K-means clustering combined with principal component analysis for material profiling in automotive supply chains
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/73558 |
Resumo: | At a time where available data is rapidly increasing in both volume and variety, descrip- tive Data Mining (DM) can be an important tool to support meaningful decision-making processes in dynamic Supply Chain (SC) contexts. Up until now, however, scarce attention has been given to the application of DM techniques in the field of inventory management. Here, we take advantage of descriptive DM to detect and grasp important patterns among several features that coexist in a real-world automotive electronics SC. Concretely, Principal Component Analysis (PCA) is employed to analyze and understand the interrelations between ten quantitative and dependent variables in a multi-item/multi-supplier environment. Afterwards, the principal component scores are character- ized via a K-means clustering, allowing us to classify the samples into four clusters and to derive di↵erent profiles for the multiple inventory items. This work provides evidence that descriptive DM contributes to find interesting feature-patterns, resulting in the identification of important risk profiles that may e↵ectively leverage inventory management for superior performance. |
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K-means clustering combined with principal component analysis for material profiling in automotive supply chainsSupply chainData miningK-means clusteringPrincipal component analysis (PCA)principal component analysisPCACiências Naturais::Ciências da Computação e da InformaçãoScience & TechnologyIndústria, inovação e infraestruturasAt a time where available data is rapidly increasing in both volume and variety, descrip- tive Data Mining (DM) can be an important tool to support meaningful decision-making processes in dynamic Supply Chain (SC) contexts. Up until now, however, scarce attention has been given to the application of DM techniques in the field of inventory management. Here, we take advantage of descriptive DM to detect and grasp important patterns among several features that coexist in a real-world automotive electronics SC. Concretely, Principal Component Analysis (PCA) is employed to analyze and understand the interrelations between ten quantitative and dependent variables in a multi-item/multi-supplier environment. Afterwards, the principal component scores are character- ized via a K-means clustering, allowing us to classify the samples into four clusters and to derive di↵erent profiles for the multiple inventory items. This work provides evidence that descriptive DM contributes to find interesting feature-patterns, resulting in the identification of important risk profiles that may e↵ectively leverage inventory management for superior performance.This work has been supported by FCT - Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. The authors want to extend grateful thanks to the editors and reviewers, whose comments have greatly improved the quality of the paper.InderscienceUniversidade do MinhoGonçalves, João N. C.Cortez, PauloCarvalho, Maria Sameiro20212021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/73558eng1751-52541751-526210.1504/EJIE.2021.114009https://www.inderscienceonline.com/doi/abs/10.1504/EJIE.2021.114009info: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:RCAAP2023-07-21T12:14:14Zoai:repositorium.sdum.uminho.pt:1822/73558Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:06:29.027311Repositório 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 |
K-means clustering combined with principal component analysis for material profiling in automotive supply chains |
title |
K-means clustering combined with principal component analysis for material profiling in automotive supply chains |
spellingShingle |
K-means clustering combined with principal component analysis for material profiling in automotive supply chains Gonçalves, João N. C. Supply chain Data mining K-means clustering Principal component analysis (PCA) principal component analysis PCA Ciências Naturais::Ciências da Computação e da Informação Science & Technology Indústria, inovação e infraestruturas |
title_short |
K-means clustering combined with principal component analysis for material profiling in automotive supply chains |
title_full |
K-means clustering combined with principal component analysis for material profiling in automotive supply chains |
title_fullStr |
K-means clustering combined with principal component analysis for material profiling in automotive supply chains |
title_full_unstemmed |
K-means clustering combined with principal component analysis for material profiling in automotive supply chains |
title_sort |
K-means clustering combined with principal component analysis for material profiling in automotive supply chains |
author |
Gonçalves, João N. C. |
author_facet |
Gonçalves, João N. C. Cortez, Paulo Carvalho, Maria Sameiro |
author_role |
author |
author2 |
Cortez, Paulo Carvalho, Maria Sameiro |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Gonçalves, João N. C. Cortez, Paulo Carvalho, Maria Sameiro |
dc.subject.por.fl_str_mv |
Supply chain Data mining K-means clustering Principal component analysis (PCA) principal component analysis PCA Ciências Naturais::Ciências da Computação e da Informação Science & Technology Indústria, inovação e infraestruturas |
topic |
Supply chain Data mining K-means clustering Principal component analysis (PCA) principal component analysis PCA Ciências Naturais::Ciências da Computação e da Informação Science & Technology Indústria, inovação e infraestruturas |
description |
At a time where available data is rapidly increasing in both volume and variety, descrip- tive Data Mining (DM) can be an important tool to support meaningful decision-making processes in dynamic Supply Chain (SC) contexts. Up until now, however, scarce attention has been given to the application of DM techniques in the field of inventory management. Here, we take advantage of descriptive DM to detect and grasp important patterns among several features that coexist in a real-world automotive electronics SC. Concretely, Principal Component Analysis (PCA) is employed to analyze and understand the interrelations between ten quantitative and dependent variables in a multi-item/multi-supplier environment. Afterwards, the principal component scores are character- ized via a K-means clustering, allowing us to classify the samples into four clusters and to derive di↵erent profiles for the multiple inventory items. This work provides evidence that descriptive DM contributes to find interesting feature-patterns, resulting in the identification of important risk profiles that may e↵ectively leverage inventory management for superior performance. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021 2021-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/1822/73558 |
url |
https://hdl.handle.net/1822/73558 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1751-5254 1751-5262 10.1504/EJIE.2021.114009 https://www.inderscienceonline.com/doi/abs/10.1504/EJIE.2021.114009 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Inderscience |
publisher.none.fl_str_mv |
Inderscience |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799132480455835648 |