Identifying the Relation Between a Supply Chain Network’s Structure and Its Overall Financial Performance


  • Penina Orenstein1 (Seton Hall University, USA)
  • Hongfei Tang1 (Seton Hall University, USA)

We construct a data set using financial performance data spanning forty publicly traded companies across several industry sectors over a three-year time-period to identify key structural features of supply networks. The data set for this study allows us to explore supply chain relationships beyond the first tier. For each network within the data set, we examine the network topology via several key structural parameters including node and edge counts, average degree, network diameter, average path length and the power law exponent. We observe that the emergent structure of supply networks is similar (inter-industry), although dominant supply networks are apparent, in some, yet not all the industry sectors. We then link the structural parameters with financial metrics and observe that higher average degree results in decreased overall financial performance of the supply network. Average degree is indicative of how many connections a firm has. A high average degree implies strong inter-connectivity among the firms in the network. Historical analysis of the data (2013-2015) points to an overall decrease in the average degree, especially at the higher tiers. Our analysis suggests that to increase the overall supply network’s financial performance, a low average degree should be targeted.

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