Market segment evaluation and selection based on application of fuzzy AHP and COPRAS-G methods.
Aghdaie, Mohammad Hasan ; Zolfani, Safaraz Hashemkhani ; Zavadskas, Edmundas Kazimieras 等
1. Introduction
Market segmentation becomes an essential element of marketing in
industrialized countries and in living of any business (Wedel, Kamakura
2000). Market segmentation is defined as the partitioning of a market
into distinct subsets of customers and any subset could be possibly
selected as a target market to be reached with a distinct marketing mix
(Kotler 1999). In other words, market segmentation makes it possible to
find homogeneous smaller markets by this means, helping marketers to
recognize marketing opportunities and to develop products and services
in a more tailor-made manner (Jang et al. 2002).
Although market segmentation was introduced into the academic
marketing literature by Smith (1956), market segmentation continues to
be an important focal point of ongoing research and marketing practices
(Chaturvedi et al. 1997; Hanafizadeh, Mirzazadeh 2011). Maybe mass
marketing will no longer exist in the coming century or it will become
vanished (Kuo et al. 2002).There are a lot of advantages of market
segmentation over mass marketing. Firstly, it repeatedly helps every
company to find a good chance to expand its own market by better
satisfying the wants of customers. Secondly, it increases the
profitability or effectiveness of the organization to the extent that
the economic benefits provided for consumers exceed the costs of the
segmentation process (Chiu et al. 2009). Thirdly, the importance of
doing marketing segmentation analysis includes better perception of the
market to truly position of a product in the marketplace, choosing the
appropriate segments for target marketing, discovering opportunities in
existing markets, and gaining competitive advantage through product
differentiation (Kotler 1980).
There are many market segmentation bases in the literature that
were used to divide a market into segments such as geographic,
demographic, life style and product benefits (Kazemzadeh et al. 2009).
Besides, there are numerous market segmentation methods such as factor
analysis, clustering, conjoint, regression, and discriminate analysis.
Also recently, using or integrating other fields including data mining,
multivariate statistical analysis, fuzzy logic, artificial neural
networks, and genetic algorithm becomes a common tool for market
segmentation.
After market segmentation, every company needs to evaluate and
select target market or markets, and then Market segmentation evaluation
is a critical management decision because all other components of a
marketing strategy follow it (Wind, Thomas 1994). Also, Market segment
evaluation can help in targeting markets, thus it is very important for
improving the probability of success in competitive market.
Although much of the marketing literature has proposed various
market segmentation techniques, but a review of academic research
reveals that existing studies have relatively neglected segment
evaluation and selection (Sarabia 1996; Ou et al. 2009). Also most
existing studies suggest some general criteria for evaluation of
attractiveness of a segment and merely present a model or method for
evaluation.
Selecting an appropriate market segment based on evaluation of
segments is one of the most complicated and time consuming problems for
many companies, due to many feasible alternatives, conflicting
objectives and variety of factors (Aghdaie et al. 2011). Market segment
evaluation and selection decisions are sophisticated by the fact that
the decision-making process must consider various criteria. Therefore
market segment evaluation and selection can be viewed as a multiple
criteria decision- making (MCDM) problem. Hence, this study has the main
objective of proposing a mechanism for market segment evaluation and
selection.
The MCDM methods deal with the process of making decisions for
finding the optimum alternative in the presence of multiple, usually
conflicting, decision criteria.
In this research a hybrid MCDM model encompassing fuzzy analytic
hierarchy process (FAHP) and the complex proportional assessment of
alternatives with grey relations (COPRAS-G method) are used for market
segment evaluation and selection. Specifically, FAHP is initially used
for calculating the weight of each criterion and COPRAS-G method is used
for ranking and selecting the best location.
The remainder of this paper is organized as follows. The related
studies are summarized in Section 2. The third section presents the
methodology including FAHP and COPRAS-G method. In Section 4, a
real-world case study is given to prove the applicability of the
proposed method on a large- sized manufacturing enterprise in Iran. In
Section 4, the results are discussed. In Section 5, finally, the article
will be concluded.
2. Literature review
Market segment evaluation and selection is one of the important
problems for every company. The major part of the related literature
concentrates on the important features for doing this evaluation and
very little research has been done on the evaluation of segment
attractiveness and market segment selection. The enormous majority of
decision-making methods identified apply to the final stage of market
segment evaluation and selection. Also, it is remarkable that
segmentation itself has many limitations in terms of product, segment
size, profitability/yield, attainability with promotion mix and supply,
doubled expenses for marketing mix, industry, etc. Generally, expert
efforts have focused on evaluating different segmentation methods and
techniques (Bonoma, Shapiro 1983; Christen 1987; Elrod, Winner 1982;
Morrison 1973; Novak et al. 1992; Wildt 1976). Even general studies of
market segmentation have paid little or no attention to the evaluation
and selection stages (Beane, Ennis 1987; Weinstein 1987; Wind 1978).
Authors generally limit themselves to analyzing how to evaluate segment
stability (Bettman 1971; Calentone, Sawyer 1978; Lehmann et al. 1982;
MacLachlan, Johansson 1981), congruence (Green 1977), internal
homogeneity and profitability (Eckrich 1984; Van Auken, Lonial 1984;
Beik, Buzby 1973), to mention only the most relevant.
Some general criteria such as identity ability, substantiality,
accessibility, stability, responsiveness, action ability have been
frequently put forward as determining the effectiveness and
profitability of market segment (Frank et al. 1972; Loudon, Della Bitta
1984; Baker 1988; Kotler 1988). Based on research of the United
Kingdom's Times Top 1000 companies, Simkin and Dibb (1998) found
that the three most important factors for selecting target markets were
profitability, market growth, and market size. McQueen and Miller (1985)
recommended the assessment of market attractiveness based upon
profitability, variability, and accessibility. In the same way, Loker
and Perdue (1992) proposed a systematic approach to evaluating segments
using a ranking procedure. They assessed segment attractiveness in terms
of profitability, accessibility, and reachability by ranking each
segment on its relative performance according to the three evaluation
criteria. Based on Kotler and Armstrong (2003) the market segments
should meet five selection criteria including: (1) measurable, (2)
accessible, (3) sustainable, (4) differentiable, and (5) actionable to
be viable. Also, Morrison (2002) added five more criteria in Kotler and
Armstrong's list for effective segmentation, including:
homogeneity, defensibility, competitiveness, durability, and
compatibility. These theoretically fundamental criteria provide
marketers with useful guidelines for targeting markets (Lee et al.
2006). Bock and Uncles (2002) suggested that, when preparing a
segmentation strategy, profitability must be considered as one of the
main selection criteria. Jang et al. (2002) incorporated the
profitability and risk concepts in evaluating segment attractiveness as
more quantifiable and comprehensive profitability measures. Most of
these studies, propose different schemes for market segmentation,
however, they have concentrated on evaluation and therefore have only
taken into account very specific criteria. Ou et al. (2009) incorporated
the famous model that was developed by Porter (1980) to evaluate each
potential segment. Companies must carefully assess and weigh key
discriminating criteria to find the "best" market segments
(Weinstein 2004).
McDonald and Dunbar (2004) prepared one of the comprehensive
criteria list for market segment evaluation. They also provide a list of
twenty-seven possible, generalized segment attractiveness factors in
five major areas: segment factors, competition, financial and economic
factors, technology, and sociopolitical factors. McDonald and Dunbar add
segment attractiveness factors be weighted based on the particular
requirements of an organization.
This study uses the McDonald and Dunbar's (2004) criteria list
as the basis for market segment evaluation. This criteria list is
depicted in Table 1.
3. Methodology
Over the past decades the complexity of economic decisions has
increased rapidly, thus highlighting the importance of developing and
implementing sophisticated and efficient quantitative analysis
techniques for supporting and aiding economic decision-making
(Zavadskas, Turskis 2011). Multiple criteria decision making (MCDM) is
an advanced field of operations research, provides decision- makers and
analysts with a wide range of methodologies, which are overviewed and
well suited to the complexity of economic decision problems (Hwang, Yoon
1981; Zopounidis, Doumpos 2002; Figueira et al. 2005). In this paper, we
proposed a combined fuzzy AHP and COPRAS-G method approach to market
segment evaluation and selection. The evaluation criteria for market
segment evaluation and selection are based on McDonald and Dunbar's
(2004) criteria list. According to these criteria, the required data
utilized in the comparisons are collected from the related decision
makers (DMs). After constructing the evaluation criteria hierarchy, the
criteria weights are calculated by applying the fuzzy AHP method.
Finally COPRAS-G method is employed to achieve the final ranking
results. The detailed descriptions of the major steps are elaborated in
the following subsections.
Fuzzy AHP
AHP is developed by Saaty (1980), maybe it is one of the famous,
dazzling and most widely used models in decision making. With the
extension of this method in fuzzy set theory, fuzzy AHP was developed.
In the proposed methodology, AHP with its fuzzy extension, namely fuzzy
AHP, is applied to obtain more decisive judgments by prioritizing the
market segment selection criteria and weighting them in the presence of
vagueness. There are numerous fuzzy AHP applications in the literature
that propose systematic approaches for selection of alternatives and
justification of problem by using fuzzy set theory and hierarchical
structure analysis (Efendigil et al. 2008; Onut et al. 2010). DMs
usually find it more convenient to express interval judgments than fixed
value judgments due to the fuzzy nature of the comparison process
(Bozdag et al. 2003). This study concentrates on a fuzzy AHP approach
introduced by Chang (1992), in which triangular fuzzy numbers are
preferred for pairwise comparison scale. Extent analysis method is
selected for the synthetic extent values of the pairwise comparisons.
Some papers published used the fuzzy AHP procedure based on extent
analysis method and showed how it can be applied to selection problems
(Cebeci, Ruan 2007; Kahraman et al. 2003, 2004). The outlines of the
fuzzy sets and extent analysis method for fuzzy AHP are given below.
A fuzzy number is a special fuzzy set F = {(x, [[mu].sub.F] (x)), x
[member of] R}, where x takes its values on the real line, R :
-[infinity] < x [less than or equal to] [infinity] and [[mu].sub.F]
(x) is a continuous mapping from R to the closed interval [0,1]. A
triangular fuzzy number (TFN) expresses the relative strength of each
pair of elements in the same hierarchy, mand can be denoted as M = (l,
m, u), where l [less than or equal to] m [less than or equal to] u. The
parameters l, m, u indicate the smallest possible value, the most
promising value, and the largest possible value respectively in a fuzzy
event. The recent applications of fuzzy AHP method, in short, are listed
below:
--Kersuliene and Turskis (2011) used fuzzy AHP and ARAS for
architect selection.
--Fouladgar et al. (2011) used fuzzy AHP and fuzzy TOPSIS for
prioritizing strategies of the Iranian mining sector.
--Lin et al. (2011) used fuzzy Delphi method, fuzzy AHP and fuzzy
theory to develop an evaluation system of knowledge management
performance.
--Nepal et al. (2010) used fuzzy AHP approach to prioritization of
CS attributes in target planning for automotive product development.
--Heo et al. (2010) used fuzzy AHP for analysis of the assessment
factors for renewable energy dissemination program evaluation.
--Haghighi et al. (2010) applied fuzzy AHP to e-banking development
in Iran.
--Tiryaki and Ahlatcioglu (2009) used fuzzy AHP for Fuzzy portfolio
selection.
--Gungor et al. (2009) used fuzzy AHP approach to personnel
selection problem. Triangular type membership function of M fuzzy number
can be described as in Equation 1.
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1)
A linguistic variable is a variable whose values are expressed in
linguistic terms (Onut et al. 2008). The concept of a linguistic
variable is very useful in dealing with situations, which are too
complex or not well defined to be reasonably described in conventional
quantitative expressions (Zadeh 1965; Zimmermann 1991; Kaufmann, Gupta
1991).
In this study, the linguistic variables that are utilized in the
model can be expressed in positive TFNs for each criterion as in Figure
1.
[FIGURE 1 OMITTED]
The linguistic variables matching TFNs and the corresponding
membership functions are provided in Table 2. Proposed methodology
employs a Likert Scale of fuzzy numbers starting from [??] to [??],
symbolized with tilde (~) for the fuzzy AHP approach. Table 2 depicts
AHP and fuzzy AHP comparison scale considering the linguistic variables
that describes the importance of criteria and alternatives to improve
the scaling scheme for the judgment matrices.
By using TFNs via pairwise comparison, the fuzzy judgment matrix
[??] ([a.sub.ij]) can be expressed mathematically as in Equation 2:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (2)
The judgment matrix [??] is a n x n fuzzy matrix containing fuzzy
numbers ay.
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (3)
Let X = {[x.sub.1],[x.sub.2],[x.sub.n]} be an object set, whereas U
= {[u.sub.1],[u.sub.2], ..., [u.sub.n]} is a goal set. According to
fuzzy extent analysis, the method can be performed with respect to each
object for each corresponding goal, [g.sub.i], resulting in m extent
analysis values for each object, given as [M.sup.1.sub.gi],
[M.sup.2.sub.gi], ..., [M.sup.n.sub.gi], i = 1,2, ..., n where all the
[M.sup.j.sub.gi] (j = 1,2, ..., m) are TFNs representing the performance
of the object xi with regard to each goal [u.sub.j]. The steps of
Chang's extent analysis (1992) can be detailed as follows (Kahraman
et al. 2003, 2004; Bozbura 2007):
Step 1: The fuzzy synthetic extent value with respect to the ith
object is defined as:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (4)
To obtain [m.summation over (j=1)] [M.sup.j.sub.gi], perform the
fuzzy addition operation m extent analysis such that operation m extent
analysis values for a particular matrix will be as follows:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (5)
then obtain [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII],
perform the fuzzy addition operation of [M.sup.j.sub.gi](j = 1,2, ...,
m) values as shown below:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (6)
then compute the inverse of the vector in Equation 6 as follows:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (7)
Step 2: The degree of possibility of [M.sub.2] [greater than or
equal to] [M.sub.1] is defined as:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (8)
and it can be equivalently expressed as follows:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (9)
where d is the ordinate of the highest intersection point D between
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] and [MATHEMATICAL
EXPRESSION NOT REPRODUCIBLE IN ASCII] (see Figure 2). To compare
[M.sub.1] and [M.sub.2], both the values of V ([M.sub.1] [greater than
or equal to] [M.sub.2]) and V ([M.sub.2] [greater than or equal to]
[M.sub.1]) are required.
Step 3: The degree of possibility of a convex fuzzy number to be
greater than k convex fuzzy numbers [M.sub.i](i = 1, 2, ..., k) can be
defined by Equation 10.
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (10)
Assume that:
d'([A.sub.i]) = min ([S.sub.i] > [S.sub.k]), (11)
where: k = 1, 2, ..., n; k [not equal to] i. Then, the weight
vector is given by as in Equation 12:
W = (d'([A.sub.1]), d'([A.sub.2]), ...,
d'[([A.sub.n])).sup.T], (12)
Where [A.sub.i](i = 1, 2, ..., n) has n elements.
Step 4: The normalized weight vectors are defined as:
W = (d'([A.sub.1]), d([A.sub.2]), ...,
d'[([A.sub.n])).sup.T], (13)
where W is a non fuzzy number.
[FIGURE 2 OMITTED]
COPRAS-G METHOD
In order to evaluate the overall efficiency of an alternative, it
is necessary to identify selection criteria, to assess information
relating to these criteria, and to develop methods for evaluating the
criteria to meet the participant's needs. Decision analysis is
concerned with the situation in which a decision-maker (DM) has to
choose among several alternatives by considering a particular set of,
usually conflicting criteria. For this reason Complex proportional
assessment (COPRAS) method that was developed by Zavadskas and
Kaklauskas (1996) can be applied. This method was applied to the
solution of various problems in construction (Tupenaite et al. 2010;
Ginevicius et al. 2008; Kaklauskas et al. 2010; Zavadskas et al. 2010).
The most of alternatives under development always deal with vague
future, and values of criteria cannot be expressed exactly. This MCDM
problem should be determined not by exact criteria values, but by fuzzy
values or by values in some intervals. Zavadskas et al. (2008) presented
the main ideas of complex proportional assessment method with grey
interval numbers (COPRAS-G) method. The idea of COPRAS-G method with
criterion values expressed in intervals is based on the real conditions
of decision making and applications of the Grey systems theory (Deng
1982; Deng 1988). The COPRAS-G method uses a stepwise ranking and
evaluating procedure of the alternatives in terms of significance and
utility degree.
The recent developments of decision making models based on COPRAS
methods are listed below:
--Uzsilaityte and Martinaitis (2010) investigated and compared
different alternatives for the renovation of buildings taking into
account energy, economic and environmental criteria while evaluating
impact of renovation measures during their life cycle;
--Chatterjee et al. (2011) presented materials selection model
based on COPRAS and EVAMIX methods;
--Zavadskas et al. (2011) presented assessment of the indoor
environment;
--Podvezko (2011) presented comparative analysis of MCDM methods
(SAW and COPRAS);
--Hashemkhani Zolfani et al. (2011) presented forest roads locating
using COPRASG method;
--Hashemkhani Zolfani et al. (2012) carried out research on quality
control manager selection applying COPRAS-G method;
--Chatterjee and Chakraborty (2012) presented materials selection
using COPRAS-G method.
The procedure of applying the COPRAS-G method consists of the
following steps (Zavadskas et al. 2009):
1. Selecting the set of the most important criteria, describing the
alternatives.
2. Constructing the decision-making matrix [cross product] X:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (14)
Here [cross product][x.sub.ji] is determined by [cross
product][[bar.x].sub.ji] (the smallest value, the lower limit) and
[[bar.x].sub.ji] (the biggest value, the upper limit).
3. Determining significances of the criteria qi.
4. Normalizing the decision-making matrix [cross product]X:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (15)
In formula (15) [[x.bar].sub.ji] is the lower value of the
criterion i in the alternative j of the solution; [[bar.x].sub.ji] is
the upper value of the criterion i in the alternative j of the solution;
m is the number of criteria; n is the number of the alternatives,
compared. Then, the decisionmaking matrix is normalized:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (16)
5. Calculating the weighted normalized decision matrix [cross
product][??]. The weighted normalized values [cross
product][[??].sub.ji] are calculated as follows:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (17)
In formula (17), qi is the significance of the i-th criterion.
Then, the normalized decision-making matrix is:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (18)
6. Calculating the sums [P.sub.j] of criterion values, whose larger
values are more preferable:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (19)
7. Calculating the sums [R.sub.j] of criterion values, whose
smaller values are more preferable:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (20)
In formula (20), (m-k) is the number of criteria which must be
minimized.
8. Determining the minimal value of [R.sub.j] as follows:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (21)
9. Calculating the relative significance of each alternative
[Q.sub.j] the expression is obtained:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (22)
10. Determining the optimal criterion K by the formula:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (23)
11. Determining the priority order of the alternatives.
12. Calculating the utility degree of each alternative by the
formula:
[N.sub.j] = [Q.sub.j]/[Q.sub.max] x 100%. (24)
Here [Q.sub.j] and [Q.sub.max] are the significances of the
alternatives obtained from equation (22).
4. Case study
A real world case problem is selected in chair manufacturing
company to illustrate the application of the proposed approach. The
selected company is Nilper Company, which is one of the well-known
brands in chair manufacturing industry in Iran. Nilper Company is a
large- sized manufacturing enterprise, which is a recognized leader in
chair manufacturing industry in Iran. Nilper Company currently offers
more than 50 models of managerial, administrative, and clinical chairs
based on customer needs and ergonomic standards. In recent years, there
has been a steady growth in demand for many models of office chairs.
Therefore, it was a matter of company's policy to undertake
marketing research in order to improve its design process based on the
main customers' wants for office chairs. Recently, this market
research project was done and three segments were defined, which are
denoted as SEG1, SEG 2 and SEG 3, respectively. Also, this company needs
to evaluate and select obtained market segments for doing other
marketing activities. Consequently, the project team including R&D
Manager, Marketing Manager, Sales Manager and two industrial engineers
working for the company was constructed. At this point, the company
needs to evaluate segments and select only one segment from them. So,
the first criteria list based on McDonald and Dunbar (2004) for the
market segment evaluation and selection was prepared. The number of
criteria was very high and it was very difficult to evaluate all of
them. So project team decided to choose some number of criteria for
evaluating. Besides, they had to consider their company conditions,
future plans, competitors, etc. For reducing the number of criteria and
in order to select the most reasonable criteria, a questionnaire
including all the first list criteria was designed. Then, the project
team have been asked to give a rate to each of the criterion containing
"not important at all", "not very important",
"important", "quite important" and "very
important" which are the verbal representation of the 1-5 numeric
scale respectively. Next, rank of each criterion was selected based on
the geometric mean of each criterion in all questionnaires. In the end
and based on these ranks, nine criteria were determined to perform the
analysis. The nine criteria are: Degree of concentration, Laws and
government agency regulations, Types of competitors, Contribution
margins, Manufacturing process technology required, Complexity, Growth
rate per year, Size, and Leveraging factors which are denoted as
[X.sub.1], [X.sub.2], [X.sub.3], [X.sub.4], [X.sub.5], [X.sub.6],
[X.sub.7], [X.sub.8], and [X.sub.9], respectively. Furthermore, project
team decided about kind of each criterion based on situations of Iran
market. After determining all selection criteria and alternatives, the
paired comparisons for criteria list (see Table 3) were made by using
the TFNs to tackle the ambiguities involved in the process of the
linguistic assessment of the data. The project team filled this table,
formed by reaching general agreement on questions related to the
importance of the criteria and alternatives via Delphi technique as a
group decision- making tool.
According to the weights in Table 3, Size, Growth rate per year and
Types of competitor were three of the most important considered
criteria.
5. Results
The aim of using fuzzy AHP is to determine importance weight of the
criteria that will be employed in COPRAS-G method. Table 3 depicts the
pairwise comparison matrix set by TFNs that matches linguistic
statements of data. The fuzzy values of paired comparison were converted
to crisp values via the Chang's extent analysis as mentioned
before. First, the fuzzy synthetic extent values were calculated by
using Equation 4 with the help of Equations 5-7. Equations 8-9 were
applied to express the degree of synthetic extent values. To have a
weight vector given by as in Equation12, Equations 10-11 were applied by
comparing the fuzzy numbers. After normalizing weight vector defined as
in Equation 13, the obtained priority weight vector of criteria is
figured out in the last column of Table 3. After this stage, project
team evaluated each segment according to each criterion and Table 4 was
developed.
V ([S.sub.C1] [greater than or equal to] [S.sub.C2], [S.sub.C3],
[S.sub.C4], [S.sub.C5], [S.sub.C6]^> [S.sub.C7], [S.sub.C8],
[S.sub.C9]) = 0.283
V ([S.sub.C2] [greater than or equal to] [S.sub.C1], [S.sub.C3],
[S.sub.C4], [S.sub.C5], [S.sub.C6]^> [S.sub.C7], [S.sub.C8],
[S.sub.C9]) = 0.441;
V ([S.sub.C3] [greater than or equal to] [S.sub.C1], [S.sub.C2],
[S.sub.C4], [S.sub.C5], [S.sub.C6], [S.sub.C7], [S.sub.C8], [S.sub.C9])
= 0.748;
V ([S.sub.C4] [greater than or equal to] [S.sub.C1], [S.sub.C2],
[S.sub.C3], [S.sub.C5], [S.sub.C6]^> [S.sub.C7], [S.sub.C8],
[S.sub.C9]) = 0.614;
V ([S.sub.C5] [greater than or equal to] [S.sub.C1], [S.sub.C2],
[S.sub.C3], [S.sub.C4], [S.sub.C6], [S.sub.C7], [S.sub.C8], [S.sub.C9])
= 0.368;
V ([S.sub.C6] [greater than or equal to] [S.sub.C1], [S.sub.C2],
[S.sub.C3], [S.sub.C4], [S.sub.C5], [S.sub.C7], [S.sub.C8], [S.sub.C9])
= 0.600;
V ([S.sub.C7] [greater than or equal to] [S.sub.C1], [S.sub.C2],
[S.sub.C3], [S.sub.C4], [S.sub.C5], [S.sub.C6]^> [S.sub.C8],
[S.sub.C9]) = 0.809;
V ([S.sub.C8] [greater than or equal to] [S.sub.C1], [S.sub.C2],
[S.sub.C3], [S.sub.C4], [S.sub.C5], [S.sub.C6], [S.sub.C7], [S.sub.C9])
= 1.000;
V ([S.sub.C9] [greater than or equal to] [S.sub.C1], [S.sub.C2],
[S.sub.C3], [S.sub.C4], [S.sub.C5], [S.sub.C6], [S.sub.C7], [S.sub.C8])
= 0.689.
It indicates the initial decision making matrix, with the criterion
values described in intervals. For the weight of criteria, we used
weights of the last column of Table 3. The initial decision making
matrix has been normalized first as discussed in section COPRAS-G
method. The normalized decision-making matrix is presented in Table 5.
Table 6 summarizes the results. The higher degree means the better
rank, so based on the results of Table 6, the ranking of the three
segments is "SEG 3>SEG 1 >SEG 2".
[P.sub.j] hybrid approach results indicate that the best
alternative with the highest degree is the best segment for doing
marketing activities. So, based on the proposed methodology, SEG 3 could
be selected as the best segment for the problem of market segment
evaluation and selection in the Nilper manufacturing company.
6. Conclusion
Market environment becomes more and more competitive and companies
should make right decisions about marketing problems. One of the
important problems is market segment evaluation and selection. Market
segment evaluation and selection is a critical managerial marketing
activity for all the companies. It helps a company choose its target
segment or segments so that company can focus its competitive
advantages, its resources, its opportunities and marketing strategies on
effectively satisfying customers' needs and wants. In this paper, a
hybrid MCDM methodology based on fuzzy AHP and COPRAS-G method for
selecting the most suitable market segment was proposed. Fuzzy AHP is
used to calculate the weight of each criterion, and COPRAS-G method is
proposed to prioritize market segments from the best to the worst ones.
This application has indicated that the model can be efficiently used in
evaluating and selecting segments. Although the application of the model
proposed in this study is specific to market segment evaluation and
selection, it can also be used with slight modifications in
decision-making process.
doi:10.3846/16111699.2012.721392
Reference
Aghdaie, M. H.; Hashemkhani Zolfani, S.; Rezaeinia, N.;
Mehri-Tekmeh, J. 2011. A hybrid fuzzy MCDM approach for market segments
evaluation and selection, in International Conference on Management and
Service Science (MASS) 1-4. Wuhan: IEEE.
Baker, M. J. 1988. Marketing Strategy and Management. New York:
Macmillan Education.
Beane, T. P.; Ennis, D. M. 1987. Market segmentation: a review,
European Journal of Marketing 21(5): 20-42.
http://dx.doi.org/10.1108/EUM0000000004695
Beik, L. L.; Buzby, S. L. 1973. Profitability analysis by market
segment, Journal of Marketing 37: 48-53.
http://dx.doi.org/10.2307/1249946
Bettman, J. R. 1971. The structure of consumer choice processes,
Journal of Marketing Research 8: 465-471.
http://dx.doi.org/10.2307/3150238
Bock, T.; Uncles, M. 2002. Taxonomy of differences between
consumers for market segmentation, International Journal of Research in
Marketing 19: 216-219. http://dx.doi.org/10.1016/S0167-8116(02)00081-2
Bonoma, T.; Shapiro, B. P. 1983. Industrial Market Segmentation: A
Nested Approach. Cambridge, MA: Marketing Science Institute.
http://dx.doi.org/10.1016/j.eswa.2006.02.006
Bozbura, F. T.; Beskese, A.; Kahraman, C. 2007. Prioritization of
human capital measurement indicators using fuzzy AHP, Expert Systems
with Applications 32: 1100-1112.
http://dx.doi.org/10.1016/S0166-3615(03)00029-0
Bozdag, C. E.; Kahraman, C.; Ruan, D. 2003. Fuzzy group decision
making for selection among computer integrated manufacturing systems,
Computers in Industry 51(1): 13-29.
http://dx.doi.org/10.1016/S0166-3615(03)00029-0"
Calentone, R. J.; Sawyer, A. G. 1978. The stability of benefit
segments, Journal of Marketing Research 15(3): 395-404.
http://dx.doi.org/10.2307/3150588
Cebeci, U.; Ruan, D. 2007. A Multi-Attribute comparison of Turkish
quality consultants by Fuzzy AHP, International Journal of Information
Technology & Decision Making 6(1): 191-207.
http://dx.doi.org/10.1142/S0219622007002423
Chang, D.-Y. 1992. Extent analysis and synthetic decision,
Optimization Techniques and Applications 1: 352-355.
Chatterjee, P.; Athawale, V. M.; Chakraborty, S. 2011. Materials
selection using complex proportional assessment and evaluation of mixed
data methods, Materials & Design 32(2): 851-860.
http://dx.doi.org/10.1016/j.matdes.2010.07.010
Chaturvedi, A.; Carroll, J. D.; Green, P. E.; Rotondo, J. A. 1997.
A feature-based approach to market segmentation via overlapping
k-centroids clustering, Journal of Marketing Research 34: 370-377.
http://dx.doi.org/10.2307/3151899
Chiu, C.-Y.; Chen, Y.-F.; Kuo, I.-T. K. 2009. An intelligent market
segmentation system using k-means and particle swarm optimization,
Expert Systems with Applications 36: 4558-4565.
http://dx.doi.org/10.1016/j.eswa.2008.05.029
Christen, F. G. 1987. Richness: a Way to Evaluate Segmentation
Systems. FL: Paper presented at the attitude research conference. West
Palm Beach.
Deng, J. L. 1982. Control problems of Grey systems, Systems and
Control Letters 1(5): 288-294.
http://dx.doi.org/10.1016/S0167-6911(82)80025-X
Deng, J. L. 1988. Introduction to Grey system theory, Journal of
Grey Theory 1: 1-24.
Eckrich, D. W. 1984. Benefits or problems as market segmentation
bases: a comment, Journal of Advertising 2: 57-59.
Efendigil, T.; Onut, S.; Kongar, E. 2008. A holistic approach for
selecting a third-party reverse logistics provider in the presence of
vagueness, Computers and Industrial Engineering 54(2): 269-287.
http://dx.doi.org/10.1016/j.cie.2007.07.009
Elrod, T.; Winner, R. S. 1982. An empirical evaluation of
aggregation approaches for developing market segments, Journal of
Marketing 46: 65-74. http://dx.doi.org/10.2307/1251363
Figueira, J.; Greco, S.; Ehrgott, M. (Eds.). 2005. Multiple
Criteria Decision Analysis: State of the Art Surveys. Springer.
Fouladgar, M. M.; Yazdani-Chamzini, A.; Zavadskas, E. K. 2011. An
integrated model for prioritizing strategies of the Iranian mining
sector, Technological and Economic Development of Economy 17(3):
459-483. http://dx.doi.org/10.3846/20294913.2011.603173
Frank, R. E.; Massy, W. F.; Wind, Y. 1972. Market Segmentation.
Englewood Cliffs, NJ: Prentice Hall.
Ginevicius, R.; Podvezko, V.; Raslanas, S. 2008. Evaluating the
alternative solutions of wall insulation by multicriteria methods,
Journal of Civil Engineering and Management 14(4): 217-226.
http://dx.doi.org/10.3846/1392-3730.2008.14.20
Green, P. E. 1977. A new approach to market segmentation, Business
Horizons 20: 61-73. http://dx.doi.org/10.1016/0007-6813(77)90088-X
Gungor, Z.; Serhadlioglu, G.; Kesen, S. E. 2009. A fuzzy AHP
approach to personnel selection problem, Applied Soft Computing 9:
641-646. http://dx.doi.org/10.1016/j.asoc.2008.09.003
Haghighi, M.; Divandari, A.; Keimasi, M. 2010. The impact of 3D
e-readiness on e-banking development in Iran: a fuzzy AHP analysis,
Expert Systems with Applications 37: 4084-4093.
http://dx.doi.org/10.1016/j.eswa.2009.11.024
Hanafizadeh, P.; Mirzazadeh, M. 2011. Visualizing market
segmentation using self-organizing maps and Fuzzy Delphi method-ADSL
market of a telecommunication company, Expert Systems with Applications
38: 198-205. http://dx.doi.org/10.1016/j.eswa.2010.06.045
Hashemkhani Zolfani, S.; Rezaeiniya, N.; Zavadskas, E. K.; Turskis,
Z. 2011. Forest roads locating based on AHP- COPRAS-G methods--an
empirical study based on Iran, E D M: Ekonomie a Management 14(4): 6-21.
Hashemkhani Zolfani, S.; Rezaeiniya, N.; Aghdaie, M. H.; Zavadskas,
E. K. 2012. Quality control manager selection based on AHP-COPRAS-G
methods: a case in Iran, Economska Istrazivanja Economic Research 25(1):
88-104.
Heo, E.; Kim, J.; Boo, K. J. 2010. Analysis of the assessment
factors for renewable energy dissemination program evaluation using
fuzzy AHP, Renewable and Sustainable Energy Reviews 14: 2214-2220.
http://dx.doi.org/10.1016/j.rser.2010.01.020
Hwang, C. L.; Yoon, K. 1981. Multiple attribute decision making: a
state of the art survey, in Lecture Notes in Economics and Mathematical
Systems. Berlin: Springer-Verlag. 186 p.
Jang, S. C.; Morrison, A. M.; O'Leary, J. T. 2002. Benefit
segmentation of Japanese pleasure travelers to the USA and Canada:
selecting target markets based on the profitability and risk of
individual market segments, Tourism Management 23: 367-378.
http://dx.doi.org/10.1016/S0261-5177(01)00096-6
Kahraman, C.; Ruan, D.; Dogan, I. 2003. Fuzzy group decision making
for facility location selection, Information Sciences 157: 135-153.
http://dx.doi.org/10.1016/S0020-0255(03)00183-X
Kahraman, C.; Cebeci, U.; Ruan, D. 2004. Multi-attribute comparison
of catering service companies using fuzzy AHP: the case of Turkey,
International Journal of Production Economics 87: 171-184.
http://dx.doi.org/10.1016/S0925-5273(03)00099-9
Kaklauskas, A.; Zavadskas, E. K.; Naimaviciene, J.; Krutinis, M.;
Plakys, V.; Venskus, D. 2010. Model for a complex analysis of
intelligent built environment, Automation in Construction 19(3):
326-340. http://dx.doi.org/10.1016/j.autcon.2009.12.006
Kaufmann, A.; Gupta, M. M. 1991. Introduction to Fuzzy Arithmetic:
Theory and Applications. New York: Van Nostrand Reinhold.
Kazemzadeh, R. B.; Behzadian, M.; Aghdasi, M.; Albadvi, A. 2009.
Integration of marketing research techniques into house of quality and
product family design, International Journal of Advance Manufacturing
Technology 41: 1019-1033. http://dx.doi.org/10.1007/s00170-008-1533-2
Kersuliene, V.; Turskis, Z. 2011. Integrated fuzzy multiple
criteria decision making model for architect selection, Technological
and Economic Development of Economy 17(4): 645-666.
http://dx.doi.org/10.3846/20294913.2011.635718
Kotler, P. 1980. Marketing Management-Analysis, Planning, and
Control. 4th ed. Prentice-Hall.
Kotler, P. 1988. Marketing Management. Englewood Cliffs, NJ:
Prentice-Hall.
Kotler, P. 1999. Marketing Management: Analysis, Planning,
Implementation, and Control. 10th edition. Englewood Cliffs, NJ:
Prentice-Hall, Inc.
Kotler, P.; Armstrong, G. 2003. Principles of Marketing. 10th ed.
Upper Saddle River, NJ: Prentice-Hall.
Kuo, R. J.; Ho, L. M.; Hu, C. M. 2002. Integration of
self-organizing feature map and A-means algorithm for market
segmentation, Computers and Operations Research 29: 1475-1493.
http://dx.doi.org/10.1016/S0305-0548(01)00043-0
Lee, G.; Morrison, A. M.; O'Leary, J. T. 2006. The economic
value portfolio matrix: a target market selection tool for destination
marketing organizations, Tourism Management 27: 576-588.
http://dx.doi.org/10.1016/j.tourman.2005.02.002
Lehmann, D. R.; Moore, W. L.; Elrod, T. 1982. The development of
distinct choice process segments over time, Journal of Marketing 46:
48-59. http://dx.doi.org/10.2307/3203340
Lin, E.-K.; Chang, C.-C.; Lin, Y.-C. 2011. Structure development
and performance evaluation of construction knowledge management system,
Journal of Civil Engineering and Management 17(2): 184-196.
Loker, L.; Perdue, R. 1992. A benefit-based segmentation of a
nonresident summer travel market, Journal of Travel Research 31(1):
30-35. http://dx.doi.org/10.1177/004728759203100107
MacLachlan, D. L.; Johansson, J. 1981. Market segmentation with
multivariate aid, Journal of Marketing 45: 74-84.
http://dx.doi.org/10.2307/1251722
McDonald, M.; Dunbar, I. 2004. Market Segmentation How to Do It How
to Profit from It. Elsevier Butterworth-Heinemann.
McQueen, J.; Miller, K. 1985. Target market selection of tourists:
a comparison of approaches, Journal of Travel Research 24(1): 2-6.
http://dx.doi.org/10.1177/004728758502400101
Morrison, D. G. 1973. Evaluating market segmentation studies: the
properties of [R.sup.2], Management Science 19(11): 1213-1221.
http://dx.doi.org/10.1287/mnsc.19.11.1213
Morrison, A. M. 2002. Hospitality and Travel Marketing. Albany, New
York: Delmar Thomson Learning.
Nepal, B.; Yadav, O. P.; Murat, A. 2010. A fuzzy-AHP approach to
prioritization of CS attributes in target planning for automotive
product development, Expert Systems with Applications 37: 6775-6786.
http://dx.doi.org/10.1016/j.eswa.2010.03.048
Novak, T. P.; De Leeuw, J.; MacEvoy, B. 1992. Richness curves for
evaluating market segmentation, Journal of Marketing Research 29:
254-267. http://dx.doi.org/10.2307/3172574
Onut, S.; Efendigil, T.; Karar, S. S. 2010. A combined fuzzy MCDM
approach for selecting shopping center site: an example from Istanbul,
Turkey, Expert Systems with Applications 37: 1973-1980.
http://dx.doi.org/10.1016/j.eswa.2009.06.080
Onut, S.; Karar, S. S.; Efendigil, T. 2008. A hybrid fuzzy MCDM
approach to machine tool selection, Journal of Intelligent Manufacturing
19: 443-453. http://dx.doi.org/10.1007/s10845-008-0095-3
Ou, C.-W.; Chou, S.-Y.; Chang, Y.-H. 2009. Using a strategy-aligned
fuzzy competitive analysis approach for market segment evaluation and
selection, Expert Systems with Applications 36: 527-541.
http://dx.doi.org/10.1016/j.eswa.2007.09.018
Podvezko, V. 2011. The comparative analysis of MCDA methods SAW and
COPRAS, Inzinerine Ekonomika - Engineering Economics 22(2): 134-146.
Porter, M. E. 1980. Competitive Strategy: Techniques for Analyzing
Industries and Competitors. New York: The Free Press.
Saaty, T. L. 1980. The Analytic Hierarchy Process. New York, NY:
McGraw-Hill.
Sarabia, F. J. 1996. Model for market segments evaluation and
selection, European Journal of Marketing 30(4): 58-74.
http://dx.doi.org/10.1108/03090569610118830
Simkin, L.; Dibb, S. 1998. Prioritizing target markets, Marketing
Intelligence and Planning 16(7): 407-417.
http://dx.doi.org/10.1108/02634509810244417
Smith, W. 1956. Product differentiation and market segmentation as
alternative marketing strategies, Journal of Marketing 21: 3-8.
http://dx.doi.org/10.2307/1247695
Tiryaki, F.; Ahlatcioglu, B. 2009. Fuzzy portfolio selection using
fuzzy analytic hierarchy process, Information Sciences 179: 53-69.
http://dx.doi.org/10.1016/j.ins.2008.07.023
Tupenaite, L.; Zavadskas, E. K.; Kaklauskas, A.; Turskis, Z.;
Seniut, M. 2010. Multiple criteria assessment of alternatives for built
and human environment renovation, Journal of Civil Engineering and
Management 16(2): 257-266. http://dx.doi.org/10.3846/jcem.2010.30
Uzsilaityte, L.; Martinaitis, V. 2010. Search for optimal solution
of public building renovation in terms of life cycle, Journal of
Environmental Engineering and Landscape Management 18(2): 102-110.
http://dx.doi.org/10.3846/jeelm.2010.12
Van Auken, S.; Lonial, S. C. 1984. Assessing mutual association
between alternative market segmentation bases, Journal of Advertising 1:
11-16.
Wedel, M.; Kamakura, W. 2000. Market Segmentation: Conceptual and
Methodological Foundations. Norwell, MA: Kluwer Academic Publishing.
Weinstein, A. 1987. Market Segmentation. Chicago, IL: Probus.
Weinstein, A. 2004. Handbook of Market Segmentation Strategic
Targeting for Business and Technology Firms. NY: Haworth Press.
Wildt, A. R. 1976. On evaluating market segmentation studies and
the properties of [R.sup.2], Management Science 22(8): 904-908.
http://dx.doi.org/10.1287/mnsc.22.8.904
Wind, Y. 1978. Issues and advances in segmentation research,
Journal of Marketing Research 15(3): 317-337.
http://dx.doi.org/10.2307/3150580
Wind, Y., Thomas, R. J. 1994. Segmenting industrial markets,
Advance in Business Marketing and Purchasing 6: 59-82.
Zadeh, L. A. 1965. Fuzzy sets, Information and Control 8: 338-353.
http://dx.doi.org/10.1016/S0019-9958(65)90241-X
Zavadskas, E. K.; Kaklauskas, A. 1996. Determination of an
efficient contractor by using the new method of multi criteria
assessment, in D. A. Langford, A. Retik (Eds.). International Symposium
for "The Organization and Management of Construction". Shaping
Theory and Practice. Vol. 2: Managing the Construction Project and
Managing Risk. CIB W 65; London, Weinheim, New
York, Tokyo, Melbourne, Madras. London: E and FN SPON, 94-104,.
Zavadskas, E. K.; Kaklauskas, A.; Turskis, Z.; Tamosaitiene, J.;
Kalibatas, D. 2011. Assessment of the indoor environment of dwelling
houses by applying the COPRAS-G method: Lithuanian case study,
Environmental Engineering and Management Journal 10(5): 637-647.
Zavadskas, E. K.; Kaklauskas, A.; Turskis, Z.; Tamosaitiene, J.
2009. Multi-attribute decisionmaking model by applying grey numbers,
Informatica 20(2): 305-320.
Zavadskas, E. K.; Kaklauskas, A.; Turskis, Z.; Tamosaitiene, J.
2008. Selection of the effective dwelling house walls by applying
attributes values determined at intervals, Journal of Civil Engineering
and Management 14(2): 85-93.
http://dx.doi.org/10.3846/1392-3730.2008.14.3
Zavadskas, E. K.; Turskis, Z. 2011. Multiple criteria decision
making (MCDM) methods in economics: an overview, Technological and
Economic Development of Economy 17(2): 397-427.
http://dx.doi.org/10.3846/20294913.2011.593291
Zavadskas, E. K.; Turskis, Z.; Tamosaitiene, J. 2010. Risk
assessment of construction projects, Journal of Civil Engineering and
Management 16(1): 33-46. http://dx.doi.org/10.3846/jcem.2010.03
Zimmermann, H. J. 1991. Fuzzy Set Theory and Its Applications. 2nd
ed London: Kluwer Academic Publishers.
Zopounidis, C.; Doumpos, M. 2002. Multi-criteria decision aid in
financial decision making: methodologies and literature review, Journal
of Multi-Criteria Decision Analysis 11: 167-186.
http://dx.doi.org/10.1002/mcda.333
Mohammad Hasan Aghdaie (1), Sarfaraz Hashemkhani Zolfani (2),
Edmundas Kazimieras Zavadskas (3)
(1,2) Department of Industrial Engineering, Shomal University, P.O.
Box 731, Amol, Mazandaran, Iran (1,2,3) Institute of Internet and
Intelligent Technologies, Vilnius Gediminas Technical University,
Sauletekio al. 11, 10223 Vilnius, Lithuania
E-mails: (1) mh_aghdaie@yahoo.com; (2) sa.hashemkhani@gmail.com;
(3) edmundas.zavadskas@vgtu.lt (corresponding author)
Received 02 February 2012; accepted 13 August 2012
Mohammad Hasan AGHDAIE was born in 1986 in Iran. In 2009 he
received a Bachelor of Industrial Engineering--Industrial Production
from Shomal University, in Amol. In 2011 he received a Master of
Industrial Engineering--Productivity and System Management from Shomal
University. His current research interests include Operations research,
Decision analysis, Multiple Criteria Decision Analysis and their
applications, especially market related decisions, Market segmentation,
Marketing research and modeling, Market Design and Engineering, Data
mining, Application of Fuzzy sets and systems, Creative Thinking and
Problem Solving and Pricing. He has published some papers in journals
and conference proceedings.
Sarfaraz HASHEMKHANI ZOLFANI got a BS in Industrial Management and
MS in Industrial Engineering-Productivity and System Management from
Shomal University of Amol, Iran. He was accepted in M.S. without
national exam because he was ranked as the top student and regarding his
good GPA in B.S. He is the author of more than 40 scientific papers in
International Conferences and International Journals which were
published, accepted or under reviewing. His research interests include
Performance Evaluation, Strategic Management, Decision-making Theory,
Supply Chain Management, (Fuzzy) Multi Criteria Decision Making and
Marketing.
Edmundas Kazimieras ZAVADSKAS. Prof, Head of the Department of
Construction Technology and Management at Vilnius Gediminas Technical
University, Vilnius, Lithuania. He has a PhD in Building Structures
(1973) and Dr Sc. (1987) in Building Technology and Management. He is a
member of the Lithuanian and several foreign Academies of Sciences. He
is Doctore Honoris Causa at Poznan, Saint-Petersburg, and Kiev
universities as well as a member of international organizations; he has
been a member of steering and programme committees at many international
conferences. E. K. Zavadskas is a member of editorial boards of several
research journals. He is the author and co-author of more than 400
papers and a number of monographs in Lithuanian, English, German and
Russian. Research interests are: building technology and management,
decision-making theory, automation in design and decision support
systems.
Table 1. The segment attractiveness criteria
Criteria Sub-criteria
Segment factors Size (money, units or both)
Growth rate per year
Sensitivity to price, service
features and external factors
Cyclicality
Seasonality
Bargaining power of upstream
suppliers
Bargaining power of downstream
suppliers
Competition Types of competitors
Degree of concentration
Changes in type and mix
Entries and exits
Changes in share
Substitution by new technology
Degrees and types of integration
Financial and economic factors Contribution margins
Leveraging factors, such as
economies of scale and experience
Barriers to entry or exit
(financial and non-financial)
Capacity utilization
Technological factors Maturity and volatility
Complexity
Differentiation
Patents and copyrights
Manufacturing process technology
required
Socio-political factors Social attitudes and trends
Laws and government agency
regulations
Influence with pressure groups and
government representatives
Human factors, such as unionization
and community acceptance
Source: adopted from McDonald and Dunbar (2004); modified from
related research.
Table 2. Linguistic variables describing weights of the criteria
and values of ratings
Linguistic scale Fuzzy numbers Membership function
for importance for fuzzy AHP
Just equal
Equal importance [??] [mu]M (x) = (3 - x)/(3 - 1)
Weak importance [mu]M (x) = (x - 1)/(3 - 1)
of one over another
[mu]M (x) = (5 - x)/(5 - 3)
Essential or strong [??] [mu]M (x) = (x - 3)/(5 - 3)
importance
[mu]M (x) = (7 - x)/(7 - 5)
Very strong [??] [mu]M (x) = (x - 5)/(7 - 5)
importance
[mu]M (x) = (9 - x)/(9 - 7)
Extremely [??] [mu]M (x) = (x - 7)/(9 - 7)
preferred
If factor i has one of the above numbers assigned
to it when compared to factor j, then j has the reciprocal
value when compared with i
Linguistic scale Domain Triangular fuzzy
for importance scale (l, m, u)
Just equal (1.0, 1.0, 1.0)
Equal importance l [less than or equal to] x (1.0, 1.0, 3.0)
[less than or equal to] 3
Weak importance l [less than or equal to] x (1.0, 3.0, 5.0)
of one over another [less than or equal to] 3
3 [less than or equal to] x
[less than or equal to] 5
Essential or strong 3 [less than or equal to] x (3.0, 5.0, 7.0)
importance [less than or equal to] 5
5 [less than or equal to] x
[less than or equal to] 7
Very strong 5 [less than or equal to] x (5.0, 7.0, 9.0)
importance [less than or equal to] 7
7 [less than or equal to] x
[less than or equal to] 9
Extremely 7 [less than or equal to] x (7.0, 9.0, 9.0)
preferred [less than or equal to] 9
Reciprocals of above
[M.sub.1.sup.-1]
[approximately equal to]
(1/[u.sub.1], 1/[m.sub.1],
1/[l.sub.1])
Table 3. Pairwise comparisons of selection criteria via TFN
[X.sub.1] [X.sub.2] [X.sub.3]
Degree of concentration 1,1,1 1,3,5 1,1,3
([X.sub.1])
Laws and government 1/5,1/3,1 1,1,1 1/9,1/7,1/5
agency regulations
([X.sub.2])
Types of competitor 1/3,1,1 5,7,9 1,1,1
([X.sub.3])
Contribution margins 3,5,7 1,3,5 1/5,1/3,1
([X.sub.4])
Manufacturing process 3,5,7 1/5,1/3,1 1/7,1/5,1/3
technology required
([X.sub.5])
Complexity ([X.sub.6]) 1/3,1,1 1/3,1,1 1/3,1,1
Growth rate per year 7,9,9 1/5,1/3,1 1/5,1/3,1
([X.sub.7])
Size ([X.sub.8]) 5,7,9 3,5,7 1/3,1,1
Leveraging factors 1,3,5 1/5,1/3,1 1,1,3
([X.sub.9])
[X.sub.4] [X.sub.5]
Degree of concentration 1/7,1/5,1/3 1/7,1/5,1/3
([X.sub.1])
Laws and government 1/5,1/3,1 1,3,5
agency regulations
([X.sub.2])
Types of competitor 1,3,5 3,5,7
([X.sub.3])
Contribution margins 1,1,1 3,5,7
([X.sub.4])
Manufacturing process 1/7,1/5,1/3 1,1,1
technology required
([X.sub.5])
Complexity ([X.sub.6]) 3,5,7 1/7,1/5,1/3
Growth rate per year 7,9,9 5,7,9
([X.sub.7])
Size ([X.sub.8]) 5,7,9 3,5,7
Leveraging factors 5,7,9 1,3,5
([X.sub.9])
[X.sub.6] [X.sub.7] [X.sub.8]
Degree of concentration 1,1,3 1/9,1/9,1/7 1/9,1/7,1/5
([X.sub.1])
Laws and government 1,1,3 1,3,5 1/7,1/5,1/3
agency regulations
([X.sub.2])
Types of competitor 1,1,3 1,3,5 1,1,3
([X.sub.3])
Contribution margins 1/7,1/5,1/3 1/9,1/9,1/7 1/9,1/7,1/5
([X.sub.4])
Manufacturing process 3,5,7 1/9,1/7,1/5 1/7,1/5,1/3
technology required
([X.sub.5])
Complexity ([X.sub.6]) 1,1,1 1,1,3 1/5,1/3,1
Growth rate per year 1/3,1,1 1,1,1 1/7,1/5,1/3
([X.sub.7])
Size ([X.sub.8]) 1,3,5 3,5,7 1,1,1
Leveraging factors 1,1,3 1,3,5 1/5,1/3,1
([X.sub.9])
[X.sub.9] Priority
weight (W)
Degree of concentration 1/5,1/3,1 0.051
([X.sub.1])
Laws and government 1,3,5 0.079
agency regulations
([X.sub.2])
Types of competitor 1/3,1,1 0.135
([X.sub.3])
Contribution margins 1/9,1/7,1/5 0.111
([X.sub.4])
Manufacturing process 1/5,1/3,1 0.066
technology required
([X.sub.5])
Complexity ([X.sub.6]) 1/3,1,1 0.108
Growth rate per year 1/5,1/3,1 0.146
([X.sub.7])
Size ([X.sub.8]) 1,3,5 0.180
Leveraging factors 1,1,1 0.124
([X.sub.9])
Table 4. Initial decision- making matrix with the criteria
values described in intervals
[cross product] [cross product] [cross product]
[x.sub.1] [x.sub.2] [x.sub.3]
opt Min Min Min
[q.sub.i] 0.051 0.079 0.135
[[x.sub.1].bar], [[x.sub.2].bar], [[x.sub.3].bar],
[bar.[x.sub.1]] [bar.[x.sub.2]] [bar.[x.sub.3]]
SEG 1 40 60 40 60 80 90
SEG 2 70 80 50 60 60 70
SEG 3 50 60 70 80 60 70
[cross product] [cross product] [cross product]
[x.sub.4] [x.sub.5] [x.sub.6]
opt Max Min Min
[q.sub.i] 0.111 0.066 0.108
[[x.sub.4].bar], [[x.sub.5].bar], [[x.sub.6].bar],
[bar.[x.sub.4]] [bar.[x.sub.5]] [bar.[x.sub.6]]
SEG 1 70 80 20 30 60 70
SEG 2 80 90 40 50 70 80
SEG 3 60 70 30 40 60 70
[cross product] [cross product] [cross product]
[x.sub.7] [x.sub.8] [x.sub.9]
opt Max Max Max
[q.sub.i] 0.146 0.180 0.124
[[x.sub.7].bar], [[x.sub.8].bar], [[x.sub.9].bar],
[bar.[x.sub.7]] [bar.[x.sub.8]] [bar.[x.sub.9]]
SEG 1 80 90 60 70 50 60
SEG 2 90 95 50 60 60 70
SEG 3 80 90 70 80 60 70
Table 5. Normalized weighted decision making matrix
[cross product] [cross product] [cross product]
[[??].sub.1] [[??].sub.2] [[??].sub.3]
Opt. Min Min Min
[[x.sub.1].bar], [[x.sub.2].bar], [[x.sub.3].bar],
[[bar.[x.sub.1]] [[bar.[x.sub.2]] [[bar.[x.sub.3]]
SEG 1 0.016 0.019 0.018 0.026 0.051 0.057
SEG 2 0.022 0.026 0.038 0.044
SEG 3 0.013 0.016 0.031 0.036 0.038 0.044
[cross product] [cross product]
[[??].sub.4] [K.sub.P] [[??].sub.6]
Opt. Max Min Min
[[x.sub.4].bar], [[x.sub.5].bar], [[x.sub.6].bar],
[[bar.[x.sub.4]] [[bar.[x.sub.5]] [[bar.[x.sub.6]]
SEG 1 0.035 0.04 0.013 0.01 0.032 0.03
SEG 2 0.04 0.045 0.026 0.032 0.037 0.043
SEG 3 0.03 0.035 0.019 0.026 0.032 0.037
[cross product] [cross product] [cross product]
[[??].sub.7] [[??].sub.8] [[??].sub.9]
Opt. Max Max Max
[[x.sub.7].bar], [[x.sub.8].bar], [[x.sub.9].bar],
[[bar.[x.sub.7]] [[bar.[x.sub.8]] [[bar.[x.sub.9]]
SEG 1 0.044 0.05 0.056 0.065 0.034 0.041
SEG 2 0.05 0.052 0.047 0.056 0.041 0.047
SEG 3 0.044 0.05 0.065 0.074 0.041 0.047
Table 6. Evaluation of utility degree
Segment [P.sub.j] [R.sub.j] [Q.sub.j] [N.sub.j]
SEG 1 0.1825 0.1399 0.3359 98.52%
SEG 2 0.189 0.154 0.3284 96.38%
SEG 3 0.1937 0.146 0.3407 100%
