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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

www.emeraldinsight.com/1359-8546.htm

Supply Chain Management: An International Journal

14/4 (2009) 314327

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):

Development of a supplier selection model using fuzzy logic

Sharon M. Ordoobadi


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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.

References

Bayou, M., Korvin, A.D. and Reinstein, A. (2007), Using

the fuzzy-analytic-hierarchical-process to select the

optim um m echanism f or developing accounting

standards, Review of Accounting and Finance, Vol. 6 No. 2,

pp. 109-30.Bhutta, K.S. and Huq, F. (2002), Supplier selection

problem: a comparison of the total cost of ownership and

analytic hierarchy process approaches, Supply Chain

Management: An International Journal, Vol. 7 No. 3,

pp. 126-35.

Bonde, A. (1997), Fuzzy logic basics, available at: www.

austinlinks.com/Fuzzy/basics.html (accessed June 6).

Chan, D.C., Yung, K.L. and Lp, A.W.H. (2002), An

application of fuzzy sets to process performance, Integrated

Manufacturing Systems, Vol. 13 No. 4, pp. 237-46.

Cheng, C-H. and Lin, Y. (2002), Evaluating the best main

battle tank using fuzzy decision theory with linguistic

criteria evaluation,European Journal of Operations Research,

Vol. 142, pp. 174-86.Cheung, S.O., Ng, T., Lam, K.C. and Sin, W.S. (2001), A

fuzzy set model for construction dispute evaluation,

Construction Innovation: Information, Process, Management,

Vol. 1 No. 2, pp. 117-27.

Cox, E. (1994), The Fuzzy System Handbook: A Practitioners

Guide to Building and Maintaining Fuzzy Systems, AP

Professional, Cambridge.

Deng, S. and Wortzel, L.H. (1995), Importer purchase

behavior: guidelines for Asian exporters, Journal of

Business Research, Vol. 32 No. 1, pp. 41-7.

Dogan, I. and Sahim, U. (2003), Supplier selection using

activity-based costing and fuzzy present worth techniques,

Logistics Information Management, Vol. 16 No. 6, pp. 420-6.

Dubois, D. and Padre, H. (1980), Fuzzy Sets and Systems:Theory and Applications, Academic Press, New York, NY.

Table IX Fuzzy scores, crisp scores, and supplier rankings

Fuzzy scores Crisp scores Rank

Supplier A ( 1.936, 8.272, 12.144, 31.648) 13.50 3

Supplier B (3.12, 11.2, 15.488, 33.328) 15.78 1

Supplier C (2.32, 9.776, 13.12, 32.56) 14.44 2

Table X Comparison of crisp scores and supplier rankings for different defuzzification approaches

Defuzzification approaches

Center of area Center of maxima Center of sums Max-membership Weighted average

Crisp scores Rank Crisp scores Rank Crisp scores Rank Crisp scores Rank Crisp scores Rank

Supplier A 13.50 3 16.79 3 10.27 3 31.28 3 10.05 3

Supplier B 15.78 1 18.22 1 12.78 1 33.33 1 13.27 1

Supplier C 14.44 2 17.44 2 11.35 2 32.56 2 11.43 2


Sharon M. Ordoobadi



325

8/10/2019 1798852

13/14

Ellram, L. (1987), The supplier selection decision in

strategic partnerships, Journal of Purchasing and Materials

Management, Vol. 26 No. 3, pp. 8-14.

Ghodsypour, S.H. and OBrien, C. (1998), A decision

support system for supplier selection using an integrated

analytical hierarchy process and linear programming,

International Journal of Production Economics, Vol. 56,

pp. 199-212.Gonzalez, E.L. and Fernandez, M.R. (2000), Genetic

optimization of a fuzzy distribution model, International

Journal of Physical Distribution & Logistic Management,

Vol. 30 Nos 7/8, pp. 681-96.

Gunasekaran, N., Rathesh, S., Rathesh, S., Arunachalam, S.

and Koh, S.C.L. (2006), Optimizing supply chain

management using fuzzy approach, J our na l o f

Manufacturing Technology Management, Vol. 17 No. 6,

pp. 737-49.

Kahraman, C., Cebeci, U. and Ulukan, Z. (2003), Multi-

criteria supplier selection using fuzzy AHP, Logistic

Information Management, Vol. 16 No. 6, pp. 382-94.

Kaufmann, A. (1975), Introduction to Theory of Fuzzy Subsets,

Academic Press, New York, NY.

Korvin, A.D., Strawser, J. and Siegel, P.H. (1995), An

application of control system to cost variance analysis,

Managerial Finance, Vol. 21 No. 3, pp. 17-35.

Lee, E-K., Ha, S. and Kim, S-K. (2001), Supplier selection

and management system considering relationships in supply

chain management, IEEE Transactions on Engineering


Lehmann, D. and OShaughnessy, J. (1974), Difference in

attribute importance for different industrial products,

Journal of Marketing, Vol. 38 No. 4, pp. 36-42.

Mummalaneni, V., Dubas, K.M. and Chao, C. (1996),

Chinese purchasing managers preferences and trade-offs

in supplier selection and performance evaluation,Industrial Marketing Management, Vol. 25 No. 2, pp. 115-24.

Naude, P. and Lockett, G. (1993), Market analysis via

judgmental modeling: an application in the UK chemical

industry, European Journal of Marketing, Vol. 27 No. 3,

pp. 5-22.

Nydick, R.L. (1992), Using the analytic hierarchy process to

structure the supplier selection procedure, International

Journal of Purchasing and Materials Management, Vol. 23

No. 2, pp. 31-6.

Ohdar, R. and Ray, P.K. (2004), Performance measurement

and evaluation of suppliers in supply chain: an evolutionary

fuzzy-based approach, Journal of Manufacturing Technology


Ross, T.J. (2004), Fuzzy Logic with Engineering Applications,Wiley, London.

Tanaka, H., Okuda, T. and Asai, K. (1976), A formulation

of fuzzy decision problems and its application to an

investment problem, Kybernetes, Vol. 5 No. 1, pp. 25-30.

Teng, G.S. and Jaramillo, H. (2005), A model for evaluation

and selection of suppliers in global textile and apparel

supply chains, International Journal of Physical Distribution

& Logistics Management, Vol. 35 No. 7, pp. 503-23.

Thomaidis, N., Nikitakos, N. and Dounias, G. (2006), The

evaluation of information technology projects: a fuzzy

multicriteria decision-making approach, International

Journal of Information Technology & Decision Making, Vol. 5

No. 1, pp. 89-122.

Thompson, K.N. (1990), Supplier profile analysis, Journal

of Purchasing and Materials Management, Vol. 26 No. 1,

pp. 11-18.

T im merm an, E. ( 19 86 ), An approach to vendor

performance evaluation, Journal of Purchasing and

Materials Management, Vol. 26 No. 4, pp. 2-8.

Ts ou rve lo ud is , N. C. an d P hi ll is , Y. A. (1 99 8),

Manufacturing flexibility measurement: a fuzzy logic

framework, IE EE Tr ansa ctio ns o n Robotic s a nd

Automation, Vol. 14 No. 4, pp. 513-24.

Willis, T.H. and Huston, C.R. (1990), Vendor requirements

and evaluation in a just-in-time environment, Int. Journal

of Operations & Production Management, Vol. 10 No. 4,

pp. 41-50.

Willis, T.H., Huston, C.R. and Pohlkamp, F. (1993),

Evaluation m easures of just-in-tim e supplier

performance, Production and Inventory Management

Journal, Vol. 34 No. 2, pp. 1-5.

Wu, W-H., Lu, H-L. and Huang, D-Y. (2007), Governmentexpenditure decision-making under fuzzy relations,

International Journal of Services and Standards, Vol. 3

No. 3, pp. 289-98.

Yaha, S. and Kingsman, B. (1999), Vendor rating for an

entrepreneur development programmed: a case study using

the analytical hierarchy process method, Journal of the

Operational Research Society, Vol. 50, pp. 916-30.

Youssef, M.A., Zairi, M. and Mohanty, B. (1996), Supplier

selection in an advanced manufacturing technology

environment: an optimization model, Benchmark

Management Technology, Vol. 3 No. 4, pp. 60-72.

Zadeh, L. (1965), Fuzzy sets, Information and Control,

Vol. 8, pp. 338-57.

Further reading

Bengtsson, L. and Berggren, C. (2004), Rethinking

outsourcing in manufacturing: a tale of two telecom

firms, European Management Journal, Vol. 22 No. 2,

pp. 211-23.

Cebi, F. and Bayraktar, D. (2003), An integrated approach

for supplier selection, Logistics Information Management,

Vol. 16 No. 6, pp. 395-400.

Choy, K.L., Lee, W.B. and Lo, V. (2004), Development of a

case based intelligent supplier relationship management

system-linking supplier rating system and product codingsystem, Supply Chain Management: An International

Journal, Vol. 9 No. 1, pp. 86-101.

Lasch, R. and Janker, C.G. (2005), Supplier selection and

controlling using multivariate analysis, International

Journal of Physical Distribution & Logistics Management,

Vol. 35 No. 6, pp. 409-25.

Masella, C. and Rangone, A. (2000), A contingent approach

to the design of vendor selection systems for different types

of co-operative customer/supplier relationships,

I nter natio na l J our na l o f O pe ra ti ons & P ro ductio n



Sharon M. Ordoobadi



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8/10/2019 1798852

14/14

Schofield, R.A. and Breen, L. (2006), Suppliers, do you

know your customers?, International Journal of Quality &

Reliability Management, Vol. 23 No. 4, pp. 390-408.

Verma, R. and Pullman, M. (1998), An analysis of the

supplier selection process, Omega. Int. J. Management

Science, Vol. 26 No. 6, pp. 739-50.

Vokurka, R., Choobineh, J. and Vadi, L. (1996), A prototype

expert system for the evaluation and selection of potentialsuppliers, International Journal of Operations & Production


Vyas, N. and Woodside, A.D. (1984), An inductive model of

industrial choice processes, Journal of Marketing, Vol. 48

No. 1, pp. 30-45.

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:

[email protected]


Sharon M. Ordoobadi



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