Hayk
Manucharyan
University
of Warsaw, Faculty of Economic Sciences, Poland
E-mail: hmanucharyan@wne.uw.edu.pl
Submission: 2/2/2020
Revision: 2/14/2020
Accept: 2/28/2020
ABSTRACT
In contemporary supply chain management, a company’s
performance is largely dependent on its strategic choice of suppliers. The
complexity of supplier evaluation and selection is driving the development of
novel support techniques and their integration into multi-criteria
decision-making processes. This review identifies the most prevalent approaches
in the supply chain management literature (1998–2018), analyzes the strengths
and weaknesses of these approaches, and discusses the most popular supplier
selection attributes. The non-conventional, emerging methods in domain
literature are also discussed, and future research directions are proposed.
Supplier selection approaches are classified into individual, integrated, and
non-conventional approaches. To overcome the limitations associated with these
tools when used individually, most of the published works have used integrated
techniques, among which integrated fuzzy and analytic hierarchy process methods
are most popular. We conclude that while some of the methodologies are common,
the more non-conventional approaches, such as market utility-based models, are
rarely used in the supplier selection literature, leaving much opportunity to
further develop these less-used approaches and, ultimately, aid decision-makers
in supply chain management.
Keywords: Supply chain management; supplier selection; vendor selection; multi-criteria decision-making; literature review.
1.
INTRODUCTION
In recent decades, good
supplier selection has grown in importance. During the late 20th
century, many companies in the manufacturing and services industry began
collaborating with their suppliers to improve organizational performance,
reduce purchasing risk, increase their competitiveness, maximize value to the
buyer, and establish long-lasting relationships along the supply chain network
(Chen et al., 2006).
Over time, the supplier
selection problem has morphed from a single-criterion cost-based approach to
complex scenarios, where multiple criteria are considered by decision-makers.
The selection process involves consideration of qualitative and quantitative factors
to select the best possible suppliers, which improves business competitiveness
and sustainability.
Aiming to develop
long-lasting supplier relationships, contemporary strategies for supplier
selection consider various factors, including cost reduction, quality
management practices, quantity, capacity, financial strength, delivery,
research and development capabilities, technology and innovation levels,
attitude, performance, communication, and co-design capabilities (Macioł et al., 2013).
Given its strategic
importance and complexity, supplier selection can be challenging, involving a
large number of tangible and intangible criteria, decisions regarding single
vs. multi-sourcing, multiple industry-specific models, the location factor, and
sustainability issues.
Although the process of
supplier selection can be arduous, consuming a significant amount of time and
resources, when done well, it can be greatly beneficial for a firm (Jayshingpure et al., 2016). Hence, a trade-off between
qualitative and quantitative factors is essential in selecting a potential
supplier (Tahriri et al., 2008).
Various multi-criteria
decision-making tools have been developed to support complex decisions. Many of
these have been applied to the supplier selection problem. The existing
literature deploys tools [such as the analytic hierarchy process, the fuzzy set
theory (FST), data envelopment analysis, and goal programming (GP)] to support
supplier evaluation and selection. In addition, there are integrated approaches
available for those tools, which aim to overcome the limitations associated
with these approaches when used in isolation (Kontis
& Vrysagotis, 2011).
Multiple literature
reviews of supplier selection have been published. Jain, Wadhwa,
and Deshmukh (2009) reviewed the main approaches to
the supplier selection problem published before 2007. Ho et al. (2010) have
analyzed multi-criteria decision-making approaches for supplier selection based
on journal articles from 2000 to 2008. These authors focused on identifying the
most popular approaches, most important supplier attributes, and inadequacies
in existing approaches.
Chai et al. (2013)
provided a systematic review of 123 journal articles published between 2008 and
2012. Bhutta and Huq’s
(2002) review included 154 articles from 68 journals and was focused on the
impact of information technologies on supplier selection. Also, there has been
a tendency towards reviewing supplier selection from a sustainability
perspective and analyzing green supplier selection (Govindan
et al., 2015; Igarashi et al., 2013).
However, most reviews
require an update as there have been significant developments in supplier
selection methodologies and techniques over the last several years. Also, the
list of relevant supplier attributes continues to expand, and some of the
recent studies have evaluated suppliers based on a broader range of criteria,
including globalization, development of e-commerce, and changes in technology.
Moreover, none of the existing supplier selection literature reviews include
non-conventional, emerging techniques, such as market utility-based approaches.
Here we provide an
extended review of multi-criteria decision-making approaches for supplier
evaluation and selection published between 1998 and 2018, which summarizes
individual, integrated, and non-conventional methods. Based on the 177 journal
articles studied, the following research questions are examined:
a) What are the most prevalent
multi-criteria decision-making approaches for supplier selection in the
operations management literature? What are the main inadequacies and
limitations of these approaches?
b) What are the most commonly
identified supplier selection criteria? How has the list of criteria changed in
recent years?
c) What non-conventional approaches
have been applied to the supplier selection problem, and how could those
techniques be further developed?
2.
REVIEW METHODOLOGY
The approach used when preforming
this review is outlined in Figure 1. First, we searched for relevant publications
within the SpringerLink, Elsevier’s ScienceDirect, IEEE Xplore,
ProQuest, Emerald, and Meta-Press databases. Articles including ‘supplier
selection’, ‘vendor selection’, ‘supply chain management’, or ‘multi-criteria
decision-making’ as keywords were selected. The scope of this review was
limited to journal articles and conference papers published between 1998 and
2018 (including those pre-published online during 2018, and subsequently
published in a 2019 journal volume).
Figure 1: The article selection process.
One-hundred-seventy-seven articles
that met our inclusion criteria were identified. These articles can be
classified as reporting individual approaches (including decision support
systems and supplier selection modeling studies), integrated approaches
[including integrated analytic hierarchy process (AHP) methods, integrated
fuzzy methods, and other hybrids], or non-conventional approaches (including
market utility-based models).
The year with the most published
articles was 2009 (n = 23). Few relevant papers were published in the early
2000s. More than 50% of the articles reviewed apply integrated approaches to
tackle the supplier selection problem. At the same time, only 8% of the studies
address non-conventional approaches. Moreover, we found only four papers that
specifically implement discrete-choice experiments to model supplier selection.
The most popular publication destinations were the International Journal of
Production Research, Expert Systems with Applications, European Journal of
Operational Research, International Journal of Integrated Supply Management,
and International Journal of Production Economics.
Figure 2: Chronological distribution of
articles.
3.
SUPPLIER SELECTION METHODOLOGIES
Supplier selection approaches can be
classified into three categories: individual, integrated, and non-conventional
approaches. The most popular individual approaches are grouped into decision
support systems and supplier selection modeling papers. The most common hybrid
methods include the integrated fuzzy approach and AHP. The discussion of
non-conventional approaches mainly develops around market utility-based models
that, so far, have been rarely used in the supplier selection literature.
3.1.
Individual approaches
3.1.1. Decision support systems
As purchasing managers deal with
significant difficulties when evaluating and choosing a supplier, there is a
need to integrate available decision-making methods into a robust system that
can assist practitioners facing multi-criteria decisions. Generally, decision
support systems (DSSs) rely on computer applications to handle business data
(collection, structuring, and analysis), thereby contributing to quality
business decision-making. In the operations management literature, supplier
selection is usually regarded as a multi-criteria decision-making (MCDM)
problem. Therefore, many MCDM support systems have been deployed to analyze and
resolve the issues related to supplier selection. The section below discusses
some of the most popular DSS techniques and their applications.
Analytic hierarchy process
Perhaps the most dominant DSS in the
supplier selection literature is AHP. AHP is a decision-making procedure
originally developed by Saaty in the 1970s. Its
primary aim is to offer solutions to decision problems in multivariate
environments. AHP is used to derive ratio-scales from both discrete and
continuous paired comparisons in multi-level hierarchical structures. The
process establishes decision weights for each criterion by organizing them in a
hierarchical manner (Saaty, 1987; Bernasconi
et al., 2010). AHP is widely applied in multi-criteria decision-making,
planning and resource allocation, and conflict resolution (Gaikwad et al., 2016).
Given a family of n ≥ 2 items of a decision problem
(e.g., three alternatives) to be compared for a given attribute (e.g., one
criterion), in AHP, a response matrix A =
[aij] is constructed with the decision
maker’s assessments aij,
taken to measure on a subjective ratio scale the relative dominance of item i over item j. For all pairs of items i, j, it is assumed:
(1)
where wi and
wj
are the underlying subjective priority weights belonging to a vector w = (w1, w2, ..., wn)′, with w1 > 0, ..., wn >
0, and by convention ∑wj = 1; and where eij is a
multiplicative term introduced to account for errors and inconsistencies in
subjective judgments typically observed in practice.
The steps for the selection of best
supplier through the AHP process are explained as follows:
Step
1: Identify a set of criteria and sub-criteria, then establish a
hierarchical structure. The process begins by determining the set of criteria
for the evaluation and selection of suppliers. After the selection of criteria,
the AHP hierarchy is constructed.
Step
2: Constructing a pair-wise comparison matrix with a scale of relative
importance. Pair-wise comparison of the attribute is made using the scale given
in Table 1.
Table 1:
Measurement scales.
Intensity importance |
Definition |
Explanation |
9 |
Absolute importance |
The evidence favoring one
activity over another is of the highest possible order of affirmation. |
7 |
Very strong importance |
An activity is favored very
strongly over another. |
5 |
Strong importance |
Experience and judgment strongly
favor one activity over another. |
3 |
Weak importance |
Experience and judgment slightly
favored one activity over another. |
1 |
Equal importance |
Two activities contribute equally
to the objective. |
2, 4, 6, and 8 |
Intermediate values between
adjacent scale values |
When compromise is needed. |
SOURCE: Saaty (1990)
Pair-wise comparisons express the
relative importance of one item versus another in meeting a goal or criterion.
An attribute compared with itself is always assigned a value of 1; therefore,
all main diagonal entries of the pair-wise comparison matrix are 1.
(1)
where
Step 3: Finding the relative normalized weight or set
of priorities of each attribute by calculating the geometric mean (GM). In the
GM approximation approach, the GM of the elements in each row of the square
decision matrix is computed. This method is used to normalize the vector so
that its elements add to unity.
Step 4: Analysis of consistency in decision-making.
Every pair-wise comparison matrix should pass the consistency test. To measure
the degree of consistency, Saaty (1980) suggested the
consistency index, which is represented by equation (2).
(2)
For each n × n pair-wise comparison matrix A, it is possible to calculate the
eigenvalue λmax
and the eigenvector w (w1,
w2,…, wn) using the theory
of eigenvector shown in equation (3).
(3)
To employ
this index, comparisons can be made with a random index (RI) using the
consistency ratio (CR) represented by equation (4).
(4)
Usually, a CR
value of 0.1 or less is considered acceptable and reveals cognizant judgment
that could be attributed to the knowledge of the analyst. Followed by supplier
evaluation, order allocation is done.
AHP has been
extensively employed in the operations management literature to address
supplier selection issues. Bali and Amin (2017) use an integrated model of AHP
and linear programming for supplier evaluation and selection. Suppliers are
selected based on multiple criteria and ranked based on their performance using
the AHP technique. This is then used to determine the optimal quantity to be
procured from each of the best-ranked suppliers.
Fuzzy set theory
Fuzzy logic is an alternative approach to
computing. Rather than applying the typical ‘true or false’ (1 or 0) Boolean
logic, fuzzy logic is based on ‘degrees of truth’. Fuzzy logic is a way of
processing data by allowing partial set membership instead of crisp set
membership or non-membership. Fuzzy logic has been extended to handle the
concept of partial truth, where the truth value can range between completely
true and completely false.
Moreover, when linguistic variables are used,
these degrees can be managed by specific functions. Fuzzy logic has proven to
be an excellent choice for many control system applications from small,
hand-held products to large computerized process control systems. The fuzzy
inference system (FIS) uses FST to map inputs to outputs. The FST was presented
by Zadeh (1965) in his seminal paper ‘fuzzy sets’ in
information and control. In FST, fuzzy numbers are used to deal with ambiguity
in decision-making. A fuzzy number is a special set , (5)
where x is the value on the real line and is a continuous mapping from R1 to the close interval
[0,1]. A triangular fuzzy number (TFN) can be donated as M = (l, m, u), its membership function is equal to:
(6)
where , l, m, and u are the lower, mode, and upper values
of the support of M, respectively. When l
= m = u, it is a non-fuzzy number by convention (CHANG, 1996). The main
operational laws for two triangular fuzzy numbers M1 and M2
are as follows (KAUFMANN, 1991):
(7)
(8)
(9)
(10)
Figure 3 shows the membership function of a triangular fuzzy number. In
Figure 3, the numbers M = (l, u) represent
lower and upper values of the fuzzy number
M, respectively, whereas m is the
middle value of M.
Figure 3: Triangular membership function.
Only a few operations
management studies report the use of the Fuzzy Logic Toolbox in multi-criteria
decision-making. Paul and Azeem (2010a) designed a
model for selecting the optimal shift numbers, considering inventory
information, customer requirements, and machine reliability using fuzzy logic.
The model was developed for any kind of manufacturing company where shift
periods affect profit and cost.
Fuzzy control is used to optimize the number of shifts under the
constraints of raw materials, delivery deadline, demand, and machine breakdown,
among other factors. Paul and Azeem (2010b) used
fuzzy sets to tackle uncertainties inherent in actual flow shop scheduling
problems to minimize work-in-process inventory. Zemkova
(2011) used fuzzy sets in the performance evaluation of employees in a company,
where a multi-criteria evaluation was utilized.
Compromise programming: TOPSIS, VIKOR
The foundation of compromise
programming methods was established in the 1970s. As typical compromise
methods, TOPSIS and VIKOR are implemented based on the aggregating function
that shows the proximity to the ideal solution. The key difference between the
two techniques is that TOPSIS uses linear optimization to eliminate the units
of criteria function, whereas VIKOR is based on vector normalization. Mirahmadi and Teimoury (2012)
used fuzzy VIKOR, a fuzzy compromise solution, to rank and select suppliers.
Fuzzy logic and trapezoidal fuzzy numbers were utilized to overcome the
ambiguity of the evaluation process.
Other DSS methods
In recent decades, researchers have
applied other DSS techniques to tackle the supplier selection problem,
including multi-objective programming, case-based reasoning, and data
envelopment analysis. Karpak et al. (2011) discussed
the role of visual interactive GP (VIG) in solving multi-objective problems
pertaining to vendor selection. Application of VIG was discussed for two
situations: for allocation of a single product to multiple suppliers; and for
multi-renewal problems related to vendor selection and order allocation.
3.1.2. Supplier selection modeling papers
While it is unlikely that a single
supplier can excel in all the evaluation criteria, it is always desirable for
purchasers to select a vendor that performs well in most dimensions.
Nevertheless, manufacturing firms must trade-off between price, quality, and
other attributes when choosing a supplier. To address these complex
decision-making situations, a variety of supplier evaluation and selection
models have been developed in recent years.
Ávila et al. (2012) identified five
broad selection criteria for supplier selection: quality, financial, synergies,
cost, and production system. These authors proposed a model of linear weighting
to reflect the significance of all the factors based on a customized survey.
The model has a hierarchical structure and can be applied with AHP or value
analysis. The authors aimed to provide a selection reference model that could
serve as a guide for decision-making during supplier selection.
Kar (2009)
modeled supplier selection in e-procurement as a multi-criteria decision-making
problem. Supplier selection criteria and constraints are modeled using fuzzy
logic, which is further modeled as a multi-objective decision-making process by
combining neural networks and the analytic hierarchy process. Suppliers are
then classified as suitable suppliers or unsuitable suppliers.
Yang et al. (2007) applied
polychromatic sets to discuss the supplier selection problem and describe the
relevant factors. Supplier performance is evaluated based on delivery, cost,
flexibility, quality, and reliability. Masella and Rangone (2000) proposed a contingency approach for supplier
selection depending on the time frame and the content of co-operative
customer/supplier relationships.
3.2.
Integrated approaches
3.2.1. Integrated AHP approaches
Hassan et al. (2015) develop a
hybrid model using AHP, artificial neural networks (ANNs), and relative
reliability risk index (R3I) to score suppliers based on their performance. AHP
is implemented to rank suppliers based on criteria using pairwise comparison,
and the output of AHP is used in the ANN model for comparison. Leanness,
agility, resilience, and greenness paradigms are used in this paper as new
criteria for evaluating and selecting the best supplier. Silva and Schramm
(2015) developed a multi-criteria DSS model, PROMETHEE II, for supplier
selection. PROMETHEE II is applied to construct a ranking of suppliers
according to the inputs from stakeholders, then the robustness of the model is
analyzed to check its suitability for the construction industry.
Scott et al. (2015) proposed an
integrated DSS model using modified AHP and quality function deployment (QFD)
for supplier selection and order allocation in a multi-criteria and
multi-stakeholder business environment (e.g., the bioenergy industry). A
conceptual model was developed to identify supplier characteristics and
stakeholder requirements, which was used to process supplier evaluation. QFD
was used for various stakeholders to express their requirements and to
translate them into multiple comparable evaluation criteria for supplier
selection.
The most important information that
QFD provides is the weights of evaluating criteria, which are derived from the
importance ratings of stakeholder requirements together with the relationship
weightings between stakeholder requirements and evaluating criteria. Generally,
both importance ratings of stakeholder requirements and relationship weightings
are arbitrarily determined by the decision-makers. This might result in a
certain degree of inconsistency and, therefore, degrade the quality of
decisions made. Hence, AHP is used to evaluate these consistently.
3.2.2. Integrated fuzzy approaches
Fuzzy AHP (FAHP) is an integrated
approach that combines FST and AHP. The first FAHP study was proposed by van Laarhoven and Pedrycz (1983),
which compares the fuzzy ratio described by a triangular fuzzy number. These
authors used the framework of the practical region of relative weight. Fuzzy
consistency was determined as the existence of relative weights within the
region. Then, the highest/lowest set ranking method was developed to obtain a
clear ranking from the global fuzzy weight. The next step of FAHP was
introduced by Chang (1996), who used the triangular fuzzy number to create a
pairwise comparison scale of FAHP.
Lee et al. (2009) used fuzzy
multiple GP to select suppliers. FAHP was first applied to analyze the
importance of multiple factors by incorporating expert opinions. These factors
included cost, yield, and the number of suppliers. Multi-choice GP was then
used to consider the limits of various resources and formulate the constraints.
Kumar et al. (2004) proposed a fuzzy
GP approach for vendor selection in the supply chain. The vendor selection
process was formulated as a fuzzy mixed integer GP vendor selection problem
that included three primary goals: minimizing the net cost, minimizing the net
rejections, and minimizing the net late deliveries. An illustration from a
realistic situation was included to demonstrate the effectiveness of the model.
3.2.3. Other integrated approaches
Compromise programming
Multiple studies have suggested new
methods based on TOPSIS and VIKOR, or even combining these with other MCDM
techniques. Shahroudi and Tonekaboni
(2012) developed a TOPSIS methodology to evaluate suppliers. Bhutia and Phipon (2012) have proposed
an integrated approach to evaluate and rank suppliers using AHP and TOPSIS. Shemshadi et al. (2011) extended the VIKOR method with a
mechanism to extract and deploy objective weights based on the Shannon entropy
concept. These authors also presented a numerical example to illustrate an
application of the proposed method.
Goal programming
GP is one of the most popular
mathematical programming techniques used in the operations management
literature. GP can be considered an extension of linear programming that is
used to deal with multiple and conflicting measures. Each of the measures is
given a goal value to be achieved. Erdem and Göçen (2012) proposed a DSS model for supplier selection
and order allocation. Initially, an AHP model was developed for qualitative and
quantitative evaluation of suppliers.
Based on these evaluations, a GP
model was developed for order allocation among suppliers. The models were
integrated into a DSS that provides a dynamic, flexible, and fast
decision-making environment. Inputs from suppliers were used for their
evaluation using AHP, and based on criteria from buyers, suppliers were weighed
or graded. Similarly, order goals from buyers were taken as inputs for order
allocation using GP.
ANP, LP, DEMATEL, and PROMETHEE
Xiaobing
and Qiang (2007) developed a vendor selection model
based on procurement cost and delivery time. A mathematical model based on an
adaptive genetic algorithm was later developed for multi-constraint supplier
selection, showing good validity and efficiency. Xu and Xiang-Yang (2007) have
proposed a multiphase supplier selection model based on supplier development
orientation. The model has three phases: pre-selection of vendor using DSS
PROMETHEE, evaluation of supplier using ANP, and supplier development using
relationship value estimation criteria.
Lin et al. (2011) used the
enterprise resource planning (ERP) system in the push and pull concept for
supplier selection based on resource and profit creation. The analytic network
process (ANP) and technique for order preference by similarity to ideal
solution (TOPSIS) were used to calculate the weights and rank suppliers. Linear
programming (LP) effectively allocated order quantity to each vendor for
real-time application in the electronics industry. Dalalah
et al. (2011) presented a hybrid fuzzy model (modified DEMATEL, modified TOPSIS
model) for group multi-criteria decision-making in supplier selection. The
modified DEMATEL deals with the influential relationship between evaluation
criteria and divides them into cause and effect groups. The modified TOPSIS
evaluates the criteria against each alternative using a fuzzy distance measure.
Integrated Delphi approaches
The Delphi method has been widely
applied in social sciences as a tool for screening criteria and finding
customized criteria and is attractive because of its ability to guide group
judgments toward a final decision (Mckenna, 1994). In
the supplier selection literature, the Delphi method has been applied in
combination with other approaches. Liao (2010) used the Delphi technique to
obtain the criteria, transferred these into the Taguchi loss function, and then
selected the best supplier in a food manufacturing factory in combination with
AHP-based weights.
To identify the most appropriate
third-party logistics provider for the Iranian automobile industry, Yazdi et al. (2018) integrated three approaches: entropy,
Delphi, and evaluation by an area-based method of ranking (EAMR). These authors
used the Delphi method to identify the criteria, the entropy method to
determine the weights of criteria, and EAMR to rank the alternatives.
Kaviani et
al. (2019) proposed a hybrid multi-criteria tool to assist with
decision-making. This tool integrates the Delphi, Shannon entropy, and
evaluation based on distance from average solution (EDAS) techniques under a
grey environment that introduces accurate computation, thereby overcoming the
weakness in the AHP, TOPSIS, and VIKOR techniques and helping with supplier
selection. There is also relevant work on applying the Fuzzy Delphi method to
validate supplier selection criteria in the fuzzy environment and then using
the integrated AHP plus DEMATEL approach to determine the weights and
cause-effects relationships of parameters (KUMAR et al., 2018).
3.3.
Non-conventional approaches
3.3.1. Market utility-based approaches
Market
utility-based approaches can evaluate the relative weights of different
value-added features of suppliers in procurement decision-making processes (Ben-Akiva; Lerman, 1991; Mcfadden, 1986; Louviere et al., 2001). Some of these
methods (e.g., discrete-choice analyses or DCAs) have been applied in the
social sciences, including marketing, transportation planning, environmental
resource economics, service design, and operations management. Examples of
discrete choice and conjoint analyses in the operations management literature
include product line decisions (Yano & Dobson, 1998), optimal service
design (Verma et al., 2001), and operations capacity
planning (Pullman; Moore, 1999). Also, discrete choice models have been applied
in a variety of operational settings (Ding et al., 2007; Victorino
et al., 2005).
Although
market utility-based approaches and, particularly, discrete-choice models are
effective in operations management, little work has been done to integrate
those techniques into the supplier selection problem. Perhaps one of the
earliest and most prominent studies in this field is the discrete choice
analysis performed by Verma and Pullman (1998).
These
authors proposed a two-staged experimental setup. In the first stage, the
participants (purchasing managers) are asked to rank supplier attributes using
Likert scale questions. The second stage involves a discrete choice experiment
examining the actual choice of suppliers. The authors claim that purchasing
managers’ stated preferences regarding the importance of supplier selection
criteria do not necessarily coincide with their actual choices. These results
indicate that although managers say that quality is the most important
attribute for a supplier, they actually choose suppliers based largely on cost
and delivery performance.
Li
et al. (2006) extended the use of DCA in the supplier selection literature by
comparing the attributes of an existing supplier to that of a new supplier.
These authors also extended the theoretical framework to include supplier
switching inertia (they prepared individually customized discrete choice
experiments asking the respondents to either switch to the new supplier or
remain with the existing supplier), confirming the existence of switching
inertia and, as a result, the competitive asymmetry between current and new
suppliers from a demand-side perspective.
Verma et al. (2008) provided an overview of the recent
advances in discrete choice modeling for applications in the service sector.
These authors provided directions for designing and executing discrete choice
studies for services and discussed several examples for multiple industries,
including healthcare, financial services, retail, hospitality, and online
services. Van der Rhee et al. (2009) explored how executives and managers
trade-off amongst various competitive dimensions, such as cost, delivery
performance, flexibility, and value-added service/support when selecting a
supplier for raw materials, with the condition that minimum acceptable quality
is guaranteed.
3.3.2. Other emerging approaches
Incorporating
risk and uncertainty into the supplier selection problem is one of the most
prominent emerging concerns in operations management literature. Micheli et al. (2009) studied the mitigation of
risks in the overall supply chain using a risk-efficiency-based supplier
selection approach in engineering, procurement, and construction industries. Li
et al. (2015) used an agent-based simulation model to deal with supplier
selection by incorporating risk scenarios under constraints based on supplier
profit and customer service level.
Ebahimipour et al. (2016) examined product structure for a
multi-criteria and multi-product supplier selection problem with uncertainty.
Evaluation indices are also addressed by the emerging approaches for supplier
evaluation and selection. Duan et al. (2010)
presented a model that uses canonical correlation discriminant analysis to
provide a flexible way of determining the evaluation indices to be used for
selecting suppliers. Tang et al. (2009) constructed a supplier evaluation index
system for a comprehensive evaluation of suppliers by the features of the
tourism supply chain and an entropy-weighted extension matter-element model.
Amongst
other non-conventional supplier selection models, the ordinal game theory
approach has also been used in several applications in the last decade. The
ordinal game theory approach helps two competing companies select the most
suitable supplier in terms of three criteria – quality, cost, and delivery
time. Kermani et al. (2012) provide a numerical
example of such a game between two manufacturers.
4.
DISCUSSION
The results of this review show that
the existing multi-criteria decision-making tools provide various solutions to
supplier evaluation and selection problems. To overcome the limitations and
weaknesses of individual tools, researchers often integrate multiple
approaches. In our review, more than 50% of the 177 papers included hybrid
approaches. The prevalence of integrated methods can be attributed to the
growing complexity of supplier selection and the need to further develop
existing approaches while utilizing their advantages.
At the same time, around 40% of
papers are still based on individual approaches, which are easy to use and do
not require significant time investment. Despite the growth of non-conventional
methods in recent years, such methods are addressed in only 8% of the reviewed
articles. This indicates that the supplier selection domain has space for
further alternative solutions. Moreover, there is a need to further test
existing non-conventional methods, such as market utility-based approaches, as
the current regional and industry scope is relatively limited.
One of the objectives of this paper
was to determine the most frequently used approaches in supplier selection
literature. The majority of the studies apply DSSs to address the challenges
associated with supplier evaluation and selection. Amongst DSSs, the AHP is,
perhaps, the most dominant technique (individually and when integrated with
other methods).
AHP has attracted significant
attention due to its simplicity and ease of use, and has been integrated with
various other techniques, including FST, artificial neural network,
bi-negotiation, data envelopment analysis, GP, grey relational analysis, and multi-objective
programming. The integration of AHP with FST is especially popular given FST’s
ability to cope with uncertain and imprecise information.
In most reviewed articles, the
decisions or judgments of decision-makers are brought out using linguistic
variables with corresponding fuzzy numbers (Zouggari
& Benyoucef, 2012; Dargi
et al., 2014; Xie et al., 2016). In addition, the
integration of FST with other MCDM methods provides more robust solutions by
overcoming the limitations of individual approaches. For instance, ANP is used
to provide weights of interdependent criteria, while suppliers are ranked using
PROMETHEE (Hanane et al., 2015). Similarly, AHP is
used to generate weights of evaluation criteria, and TOPSIS is used to rank
alternative suppliers (Wang & Zhou, 2011). Most of these applications use
FST to address the imprecise judgments of decision-makers.
Below, the most prevalent approaches
in supplier selection literature are critically analyzed, discussing their
major strengths and weaknesses. DSSs, such as AHP and ANP, are commonly used to
address the supplier selection problem. Since the usual structure of the
problem involves a finite number of alternatives under a finite number of
evaluation criteria, the use of DSSs is considered the most plausible approach
(Ho et al., 2010, Yildiz & Yayla,
2015).
AHP is widely used due to its
simplicity and ease of use, but it also has limitations. Whenever faced with a
large number of alternative criteria, the pairwise comparison process becomes
time-consuming. If a problem involves n
criteria and m suppliers, the total
number of pairwise comparisons is (n(n-1)
+ nm(m-1)) / 2. Increasing n, m, or both will result in a second-order
increase in the number of evaluations to be performed by decision-makers.
Moreover, Saaty
and Özdemir (2003) have noted that the number of
elements to be compared in AHP should not be more than seven (or seven
plus/minus two) to maintain consistency in judgments. This limitation poses a
drawback to some supplier selection problems as a large number of candidate
suppliers can cause problems.
The reviewed papers (Blaszczyk & Wachowicz, 2010;
Wang & Sun, 2011; Xu et al., 2013) satisfy this requirement, except for
Mukherjee et al. (2009), with ten alternative suppliers, which might raise
questions about the validity of the results. Furthermore, the issue of rank
reversal, raised by Forman and Gass (2001), has
become a major drawback of the AHP method, which states that when an additional
alternative is added, axioms of decision theories state that the ranking of the
original alternatives must not be changed (prohibiting rank reversal).
However, the original AHP
formulation allows for rank reversal. In supplier selection problems, this
issue might pose serious implications as it can alter the rankings of candidate
suppliers when a new supplier is considered. Finally, the assumption of
independence among and within criteria and alternatives is also a drawback of
the AHP as real-world supplier selection problems have inherent
interdependencies of criteria and/or alternatives. For instance, many studies (Wang
& Sun, 2011) use AHP assuming that cost and quality are not interdependent,
whereas these two criteria are related.
While the use of ANP in supplier
selection problems addresses the issue of both rank reversal and
interdependence, the number of pairwise comparisons increases with the number
of interdependencies introduced into the decision system. As a benefit, it
reflects case-specific scenarios in supplier selection problems. As a drawback,
the interdependencies might provide general insights, and their introduction to
the problem might not always be valid (Yulugkural et
al., 2013).
TOPSIS is also widely used as a
multi-criteria decision-making tool in supplier selection problems due to its
operational simplicity (Huo et al., 2011; Jiang et
al., 2009; Wang & Song, 2010). It also overcomes the ‘seven plus or minus
two’ limitation of the AHP/ANP approaches; it can evaluate a finite large
number of suppliers that reflect real-life conditions. However, the use of
Euclidean distance as the basis for ranking suppliers does not consider the
interdependencies of evaluation criteria. Furthermore, Madi
et al. (2016) claim that TOPSIS might have issues around reliability and consistency,
which can affect supplier selection.
The review of non-conventional
approaches is an important part of this study. While most of the empirical
articles are based on the managers’ rating of the perceived importance of
different supplier attributes, this group of papers studies how managers
actually choose suppliers. An actual choice of supplier involves evaluating the
characteristics of the suppliers based on their attributes and selecting one or
more supplier(s) that best suit the needs of the firm. Market utility-based
approaches, such as discrete-choice analyses, can assess the relative weights
of price, quality, delivery, flexibility, and various value-added features in
various managerial decision-making processes (Louviere et al., 2001).
These methods have been widely used
in many social sciences, including marketing, transportation planning,
environmental resource economics, service design, and operations management (Pullman
& Moore, 1999; Pullman et al., 2001; Verma et
al., 2001). Examples of discrete choice and conjoint analysis in operations
management include product line decisions (Yano & Dobson, 1998), optimal
service design (Verma et al., 2001), and operations
capacity planning (Pullman & Moore, 1999).
Also, Ding et al. (2007) and Victorino et al. (2005) have applied discrete choice models
in a variety of operational settings. Furthermore, an emerging emphasis on
incorporating behavioral aspects into manufacturing and service operations
models (Bendoly et al., 2006) suggests that future
growth of discrete-choice analyses and related approaches is likely within the
operations management literature.
Finally, this paper is aimed at
analyzing the development of the relevance of supplier attributes and assessing
the most common evaluation criteria. First, it was observed that price is not
the only attribute considered when evaluating and choosing suppliers. Moreover,
price is no longer the criterion with the highest perceived importance.
Instead, when making decisions, factors such as quality or delivery performance
are often considered. This is in line with the growing consensus that
traditional single-criterion approaches based on the lowest cost are neither
supportive nor robust enough.
More than 80% of the publications
consider quality in supplier selection. Various quality-related attributes have
been identified among the papers, such as ‘acceptable parts per million’,
‘compliance with quality’, ‘quality control rejection rate’, and ‘end-customer
rejection rate’. The second most popular criterion is delivery (slightly below
80%). The most common attributes include ‘compliance with the due date’, ‘fill
rate’, ‘delivery lead time’, and ‘waiting time’.
Finally, more than 70% of articles
mention price/cost as a key supplier selection criterion. Cost-related
attributes include ‘purchase price’, ‘logistics costs’, ‘cost reduction
performance’, ‘direct cost’, ‘fluctuation on costs’, and ‘manufacturing cost’.
Based on these findings, price/cost is not the most widely adopted criterion.
The traditional single-criterion approach based on the lowest cost bidding is
no longer supportive or robust enough in contemporary supply chain management.
Moreover, with increasing
globalization, changing customer preference, speed of delivery, increased supply
chain risk, increased number of suppliers, increased outsourcing, improved
purchasing function, use of the Internet, increased options, and government
regulation and transparency, more complex supplier attributes have been
introduced since 1998, which now play key roles in purchasing decisions (Boer et
al., 2001). Table 2 provides a comprehensive list of the most recent supplier
rating criteria.
Table 2.
Criteria for supplier selection rating system (from 1998 to 2018).
Supplier attribute |
Frequency |
Representative authors |
Quality |
143 |
Verma and Pullman (1998), Lee et al. (2003) |
Delivery |
136 |
Pullman et al. (2001),
van der Rhee et al. (2009) |
Cost |
127 |
Verma and Pullman (1998), Lin et al. (2005) |
Financial stability |
102 |
Masella and Rangone (2000), Wang et al. (2004) |
Service |
101 |
Vonderembse and Tracey (1999), Lee et al. (2003) |
Communication |
97 |
Louviere et al. (2000), Liao and Rittscher (2007) |
Risk management |
96 |
Li et al. (2006), Karpak et al. (1999) |
Reputation and
credibility |
92 |
Kaplan and Sawhney
(2000), Kannan and Tan (2002) |
Facilities and capacity |
86 |
Karpak et al. (1999), Basnet and Leung (2005) |
Repair services |
84 |
Bhutta and Huq (2002), Chan (2003) |
Promotion potential |
80 |
Carter and Jennings (2004), Choy et al.
(2004) |
Performance history |
72 |
Liao and Rittscher
(2007), Li et al. (2006) |
Quality management
practices |
72 |
Pullman and Moore (1999), Talluri (2006) |
Location |
70 |
Sarkis and Semple (1999), Pullman et al. (2001) |
Automated storage and
retrieval systems |
67 |
Wang et al. (2004), Hanfield
and Nichols (1999) |
Rapid prototyping
techniques |
65 |
Huang and Keskar
(2007), Ittner and Larcker
(1999) |
Computer-aided design,
manufacturing and/or engineering |
64 |
Gonzàlez et al. (2004), Eltantawy et al. (2003), Vonderembse and Tracey (1999) |
Automated handling |
60 |
Ding et al. (2007), Choy et al. (2003) |
Integration
capabilities |
56 |
Chen et al. (2006), Carter and Jennings
(2004) |
Experience |
51 |
Cakravastia and Takahashi (2004), Chan (2003) |
Management and
organization |
49 |
Amoako-Gyampah and Acquaah (2008) |
Insurance and warranty
policies |
48 |
Alidrisi (2014), Amin and Razmi (2009) |
Environmental and
social responsibility |
46 |
Amid et al. (2009), Dai und Blackhurst (2012) |
Continuous improvement |
45 |
Dayama and Jidugu (2009), Hamdi
et al. (2014) |
R&D and innovation |
41 |
Kasirian and Yusuff (2013), Ma and Liu (2011) |
Transportation and
logistics |
39 |
Opricovic (1998), Osman and Demirli (2009) |
Minimum and maximum
order quantities |
32 |
Pan et al. (2014), Saaty
and Sagir (2009) |
Financial terms of the agreement |
31 |
Wu et al. (2008), Schneider et al. (2009) |
Technical capabilities |
26 |
Ren and Lin (2009), Ravindran
et al. (2010) |
Strategic fit |
26 |
Peng and Wang (2011), Qin et al. (2017) |
Impression |
24 |
Omosigho and Omorogbe (2014) |
Attitude |
21 |
Mian (2011), Kumaraswamy et al. (2011) |
Labor relations record |
19 |
Karimi and Rezaenia (2014), Lima et al. (2013) |
Training aids |
18 |
Shen et al. (2010), Yu and Wong (2014) |
In summary, for any firm, supplier
selection is a crucial decision-making process that directly affects purchasing
and overall operations. Choosing the optimal suppliers enables a firm to remain
competitive. While there is no ideal methodology, various approaches can be
efficient depending on supplier selection setup and complexity. Also, as noted
here, given the changing nature of the operations management domain, several
emerging approaches are expected to contribute to the supplier selection
literature.
5.
CONCLUDING REMARKS AND DIRECTIONS
FOR FURTHER RESEARCH
Global competition, high customer
expectations, and harsh economic conditions force companies to rely on external
vendors and suppliers. Supplier selection is a strategic decision that requires
companies to trade-off between multiple criteria, thereby creating ambiguity
and imprecision. Over time, the complexity of supplier selection has
significantly increased, and new methods have emerged for evaluating and
selecting suppliers. Moreover, to overcome limitations inherent in single
methods, integrated techniques are also being used to solve real-life problems.
This review of the supplier
selection literature (from 1998 to 2018) included 170 articles that have
applied a variety of approaches. The reviewed literature includes articles that
have proposed individual methods, as well as articles reporting integrated
techniques. Among those articles that applied a single method, AHP was most
common. The simplicity and ease of use of AHP make it a valuable tool for
researchers dealing with multi-criteria decision-making. It was also found that
the most prevalent integrated approaches were based on FST.
These findings show that recent
studies on supplier selection have placed much emphasis on overcoming the
subjective and human factors inherent in supplier selection problems. In
addition to the traditional approaches to supplier selection (individual and
integrated), this review also identified a third category of articles: those
applying non-conventional, emerging approaches (methodologies addressing
specific supplier selection themes and aspects that have not yet been
well-studied).
A strength of this review is that,
after identifying and categorizing the published methods, their strengths and
weaknesses have been investigated, while also analyzing the most prevalent
attributes used by the various articles. To our knowledge, this is the first
review article to address non-conventional approaches, such as market
utility-based methods. Furthermore, we include an updated list of attributes
with emerging supplier selection criteria used in recent studies.
Based on our findings, we conclude
that there remains much opportunity for future work in the supplier selection
domain. First, some of the existing prevalent methods (e.g., FST) could be
developed further and tested on new data samples. A limited number of
evaluation criteria are used for this method, and it should be possible to test
a more comprehensive list of attributes. Furthermore, the membership functions
and criterion importance and/or performance scale intervals are unchanged from
one study to another.
Given that fuzzy logic can be
applied based on other membership functions and importance/performance scales,
it would be interesting to test those and compare the results across different
model specifications. Also, most of the studies have examined the topic for
automotive and assembly, chemical, or metal processing industries. Similarly,
they have mainly developed case studies based on a single country or a region.
These lists could be extended to test the proposed models among richer
industry-country cohorts.
6.
ACKNOWLEDGEMENTS
The author gratefully acknowledges
the support of the International Visegrad Fund from
the Visegrad Scholarship Program. The author wishes
to thank Mikołaj Czajkowski
for his advice and guidance.
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