Analysis of diversity of characteristics of auto-parts and product portfolios of suppliers by bipartite network projection

Tomomi Kito*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


This study aims to investigate the structural heterogeneity of real-life supply networks in the automotive industry, through complex network analysis of large-scale empirical data. The concept of complex adaptive systems, which considers the supply network structures as the result of emergence, has recently been well accepted, and a considerable number of models have been proposed. However, such models fail to capture how supply network structures may reflect the effects of various factors - product characteristics differ in terms of the degree of standardization, modularization and technological advancement, suppliers have different production capabilities, and different car assemblers may have different production strategies. The unique data we collected provides information about "which suppliers supply which auto-parts to which car manufacturers", which allowed us to first confirm the scale-free property of the distribution of suppliers' production capabilities, and to highlight a wide diversity of product characteristics. Furthermore, the bipartite network constructed based on the collected data was projected onto another information space, which resulted in a product network, exhibiting proximity between products. Analysis of this product network elucidated a high degree of structural heterogeneity of the auto-parts supply network, in which various factors may be reflected. The results of this study will contribute to the establishment of more realistic supply network models.

Original languageEnglish
Pages (from-to)721-728
Number of pages8
JournalTransactions of the Japanese Society for Artificial Intelligence
Issue number6
Publication statusPublished - 2015 Oct 27
Externally publishedYes


  • Complex network
  • Empirical data
  • Portfolio
  • Supply network

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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