A data-analytics approach to identifying hidden critical suppliers in supply networks: Development of nexus supplier index

Recent events involving supplier-caused business disruptions bring to the forefront the issue of managing hidden yet critical suppliers that may exist deep in the supply network. While managing prominent strategic suppliers in the top tier is well understood, we have only just begun to recognize a different type of critical suppliers called nexus suppliers. Nexus suppliers are critical because of their structural positions in the supply network. They can be several tiers removed in the extended supply network and hence may not have direct contact with, and not be visible to, the focal buying firm. In this study, we explore the identification and categorization of nexus suppliers. Based on the theory of nexus supplier and data envelopment analysis (DEA), we propose a data-analytics approach to compute what we call Nexus Supplier Index (NSI). It is a measure that combines various network centrality measures to capture and reflect different aspects of a supplier's structural importance. The contribution of our study is to take the concept of nexus suppliers that exists only in theory to practice and demonstrate how to look for nexus suppliers in the real world. To achieve this aim, we develop a mathematical model for NSI, compile a large data set using Bloomberg Terminal, and engage in computations to identify and categorize nexus suppliers. The target company is Honda, and we review the results with the top supply management team at Honda of America. Implications for practice and future research are discussed.