Risk management framework for evaluating suppliers

Abstract

Sikorsky Aircraft Co. currently finds itself in a critical growth period, in terms of both sales contracts and supplier agreements. Popular supply chain strategies preach reduction and simplification of the supply base, but Sikorsky encounters "must-grow" situations with their supply base, due to factors like international offset provisions and capacity needs. Growth in the number of supplier relationships each year strains the supply management department and makes it difficult to complete full analyses of new suppliers. The goal of this research is to provide tools that combine the knowledge of experienced supply chain employees with statistical analysis in a package that will allow any member of the supply chain group to complete a thorough supplier risk analysis in the minimum amount of time. To address Sikorsky's supply chain risk, a concrete framework is desired that will ask the right questions about a supplier and produce an indicator of the level of risk involved in a supplier agreement. This project sets out to identify the connections between the sources of risk (risk drivers) and affected performance metrics (effects). These connections can be presented in an easy-to-use tool that enables quick yet thorough analyses. The framework links supplier analyses with the resulting performance, and uses the results to make data driven inferences about future supplier relationships. This allows quick and informed assessments by anyone in the supply chain group, regardless of their level of experience. The result of this project is a software-based risk assessment framework with scoring based on historical Sikorsky supplier performance.

(cont.) The data have revealed through statistical regression analysis strong correlations between a number of risk drivers and resulting supplier performance. These correlations can be used to score suppliers with similar attributes through the model. In addition, the model can be used as a knowledge retention mechanism of supplier performance data to facilitate future refinements of both the model and risk driver/effect correlations.