Big data analytics for intra-logistics process planning in the automotive sector

Detalhes bibliográficos
Autor(a) principal: Lourenço, Luís Carlos Guimarães
Data de Publicação: 2020
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/106551
Resumo: The manufacturing sector is facing an important stage with Industry 4.0. This paradigm shift impulses companies to embrace innovative technologies and to pursuit near-zero fault, near real-time reactivity, better traceability, and more predictability, while working to achieve cheaper product customization. The scenario presented addresses multiple intra-logistic processes of the automotive factory Volkswagen Autoeuropa, where different situations need to be addressed. The main obstacle is the absence of harmonized and integrated data flows between all stages of the intra-logistic process which leads to inefficiencies. The existence of data silos is heavily contributing to this situation, which makes the planning of intra-logistics processes a challenge. The objective of the work presented here, is to integrate big data and machine learning technologies over data generated by the several manufacturing systems present, and thus support the management and optimisation of warehouse, parts transportation, sequencing and point-of-fit areas. This will support the creation of a digital twin of the intra-logistics processes. Still, the end goal is to employ deep learning techniques to achieve predictive capabilities, all together with simulation, in order to optimize processes planning and equipment efficiency. The work presented on this thesis, is aligned with the European project BOOST 4.0, with the objective to drive big data technologies in manufacturing domain, focusing on the automotive use-case.
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spelling Big data analytics for intra-logistics process planning in the automotive sectorIndustry 4.0Data MiningMachine LearningBig DataDigital-TwinDomínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e InformáticaThe manufacturing sector is facing an important stage with Industry 4.0. This paradigm shift impulses companies to embrace innovative technologies and to pursuit near-zero fault, near real-time reactivity, better traceability, and more predictability, while working to achieve cheaper product customization. The scenario presented addresses multiple intra-logistic processes of the automotive factory Volkswagen Autoeuropa, where different situations need to be addressed. The main obstacle is the absence of harmonized and integrated data flows between all stages of the intra-logistic process which leads to inefficiencies. The existence of data silos is heavily contributing to this situation, which makes the planning of intra-logistics processes a challenge. The objective of the work presented here, is to integrate big data and machine learning technologies over data generated by the several manufacturing systems present, and thus support the management and optimisation of warehouse, parts transportation, sequencing and point-of-fit areas. This will support the creation of a digital twin of the intra-logistics processes. Still, the end goal is to employ deep learning techniques to achieve predictive capabilities, all together with simulation, in order to optimize processes planning and equipment efficiency. The work presented on this thesis, is aligned with the European project BOOST 4.0, with the objective to drive big data technologies in manufacturing domain, focusing on the automotive use-case.Gonçalves, RicardoCosta, RubenRUNLourenço, Luís Carlos Guimarães2020-11-03T15:52:27Z2020-0720202020-07-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/106551enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:51:27Zoai:run.unl.pt:10362/106551Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:40:44.179764Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Big data analytics for intra-logistics process planning in the automotive sector
title Big data analytics for intra-logistics process planning in the automotive sector
spellingShingle Big data analytics for intra-logistics process planning in the automotive sector
Lourenço, Luís Carlos Guimarães
Industry 4.0
Data Mining
Machine Learning
Big Data
Digital-Twin
Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
title_short Big data analytics for intra-logistics process planning in the automotive sector
title_full Big data analytics for intra-logistics process planning in the automotive sector
title_fullStr Big data analytics for intra-logistics process planning in the automotive sector
title_full_unstemmed Big data analytics for intra-logistics process planning in the automotive sector
title_sort Big data analytics for intra-logistics process planning in the automotive sector
author Lourenço, Luís Carlos Guimarães
author_facet Lourenço, Luís Carlos Guimarães
author_role author
dc.contributor.none.fl_str_mv Gonçalves, Ricardo
Costa, Ruben
RUN
dc.contributor.author.fl_str_mv Lourenço, Luís Carlos Guimarães
dc.subject.por.fl_str_mv Industry 4.0
Data Mining
Machine Learning
Big Data
Digital-Twin
Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
topic Industry 4.0
Data Mining
Machine Learning
Big Data
Digital-Twin
Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
description The manufacturing sector is facing an important stage with Industry 4.0. This paradigm shift impulses companies to embrace innovative technologies and to pursuit near-zero fault, near real-time reactivity, better traceability, and more predictability, while working to achieve cheaper product customization. The scenario presented addresses multiple intra-logistic processes of the automotive factory Volkswagen Autoeuropa, where different situations need to be addressed. The main obstacle is the absence of harmonized and integrated data flows between all stages of the intra-logistic process which leads to inefficiencies. The existence of data silos is heavily contributing to this situation, which makes the planning of intra-logistics processes a challenge. The objective of the work presented here, is to integrate big data and machine learning technologies over data generated by the several manufacturing systems present, and thus support the management and optimisation of warehouse, parts transportation, sequencing and point-of-fit areas. This will support the creation of a digital twin of the intra-logistics processes. Still, the end goal is to employ deep learning techniques to achieve predictive capabilities, all together with simulation, in order to optimize processes planning and equipment efficiency. The work presented on this thesis, is aligned with the European project BOOST 4.0, with the objective to drive big data technologies in manufacturing domain, focusing on the automotive use-case.
publishDate 2020
dc.date.none.fl_str_mv 2020-11-03T15:52:27Z
2020-07
2020
2020-07-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/106551
url http://hdl.handle.net/10362/106551
dc.language.iso.fl_str_mv eng
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instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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