The global market for automotive parts is typically characterized by the strong presence of global suppliers, which are continually pressured to reduce costs, and increase productivity and competitiveness. In this context, this paper describes a new FGPDEA model that combines Data Envelopment Analysis (DEA) and Fuzzy Goal Programming, thereby aiming to increase the discrimination among Decision Making Units (DMUs) in an environment under uncertainty. A real application of the FGPDEA model was carried out to evaluate the efficiency of seven mini-factories (DMUs) of the auto parts segment. The results obtained in this real problem were adherent to the reality studied, reliably identifying which mini-factories were efficient and which ones were more sensitive to the effect of uncertainty.
Efficiency; Goal Programming; Data Envelopment Analysis; Fuzzy Theory; Auto Parts Segment
