warranty 4.0 Project - Big Data Analytics applied to the automotive warranty sector.

Rel. Marco Cantamessa. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Gestionale (Engineering And Management), 2022


Nowadays the automotive industry is challenged in several areas such as, advancements in technologies, change in customer behavior, strict regulation and narrow competition. On one hand the focus is to be competitive on the market by embracing the trend of “EACSY” (Electrified, Autonomous, Connected, Shared and Yearly updated) vehicles. On the other hand, processes optimization and reengineering should not be neglected. Among them, the warranty management is a key asset for automakers to decrease wastes and improve the efficiency of the related activities of the value chain, such as design, production, marketing and after sales. In this context fits the “Warranty 4.0”, strategic project of a multinational vehicle manufacturing company, realized by the consulting company hosting the internship and thesis work. The Warranty 4.0 project is designed to improve the warranty management process through automation and engineering of the processes, leveraging on Big data Analytics technologies. To contextualize the development of the Warranty 4.0, the thesis offers an overview of the state-of-art of Big Data Analytics and examines their potential applications in light of the worldwide automotive industry trends. After this, the paper focus on the Warranty 4.0 project comparing the automaker warranty management process before and after the project start, with particular attention to the improvements made and results achieved during the internship period.

Relators: Marco Cantamessa
Academic year: 2021/22
Publication type: Electronic
Number of Pages: 144
Additional Information: Tesi secretata. Fulltext non presente
Corso di laurea: Corso di laurea magistrale in Ingegneria Gestionale (Engineering And Management)
Classe di laurea: New organization > Master science > LM-31 - MANAGEMENT ENGINEERING
Aziende collaboratrici: Accenture SpA
URI: http://webthesis.biblio.polito.it/id/eprint/22531
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