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Research paper
Development of a supplier selection model
using fuzzy logicSharon M. Ordoobadi
Charlton College of Business, University of Massachusetts-Dartmouth, Dartmouth, MA, USA
AbstractPurpose This paper aims to provide a tool for decision makers to help them with selection of the appropriate supplier.Design/methodology/approach Companies often depend on their suppliers to meet customers demands. Thus, the key to the success of thesecompanies is selection of the appropriate supplier. A methodology is proposed to address this issue by first identifying the appropriate selection criteriaand then developing a mechanism for their inclusion and measurement in the evaluation process. Such an evaluation process requires decision makerspreferences on the importance of these criteria as inputs.Findings Human assessments contain some degree of subjectivity that often cannot be expressed in pure numeric scales and requires linguisticexpressions. To capture this subjectivity the authors have applied fuzzy logic that allows the decision makers to express their preferences/opinions in
linguistic terms. Decision makers preferences on appropriate criteria as well as his/her perception of the supplier performance with respect to thesecriteria are elicited. Fuzzy membership functions are used to convert these preferences expressed in linguistic terms into fuzzy numbers. Fuzzymathematical operators are then applied to determine a fuzzy score for each supplier. These fuzzy scores are in turn translated into crisp scores to allowthe ranking of the suppliers. The proposed methodology is multidisciplinary across several diverse disciplines like mathematics, psychology, andoperations management.Practical implications The procedure proposed here can help companies to identify the best supplier.Originality/value The paper describes a decision model that incorporates decision makers subjective assessments and applies fuzzy arithmeticoperators to manipulate and quantify these assessments.
Keywords Suppliers, Fuzzy logic, Linguistics, Supplier evaluation
Paper type Research paper
1. Background and motivationIn todays competitive market proper management of the
supply chain is the key to success of every company. Selection
of the appropriate supplier is a major requirement for an
effective supply chain. Thus, the subject has been the focus of
numerous studies both theoretical and empirical. The work of
Dickson (1966) was one of the original studies in the supplier
selection area. He identified 23 criteria for assessing the
performance of suppliers based on responses from 170
managers and purchasing agents. In addition the respondents
were asked to specify the importance of each criterion on a
five-point scale and the average values over all the respondents
were calculated to provide the ranking of these criteria.
Majority of the studies that followed have used results of
Dicksons study as a foundation and recommended varioustechniques for ranking of these attributes. A brief overview of
the approaches for evaluation and selection of suppliers that
were uncovered in the literature follows:
. Categorical method (Timmerman, 1986; Willis and
Huston, 1990). Once the list of attributes to use in the
evaluation process is established, the suppliers
performance on each attribute is assessed in categorical
terms such as good, fair, and poor. The supplier
receiving the most good rating is considered the best.
This method is easy to use, inexpensive, and requires
minimum data. However, it is largely an intuitive process,
heavily dependent on personal judgment of the evaluator,
and all criteria are assumed to have equal importance.. Linear weighted average method (Timmerman, 1986). This
method assigns relative importance weight to each
criterion. The evaluator then rates the performance of
suppliers with respect to each criterion. The supplier
performance ratings are multiplied by criterion
importance weights to calculate a weighted score. Theseweighted scores are then summed over all the criteria to
obtain one aggregate weighted score for each supplier.
The supplier with the highest weighted score is the best.
Although this method no longer treats the criteria as
having equal importance, the subjectivity of the decision
maker in assigning weights remains as an issue.. Cost-ratio method (Timmerman, 1986; Dobler et al.,
1990). The total cost related to quality, delivery, and
service are calculated and expressed as a proportion of the
total firms purchase price. The supplier who can provide
the lowest cost is the best. This method is more precise
The current issue and full text archive of this journal is available at
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Supply Chain Management: An International Journal
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q Emerald Group Publishing Limited [ISSN 1359-8546]
[DOI 10.1108/13598540910970144]
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compare to the other aforementioned methods. However,
it requires a comprehensive cost-accounting system to
identify the precise cost data.. Vendor profile analysis(Thompson, 1990). This is a modified
weighted average method in order to reduce the uncertainty
involved in the assignment of the ratings. A Monte Carlo
simulation technique is used to replace the rating based
solely on intuitive judgment. The use of Monte Carlosimulation has two advantages over the weighted average
technique. It simplifies the decision makers input to the
evaluation process and provides output that has
considerably more information for the decision maker.. Dimensional analysis (Willis et al., 1993; Youssef et al.,
1996). The evaluation process involves a series of one-on-
one comparisons and can compare only two suppliers at a
time. The dimensional analysis ratio can be greater than
one, equal to 1, or less than one. The main difficulty is
that the process becomes very time consuming if there are
a large number of suppliers that should be evaluated.. Vendor rating with analytical hierarchy process (Nydick,
1992; Ghodsypour and OBrien, 1998; Yaha and
Kingsman, 1999; Bhutta and Huq, 2002; Kahraman
et al., 2003; Teng and Jaramillo, 2005). One of the major
difficulties of the aforementioned methods was the
assigning of the weights to the attributes. These weights
were assigned purely based on personal judgment and
intuition of the decision maker. To overcome this difficulty
researchers proposed the use of analytical hierarchy
process (AHP). AHP provides a systematic way for
determining the attributes weights by a series of pair wise
comparisons of all attributes. Once weights of the
attributes are determined by AHP they are used to
construct a vendor evaluation and selection system.
Majority of the above mentioned models focus on identifying
supplier attributes and then using various techniques for
evaluation of these attributes. A common feature among thesetechniques is how the rankings of the potential suppliers are
determined. Most often these rankings are assigned based on
two factors: the importance weight of the attributes, and
suppliers performance with respect to these attributes. Both
of these factors are decision maker-specific and thus should be
solicited from the individuals. Often elicitation process is
conducted by asking the decision makers to express their
preferences in pure numeric scales. The main difficulty with
such an elicitation procedure is that the subjectivity and
imprecision associated with perceptions are lost by forcing the
decision makers to use numeric scales.
To overcome the above mentioned difficulty there is a need
for a mechanism that captures the subjectivity involved in
expressing individual preferences. Subjectivity of human
assessments and beliefs can best be expressed in linguistic
terms without the limitation of the numeric scales boundaries.
A methodology that allows decision makers preferences to be
expressed in linguistic terms is fuzzy logic. Fuzzy set theory is
a powerful tool for solving many real world problems (like
supplier selection) that involve some degree of imprecision
and ambiguity. Thus, this methodology is applied in our
proposed model.
The proposed model starts by identifying the appropriate
selection criteria. These criteria arethen used forevaluation and
ranking of the potential suppliers. Such an evaluation is
performed based on the importance of the selection criteria to
the decision maker as well as his/her perception of the suppliers
performance with respect to these criteria. Using fuzzy
membership functions and fuzzy mathematical operators a
fuzzy score is determined for each supplier. These fuzzy scores
are then converted to crisp values through defuzzification
process to make the ranking of the suppliers a straightforward
task. The supplier with the highest ranking is selected.
The rest of the paper is organized as follows: researchdesign and identification of the selection criteria are covered
in section 2. A brief overview of fuzzy set theory and fuzzy
arithmetic operators as well as a review of fuzzy logic
applications are provided in section 3. Section 4 covers the
development of the evaluation methodology. A numerical
example is provided in section 5 to illustrate the application of
the proposed model. Finally the paper concludes with
summary and suggestions for future research in section 6.
2. Research design
The purpose of this research is to help decision makers with
management of their supply chain by providing them a
guideline for selection of an appropriate supplier. This task is
done in a two-step process:
1 Identification of the supplier selection criteria.
2 Development of a methodology that uses these criteria for
evaluation and ranking of the suppliers.
The first step is detailed in the following section and the
second step is explained in section 4.
2.1 Identification of the supplier selection criteria
To identify a set of criteria that is well accepted we surveyed
the vendor selection literature (both empirical and
theoretical). Table I summarizes the supplier attributes
found in the literature along with their corresponding authors.
After careful review of the criteria uncovered in the
literature and eliminating the duplications five main criteriaand several sub-criteria were identified. Figure 1 shows these
criteria and their sub-criteria in a tree hierarchical
configuration. Of course the factors considered in supplier
selection are situation-specific and each company will develop
its own selection criteria when facing with finding appropriate
suppliers. We are using these criteria in the development of
our methodology however an individual decision maker can
easily customize the criteria to fit his/her situation.
Once these criteria are set then there is a need for a
mechanism that allows comparison among alternatives. To
develop such a mechanism decision makers inputs are
required in two areas. First, relative importance of each
criterion to the decision maker and second his/her perception
of supplier performance with respect to the selection criteria.
Both of these involve subjective assessments and to capture
such subjectivity fuzzy logic is used to elicit the decision
makers preferences. A brief overview of fuzzy set theory along
with a review of its applications in managerial decision-
making is provided in the next section.
3. Fuzzy set theory
Fuzzy set theory introduced by (Zadeh, 1965) is used to
represent the vagueness of human thinking; it expands
traditional logic to include instances of partial truth (Bonde,
1997):
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TableIListofsupplierattributesan
dtheircorrespondingauthors
Authors
Supplierattributes
Dickson
(1966)
Yahaand
Kingsman
(1999)
Parasuramann
etal.(1988)
Leeetal.
(2001)
Ka
hraman
etal.(2003)
Ohdarand
Ray(2004)
SupplyChain
Council(1999)
Ellram
(1987
)
Lehmannand
OShaughnessy
(1974)
Naudeand
Lockett
(1993)
Mumm
alaneni
etal.
(1996)
Dengand
Wortzel
(1995)
Quality
Delivery
Performancehistory
Warrantiesandclaim
policies
Productionfacilities
Price
Technicalcapability
Innovativeness
Financialposition
Responsiveness
Proceduralcompliance
Industryreputation
Operatingcontrols
Service
Packagingability
Laborrelationsrecord
Pastbusiness/experience
Geographicallocation
Managementattitude
Reliability
Assurance
Empathy
R&Dcapability
Globalization
Value-addedproductivity
Productionflexibility
Assets
Futuremanufacturing
capabilities
Safetyrecord
Trainingaids
Professionalism
Competence/expertise
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Figure 1Hierarchy of supplier selection criteria
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Fuzzy set theory encompasses fuzzy logic, fuzzy arithmetic, fuzzy
mathematical programming, fuzzy topology, fuzzy graph theory, and fuzzydata analysis, though the term fuzzy logic is often used to describe all of these(Kahramanet al. 2003).
A review of applications of fuzzy sets is provided in the
following section.
3.1 A review of fuzzy sets applicationsFuzzy set theory has diverse applications and has been used in
the managerial decision making for handling uncertainties
and imprecise information involved in the process. Several
researchers have applied fuzzy sets to address various issues in
the supply chain management field. (Gonzalez and
Fernandez, 2000) have applied fuzzy set theory to represent
imprecise information related to distribution problems. A
fuzzy multi-criteria decision-making procedure is applied to
find a set of optimal solution with respect to the suppliers
performance (Gunasekaran et al., 2006). An evolutionary
fuzzy system has been used by (Ohdar and Ray, 2004) to
evaluate suppliers performance in supply chain. Kahraman
et al. (2003) have applied fuzzy analytic hierarchy process for
selection of the best vendor for outsourcing purposes. Dogan
and Sahim (2003) have used activity-based costing and fuzzy
present-worth technique to develop a methodology for
supplier selection.
In addition to supply chain management, fuzzy sets have been
applied in several other areas. Researchers in the accounting
and finance field have used fuzzy sets to develop guidelines for
investment decisions (Korvin et al., 1995; Tanaka et al., 1976).
While fuzzy analytical hierarchy process has been used by others
(Bayou et al., 2007) to select the optimum mechanism for
developing accounting standard. Managers have used fuzzy sets
to evaluate the seriousness of construction dispute of a
construction project in order to take appropriate corrective
actions (Cheung et al., 2001). To select the appropriate process
for quality improvement (Chanet al., 2002) have applied fuzzy
sets for evaluation purposes. A methodology for measuringmanufacturing flexibility using fuzzy logic has been developed
by Tsourveloudis and Phillis (1998). Wu et al. (2007) has
combined fuzzy multilayered analytic hierarchy process
(FMAHP) with group decision-making process to seek the
consensus of experts. A fuzzy multicriteria decision-making
approach has been developed by Thomaidis et al. (2006) for
evaluation of information technology (IT) projects.
3.2 A brief overview of fuzzy set theory
In traditional set theory, elements have either complete
membership or complete non-membership in a given set.
With fuzzy set theory, intermediate degrees of membership
are allowed. The coding of the degree of membership to each
of the elements in the set is defined as the membership
function of the fuzzy set. The membership function is
commonly depicted as a membership curve. The membership
curve contains three main components: the horizontal axis
consisting of domain elements (usually real numbers) of the
fuzzy set, the vertical axis consisting of the degree of
membership scale from 0 to 1, and the surface of the set itself
which relates the degree of membership to the domain
element. These membership curves can take on several
s ha pe s, s uc h a s l in ea r r ep res en ta ti on s, S- cu rve
representations, triangular and trapezoidal representations,
and bell curve representations (Cox, 1994). The triangular
and trapezoidal are the most frequently used since they are
easily understood by the decision makers and have a good
suitability to different real situations (Kaufmann, 1975). The
two extreme points as well as the average or most likely values
are easily depicted by this form of membership curves. These
features of the triangular and trapezoidal representations
make them quite useful for application to various managerial
situations. These membership functions are utilized in the
current research as well.Fuzzy logic is very useful when the model requires human
perceptions as inputs where ambiguity and vagueness exists.
In particular, systems requiring linguistic descriptions (i.e.
delivery performance is excellent) are more easily modeled
using fuzzy sets. The main inputs to the supplier selection
process are the decision makers perceptions of importance of
the selection attributes and supplier performance with respect
to these attributes. However, it is very difficult to obtain exact
assessments from the decision maker. The nature of these
assessments is often subjective and thus forcing the decision
makers to express their opinion in pure numeric scales does
not allow any room for subjectivity. Subjectivity of human
assessments and beliefs can best be expressed by using
linguistic terms such as low importance or excellent
performance. The fuzzy set theory and fuzzy numbers allow
such qualitative expressions. As a result their use in modeling
of our proposed system seems a logical choice. In the next
section the membership functions that are used in the
evaluation process are introduced.
3.3 Fuzzy membership functions
In the present research the decision makers perceptions are
solicited in two areas: importance weights of the selection
attributes, and performance ratings of the suppliers. Thus we
define two fuzzy membership functions: one for assessment of
the attribute weights and one for performance ratings of the
suppliers.
3.3.1 Assessing importance of attributesThe importance of each supplier selection criterion is
evaluated by a question with the answer set of low
importance, moderate importance, high importance,
and very high importance. These values correspond to
fuzzy numbers on the numeric scale 0-1. Figure 2 illustrates
these four membership functions. For each membership
function, the average value is the point at which the degree of
membership reaches one, or full membership for that set. The
upper and lower limits are those points at which the degree of
membership reaches zero, or no membership. Other degrees
of membership between these two extremes are determined
Figure 2The membership functions of the linguistic importance weight
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from the membership curve. The linguistic scales for the
membership functions are illustrated in Table II.
3.3.2 Assessing performance rating of suppliers
The performance of a supplier with respect to each criterion is
evaluated by a question with the answer set of excellent,
very good, good, and poor. These values correspond to
fuzzy numbers on the numeric scale 0-10. The membershipf unctions are shown in F igure 3 and the l inguistic
performance scale is described in Table III.
Now that a review of fuzzy set theory and fuzzy membership
functions is completed there is a need to illustrate how
mathematical operations are applied to manipulate fuzzy
numbers. Thus, a brief overview of fuzzy arithmetic operators
is provided in the following section.
3.4 Fuzzy operators
The basic arithmetic operations used to manipulate fuzzy sets
are very similar to those used in traditional statistical setting.
These operations are used to represent combination of two or
more fuzzy sets to arrive at a final membership value. The two
main algebraic operations that are used in this study areadditions and multiplications. A brief overview of these
operations follows.
Let X x1; x2; x3; x4 and Y y1;y2;y3;y4 b e t wopositive trapezoidal fuzzy numbers. Then the algebraic
operators are (Dubois and Padre, 1980):
X Y x1y1; x2y2; x3 y3; x4y4 1
X Y < x1y1; x2y2; x3y3; x4y4 2
In addition, often there is a need for ranking of the fuzzy
numbers to allow comparison among the competing
alternatives. However ranking of the alternatives (e.g.
suppliers) is not easy with fuzzy numbers since the ordering of
fuzzy numbersis not as obvious as real numbers. Thus there is a
need for translating fuzzy numbers into real numbers to make
the ranking of the alternatives a straightforward task. Popular
defuzzification approaches include the weighted average
method, the centroid method, the mean-max membership,
the center of sums, the max-membership principle, and the first
(or last) of maxima (Cheng and Lin, 2002; Ross, 2004). The
most common approach is center of area (COA) or centroid
method. For a trapezoidal fuzzy number (x1, x2, x3, x4), the
center of area is calculated as: x1 x2 x3 x4=4. In this
study the COA defuzzification approach is applied to translate
fuzzy numbers into real numbers.
4. Development of the methodology
Now that the first step, identification of the selection criteria,
is completed and appropriate fuzzy membership functions
are introduced it is necessary to outline the detail of the
proposed methodology. The objective is to develop a
mechanism to evaluate the attributes and measure the
performance of the suppliers with respect to these attributes.
This information then becomes an input into the choice
process for selection among the alternatives. The evaluation
and selection process consists of nine main steps each of
which is further explained below. A ( *) preceding the
description of a step indicates that this particular step is an
internal function performed within the system and requires
no input or action from the decision maker:. Present the decision maker with a list of selection criteria
and ask him/her to choose the ones relevant to the situation
athand. Let the numberof criteriaselected be denoted byn.. Have the decision maker express his/her perception of the
importance of these criteria in linguistic terms low,
moderate, high, or very high.. * Using the appropriate fuzzy membership functions,
convert the linguistic terms into fuzzy weights. Let widenotes the fuzzy importance weight of criterion i. Where
i 1; 2;. . .; n. For instance if the importance of criterion 1
to the decision maker is High then the fuzzy importance
weight w1 0:4; 0:6; 0:6; 0:8 according to the linguistic
scales of Table II..
Have the decision maker identify the potential suppliershe/she wants to consider for selection. Let the number of
candidates selected be denoted by m.. Solicit the decision makers perception of each suppliers
performance with respect to the pertinent criteria in
linguistic terms excellent, very good, good, or
poor.. * Using the appropriate fuzzy membership functions,
convert the linguistic terms into fuzzy performance
ratings. Let rji denotes the fuzzy performance rating of
supplier jwith respect to criterion i. Where i 1; 2;. . .; n
and j 1; 2; . . .; m.
Figure 3The membership functions of the linguistic performance rate
Table III The linguistic performance scale
Poor performance (P) (0, 0, 2, 4)
Good performance (G) (2, 4, 4, 6)
Very good performance (VG) (4, 6, 6, 8)
Excellent performance (EX) (6, 8, 10, 10)
Table II The linguistic importance scale
Low importance (L) (0.0, 0.0, 0.2, 0.4)Moderate importance (M) (0.2, 0.4, 0.4, 0.6)
High importance (H) (0.4, 0.6, 0.6, 0.8)
Very high importance (VH) (0.6, 0.8, 1.0, 1.0)
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ssj Srjiwi.ssjdenotes supplier js aggregate fuzzy score forall the pertinent criteria. Where i 1; 2;. . .; n and
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. * Convert the aggregate fuzzy scores into crisp scores
using the Center of Area (COA) defuzzification approach.. * Rank the suppliers according to their crisp scores. The
supplier that has the highest overall crisp score is assigned
the highest ranking and will be selected.
5. An illustrative example
Following hypothetical example is presented here to illustrate
the application of the proposed model. The XYZ Co. is a
Widget manufacturing company. The parts are beingpurchased from external suppliers and assembled in the
company. The president of the company has been able to
narrow down the list of the potential suppliers to three. Now
this company is faced with the task of selecting an appropriate
supplier from this list. These suppliers are hereafter referred
to as supplier A, supplier B, and supplier C. The application
of the proposed model is shown by first implementing the
elicitation process to seek decision makers inputs. These
inputs are then used to perform fuzzy calculations and to
determine the ranking of the suppliers. The detailed
explanation of each follows.
5.1 Elicitation process
This process is performed by going through the following
steps:
1 A master list of selection criteria and sub-criteria is
presented to the decision maker. This list is narrowed
down to include only the criteria/sub-criteria that the
decision maker feels are pertinent to the situation at hand.
The calibration procedure is shown in Table IV.2 The decision makers preferences regarding importance
weights of the selected criteria are elicited. This elicitation
procedure is shown in Table V. The result of this
elicitation process is illustrated in Figure 4 in a tree
Table IV A sample calibration procedure
Criteria Sub-criteria Relevant?
Quality Yes No
Quality control
rejection rate
Yes No
Customer rejection
rate
Yes No
Delivery Yes No
Compliance with
due date
Yes No
Fill rate Yes No
Delivery lead time Yes No
Flexibility Yes No
Change in delivery
date
Yes No
Special requests Yes No
Meeting demand
fluctuations
Yes No
Service Yes No
Reliability Yes No
Responsiveness Yes NoEmpathy Yes No
Communications Yes No
Access Yes No
Understanding Yes No
Assurance Yes No
Competence Yes No
Courtesy Yes No
Credibility Yes No
Costs Yes No
Purchase price Yes No
Logistics costs Yes No
Product Yes No
Product range Yes No
New productavailability
Yes No
Additional
features
Yes No
Recycled materials Yes No
Ergonomic
features
Yes No
Note:This is a list of criteria and sub-criteria to be considered for supplierselection. Please identify the criteria/sub-criteria that are relevant to yourcompany. In addition please add any other criterion you believe is relevantbut not listed
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hierarchical form where the importance weight of each
criterion is listed in parenthesis next to the criterion. As
can be seen from the above tree ten sub-criteria were
chosen by the decision maker as being pertinent for the
evaluation of the suppliers. These sub-criteria are utilized
in the next step of the elicitation process.
3 The decision makers perceptions of the suppliers
performances with respect to the selection criteria areelicited as shown in Table VI. The result of the elicitation
process is shown in Table VII.
5.2 Fuzzy calculations
Now that the elicitation process is completed fuzzy operators
are applied to manipulate the inputs that have been collected
from the decision maker. The rest of the steps are shown here
for illustration purposes only. The proposed process can easily
be implemented into a software or an internet-based tool
where all the calculations can be done internally within the
system. Following steps illustrate the fuzzy calculations and
ranking of the suppliers.
1 The linguistic importance weight of each node of the tree
in Figure 4 is translated into fuzzy weights using the
linguistic scales of Table II. The fuzzy importance weights
of the end nodes of each branch are then calculated bytracing down all the nodes on that branch. For example
w2 the importance weight of the end node of the second
branch is computed by multiplying the importance weight
of quality (VH) by importance weight of the customer
rejection rate ( M) . T hus, w2 0:6; 0:8; 1:0; 1:00:2; 0:4; 0:4; 0:6 0:12; 0:32; 0:4; 0:6. The rest of theimportance weights are calculated in the same manner.
The result is illustrated as follows:. w1 (0.24, 0.48, 0.6, 0.8).. w2 (0.12, 0.32, 0.4, 0.6).. w3 (0.16, 0.36, 0.36, 0.64).. w4 (0.032, 0.144, 0.144, 0.384).. w5 (0.064, 0.216, 0.216, 0.512)..
w6
(0.04, 0.16, 0.16, 0.36).. w7 (0.0, 0.0, 0.032, 0.144).. w8 (0.008, 0.064, 0.064, 0.216).. w9 (0.24, 0.48, 0.6, 0.8).. w10 (0.16, 0.36, 0.36, 0.64).
2 The linguistic supplier performance ratings of Table VII
are translated into fuzzy performance ratings. For
example, the decision maker believes that performance
of supplier A with respect to honoring special requests
is good. The fuzzy number representing this level of
performance based on the linguistic scales of Table III is
(2,4,4,6). The rest of the linguistic performance ratings
are translated into fuzzy numbers in the same manner.
The result is illustrated in Table VIII.
3 The suppliers fuzzy scores are calculated by multiplying
fuzzy performance ratings matrix of Table IX by fuzzy
importance weights matrix of Table VIII. These fuzzy
scores are defuzzified and converted into crisp scores
using the centroid method. Suppliers are then ranked
according to their crisp scores and the supplier with
Table V Elicitation procedure for criteria importance weights
Criteria Sub-criteria Importance
Quality L M H VH
Quality control rejection rate L M H VH
Customer rejection rate L M H VH
Delivery L M H VH
Delivery lead time L M H VH
Flexibility L M H VH
Change in delivery date L M H VH
Special requests L M H VH
Service L M H VH
Reliability L M H VH
Empathy L M H VH
Access L M H VH
Understanding L M H VH
Costs L M H VH
Purchase price L M H VH
Logistics costs L M H VH
Note: This is a list of the attributes that you have identified as beingrelevant for supplier selection. Please specify the importance of eachattribute as VH: Very High, H: High, M: Moderate or L: Low
Figure 4The hierarchy of selection criteria and sub-criteria
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TableVIElicitationprocedureforsu
pplierperformanceratings
Selectioncriteria
Customer
rejectrate
Qualitycontrol
re
jectrate
Deliverylead
time
Changein
deliveryda
te
Special
requests
Reliability
Acc
ess
Understanding
Purchaseprice
Logisticscosts
SupplierA
P,G,V
G,
EX
P,G,V
G,
EX
P,G,V
G,
EX
P,G,V
G,
EX
P,G,V
G,
EX
P,G,V
G,
EX
P,G,V
G,
EX
P,G,V
G,
EX
P,G,V
G,
EX
P,G,V
G,
EX
SupplierB
P,G,V
G,
EX
P,G,V
G,
EX
P,G,V
G,
EX
P,G,V
G,
EX
P,G,V
G,
EX
P,G,V
G,
EX
P,G,V
G,
EX
P,G,V
G,
EX
P,G,V
G,
EX
P,G,V
G,
EX
SupplierC
P,G,V
G,
EX
P,G,V
G,
EX
P,G,V
G,
EX
P,G,V
G,
EX
P,G,V
G,
EX
P,G,V
G,
EX
P,G,V
G,
EX
P,G,V
G,
EX
P,G,V
G,
EX
P,G,V
G,
EX
Note:Thefollowing
tablepleasespe
cifyyourperceptionofhow
each
supplierperformswith
respecttotheselection
criteriaasP:Poorperformance,
G:Good
performa
nce,
VG:VeryGood
performance,orEX:Excellentperformance
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TableVIISuppliersperformancera
tingswithrespecttotheselectioncriteria
Selectioncriteria
Customerreject
rate
Qualitycontrol
re
jectrate
Deliverylead
time
Changein
delivery
date
Specialrequests
Reliability
Access
Understanding
Purchaseprice
Logisticscosts
SupplierA
G
VG
P
EX
G
P
VG
G
P
VG
SupplierB
EX
VG
G
P
P
G
EX
P
G
G
SupplierC
VG
G
P
VG
VG
EX
P
G
G
P
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TableVIIIFuzzysupplierperforman
ceratings
Selectioncriteria
Customerreject
rate
Qu
alitycontrol
rejectrate
Deliverylead
time
Changein
delivery
date
Specialrequests
Reliability
Access
Understanding
Purchasep
rice
Logisticscosts
SupplierA
(2,4,4,6
)
(4,6,6,8
)
(0,0,2,4
)
(6,8,10
,10)
(2,4,4,6
)
(0,0,2,4
)
(4,6,6,8
)
(2,4,4,6
)
(0,0,2,4
)
(4,6,6,8
)
SupplierB
(6,8,1
0,1
0)
(4,6,6,8
)
(2,4,4,6
)
(0,0,2,4
)
(0,0,2,4
)
(2,4,4,6
)
(6,8,1
0,1
0)
(0,0,2,4
)
(2,4,4,6
)
(2,4,4,6
)
SupplierC
(4,6,6,8
)
(2,4,4,6
)
(0,0,2,4
)
(4,6,6,8
)
(4,6,6,8
)
(6,8,1
0,1
0)
(0,0,2,4
)
(2,4,4,6
)
(2,4,4,6
)
(0,0,2,4
)
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highest ranking will be selected. The result is summarized
in Table IX.
Thus, supplier B is identified as the best supplier based on the
selection criteria and decision makers perception of the
suppliers performances with respect to these criteria.
In order to test the robustness of the supplier rankings,
several defuzzification approaches have been used to calculate
crisp scores for the three suppliers. In particular, crisp scores
and supplier rankings were determined based on five
defuzzification approaches (center of area, center of
maxima, center of sums, max-membership, and weighted
average). The crisp scores and rankings of the suppliers
resulting from application of these defuzzification techniques
are summarized in Table X. As can be seen from this table,
the supplier rankings are robust to the alternative
defuzzification methods.
6. Conclusions and suggestions for futureresearch
A decision model is proposed to help decision makers with
their decisions regarding rating and selection of the
appropriate suppliers. First a master list of supplier
attributes is prepared for the decision makers review. Once
the decision maker has identified relevant selection criteria,
the elicitation process is implemented to solicit the decision
makers preferences. The decision makers are asked to express
their preferences in linguistic terms to allow room for
subjectivity. These preferences are used as inputs into the
selection process where selection criteria are evaluated and
suppliers performances are measured. These tasks are
accomplished by applying fuzzy set theory. Fuzzy operators
are used to calculate fuzzy scores for each potential supplier.
These scores are then translated into crisp values to make
rankings of the suppliers a straightforward task. The supplier
with the highest ranking is then selected.The proposed methodology could be very beneficial to the
practitioners. It opens up new approaches to supplier
selection for those who pursue new and unconventional
techniques to the more current day supplier selection
methods. It allows practitioners to look at the supplier
selection process in a whole new way. Both subjective natures
of the decision makers preferences as well as necessary
quantitative ranking systems are considered without having to
compromise one for the other.
Several areas for further research have been identified.
First, it is recommended to implement the proposed
methodology into computer software or an internet-based
tool. This allows the application of fuzzy operators and
calculation of supplier fuzzy scores as well as defuzzification
process to be performed more accurately. In addition it allowseasy access to those who whish to use the proposed system.
Second, the list of selection criteria can be modified to include
the criteria that the managers use in actual practice. The
criteria used in the proposed model were based on academic
studies and definitely can be enriched by adding practitioners
point-of-view.
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About the author
Sharon M. Ordoobadi is an assistant professor of operations
management at the University of Massachusettss college of
business. She received both her MS and PhD in Management
from Purdue universitys Krannert Graduate School of
Management. Her areas of expertise include operations
management, decision modeling, logistics, engineering
economics, total quality management, and engineering
m anagem ent. S he has vast experience in teaching
quantitative courses in business and engineering colleges.
Her research focuses on decision modeling in the areas of
supply chain management and management of technology.
She has received two grants from National Science
Foundation to support her research. For the past several
years she has been actively involved in two professional
organizations: Institute for Operations research and
Management Science (INFORMS), and Decision Science
Institute (DSI). She also serves as reviewers for several
professional journals. Sharon Ordoobadi can be contacted at:
Development of a supplier selection model using fuzzy logic
Sharon M. Ordoobadi
Supply Chain Management: An International Journal
Volume 14 Number 4 2009 314327
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