K-means clustering combined with principal component analysis for material profiling in automotive supply chains

João N. C. Gonçalves*, Paulo Cortez, M. Sameiro Carvalho

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

At a time where available data is rapidly increasing in both volume and variety, descriptive 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 SC. Principal component analysis (PCA) is employed to analyse and understand the interrelations between ten quantitative and dependent variables in a multi-item/multi-supplier environment. Afterwards, the principal component scores are characterised via a K-means clustering, allowing us to classify the samples into four clusters and to derive different 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 effectively leverage inventory management for improved SC performance.
Original languageEnglish
Pages (from-to)273-294
Number of pages22
JournalEuropean Journal of Industrial Engineering
Volume15
Issue number2
DOIs
Publication statusPublished - 2021
Externally publishedYes

Keywords

  • Supply chain
  • Data mining
  • K-means clustering
  • Principal component analysis
  • PCA

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