Advances in Management & Applied Economics, vol.1, no.2, 2011, 207-219
ISSN: 1792-7544 (print), 1792-7552 (online)
International Scientific Press, 2011
Supplier selection problem: A literature review of
Multi-criteria approaches based on DEA
Alexios-Patapios Kontis1 and Vassilios Vrysagotis2
Abstract
Supplier selection problem usually is very complicated, because variety of
uncontrollable and unpredictable factors affect on evaluation and decision-making
process. Various decision making approaches have been proposed to tackle the
problem as part of a general tendering process, particularly those of multi-criteria
analysis which use both quantitative and qualitative data. Specifically for this
problem have been proposed methodologies and techniques included methods
such as Analytic Hierarchy Process (AHP), Analytic Network Process (ANP),
Case-Based Reasoning (CBR), Genetic Algorithms (GA), SMART theory (Simple
Multi-Attribute Rating Technique) and Data Envelopment Analysis (DEA). This
paper reviews the literature of the multi-criteria decision making approaches for
evaluation and selection supplier based on Data Envelopment Analysis (DEA) and
(its their) combinations.
1
2
Department of Logistics Management, Technological Educational Institute of Chalkida,
Greece, e-mail: alexis.kontis@gmail.com
Department of Logistics Management, Technological Educational Institute of Chalkida,
Greece, e-mail: brisxri@otenet.gr
Article Info:
Revised: October 24, 2011.
Published online: October 31, 2011
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Supplier selection problem: A literature review … on DEA
JEL Classification: C81
Keywords: Multi-criteria decision making, Data Envelopment Analysis (DEA),
supplier evaluation and selection
1
Introduction
In an era of global sourcing, business’s success often hinges on the most
appropriate selection of its partners and suppliers. Procurement is an increasingly
important activity within most firms, and severe financial and operational
consequences can result from the failure to optimize the procurement function.
Specifically, appropriate suppliers selection is one of the fundamental strategies
for enhancing the quality of output of any organization, which has a direct
influence on the company’s competitiveness and reputation. Suppliers’ evaluation
and selection process became vital for the entire width of business sizes and
activities [1]. The selection process involves the determination of quantitative and
qualitative factors so as to select the best possible suppliers, which ensure business
competitiveness, sustainability and success. Consequently, the supplier selection
problem requires the consideration of multiple objectives, and hence can be
viewed as a multi-criteria decision-making (MCDM) problem [2]. Over the last
decade the research community has extensively studied the problem of evaluating
and selecting suppliers, adopting approaches and implementations from a wide
range of mathematical practices and methodologies.
Consequently, numerous
multi-criteria decision support tools have been developed for structuring and
supporting such decisions [3] [4].
Major part of total body of research for
supplier’s evaluation and selection problem, consists from Data Envelopment
Analysis (DEA) approaches.
Other related approaches based on AHP (Analytic
Hierarchy Process), Fuzzy Sets Theory, Goal Programming and combination of
them have been proposed to solve the suppliers decision-making problem.
Data
A-P. Kontis and V. Vrysagotis
209
envelopment analysis was developed by Rhodes [5] and initially detailed and
publicized by Charnes, Cooper and Rhodes [6] for evaluating more than two
decision outcomes and/or Decision Making Units (DMUs) with respect to their
relative efficiencies, based upon multiple criteria. DEA is built on the theoretical
foundations provided by Farrell [7] and continues to be popular for a wide variety
of applications [8] [9]. Beyond identifying efficient outcomes, DEA has been used
to identify the existence of technical and managerial efficiencies.
2
Implementations of Data Envelopment Analysis (DEA) in
supplier evaluation and selection
Much of the research for suppliers evaluation and selection lies in Data
Envelopment Analysis (DEA) methods and its variants, because DEA is a
non-parametric, linear programming based technique for measuring the relative
efficiency of homogeneous units that consume incommensurable multiple inputs
and produce multiple outputs. The inputs and outputs are assumed to be
continuous positive variables and the weights are estimated in favor of each
evaluating unit to maximize its efficiency. DEA achieves to classify the units into
efficient units that achieve efficiency scores equal to the upper bound and
inefficient units that are those that do not succeed to do so. DEA is a widely
accepted evaluation method by researchers and practitioners because it has
repeatedly proved its ability to effectively handle multiple conflicting properties
associated with the modern requirements of administrative sciences inherent in
supplier selection and beyond.
In order to facilitate readers, presented DEA approaches divided in individual and
integrated.
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Supplier selection problem: A literature review … on DEA
2.1 Evaluation and selection processes based on individual
approaches of DEA

Weber in 1996 [10] suggests that Data Envelopment Analysis (DEA) can be
used as an objective method to evaluate suppliers based on multiple criteria
and identified benchmarking values.

Four years later, Liu, Ding an Lall [11] recognizing the usefulness of DEA
as a multi-criteria evaluating methodology and the need for more functional
and efficiently suppliers selection systems, proposed a simplified model of
DEA to evaluate the overall performance of suppliers, considering three
inputs and two outputs. The model aimed to highlight the supplier who
offering the greatest supplies variety.

Forker and Mendez [12] applied DEA in order to benchmarking the
comparative efficiency of suppliers in order to help companies save time
and resources by identify the “best peer” supplier(s). “Best peer” suppliers
can be imitated by companies with similar organizational structures by
paying the least amount of effort. Forker and Mendez method for each
supplier calculated the maximum ratio of multiple outputs for each single
input and use Cross-Efficiency to filter the total results.

Narasimhan, Talluri and Mendez [13] recognizing that business
performance based on total performance of suppliers network propose a
DEA model for effective supplier performance evaluation, based on eleven
critical factors, six inputs and five outputs. Combining the results of DEA
with managerial performance rating, suppliers identifying into 4 clusters:
High performance and Efficient (HE), High performance and Inefficient
(HI), Low performance and Efficient (LE) and Low performance and
Inefficient (LI). Based on this categorization firms on HI, LE and LI
suppliers clusters could improve their operations across a variety of
dimensions by benchmarking and analyzing High performance and
Efficient (HE) suppliers.
A-P. Kontis and V. Vrysagotis

211
Talluri and Sarkis [14] using DEA to formulate a new model for
performance monitoring of suppliers. The aims of this paper were to apply
a new multi-criteria evaluation model for supplier performance evaluation
by considering various performance criteria, such as to serve a monitoring
and control mechanism for the performance of suppliers. The new model
and its apply based on data of a previously published illustrative case study
of Talluri and Baker [15].

Talluri and Narasimhan [16] aiming to propose an objective framework for
effective supplier sourcing, which considers multiple strategic and
operational factors in the evaluation process, applied a similar technique to
Narasimhan, Talluri and Mendez (2001) based on statistical indicators and
Cross-Efficiencies to classify suppliers compared to their effectiveness.

Ross, Buffa, Dröge and Carrington [17] at their Action Research (AR)
framework developed and suggested a DEA model which examines
supplier’s efficiency focused on buyer's perspective.

Garfamy [18] focused on Total Cost of Ownership (TCO) applied a DEA
model to evaluate the overall performance of suppliers, claiming that the
supplier with the lowest cost per unit is the most efficient supplier.

Sean’s [19] objective is to propose an innovative method based on DEA for
selecting suppliers in the presence of nondiscretionary factors from
supplier’s perspective. He introduce in evaluating process quality factors
that measured by ordinal data.
At this paper the factor "know-how
transfer" was assessed on a qualitative scale of 5 points.

In Seydel’s [20] research, the suppliers’ evaluation model does not focus on
inputs but on outputs which have qualitative characteristics rated on a seven
level scale. Proposed method is compared with the SMART methodology,
noting that requires less participation of decision maker and less data.

Talluri, Narasimhan and Nair [21] addressing the complexities associated
with stochastic approaches for effectiveness assessment, suggest a
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Supplier selection problem: A literature review … on DEA
“Chance-Constrained Data Envelopment Analysis” (CCDEA) approach in
the presence of multiple performance measures that are uncertain. In this
model, the price used as an input while the quality and delivery is
considered as outputs.

Smirlis, Panta, Kaimakamis and Sfakianakis [22], in order to develop a
systematic and reliability evaluation model for selecting Third Party
Logistics (3PL) partners, suggest a DEA methodology based on the
estimation of a superiority index which achieves to discriminate them into
beneficiary and non beneficiary.

Saen [23] facing with a main problem of supplier selection process, the use
both quantitative and qualitative data, present an innovative variation of
DEA (imprecise DEA) based on non-precise of Fuzzy data which include
verity of cardinal and ordinal data. The proposed model can handle
non-precise of fuzzy data as precise.

Following the same path, Wu, Shunk, Blackhurst and Appalla [24] propose
an improved version of supplier selection model based on non-precise data
which had potential for further discrimination of efficiently selection.

Wu and Blackhurst [25] in aim to eliminate the potential weaknesses of
basic DEA model as well as the cross-efficiency and super-efficiency
models,
suggest
an
Augmented
DEA
approach
with
enhanced
discriminatory power that can be used in supplier evaluation and selection.
They have added two enhancements to the basic DEA model: the inclusion
of “virtual standards” which are spanned across a range (rather than being a
single point or benchmark); and the introduction of weight constraints.

Panta, Smirlis and Sfakianakis [26] in order to propose a efficient decision
making approach in relation with the existing fixed weighted framework for
the procurement of products and services for Public Organizations, develop
a DEA model that overcomes the above mentioned shortcoming. The main
characteristics of this model are the ability to incorporate different price
A-P. Kontis and V. Vrysagotis
213
levels and the flexibility of setting the weights in favor of each evaluated
bid, ensuring that the winner has uncontested superiority.
2.2
Evaluation and selection processes based on integrated
approaches of DEA
Except the approaches which use classical implementation of DEA to solve
suppliers decision-making problem, research community suggest numerous of
innovative combinations approaches whose at least a part based on DEA.

Weber, Current and Desai [27] aiming to determine the optimal order
quantity, suggests DEA in conjunction with Multi-Objective Programming
(MOP). According to this approach, MOP is used to develop vendor-order
quantity solutions, whom calls “supervendors” and then evaluating the
efficiency of these “supervendors” on multiple criteria using DEA.

Braglia and Petroni [28] applied Data Envelopment Analysis to measure the
efficiencies of candidates’ suppliers.
In order to evaluate and rank
alternative suppliers they use nine key factors.
For ensuring selection
process they apply both Cross-Efficiency and Maverick index.

Talluri and Baker [15] present a multi-phase mathematical programming
approach for designing effectively supply chain networks. Specifically, they
develop and apply a combination of multi-criteria efficiency models, based
on Game Theory concepts, and Linear and Integer Programming methods.
In first stage, potential suppliers and manufacturers were evaluated
separately in two inputs and four outputs using DEA. Based on the results
of this phase, at the second phase developed a supply and distribution
model of goods in warehouses by identifying the optimal number of
suppliers, manufactures and distributors. Finally, the third phase involves
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Supplier selection problem: A literature review … on DEA
the initial deployment plans, which identify the optimal routing of material
from selected suppliers to manufacturers by minimizing the total cost.

Saydel [29] aiming to support sole-sourcing decision-making discussion,
uses an example of ten supplier to present SMART (Simple Multi-Attribute
Rating Technique) and DEA and demonstrate their efficiency.
The results
from these two approaches are compared to those based upon a pure
aggregation and averaging procedure.

Saen [30] aiming to evaluating and selecting slightly non-homogeneous
suppliers proposed an innovative approach which combines AHP and DEA.
Recognizing that suppliers do not consume common inputs to supply
common outputs, used AHP to identify the relative weight of each supplier
and DEA to compute the relative efficiency of each supplier.

Ramanathan [31] study attempted to extend the analysis proposed by Bhutta
and Huq [2], which evaluate suppliers performance using Total Cost of
Ownership (TCO) considering the quantitative factor of cost, and Analytical
Hierarchy Process (AHP) to consider a mix of qualitative factors. Through
his research suggests a combination of objective and subjective information
provided by the results of the Total Cost of Ownership (TCO) and
Analytical Hierarchy Process (AHP) approaches via Data Envelopment
Analysis (DEA).

In order to select competitive suppliers in a supply chain, Ha and Krishnan
[32] outlined a hybrid method, which incorporates multiple techniques (i.e.,
AHP, DEA, and ΑNN) into an integrated process. Proposed hybrid method
uses an AHP to assign weight to the qualitative selection criteria, and
combination of Artificial Neural Network (ANN) and DEA in order to rated
the overall performance index of vendors. Final selection process is based
in cluster analysis which returns a Supplier Map (SM) that imprints
suppliers position within different segments, according to performance of
qualitative and quantitative dimensions.
A-P. Kontis and V. Vrysagotis

215
Ozdemir and Temur [33] developed an expert system on supplier evaluation
process by designing Artificial Neural Network (ANN) that is supported
with Data Envelopment Analysis (DEA). Proposed model uses a total of
variables such as Material Quality (MQ), Discount Of Amount (DOA),
Discount Of Cash (DOC), Payment Term (PT), Delivery Time (DT) and
Annual Revenue (AR).

Desheng Wu [34] presents a hybrid model which combining Data
Envelopment Analysis (DEA), Decision Trees (DT) and Neural Networks
(NNs) to evaluate supplier performance aiming to a favorable classification
and prediction accuracy rate. Proposed hybrid model is consisted of two
modules, can function as both a classification model and a regression
model. Module 1 applies two-stage DEA and classifies suppliers into
efficient and inefficient clusters based on the computed efficiency scores.
Module 2 is a classification or regression module based on the Decision
Tree or the Neural Network.
3
Observations and recommendations
The primary advantages of Data Envelopment Analysis are that it considers
multiple factors and does not require parametric assumptions of traditional
multivariate methods. However, in the application of DEA models there are some
critical factors which have to be considered. Efficiency scores could be very
sensitive to changes in the data and depend heavily on the number and type of
input and output factors considered. In general, inputs can include any resources
utilized by a DMU, and the outputs can range from actual products to a range of
performance and activity measures. The size of the data set is also an important
factor when using some of the traditional DEA models, however, some of these
sample size problems can be overcome by using cross-efficiency models.
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4
Supplier selection problem: A literature review … on DEA
Conclusions
In this paper we review recently literature about solving supplier's evaluation
and selection problem, based on DEA and some methodological extensions that
could be utilized to improve its discriminatory power in performance evaluation,
therefore by no means exhausts all the developments occurring on DEA technique.
In general literature, in the last period there is significant work in the field of
sensitivity analysis in DEA, target setting in DEA, stochastic DEA, profiling in
DEA, among other developments.
In the field of supplier evaluation, it’s clear
over the time, that DEA methodology develops, enriches and improves both the
discretionary ability and effectiveness in managing multi-criteria, decisions
-making problems. Moreover, research community adds new, innovative and
integrated methodological approaches utilizing the multiple capabilities of DEA
creatively, making the method more user-friendly, handling and functional.
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