Abstract:
Supply chains in the aerospace sector are becoming more complex than ever
before, frequently causing delays on the production process. Complexity gave
rise to the term “supply networks”, changing the way we view supply chains
from a structural point of view. Structural properties are important to investigate
as they help define robustness and efficiency of systems. Although complexity
in structure is suspected by previous researchers who studied these networks,
empirical data to characterise what complexity means, and how it effects
properties of networks has been largely absent from literature. If empirical data
is available, network science can be used to understand structural properties of
such complex supply networks. Network science is a suitable Mathematical tool
for analysing the complex relationships and collaborations in the network and
summarizing the properties of network from a fundamental, structural
perspective. In this report, the author will apply network science to analyse the
structure of the Airbus supply network. Due to the lack of aerospace supply
chain data, firstly an empirical database is built. Analysis then focuses on the
real structure of Airbus supply network and identification of key firms or
communities under two scenarios: a non-weighted network in which the value of
link is either 1 or 0, and a weighted network in which the value of link presents
the strength of relationships among firms. While the weighted network indicates
more informed features of the supply network structure by considering the
weight of relationships, the non-weighted network can help us understand
fundamental patterns that determine the structure of the connections in the
network. The analysis indicates the Airbus supply network carries a power law
distribution, which means most resources are dominated by few firms, and the
network is robust to random firm failure but vulnerable to hub failure. The
network contains communities with strong relationships between them.These
communities do not only belong to the same industry and same region but have
emerged as the result of an interaction between the two effects. Some key firms
in the network own significant power of control the supply chain and fiancial
resources, occupying key positions that bridge communities in the network.The
study presents key structural features of a large scale network using empirical
data and act as a case example for using network science based analysis in
supply chains.
