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Data Analysis and Digitalization for the Industry 4.0

Pierpio Lupo

Data Analysis and Digitalization for the Industry 4.0.

Rel. Giulia Bruno, Emiliano Traini. Politecnico di Torino, Corso di laurea magistrale in Automotive Engineering (Ingegneria Dell'Autoveicolo), 2021


The objective of this thesis is to support the digital transformation in manufacturing operations analysing dataset and using several tools of I4.0, in order to provide improvement actions which, allow company increase flexibility and productivity. TE Connectivity is one of the most important electronic leaders worldwide that designs and manufactures electronic connections for several fields. After the digitalization in Assembly department with some pilot cycles, the company needed to extend this transformation at the most profitable department in terms of costumers, but also that one that needs the highest number of inspections due to the products’ complexity, in order to enhance its performances and evaluate potential market' opportunities. The thesis describes which are the most powerful tools used in the shop floor to manage the Big Data generated by quality process, in order to enable managers, supervisors and operators make more accurate decisions at any level during the production operations. The streamlining and improvement of this Big data has been performed through Hydra, application of manufacturing execution system, which is the main part and the data hub of manufacturing environment, and Microsoft Office. My activities have started following step by step in an entire shift, the most trained operators and quality supervisors, to understand which are the working procedures during which they execute the quality controls at determined frequency and how machines generate so many controls for each factory order. Besides, throughout some focus groups and brainstorming with the Product engineer, Quality manager and Continuous Improvement manager, we have decided to focus on the reorganization of these procedures in a way that the machines connected to the MES were able to create lower number of inspections defined only for the most critical dimensions for each kind of product. Therefore, the reorganization of these procedures, reported in this thesis, will enable to continuous improvement manager to monitor constantly the DF productivity, to quality engineer to manage in a faster way the claims received from the customers, having a trackability of the product inside and outside the factory and going directly to verify the feature reclaimed and the operating conditions at the exact time when the piece comes out from the press.

Relators: Giulia Bruno, Emiliano Traini
Academic year: 2020/21
Publication type: Electronic
Number of Pages: 102
Additional Information: Tesi secretata. Fulltext non presente
Corso di laurea: Corso di laurea magistrale in Automotive Engineering (Ingegneria Dell'Autoveicolo)
Classe di laurea: New organization > Master science > LM-33 - MECHANICAL ENGINEERING
Aziende collaboratrici: TE Connectivity Italia SRL
URI: http://webthesis.biblio.polito.it/id/eprint/17727
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