Supplier selection using chance-constrained data envelopment analysis with non-discretionary factors and stochastic data

The changing economic conditions have challenged many organisations to search for more efficient and effective ways to manage their supply chain. During recent years supplier selection decisions have received considerable attention in the supply chain management literature. There are four major decisions that are related to the supplier selection process: what product or services to order, from which suppliers, in what quantities and in which time periods? Data envelopment analysis (DEA) has been successfully used to select the most efficient supplier(s) in a supply chain. In this study, we introduce a novel supplier selection model using chance-constrained DEA with non-discretionary factors and stochastic data. We propose a deterministic equivalent of the stochastic non-discretionary model and convert this deterministic problem into a quadratic programming problem. This quadratic programming problem is then solved using algorithms available for this class of problems. We perform sensitivity analysis on the proposed non-discretionary model and present a case study to demonstrate the applicability of the proposed approach and to exhibit the efficacy of the procedures and algorithms.