Network algorithm to model automotive supply chain structure

Abstract

A network algorithm that models the structure of automotive supply chains, compiled from a proprietary database, is presented. An initial structural analysis was conducted using key performance indicators, including average path length, clustering coefficient, and degree distribution, to assess network configurations. The networks were then partitioned into subnetworks, with an emphasis on reflecting the operational dynamics of supply chain activities. Regression analysis was applied to each subnetwork, using the number of vertices as the independent variable, to develop an algorithm for generating synthetic networks. These synthetic constructs serve as benchmarks for the automotive sector and have shown a strong average correlation (0.94) with the structure of actual supply networks. This methodological contribution provides tools for analysing and optimising supply chain structures that underpin automotive engineering and manufacturing, ensuring robustness and efficiency in vehicle production systems. The prevalence of tree-like structures within supply networks challenge conventional beliefs regarding the complexity of automotive supply chains and prompts further investigation into the determinants of their resilience.