Examining Operational Risks in Supply Chains

Abstract

Since 2000, many organizations have become members of formalized extended enterprises known as supply chains. These entities can be characterized as organizational networks designed to help firms achieve a competitive advantage via improved market responsiveness and cost reductions. Additionally, supply chain networks are created to counteract the effects of increasing levels of global competition, demanding customers and employees, shrinking product lifecycles, and decreasing acceptable response times. However, as organizations Increase their dependence on these networks, they become more vulnerable to their suppliers' operational risk profiles as well as other categories of risk associated with supply chains. Supplier operational risk profiles consist of risk events that can directly affect their internal operations. Suppliers with a high probability of operational risk event occurrences can have a significant impact on the revenues realized by organizations that rely on their inputs. Thus, it is essential that supply chain network participants are capable of evaluating the operational risks associated with their supplier base. This article presents a methodology for examining supplier operational risk profiles in supply chains through the creation of Bayesian networks. The networks are used to determine a supplier's external, operational, and network risk probability, and the potential revenue impact a supplier can have on the organization. The results of this study show that Bayesian networks can be used as an effective tool to assist managers in making decisions regarding current or prospective suppliers based upon their potential revenue impact, as illustrated through their associated operational risk profiles. [ABSTRACT FROM AUTHOR] Copyright of Supply Chain Forum: International Journal is the property of KEDGE Business School and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)