a hybrid business strategy selection process for a textile company using swot and fuzzy anp–a case...

7/30/2019 A HYBRID BUSINESS STRATEGY SELECTION PROCESS FOR A TEXTILE COMPANY USING SWOT AND FUZZY ANPA CA

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International Journal of Management (IJM), ISSN 0976 6502(Print), ISSN 0976

6510(Online), Volume 3, Issue 2, May-August (2012)

A HYBRID BUSINESS STRATEGY SELECTION PROCESS FOR A

TEXTILE COMPANY USING SWOT AND FUZZY ANPA CASE

STUDY

K.L. Jeyaraj*,a

, C. Muralidharanb, T. Senthilvelan

cand S.G. Deshmukh

d

aDepartment of Manufacturing Engineering, Annamalai University,Chidambaram608002, TamilNadu, India.Email: [email protected]

bDepartment of Manufacturing Engineering, Annamalai University,Chidambaram608002, TamilNadu, India.

cDepartment of Mechanical Engineering, Pondicherry Engineering College,Pondicherry605014, India.

dDepartment of Mechanical Engineering, Indian Institute of Technology Delhi, New Delhi110016, India.

AbstractMany companies are conducting a SWOT analysis as part of their strategic planning. This is

the process to identify the strengths (S), weaknesses (W), opportunities (O) and threats (T)

before proceeding to the formulation of their long and short term strategy. This work initiates

with the formation of SWOT matrix, which contains the SWOT factors, sub-factor and

strategies. To ensure the successful implementation of the best strategy, here raises a critical

issue of how firms can better evaluate and select a best strategy before implementation. A

framework is proposed to address the inner dependence relations of SWOT factors and sub-

factors with the aid of analytical network process (ANP). The results obtained through the

proposed approach are more objective and unbiased due to two reasons. Firstly, the results

are generated by decision makers in the presence of multiple criteria. Secondly, the fuzzy

linguistic approach employed has more advantage to reduce distortion and losing of

information. An empirical study is presented to illustrate the application of the proposed

approach. The ANP algorithm suggests to implement and optimize an innovative process

(Colour Fast FinishCFF) to reduce water, power, fuel and effluent load (ST1). Finally this

article concludes with the force field analysis which will provide the guidelines for the

implementation of selected strategy.

Key words: SWOT analysis, strategic planning, Fuzzy ANP, Force field analysis.

1.0 An Overview of Indian Textile Industry

The textile industry in India plays a pivotal role through its contribution to industrial output,

employment generation, and the export earnings of the country. It contributes about 14 per

cent to industrial production, 4 percent to the GDP, and 16.63 percent to the country's export

earnings. It would provide direct employment to over 35 million people by 2010 to 2011

(Texmin, 2005). Thus, the growth and all round development of this industry has a direct

INTERNATIONAL JOURNAL OF MANAGEMENT (IJM)

ISSN 0976 6367(Print)ISSN 0976 6375(Online)

Volume 3, Issue 2, May- August (2012), pp. 124-143

IAEME: www.iaeme.com/ijm.html

Journal Impact Factor (2011): 1.5030 (Calculated by GISI)

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IJM I A E M E


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bearing on the improvement of the economy of the nation. In Indias current scenario, textile

industry is facing more challenges (cotton and yarn price fluctuation, effluent treatment and

discharge, customer expectation on high quality and disposal of solid waste) from all the

areas of business. For facing these challenges, organization has to plan their effective long

and short range strategy. Before starting the strategic planning, the organization has to

identify their internal capabilities and their competitive environment. SWOT analysis is oneof the self evaluating tools to measure the company internal capabilities and external

competitive environment. This article concentrates the textile processing units business

strategy selection of a leading textile company in south India. This company is having its

entire value chain of textile process. Our research interest is the processing unit (dyeing and

finishing) of that company.

1.1 Introduction to SWOT Analysis

Many companies are conducting a SWOT analysis as part of the strategic planning process to

identify strengths, weaknesses, opportunities and threats before proceeding to the formulation

of a strategy (Houben et al., 1999). SWOT analysis, meaning the analysis of key or

critical success factors, belongs to the highest ranked set of techniques of strategic analysisused by firms in empirical surveys (Glaister and Falshaw, 1999). Most of literatures are

covering the strategic planning process; most approaches include a cyclic iteration of the

following five elements. (1) Strategic planning process begins with a statement of the

corporate mission and goals. (2) Analysis of the organizations external competitive

environment. (3) Analysis of the organizations internal operating environment. (4) Selection

of focused organization strategies. (5) Implementation of the selected strategies. The last step

also involves the design of the organizational structure and control systems necessary to

implement the chosen strategy (Hax and Majluf, 1991). The focus of this article lies upon

step 4, selection of the strategy which is best among the alternative strategies. (Weihrich,

1982, 1999) modified SWOT (or TOWS) into the format of a matrix, matching the internal

factors (i.e., the strengths and weaknesses) of an organization with its external factors (i.e.,

opportunities and threats) to systematically generate responses that ought to be undertaken bythe organization. In many cases there is dependencies found among the external and internal

factor effects. This dependency needs to be taken into account during the strategic planning

(Yuksel and Dagdeviren, 2007). If it used properly, SWOT can provide a good basis for

strategy formulation (Kajanus et al., 2004). However, SWOT analysis is having deficiencies

in the measurement and evaluation steps (Hill and Westbrook, 1994, Christianson, 2002). In

conventional SWOT analysis, the magnitude of the factors is not quantified to determine the

effect of each factor on the proposed plan or strategy. In other words, SWOT analysis does

not provide an analytical means to determine the relative importance of the factors, or the

ability to assess the appropriateness of decision alternatives based on these factors (Kajanus

et al., 2004). For this reason, SWOT analysis alone cannot comprehensively appraise the

strategic decision-making process (Hill and Westbrook, 1994).

1.2 Introduction to Analytical Network Process (ANP)

Structuring of a decision problem with functional dependencies that allows for feedback

among clusters is considered to be a network system. Saaty suggested the use of AHP to

solve the problem of independence among alternatives or criteria, and the use of ANP to

solve the problem of dependence among alternatives or criteria (Saaty, 1978). The ANP, also

introduced by Saaty, is a generalization of the AHP (Chang and Huang, 2006). While the


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AHP represents a framework with an un-directional hierarchical relationship, the ANP allows

for complex interrelationships among decision levels and attributes. The ANP feedback

approach replaces hierarchies with networks in which the relationships between levels are not

easily represented as higher or lower, dominant or subordinate, direct or indirect. ANP can be

described in the following steps (Chung et al., 2005): Step 1. Model construction and

problem structuring, Step 2. Pair-wise comparison matrices and priority vectors, Step 3.Supermatrix formation and Step 4. Selection of the best alternatives. A system with feedback

(inner or outer dependence) can be represented by a network.

1.2.1 Fuzzy Set theory for ANP

Fuzzy set theory can express and handle vague or imprecise judgments mathematically. In

fuzzy logic, each number between 0 and 1 indicates a partial truth, whereas crisp sets

correspond to binary logic [ ]10, . Hence, fuzzy logic can express and handle vague orimprecise judgments mathematically (Al-Najjar and Alsyouf, 2003). In particular, to tackle

the ambiguities involved in the process of linguistic estimation, it is a beneficial way to

convert these linguistic terms into fuzzy numbers. This study builds on some important

definitions and notations of fuzzy set theory and this has been given in Appendix 1. In orderto perform a pair wise comparison among the parameters, a linguistic scale has been

developed. The scale is depicted in Figure 1 and the corresponding explanations are provided

in Table 1.

Figure 1. Triangular fuzzy membership function and fuzzy number N~

Table 1. Linguistic variables and fuzzy numbers for the importance weight

Linguistic

scaleExplanation

Fuzzy

numbers

The inverse of

fuzzy numbers

Equal

Importance

Two activities contribute equally to the

objective(1,1,1) (1,1,1)

ModerateImportance Experience and judgment slightly favor oneactivity over another (1,3,5) (1/5,1/3,1)

Strong

Importance

Experience and judgment strongly favor one

activity over another(3,5,7) (1/7,1/5,1/3)

Very strong

Importance

An activity is favored very strongly over

another; its dominance demonstrated in practice(5,7,9) (1/9,1/7,1/9)

Demonstrated

ImportanceThe evidence favoring one activity over another

is highest possible order of affirmation(7,9,11) (1/11,1/9,1/7)

1 2 3 4 5 6 7 8 9 11 l m u X

(x)~N

0

1


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The main objectives of this paper are to:

Form the SWOT matrix of a textile company. Build the hierarchy and network model of the SWOT matrix. Create complete inner dependence relationship of network model. Incorporate fuzziness in pairwise comparison matrix to eliminate the datavagueness and Create super matrix. Find the best strategy for the textile company. Force field analysis of the selected strategy.

This paper is organized as follows: The literature review is presented followed by the

methodology used for the study with a case of a textile company is given. A step by step

approach to select the best strategy is given based on fuzzy ANP. Finally this article

concludes with the force field analysis which will provide the guideline to the

implementation of selected strategy.

2.0 Literature reviewThe following are the review of literatures on SWOT analysis in the area of textile sector. Sandeep

and Goswami, (2007), have applied SWOT analysis in Indian handmade carpets industry and made aSWOT matrix. The SWOT analysis confirmed that engaging handmade carpet work produces gainfulemployment resulting socio-economic growth. Ramesh (2006) applied SWOT analysis of garment

industry and analyzed the barriers in the garment exporters in district Salem, India. In his article,SWOT analysis is used to determine the textile companys long and short range strategic planning.Dadashian et al., (2007) have applied AHP for developing the balanced scorecard on an alliance-making of eight textile companies. The work has been conducted as a qualitative case study at thetextile firms in Isfahan. But the selected factors for study are assumed to have no inner dependence

between them. Hussain et al., (2009) have made a study to identify internal and external factors

relevant to textile and clothing supply chain in Pakistan using SWOT-AHP / ANP analysis. The

identified factors played an important role in the development of strategies which are useful for

improving the competitiveness of the chain. AHP and ANP were used to find the potential strategy.But in their paper the authors dealt only the 1

stlevel of inner dependence of the SWOT main factors.

The sub factors inner dependence is not accounted. Hussain et al., (2010) have focused to examine the

potential of different strategies formulated by experts on Pakistans textile and clothing supply chaincase. The intension was to formulate a decision structure based on external view of the chain and with

more generalized criteria based on SWOT AHP / ANP. The inner dependence between the sub-factors is not considered even in their study. Hussain et al., (2011) have made a future opportunity

based planning analysis for Pakistans textile and clothing supply chain case. They have formed amethodology for developing a planninglink between chain entities and opportunities based on

SWOT ANP, assuming sub-factors as independent. Rezaie et al., (2010) have made a novel

systematic method to obtain the most proper organizational safety strategies in a textile company in

Iran by utilizing SWOT concepts. Yuksel and Dagdeviren, (2007) have applied the strategic decision

making SWOT ANP analysis for a textile company. Their paper demonstrated the process for

quantitative SWOT analysis when there is dependence among strategic factors. In this study a

complete inner dependency analysis with ANP is suggested with a textile company case. SWOT mainfactor and SWOT sub factors inner dependence is measured and incorporated for strategy selection. Anew approach of network interdependence and super matrix is constructed. More over, the literatures

show that researchers have not considered the vagueness in the decision makers impreciseness inSWOT AHP / ANP pair wise comparison matrix. To eliminate the data vagueness in this study,

fuzzy set theory is used as an improvement process from the existing study.

3.0 MethodologyThe proposed methodology is given as a flow chat (Figure 2), which starts from SWOT matrix. For

selection of the best strategy from the SWOT matrix, the proposed Fuzzy ANP algorithm is as

follows:


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Step 1: Identify SWOT sub-factors, determine the alternative strategies according to SWOT sub-

factors and form the SWOT matrix. Table 2 shows the textile companys key internal and external

strategy for business improvement. Internal strength and weakness variables, external opportunity and

threat variables are also given in Table 2 to plan the strategies. This matrix is developed based on thequantitative analysis of each SWOT factors and sub-factors. The thirty three SWOT sub-factors are

identified based on the prior experience of the researcher, from literature and also with the help of

brainstorming method. The same is made as rating type questionnaire. All the thirty threequestionnaires are evaluated in qualitative nature. The questionnaires are surveyed by interview(Edward, 2002 and Gorsuch, 1983) through all the (125 no) staff members in top management,

managerial level and supervisory level of the company (Jeyaraj et al., 2011). The results of the

matching are listed in the four separate quadrants (i.e. MaxiMaxi SO; MiniMaxi WO; MaxiMini

ST and MiniMini WT strategies).

Figure 2. Flow chart for the methodology used

Formation of SWOT matrix

Problem structure and construction of

hierarchy model

Construction of network model and

inner dependence model

Pair wise comparison matrices

Formation of supermatrix

Selection of best strategy

Force filed analysis

For matching external and

internal SWOT factors for

strategy building

To classify SWOT

factors, sub factors and

alternate strategies with

a linka e

To study the inner

dependence between the

SWOT factors and sub

factors

To find the importanceweight of factors, sub

factors and alternatives.

Also to find the inner

dependence weight

A matrix format to

incorporate inner

dependence to the

factor wei hts

Selection of best

strategy based on ANP

weight-age

To analyze the positive

negative environment to

implementation of the

selected strate

Entire ANP

activity

Fuzzy ANP

activity


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Table 2. SWOT matrix of the textile company

SWOT

Internal Strength Internal weakness

1. (C6S) Raw material supply 1. (C12W) Operatives fatigue

2. (C4S) Low labour cost 2. (C15 W) Fragmented company3. (C1S) Mass production setup 3. (C11W) Textile engineering Skills

4. (C2S) Cost conscious business 4. (C13 W) Effluent treatment capacities

5. (C5S) Capital investment

availability5. (C14W) Availability of water

6. (C3S) Strong R & D for dyeing and

finishing6. (C10W) Work environment

External Opportunity Maxi - Maxi (SO strategy) Mini - Maxi (WO strategy)

1. (C18O)Market orders Exports / Locals

(SO1) Production and ETP line

balancing, can be achieved by

changing product mix and production

plan

(WO1) Implementation of OHASAS

and EMS

2. (C19O) Marine discharge facility (SO2) Development of own retail

market across the south India

3. (C20O) Technical textile(SO3) Production and ETP line

balancing

4. (C21O) New developments in dyes,

pigments and chemicals

External Threat Maxi - Mini (ST strategy) Mini - Mini (WT strategy)

1. (C30T) High water consumption /

effluent generation

(ST1) Implement and optimize an

innovative process (Colour Fast Finish

- CFF) to reduce water, power, fuel

and effluent load

(WT1) Time and motion study and

implementation

2. (C26T) Product lead time

(ST2) Capacity improvement of power

generation by own generation or from

3rd party or from wind mills

(WT2) Ergonomics study and

implementation

3. (C31T) Disposal of solid waste

generated from effluent

(ST3) Capacity improvement of steam

generation by install new boiler units

(WT3) Training and skill development

campaign in production and services

for production

4. (C29T) Availability of electrical

power

(ST4) Production, lead time and

quality improvement through TPM

and TQM

(WT4) Re locate and integration of the

value chain of the company by

considering the cost benefit

5. (C25T) Availability fuel for steam

generation(ST5) Implementation of SA 8000

6. (C24T) Ecological product requirement

7. (C28T) Social awareness

8. (C27T) Market competition

Step 2: Specify the problem structure of the SWOT matrix and the hierarchical representation of the

SWOT matrix. The problem is transformed into a hierarchical structure, as suggested by Saaty and

Mariya, (2008), for decision making. The proposed hierarchical model structure is constructed using

the Web-HIPRE1

software and it is presented here in Figure 3. The overall problem consists of the

goal, Determining Best Strategy which is based on four factors: strengths, weaknesses,opportunities and threats (Yuksel and Dagdeviren, 2007). These factors are further added with sub

factors of relevant importance to the chain and included six strengths, six weaknesses, four

opportunities and eight threats. Linking the goal with the factors and sub-factors, thirteen strategies

are suggested which may have potential contributions in a textile company strategic planning.


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Step 3: Construct the network of the SWOT model and inner dependence among the SWOT factorsand SWOT sub-factors. The hierarchy and network model proposed in this study for SWOT analysis

is composed of four levels, as shown in Figure 4.

Figure 3. Hierarchical model for determining the best strategy of a textile company

Figure 4. Hierarchy and network representation of SWOT model

Alternative

(b) The network of SWOT model

S W O T

factors

Goal

Criteria

Sub

Criteria

BestStrategy

S W O T

sub-factors

Alternate

Strategy

Alternative

S W O T

factors

Goal

Criteria

Sub

Criteria

BestStrategy

S W O T

sub-factors

Alternate

Strategy

Inner

dependence

Inner

dependence(w21)

(w32)

(W43)

(w1)

(W2)

(w3)(W4)

(W5)

(a) The hierarchy of SWOT model

Cluster

Element


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The goal (best strategy) is indicated in the first level, the criteria (SWOT factors) and sub-

criteria (SWOT sub-factors) are found in the second and third levels respectively, and the last

level is composed of the alternatives (alternative strategies). The supermatrix of a SWOT

hierarchy with four levels is as follows:

=

IW

w

w

esAlternativ

factorsSWOT sub rsSWOT facto

Goal

W

43

32

21

00

000

000

0000

(12)

Where w21 is a vector which represents the impact of the goal on the criteria, w32 is a matrix

that represents the impact of the criteria on each of the sub-criteria, W43 is a matrix that

represents the impact of the sub-criteria on each of the alternatives, and I is an identity

matrix. A hierarchical representation of the SWOT model is given in Figure 4a and its

general network representation is presented in Figure 4b. The network model (Figure 4b)

illustrates the case of a hierarchy with inner dependence within clusters but without feedback.

SWOT factors, sub-factors and strategies are used in place of criteria, sub-criteria and

alternatives respectively in Figure 4b. The SWOT factors and sub-factors have inner

dependencies with in their clusters. A network can be organized to include source clusters,

intermediate clusters and sink clusters. Relationships in a network are represented by arcs,where the directions of arcs signify directional dependence (Chung et al., 2005).

Interdependency between two clusters, termed outer dependence, is represented by a two-way

arrow. Inner dependencies among the elements of a cluster are represented by looped arcs

(Chung et al., 2005). The letters in brackets in Figure 4b represents the relationship that will

be signified by sub-matrices for supermatrix evaluation. Based on the schematic

representation of Figure 4b, the general sub-matrix notation for the SWOT model used in this

study is as follows:

=

IW

Ww

Ww

esAlternativ

factorsSWOT sub

rsSWOT facto

Goal

W

5

43

21

00

00

00

0000

(13)

Where, w1 is a vector that represents the impact of the goal, namely, selecting the best

strategy according to SWOT factors. The W2 is a matrix that represents the inner dependence

of the SWOT factors. The w3 is a vector that denotes the impact of the SWOT factor on each

of the SWOT sub-factors. The W4 is a matrix that represents the inner dependence of the

SWOT sub-factors and W5 is a matrix that denotes the impact of the SWOT sub-factors on

each of the alternative strategies. Using matrix operations is preferred in order to show the

details of the calculations in this algorithm.

Step 4: Assume that there is no dependence among the SWOT factors; determine the

importance degrees of the SWOT factors with a 5 point linguistic scale (i.e. calculate w1).

Expert team of six key staff members of the company (with author) constructed the inner

dependence models for factors and sub-factors. The expert team has selected the pairwise

importance, inner dependence through fuzzy linguistic scale. Saaty and Mariya (2008) haveemphasized maintaining the consistency ratio below 0.1 when constructing the comparison

matrices of orders larger than 55. In this study for all comparison matrices, the consistency

ratio is calculated based on fuzzy preference ratio method (Modarres et al., 2010).The

pairwise comparison matrix for the SWOT factors with respect to the best strategy (goal) is

constructed first (Table 3).


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Table 3. Pairwise comparison of SWOT factors for the selection of best strategy

Best Strategy Strength Weakness Opportunity ThreatImportance weight of

SWOT factors

Strength (1,1,1) (3,5,7) (1,3,5) (1,1,1) 0.343

Weakness (1/7,1/5,1/3) (1,1,1) (1,1,1) (1/9,1/7,1/5) 0.082

Opportunity (1/5,1/3,1) (1,1,1) (1,1,1) (1/7,1/5,1/3) 0.094

Threat (1,1,1) (5,7,9) (3,5,7) (1,1,1) 0.480(CR 0.07)The importance weight of SWOT factors is calculated by the equation 5 to 10 (Appendix 1).

The matrix w1 is the importance weight of the SWOT factors from Table 3.

Step 5: Determine with a 5 point linguistic scale, the inner dependence matrix of each SWOT

factor with respect to the other factors by using the schematic representation (Figure 5a) of

inner dependence among the SWOT factors (i.e. calculate W2). To calculate the inner

dependence in SWOT factors, the inner dependence model presented in Figure 5a is

followed. The inner dependence comparison matrices of SWOT factors are presented in

Tables 4 through Table 6. The relative importance weight of SWOT factors inner

dependence matrix is calculated by the equation 5 to 10 (Appendix 1). As SWOT factor

opportunity is affected only by the strength, so no pairwise comparison matrix is formed with

respect to opportunity. The comparison matrix (W2) of inner dependences of SWOT factors isconstructed as follows. Initially 4 4 identity matrix is made. The four rows and columns are

strength, weakness, opportunity and threat. The first column (strength) is filled with relative

importance weight from Table 4. The second column (weakness) is filled with the relative

importance weight from Table 5. The third column (opportunity) is filled with zero except

strength row, since it is dependent only with strength which is filled by value one (Figure 5a).

The fourth column is filled with relative importance weight from Table 6.

Table 4. Inner dependence of SWOT factors with respect to strength

Strength Weakness Opportunity ThreatRelative importance weight

of SWOT factors

Weakness (1,1,1) (1,1,1) (1/9,1/7,1/5) 0.124

Opportunity (1,1,1) (1,1,1) (1/7,1/5,1/3) 0.129Threat (5,7,9) (3,5,7) (1,1,1) 0.748

(CR 0.051)

Table 5. Inner dependence of SWOT factors with respect to weakness

Weakness Strength ThreatRelative importance weight of

SWOT factors

Strength (1,1,1) (1/5,1/3,1) 0.274

Threat (1,3,5) (1,1,1) 0.726

(CR Not applicable)

Table 6. Inner dependence of SWOT factors with respect to threat

Threat Strength ThreatRelative importance weight of

SWOT factors

Strength (1,1,1) (1/5,1/3,1) 0.171

Threat (1,3,5) (1,1,1) 0.829

(CR Not applicable)

The remaining unfilled references of W2 matrix are filled with zero. The matrix W2 is

presented as follows:


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=

0001000072607480

0000000100001290

8290000000011240

1710000127400001

2

....

....

....

....

W

Step 6: Determine the inner dependence priorities of the SWOT factors (i.e. calculate wfactors

= W2 w1). The (1st

level) SWOT wfactors are local priority weights. These are the results of

the normalized eigenvectors with considering the inner dependence. It can be explained as arelative grade of the SWOT factor group. If it is high for a factor, that will be high priority

factor at local level. The matrix (Wfactors) of inner dependence priority of SWOT factors is

calculated by multiplying w1 with W2 and normalized values of these weights are presented

as follows:

=

=

==

3980

0690

2610

2710

;

7990

1390

5220

5420

4800

0940

0820

3430

0001000072607480

0000000100001290

8290000000011240

1710000127400001

12

.

.

.

.

W

.

.

.

.

.

.

.

.

....

....

....

....

wWWd)(Normalize

factorfactors

Step 7: Assume that there is no inner dependence among the SWOT sub-factors; determine

the importance degrees of the SWOT sub-factors with a 5 point linguistic scale given in

Table 1 (i.e. calculate w3). The pairwise comparison matrices of SWOT sub-factors for local

priority are constructed. SWOT strength category sub-factors pairwise comparison matrix is

given in Table 7.

Table 7. Pairwise comparison of strength - SWOT sub-factors for the local priority (CR 0.087)

Strength C1S C2S C3S C4S C5S C6S Local weight

C1S (1,1,1) (1,1,1) (3,5,7) (1,1,1) (1,3,5) (1/5,1/3,1) 0.166

C2S (1,1,1) (1,1,1) (1,3,5) (1/5,1/3,1) (1,1,1) (1/7,1/5,1/3) 0.098

C3S (1/7,1/5,1/3) (1/5,1/3,1) (1,1,1) (1/9,1/7,1/5) (1,1,1) (1/11,1/9,1/7) 0.044

C4S (1,1,1) (1,3,5) (5,7,9) (1,1,1) (3,5,7) (1,1,1) 0.260

C5S (1/5,1/3,1) (1,1,1) (1,1,1) (1/7,1/5,1/3) (1,1,1) (1/9,1/7,1/5) 0.056

C6S (1,3,5) (3,5,7) (7,9,11) (1,1,1) (5,7,9) (1,1,1) 0.376

The local weight of the SWOT sub-factors is calculated by the equations 5 to 10 (Appendix

1). Similar to the Table 7, other three sub-factors category (weakness, opportunity and

threat) pairwise comparison matrices for local priority are constructed and local weights have

been calculated. The matrix w3 is the local weight of pairwise comparison of strength

category SWOT sub-factors from Table 7.

Step 8: Determine with a 5 point linguistic scale, the inner dependence matrix of each SWOT

sub-factor with respect to the other sub-factors by using the schematic representation (Figure

5b, 5c, 5d and 5e) of inner dependence among the SWOT sub-factors (i.e. calculate W 4).

Similar to the SWOT factors inner dependence matrices (Table 5 to Table 7), SWOT sub-

factors (strength, weakness, opportunity and threat) inner dependence matrices are formed

and weights have been calculated. The comparison matrix (W4) of inner dependences of

strength category SWOT sub-factors is constructed as follows. Initially 6 6 identity matrixis made. The six rows and columns are C1S to C6S. Each column of the matrix (W4) is filled

with the relative importance of SWOT sub-factors local weight similar to matrix W2. The

matrix W4 is presented as follows:


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=

000100000000775025007480

000000010000000017800000

000000000001000024900000

059000000000000117801290

423000010001116000011230

517000000000109014400001

W 4

......

......

......

......

......

......

Similar to the strength category inner dependence comparison matrix, other categories

(weakness, opportunity and threat) inner dependence comparison matrices are formed.

Step 9: Determine the inner dependence priorities of the SWOT sub-factors (i.e. calculate

wsub-factors (Local) = W4 w3). The (2nd

level) SWOT wsub-factors (Local) are also local priority

weights. These are the results of the normalized eigenvectors with considering the inner

dependence. It can be explained as a relative grade of the SWOT sub-factor group (Example

strength category).

Figure 5. Inner dependence representation of SWOT model

If it is high for a sub-factor, that will be high priority sub-factor at the local level. Inner

dependence priority of strength category sub-factors (Wsub-factors) are calculated by

multiplying w3 with W4 and normalized values of these weights are presented as follows:

O

S

W

T

(a) Inner dependence

among SWOT factors

C1S

C3S

C6S

C2S

C4S

C5S

(b) Inner dependence among

SWOT sub-factors (strength)

C11W

C13W

C14W

C15W

C12W

C10W

(c) Inner dependence among

SWOT sub-factors (weakness)

C21O

C19O

C20O

C18O

(d) Inner dependence among

SWOT sub-factors (opportunity)

C29T

C13W

C14W

C26T

C28T

C27T

C25T

C24TC30T

C31T

(e) Inner dependence among SWOT sub-factors (threat)


12/20



=

==

5580

0740

2850

1050

5990

3800

3760

0560

2600

0440

0980

1660

000100000000775025007480

000000010000000017800000

000000000001000024900000

059000000000000117801290

423000010001116000011230

517000000000109014400001

34)(

.

.

.

.

.

.

.

.

.

.

.

.

......

......

......

......

......

......

wWWStrength

Localssub-factor

Similar to inner dependence priority of strength category sub-factors, other categories(weakness, opportunity and threat) inner dependence priority sub-factors are calculated. The

normalized weight of those sub-factors inner dependence priority is given as follows:

=

=

=

=

105.0

180.0

072.0

088.0

0940

2490

0520

1600

;

2850

1060

2720

33690

;

322.0

042.0

0610

2120

2390

1210

;

279.0

037.0

1420

0520

2990

1900

(Local)(Local)(Local)(Local)

.

.

.

.

W

.

.

.

.

W.

.

.

.

W.

.

.

.

W

d)(NormalizeThreat

factorsub

d)(Normalize

yOpportunit

factorsub

d)(NormalizeWeakness

factorsub

d)(Normalize

Strength

factorsub

Step 10: Determine the global priority of the SWOT sub-factors (i.e. calculate wsub-factors (Global)

= wfactors wsub-factors (Local)). The (1st

level and 2nd

level) SWOT wsub-factors (Global) are combinedlocal priority (the combination of two local priority is called global priority of entire criteria).

The eigenvectors are then normalized with considering the inner dependence. It can be

expressed as a relative grade of the complete SWOT criteria (factor and sub-factor). If it is

high, it will have high priority factor in the global level. Local priorities of sub-factors are

converted into global priorities by multiplying these Wfactors with wsub-factors (Local). Global

priorities of sub-factors (wsub-factor (Global)) are presented in Table 8.

Table 8. Conversion of local priority of sub-factors into global priority

SWOT factorsInner dependencepriority of factors

Wfactor

SWOT

sub-factors

Inner dependencelocal priority of sub-factors

Wsub-factor (Local)

Global priority of sub-factors

Wsub-factor (Global)

Strength 0.271

C1S 0.190 0.051

C2S 0.299 0.081C3S 0.052 0.014

C4S 0.142 0.039

C5S 0.037 0.010

C6S 0.279 0.076

Weakness 0.261

C10W 0.121 0.032

C11W 0.240 0.063

C12W 0.212 0.056

C13W 0.061 0.016

C14W 0.042 0.011

C15W 0.323 0.084

Opportunity 0.069

C18O 0.337 0.023

C19O 0.272 0.019

C20O 0.106 0.007

C21O 0.285 0.020

Threat 0.398

C24T 0.161 0.064

C25T 0.052 0.021

C26T 0.249 0.099

C27T 0.094 0.037

C28T 0.088 0.035

C29T 0.072 0.029


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C30T 0.180 0.072

C31T 0.105 0.042

Step 11: Determine the priority of the alternative strategies with respect to each SWOT sub-

factor with a 5 point linguistic scale (i.e. calculate W5). The comparison matrix for the

alternative strategies with respect to SWOT sub-factor (mass production setup C1S) is

constructed (Table 9).

Table 9. Pairwise comparison matrix of alternatives priority with respect to sub-factor (C1S)

C1S SO1 SO2 SO3 WO1 ST1 ST2 ST3 ST4 ST5 WT1 WT2 WT3 WT4Local

Weight

SO1 (1,1,1) (1,1,1) (1,3,5) (1,3,5) (1/5,1/3,1) (1,1,1) (1,1,1) (1,1,1) (1,3,5) (1,3,5) (3,5,7) (3,5,7) (1,3,5) 0.114

SO2 (1,1,1) (1,1,1) (1,3,5) (1,3,5) (1/5,1/3,1) (1,1,1) (1,1,1) (1,1,1) (1,3,5) (1,3,5) (3,5,7) (3,5,7) (1,3,5) 0.114

SO3 (1/5,1/3,1) (1/5,1/3,1) (1,1,1) (1,1,1) (1/9,1/7,1/5) (1/5,1/3,1) (1,1,1) (1,1,1) (1,1,1) (1,1,1) (1,1,1) (1,1,1) (1,1,1) 0.040

WO1 (1/5,1/3,1) (1/5,1/3,1) (1,1,1) (1,1,1) (1/9,1/7,1/5) (1/5,1/3,1) (1,1,1) (1,1,1) (1,1,1) (1,1,1) (1,1,1) (1,1,1) (1,1,1) 0.040

ST1 (1,3,5) (1,3,5) (5,7,9) (5,7,9) (1,1,1) (1,3,5) (3,5,7) (3,5,7) (5,7,9) (5,7,9) (7,9,11) (7,9,11) (5,7,9) 0.272

ST2 (1,1,1) (1,1,1) (1,3,5) (1,3,5) (1/5,1/3,1) (1,1,1) (1,1,1) (1,1,1) (1,3,5) (1,3,5) (3,5,7) (3,5,7) (1,3,5) 0.114

ST3 (1,1,1) (1,1,1) (1,1,1) (1,1,1) (1/7,1/5,1/3) (1,1,1) (1,1,1) (1,1,1) (1,1,1) (1,1,1) (1,3,5) (1,3,5) (1,1,1) 0.061

ST4 (1,1,1) (1,1,1) (1,1,1) (1,1,1) (1/7,1/5,1/3) (1,1,1) (1,1,1) (1,1,1) (1,1,1) (1,1,1) (1,3,5) (1,3,5) (1,1,1) 0.061

ST5 (1/5,1/3,1) (1/5,1/3,1) (1,1,1) (1,1,1) (1/9,1/7,1/5) (1/5,1/3,1) (1,1,1) (1,1,1) (1,1,1) (1,1,1) (1,1,1) (1,1,1) (1,1,1) 0.040

WT1 (1/5,1/3,1) (1/5,1/3,1) (1,1,1) (1,1,1) (1/9,1/7,1/5) (1/5,1/3,1) (1,1,1) (1,1,1) (1,1,1) (1,1,1) (1,1,1) (1,1,1) (1,1,1) 0.040

WT2 (1/7,1/5,1/3) (1/7,1/5,1/3) (1,1,1) (1,1,1) (1/11,1/9,1/7) (1/7,1/5,1/3) (1/5,1/3,1) (1/5,1/3,1) (1,1,1) (1,1,1) (1,1,1) (1,1,1) (1,1,1) 0.033

WT3 (1/7,1/5,1/3) (1/7,1/5,1/3) (1,1,1) (1,1,1) (1/11,1/9,1/7) (1/7,1/5,1/3) (1/5,1/3,1) (1/5,1/3,1) (1,1,1) (1,1,1) (1,1,1) (1,1,1) (1,1,1) 0.033

WT4 (1/5,1/3,1) (1/5,1/3,1) (1,1,1) (1,1,1) (1/9,1/7,1/5) (1/5,1/3,1) (1,1,1) (1,1,1) (1,1,1) (1,1,1) (1,1,1) (1,1,1) (1,1,1) 0.040

(CR 0.065)

The local weight of the alternative strategy with respect to C1S is calculated by equation 5 to

10 (Appendix 1). Similar to the Table 9, other twenty three SWOT sub-factors alternative

strategy matrices are constructed and local weights have been obtained. The local weights of

twenty four alternative strategies with respect to each sub-factor are included in matrix W 5(matrix size 24 x 13). The local weights transferred into the each column of the W5 matrix as

follows:

=

00100020001000100010003000100020004000000020001000400020

00300040002000200020008000100040004000000020001000400020

00300040001000300020008000100040004000000020001000400020

00300040002000300030008000100040004000000020001000400020

00300060002000300010006000100050004000100030001000400020

00300030001000100030008000100030004000100020001000400030

00300040003000100030006000200040004000100020001000400030

00200030003000200030006000200030004000100020001000400060

00600110005000500050015000400100024000300110004002400140

00300060002000300010006000100050004000000020001000400020

00300060001000200020004000100050004000000020001000400020

00300040002000100030006000100040008000100030001000900060

00300060002000200020004000100040004000000020001000900060

5

..............

..............

..............

..............

..............

..............

..............

..............

..............

..............

..............

..............

..............

W

KKKKK

KKKKK

KKKKK

KKKKK

KKKKK

KKKKK

KKKKK

KKKKK

KKKKK

KKKKK

KKKKK

KKKKK

KKKKK

Step 12: Determine the global priority of the alternative strategies, reflecting the

interrelationships within the SWOT factors (i.e. calculate walternatives = W5 wsub-factors (Global)).

Finally the global priorities of alternative strategies are established by multiplying the

priorities of alternative strategies calculated with respect to sub-factors (W5) and global

priorities of sub-factors (wsub-factors (Global)). walternatives is the relational grade of strategies based

on the proposed ANP algorithm. It includes the priorities of the alternatives and also the

global priority of complete criteria set. This aids the selection of best strategy. This has been

presented in Table 10.


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Table 10. Global priority of strategies and ranking of the strategies

Strategy SO1 SO2 SO3 WO1 ST1 ST2 ST3 ST4 ST5 WT1 WT2 WT3 WT4

P

riorities Strength 0.022 0.028 0.013 0.013 0.080 0.018 0.015 0.015 0.014 0.014 0.013 0.013 0.013

Weakness 0.027 0.026 0.029 0.032 0.069 0.026 0.029 0.026 0.031 0.032 0.030 0.029 0.012

Opportunity 0.006 0.006 0.005 0.003 0.015 0.005 0.006 0.004 0.003 0.003 0.003 0.007 0.003

Threat 0.030 0.022 0.032 0.049 0.129 0.045 0.037 0.037 0.025 0.040 0.038 0.038 0.054Global priority of

alternative strategies0.085 0.082 0.079 0.097 0.292* 0.095 0.087 0.082 0.073 0.089 0.084 0.088 0.081

Ranking 7 9 12 2 1* 3 6 10 13 4 8 5 11

(* Best strategy)

Step 13: Selection of best strategy based on the global priority of the alternative strategies

(i.e. calculate rank of walternatives). The ANP algorithm indicates that implement and optimize

an innovative process (Colour Fast FinishCFF) to reduce water, power, fuel and effluent

load (ST1) is the best strategy with a global priority of alternative strategies value of 0.292

(Table 10). Further the ranking which are achieved with inner dependencies in SWOT model

(both SWOT factors and sub-factors) is presented in Table 10. This Table 10 also shows the

contributions of SWOT main factors for the global priorities of alternative strategies. Theglobal priority of the strategies confirms that ST1 strategy is un-doubted best strategy. The 1st

ranked strategy ST1s relative global priority 0.292 but the 2nd

ranked WO1s relative global

priority is 0.097. This huge difference proves that ST1 is to be studied and implemented

without any ambiguity by the top management of the company. The other strategies expect

ST1 are having the less relative global priority variance. The contribution of SWOT factors to

the ST1 strategy is very high relative values compare to the other strategies. The threat and

strength category factors and its sub-factors are the main contributing factors for the relative

global priority of strategy ST1 and others. The threat category sub-factors mainly deals with

the environment impact. Strength category sub-factors mainly focus companys facilities. At

the moment the perennial issue for the society is to provide safe environment to the people

and living hoods. The selected strategy is directly related to the goodness of environmental

impacts. First and foremost work for the textile company is to implement the ST1 strategy;the other strategies can be given least importance.

Step 14: Analyze the selected strategy using Force field analysis. The first step is to agree the

area of change needs to be discussed. This might be written as a desired policy goal or

objective. All the forces in support of the change are then listed in a column to the left

(driving the change forward), whereas all forces working against the change are listed in a

column to the right (restraining the change holding it back). Driving forces are those forces

affecting a situation and which are attempting to push it in particular direction. These forces

tend to initiate change or keep it going. Restraining forces are forces acting to restrain or

decrease the driving forces. The forces working against the change needs to be minimized

and the forces support of the change needs to be maximized. A state of equilibrium is reached

when the sum of the driving forces equals the sum of the restraining forces. The net resulted

(equilibrium forces or positive forces) forces will create the climate to implement the goal orobjective. Holmes, T.A., (2011) formulated three fundamental assertions about force fields

and change as follows:

1. Increasing the driving forces results in an increase in the resisting forces; the current

equilibrium does not change but is maintained under increased tension.

2. Reducing resisting forces is preferable because it allows movement towards the desired

state, without increasing tension.

3. Group norms are an important force in resisting and shaping organizational change.


15/20



In this paper, the selected best strategy from the Fuzzy ANP methodology is under gone force

filed analysis to find the driving and restraining forces for its implementation. Figure 6,

shows the force filed analysis of ST1 strategy. In this analysis, the restraining forces are

scoring high value (-15, sum of restraining forces score) comparing to the driving force (+6,

sum of driving forces score). The driving force consists of the following force components;

management commitment, technical support from supplier and machinery and facility

availability. The restraining force consists of parameter study, un-optimized of parameters,process variability, multiple quality requirement and process understanding. The restraining

forces score needs to be minimized so as to remove the barriers to implement the strategy

effectively.

Figure 6. Force filed analysis of ST1 strategy

The team of the textile company has to work on the restraining forces so as to covert the

restraining forces to driving forces or reduce the restraining forces total score. 1st

force, theparameter study needs to be concentrated, which will identify the process parameter involves

in the Colour Fast Finish (CFF) process both controlled and uncontrolled parameters. 2nd

force, optimization of the parameters is identified from the 1st

force. 3rd

force, variability

reduction of the Colour Fast Finish (CFF) process by which the process can be controlled and

can be improved. Further it will lead to the process capability studies. 4th

force, multiple

quality requirements which are needs to be optimized with respect to 1st

and 2nd

force. 5th

force, process learning and expertise (through training) will be the base for successful

implementation of the strategy.

6.0 Conclusion

Fuzzy ANP method used in this study, offers a complete analysis by additionally consideringinner dependence relationships. In this study inner dependence considered for both SWOT

factors and sub-factors. In sub-factors all the four categories are considered the inner

dependences. This kind of completed consideration of inner dependences will provide the

more actual results compare the AHP or 1st

level inner dependence ANP methods. So the

selection of best strategy will resemble its trueness of selection. But considering more inner

dependence requires more time and effort (additional interdependency relationships increase

geometrically the number of pairwise comparison matrices). For this reason, an application of

the fuzzy ANP approach, as proposed in this study targeted at more strategic decisions in a


16/20



human oriented thinking and reasoning approach. Triangular number over comes the

vagueness in the ordinary Likert scale data when it is compare to classical ANP. Fuzzy

triangular number also increases the resolution (difference between the two values) of the

global priority of the alternative strategies. The inner dependencies make the decision makers

work easy. If all the inner dependences considered in the selection stage itself, then the

decision maker wont have any question of negative probability in his mind. The results of

this empirical study show that the most desired strategy is to implement and optimize aninnovative process (Colour Fast FinishCFF) to reduce water, power, fuel and effluent load

(ST1). This strategy had a global priority of alternative strategies value of 0.292.

Considering other strategies ST1 is having very high potential for especially long-term

environment safety, profit, and competitiveness considerations. Additionally, the

implementation of this strategy is planned through force filed analysis. High scoring

restraining forces are provided a way to implement the strategy (ST1). The restraining forces

emphasis the study related to process parameters and multiple quality characteristics will

minimize the distance between the restraining and driving forces. Strategy ST1 can be

implemented in an effective manner by force filed analysis. The effectiveness of the study on

process parameters and multiple quality characteristics will make a climate to measure the

success of the strategy ST1. This paper addresses the need for a strategic analysis model to

assist management in evaluating and selecting the strategies for business growth. Through

this line, an evaluation model is developed based on a literature survey and refined with

industrial experts. The proposed evaluation framework is generic. However, the model is

implemented in a famous textile company, South India. The results, based on the synthesized

judgments, indicate that textile processing industry should focus on innovative process to

reduce energy for effective strategic planning. This research aims to help practitioners to

understand the relative importance of the factors and set then effective improvement plans as

they may not have sufficient resources to deal with all the factors at the same time. The

evaluation sub-factors are comprehensive but they may change rapidly; thus, the checklist of

SWOT sub-factors must be updated in the future (frequency, every 3 years).

Acknowledgement The authors would like to thank the Managing Director (Mr. Prithiv), Gee Kay PrintingMills, Tirupur and his subordinates for their support and committed co-operation to this study.

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Appendix 1. (Fuzzy Set theory concepts for ANP)

To measure the pair-wise comparison between criteria { }niCC i ,,2,1 K== , a decision group of p expertsare asked to make sets of pair-wise comparisons in terms of linguistic terms. Hence, p fuzzy matrices

)()2()1( ~,,

~,

~ pZZZ K , each corresponding to an expert and with triangular fuzzy numbers as its elements, are

obtained (Ming-Lang et al., 2011). Denote)(~ kZ as:

=

0~~

~0

~

~~0

~

21

2

112

ZZ

ZZ

ZZ

Z

(k)

n

(k)

n

(k)

n

(k)

(k)

n

(k)

(k)

K

MOMM

K

K

(1)

A triangular fuzzy number N~

can be defined as a triplet (l, m, u) , and the membership function (x)~N

is

defined as (Figure 1):

=

ux,

ux), m(u-x)/(u-m

mx), l(x-l)/(m-l

lx,

(x)~N

f

p

0

0

(2)

Where ml , , and u are real numbers and uml .

Let ( )(k)ij

(k)

ij

(k)

ij

(k)

ij ,u,mlZ =~

.Without loss of generality, elements ( ),n,,iZ(k)ii K21~

== will be regarded as a

triangular fuzzy number whenever it is necessary. Fuzzy matrix(k)Z

~is called the initial pair-wise comparison

fuzzy matrix of expert k. Acquire the normalized pair-wise comparison fuzzy matrix, Let)(~ k

ia be the triangular

fuzzy number:

==

====

n

j

(k)

ij

n

j

(k)

ij

n

j

(k)

ij

n

j

(k)

ij

(k)

i u,m,lZa1111

~~ and

=

=

n

j

k

ijni

(k)ur

1

)(

1max (3)

The linear scale transformation is then used as a normalization formula to transform the criteria scales into

comparable scales. The normalized pair-wise comparison fuzzy matrix of expert k, denoted as (k)X~

, is givenby:

=

(k)

nm

(k)

n

(k)

n

(k)

n

(k)(k)

(k)

n

(k)(k)

(k)

XXX

XXX

XXX

X

K

MOMM

K

K

21

22221

11211

~ ; pk ,,2,1 K= (4)

Where,

==

(k)

(k)

ij

(k)

(k)

ij

(k)

(k)

ij

(k)

(k)

ij(k)

ijr

u,

r

m,

r

i

r

zX~


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As that in crisp value assumes at least one, such that=

a hybrid business strategy selection process for a textile company using swot and fuzzy anp–a case...

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