Article: Supplier selection using chance-constrained data envelopment analysis with non-discretionary factors and stochastic data Journal: International Journal of Industrial and Systems Engineering (IJISE) 2012 Vol.10 No.2 pp.167 - 196 Abstract: 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. Inderscience Publishers - linking academia, business and industry through research

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

Authors: Majid Azadi; Reza Farzipoor Saen; Madjid Tavana

Addresses: Faculty of Economic and Management, Islamic Azad University – Sciences and Researches Branch, Tehran, Iran ' Department of International Business and Asian Studies, Griffith University – Gold Coast Campus, Gold Coast, Queensland 4222, Australia ' La Salle University, Philadelphia, PA 19141, USA

Abstract: 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.

Keywords: supplier selection; SCM; supply chain management; chance-constrained DEA; data envelopment analysis; chance-constrained programming; non-discretionary factors; stochastic data; quadratic programming.

DOI: 10.1504/IJISE.2012.045179

International Journal of Industrial and Systems Engineering, 2012 Vol.10 No.2, pp.167 - 196

Published online: 20 Dec 2014 *

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

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