Evaluating green suppliers: improving supplier performance with DEA in the presence of incomplete data

Abstract

The role of the supplier and relationship management with the supply base in purchasing is increasingly appreciated. This highlights the importance of pre- and post-qualification data, and the need for the focus to shift from selection to rating and mapping opportunities for development in the process of relationship management. From a decision-theory perspective, this means that suppliers need to be informed about how specific performance indicators should be improved to increase their prospects of qualifying for selection. From the supply management point of view, it is important that the efficiency of suppliers is increased to the level at which criteria are met. We employ a DEA model to parameterize the related data, making it treatable using fuzzy or interval DEA models. The problems associated with missing and imprecise data in such models can also be solved using parametric linear programming. However, this approach means that technology coefficients are also parameterized, for which good analytical solutions are lacking. We therefore approach the problem using simulation.