Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/73558
Título: | K-means clustering combined with principal component analysis for material profiling in automotive supply chains |
Autor(es): | Gonçalves, João N. C. Cortez, Paulo Carvalho, Maria Sameiro |
Palavras-chave: | Supply chain Data mining K-means clustering Principal component analysis (PCA) principal component analysis PCA |
Data: | 2021 |
Editora: | Inderscience |
Revista: | European Journal of Industrial Engineering |
Resumo(s): | 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. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/73558 |
DOI: | 10.1504/EJIE.2021.114009 |
ISSN: | 1751-5254 |
e-ISSN: | 1751-5262 |
Versão da editora: | https://www.inderscienceonline.com/doi/abs/10.1504/EJIE.2021.114009 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CAlg - Artigos em revistas internacionais / Papers in international journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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EJIE_2021.pdf | 6,21 MB | Adobe PDF | Ver/Abrir |