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A novel data envelopment analysis model for solving supplier selection problems with undesirable outputs and lack of inputs

Version 2 2024-06-13, 10:27
Version 1 2017-03-29, 12:04
journal contribution
posted on 2024-06-13, 10:27 authored by M Mahdiloo, RF Saen, M Tavana
Supplier evaluation and selection problems are inherently multi-criteria decision problems. Numerous analytical techniques ranging from simple weighted scoring to complex mathematical programming approaches have been proposed to solve these problems. Data Envelopment Analysis (DEA) has been used to evaluate suppliers' performance when there are multiple inputs and outputs in the supplier selection problem. The DEA determines the relative efficiencies of multiple suppliers. These relative efficiencies are then used to provide benchmarking data for reducing the number of suppliers. The DEA models used for supplier selection require numerical data for all the inputs and outputs for all the suppliers. However, this information may not be readily available in real-world problems. In this paper, we propose a novel DEA model that addresses this gap in the supplier evaluation literature. The proposed model can measure suppliers' efficiency in problems exhibiting: the presence of undesirable outputs; the lack of input variables and the presence of zero or negative values in the data set. We also present a case study at Saipa, Iran's second-largest car maker, to demonstrate the applicability of the proposed framework and exhibit the efficacy of the procedures and algorithms.

History

Journal

International journal of logistics systems and management

Volume

11

Pagination

285-305

Location

Olney, Eng.

ISSN

1742-7967

eISSN

1742-7975

Language

eng

Publication classification

C Journal article, C1.1 Refereed article in a scholarly journal

Copyright notice

2012, Inderscience Enterprises

Issue

3

Publisher

Inderscience Publishers