Steven Carnovale

An analytical framework for supply network risk propagation: A Bayesian network approach

There are numerous examples of supply chain disruptions that have occurred which have had devastating impacts not only on a single firm but also on various other firms in the supply network. We utilize a Bayesian Network (BN) approach and develop a model of risk propagation in a supply network. The model takes into account the inter-dependencies among different risks, as well as the idiosyncrasies of a supply chain network structure. Specific risk measures are derived from this model and a simulation study is utilized to illustrate how these measures can be used in a supply chain setting.

The Impact of Supply Network Structure on the Financial Performance of the Firm

Supply chain finance (SCF) as a topic of academic interest and professional practice is rather nebulous, in the sense that the financial aspects of the supply chain have far reaching and complex effects on the entire enterprise. Firms that properly manage their SCF initiatives experience significant benefits to market capitalization and cash flow performance throughout the extended enterprise. Additionally, recent research has emerged that conceptualizes supply chains under the guise of networks. However, even with the important role that SCF plays throughout the supply network, there has been little research that specifically examines the characteristics of a firm’s supply network structure and its impact on financial performance. Consequently there is a gap in the literature dealing with this relationship and thus the primary undertaking in this research is to answer the following research question: what role does a firm’s supply network structure have in explaining its financial performance? We estimate several random effects GLS regressions to test several theoretically driven hypotheses. Our results suggest that firms that can understand and optimize their network structures can use them to their advantage to generate significant improvements in ROA, ROI, ROE, and ROS.