A data-analytics approach to identifying hidden critical suppliers in supply networks

Development of nexus supplier index

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)37-48
Number of pages12
JournalDecision Support Systems
Volume114
DOIs
StatePublished - Oct 1 2018

Fingerprint

Data envelopment analysis
Industry
Mathematical models
Network development
Supply network
Suppliers
Nexus
Theoretical Models

Keywords

  • Centrality measures
  • Data analytics
  • Data envelopment analysis
  • Hidden suppliers
  • Nexus supplier index
  • Social network analysis

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management

Cite this

@article{e26b19a1a50a4a978502f879be7bab2f,
title = "A data-analytics approach to identifying hidden critical suppliers in supply networks: Development of nexus supplier index",
abstract = "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.",
keywords = "Centrality measures, Data analytics, Data envelopment analysis, Hidden suppliers, Nexus supplier index, Social network analysis",
author = "Benjamin Shao and Zhan Shi and Thomas Choi and Sangho Chae",
year = "2018",
month = "10",
day = "1",
doi = "10.1016/j.dss.2018.08.008",
language = "English (US)",
volume = "114",
pages = "37--48",
journal = "Decision Support Systems",
issn = "0167-9236",
publisher = "Elsevier",

}

TY - JOUR

T1 - A data-analytics approach to identifying hidden critical suppliers in supply networks

T2 - Development of nexus supplier index

AU - Shao, Benjamin

AU - Shi, Zhan

AU - Choi, Thomas

AU - Chae, Sangho

PY - 2018/10/1

Y1 - 2018/10/1

N2 - 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.

AB - 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.

KW - Centrality measures

KW - Data analytics

KW - Data envelopment analysis

KW - Hidden suppliers

KW - Nexus supplier index

KW - Social network analysis

UR - http://www.scopus.com/inward/record.url?scp=85051950504&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85051950504&partnerID=8YFLogxK

U2 - 10.1016/j.dss.2018.08.008

DO - 10.1016/j.dss.2018.08.008

M3 - Article

VL - 114

SP - 37

EP - 48

JO - Decision Support Systems

JF - Decision Support Systems

SN - 0167-9236

ER -