Vendor Performance With Supply Risk: A Chance-Constrained DEA Approach

Abstract

The strategic importance of vendor evaluation is well established in the purchasing literature. Several evaluation methodologies that consider multiple performance attributes have been proposed for vendor evaluation purposes. While these techniques range from scoring models that utilize prior articulation of weights to derive composite scores for vendors to advanced mathematical models, methods that incorporate the inherent variability in vendor's performance attributes have been limited. The primary reason for the lack of development of such models is due to the complexities associated with stochastic approaches. In order to more accurately evaluate the performance of vendors, itis critical to consider variability in vendor attributes. This paper is an attempt to fill this void in vendor evaluation models by presenting a chance-constrained data envelopment analysis (CCDEA) approach in the presence of multiple performance measures that are uncertain. Our paper effectively demonstrates the first application of CCDEA in the area of purchasing, in general, and vendor evaluation, in particular. The model is demonstrated by applying it to a previously reported dataset from a pharmaceutical company.

Keywords: Chance-constrained programming, Non-linear programming, Purchasing, Stochastic data envelopment analysis