locating the competitive relation of global logistics hub using quantitative swot analytical method

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Qual Quant (2009) 43:87107DOI 10.1007/s11135-007-9087-1

O R I G I N A L PA P E R

Locating the competitive relation of global logistics hub

using quantitative SWOT analytical method

Kuo-Liang Lee Wen-Chih Huang Junn-Yuan Teng

Received: 15 April 2006 / Accepted: 15 August 2006 / Published online: 29 March 2007 Springer Science+Business Media B.V. 2007

Abstract Evaluating the competitive position of location develops global logisticshub (GLH) is a multiple criteria decision-making (MCDM) problem and it isimportant for governor to implement suitable strategies appropriate its environment.SWOT analysis is very important in the process of strategic formulation. Under manyconditions, the evaluative criteria (indicators) are mixed with quantitative/qualitativevalues and the values for qualitative criteria are often imprecisely defined for decision-

makers. A quantified SWOT analytical method, that integrates the method of fuzzyAnalytic Hierarchy Process (AHP) method, was proposed to provide more detailedand quantified data for SWOT environmental analysis to assess the competitive rela-tion for locations develop different types GLH in PacificAsian region. Integratingthe concept of Grand Strategy Matrix (GSM), a suitable competing strategy could besuggested for location developing GLH in accordance with its competitive position.

Keywords Global logistics hub Competitive position Quantitative/qualitativecriteria Quantitative SWOT Fuzzy AHP Competing strategy

K-L. Lee (B)Department of Marketing and Distribution Management, Overseas Chinese Institute ofTechnology, No.100, Chaio Kwang Rd., Taichung, 407, Taiwan, R.O.C.e-mail: [email protected]

W-C. Huang

Laboratory of Port and Logistics of National Taiwan Ocean University, P.O. BOX 7-107, Keelung,Taiwan, R.O.C.e-mail: [email protected]


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88 K-L. Lee et al.

1 Introduction

The way of modern commodities distribution changes from anticipatory logisticsto response-based logistics; namely, which focuses on predicting the final product

demand now turns to emphasis quickly response the customer demand. The decisionof logistics service providers (LSPs) and international firms, on concentrating logisticsfunction in a particular global logistics hub (GLH) is critical importance. Hence,the role of GLH location as home-based for providing these logistics function hasbecomeincreasingly important. In order for a location in developing a successful GLH,the governor is required to design and implement proper strategies for attractinginternational firms (Tao and Park 2004; Sheu 2004).

SWOT analytical method is very important in the process of strategy formulation(David 1998; Chang and Huang 2006). Analysis on internal strengths and weaknessesis mainly to evaluate how an enterprise carries out its internal work, such as mana-

gement, work efficiency, research, and development, etc. SWOT analysis is able tohelp the enterprises evaluate their position in the competition and can be used asfoundation for the development of policies. Analysis of external opportunities andthreats is mainly to evaluate whether an enterprise can seize the opportunities andavoid the threats when facing an uncontrollable external environment. With the helpof SWOT analysis, the location can get to know its position when it is faced with thecompetitive environment so that it can function as the basis to propose the strategies.

The quantitative analysis on the environment mainly aims at analyzing data statisti-cally, hence, it is much more objective for the analytical results when using the method,

and it is different from the subjective estimation in words of the quality mode, suchtraditional SWOT analysis method.David (1998, 2001)summarized various SWOTquantitative analysis methods, including External Factor Evaluation Matrix (EFE),Internal Factor Evaluation matrix (IFE). Kurttila et al. (2000) and Stewart et al. (2002)combined the Analytic Hierarchy Process (AHP) with SWOT to provide a new hybridmethod for improving the usability of SWOT analysis. Although a consistency test isused to ensure the weight that was scored objectively by the evaluative group, to carryout SWOT analysis comparison on several enterprises simultaneously is difficult.

A suitable location decision for MNCs selecting a GLH in accordance with twoor more criteria is a multiple criteria decision-making (MCDM) problem. However,

the criteria of GLH competition differ according to the criteria for judging subjects,circumstances, the degree of knowledge, etc. Also, their degree of strength is tobe changed as per the different ways of thinking in depth. Moreover, the criteriaare mixed with quantitative and qualitative values, and have reciprocal organic andcomplex relationships each other. These many criteria have the problems of complexand organic relationships. Under many conditions, the values for qualitative criteriaare often imprecisely defined for decision-makers. Besides, the desired values andimportance weighting of criteria are usually described in linguistic terms, e.g., low,medium, high, very high, etc. It is not easy to precisely quantify the rating

of each alternative location selection problem and the precision-based methods asstated above are not adequate to deal with the GLH location selection problem.Fuzzy set theory was developed exactly based on the premise that the key indicators
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Locating the competitive relation of global logistics hub 89

In this paper, a quantified SWOT analytical method, that integrates the conceptof MCDM and fuzzy AHP method, was proposed to improve the above methods. Bythe analytical method, we evaluate locations developing GLH in Pacific Asia regioncan not only realize their position in the competition be shown on the 4-quadrant

coordinate but also have a reference for developing strategies.

2 Methodology

2.1 Fuzzy AHP method

2.1.1 AHP method

The AHP was initially presented bySaaty (1980)for solving multiple criteria deci-

sion problems. Using a systematic hierarchy structure, complex estimation criteriacan be represented clearly and definitely. Ratio scales are utilized to make recipro-cal comparisons for each element and each layer. After completing the reciprocalmatrix, one can obtain comparative weights for each element. Considering the cri-teriaC1, . . . , Ci, . . . , Cj, . . . , Cn, some one level in hierarchy. One wishes to find theirweights of importance,w1, . . . , wi, . . . , wj, . . . , wn, on some elements in the next level.Obtaining an exact priority vector w = (w1, . . . , w2, . . . , wj, . . . , wn) is complex, sothis paper uses the Normalization of Row Average(NRA) (Saaty and Vargas 1982)method to replace the more complex operation. This method sums up each row ele-

ment and standardizes it by summing all elements of the matrix. That is, allowingaij, i,j = 1,2, . . . , n, to be the importance strength ofCiwhen compared withCj, then

wi =

nj=1

aij

ni=1

nj=1

aij

, i = 1,2, . . . , n. (1)

Consistency testing is an important issue for using Eq. 1to find the priority vectorand it contains two layers. One is to check whether the pairwise comparative matrix

which answers by decision makers is a consistency matrix or not. The other is tocheck the consistency of hierarchy structure. The ratio to estimate the consistencyis Consistent Ratio (CR). The CR tells us how consistent we are with our answers.A higher number means we are less consistent, while a lower number means that weare more consistent. In general, if the CR is less than or equal to 0.1, the consistencywill be guaranteed.

The ratio is equal to the consistency index (CI) divided by the random index (RI).

CR =CI

RI. (2)

The formula for C.I. is:

C I = n

(3)


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90 K-L. Lee et al.

Due to the pairwise comparisons utilized in AHP facilitate the conveyance ofresponsors preference, and the measure of consistency enables us to return to thejudgments modifying them here and there to improve the overall consistency. TheAHP method will be utilized to find the criteria weight.

2.1.2 Fuzzy set theory

In a universe of discourse ofX, a fuzzy subsetA ofXis characterized by a membershipfunctionfA, which maps each element xin Xto a real number in the interval [0, 1].The function value represents the grade of membership ofxin A. A fuzzy numberA(Dubois and Prade 1978; Dornier et al. 1998; Laarhoven 1983) in (real line) is atriangular fuzzy number if its membership function fA :

0, 1

is

fA(x) =

xc

ac, c x a,

dxda

, a x d,

0, otherwise.

(4)

With < c a d < , the triangular fuzzy number A can be representedby (c, a, d). Here, the triangular fuzzy numbers are used to denote the approximatereasoning of linguistic values (Zadeh 1975, 1976).Theyareusedtocoveythesubjectiveevaluation of decision-makers. The reason of using triangular fuzzy number is that itis easy to use. For example, performance is a linguistic variable, its values are very

low, low, medium, high, very high, etc. Linguistic value can also be represented bythe approximate represented by the approximate reasoning of fuzzy set theory. Forexample, the linguistic value Good can be denoted by (0.5, 0.7,1). An exact numbera can be represented by (a, a, a). In this paper the linguistic values are utilizedto assess the linguistic ratings given by decision-makers, as well as the linguisticweights assigned to various selection criteria. By the extension principle (Zadeh1965) the extended algebraic operations of any two triangular fuzzy numbers A1 =(c1, a1, d1),A2 = (c2, a2, d2)can be expressed as:

A1 A2 = (c1 + c2, a1 + a2, d1 + d2), KA1 = (kc1, ka1, kd1), k R, k 0.

2.1.3 Ranking of triangular fuzzy numbers

Many fuzzy ranking methods have been proposed (Bortolan and Degani 1985; Luisand Antonio 1989; Kim and Park 1990). Because of the graded mean integrationrepresentation method (Chen and Hsieh 2000) not only improve some drawbacksof the existing method, but also possess the advantage of easy implementation, andpowerfulness in problem solving, it will be used to rank the final superiority ratingsof all alternatives.

Let Ai

= (ci, a

i, d

i), i = 1,2, , n, be n triangular fuzzy numbers. The graded

mean integration representationR(Ai)ofAiis

4 d
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Ai > Aj R(Ai) >R(Aj),

Ai = Aj R(Ai) = R(Aj),

Ai < Aj R(Ai)


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92 K-L. Lee et al.

Questionnaire

investigation

Calculating the internal and

external weight score and

determining the benchmarks

of each type GLH

Deciding the

competitive locations

Distinguish of internal and

external environmentindicators of various types

GLH

Normalize the

performance

Build a hierarchical structure

of various types GLH

Calculating the internal and

external coordinate values of

each type GLH

Judging the competitive

positions of all locations onthe 4-quadrant coordinate

of each type GLH

Data collection

Objective and quantified

Performance value

The linguistic quality

Performance value

Weights of key

indicators using AHP

method

Fig. 1 Quantified SWOT procedures evaluating competitive position of GLH

0 Eij 1

j

Eij= 1,

wherePijandEij, respectively, represent the non-normalized and normali-

zed performance value of thejlocation of theith evaluative indicators.Step 7 Calculating the weight score of all locations (as shown in Table1), whichis calculated by weigh (wi) fuzziness performance value (Ej(cj, aj, dj)) and


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Table 1 The assessment of weighted score among competitive locations

Criteria Weight Unit Locations (Lj)performance value(Ci) (wi)

L1 L2 Ln

C1 w1 Q E11(c11, a11, d11) E12(c12, a12, d12) E1n(c1m, a1m, d1m)C2 w2 N E21(a21, a21, a21) E22(a22, a22, a22) E2n(a2m, a2m, a2m)

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

Cm wn Q Em1(cm1, am1, dm1) Em2(cm2, am2, dm2) Emn(cmn, amn, dmn)Weight 1 E1(c1, a1, d1) E2(c2, a2, d2) En(cn, an, dn)score R1 R2 Rn

Remark: 1. Q: Quality; N: Quantity

2.Ej(cj, aj, dj) =m

i=1

wi Eij, Rj=cj+4aj+dj

6 .

AI=I1 +I2 + +In

n , j= 1, 2 . . . n, (8)

AE =E1 + E2 + + En

n , j= 1, 2 . . . n, (9)

whereAIandAE, respectively, represent the benchmark of the internal andenvironment evaluation,Ijand Ej, respectively, represent the weight score

of thejports internal and external environment.Step 9 When the weight scores of the internal and external environments of theresearch location subtract the benchmarks of the internal and external en-vironments, the results are coordinate values of the research objects in fourquadrants of SWOT.

ISj= Ij AIj, j = 1, 2 . . . n, 1 IS +1, (10)

ESj= Ej AEj, j = 1, 2 . . . n, 1 ES +1, (11)

whereISjrepresents the coordinate value of the jports internal environ-

ment, andESjrepresents the coordinate value of thejports external envi-ronment.

Step 10 Finally, all candidate locations are illustrated in the SWOT Matrix to judgethe competitive profiles, positions, of all locations.

3 Empirical analysis

With strong economic developments since the early 1980s and a shift in the global

center of manufacturing to Asia, major ports in Far Eastern region have expandedrapidly. The demand for cargos in Far Eastern region will further increase in the future(Chou et al. 2003). We note that Hong Kong (China), Singapore, Shanghai (China),


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Table 2 The function of various types of GLH

Functions Stage Providing firms Types

Transportation Transportation Carriers

Warehousing Forwarder TransshipmentConsolidation CFSDistribution Custom brokers

Assembly Reprocessing Manufacturing firmsLabeling DC firms Re-export (export or transshipment +

reprocessing)InspectingPacking

3.1 Types of GLH

Logistics activities provide a number of functions, including transportation, storage,consolidation, assembly, inspection, labeling, packing, documentation, and R&D ser-vices (Lu 2003; Sheu 2004). In accordance with relations among businesses thatparticipate in the process of the supply of and demand for products and services,the supply chain participants could be classified into primary and specialized types(Bowersox and Closs 1996; Sheu 2004). The primary participants, the demanders oflogistics activities, include manufacturing firms, wholesalers, and retailers that providemanufacturing, assembly, distribution, and retail services for products. The speciali-

zed participants, the suppliers of logistics activities, include functional and supportingparticipants. The functional participants include carriers, forwarders, container freightstation (CFS) operators, customs brokers, and other logistics integration companiesthat provide the transportation, storage, consolidation, assembly, inspection, labeling,packing, and documentation services and charge fees and tariffs from the primaryparticipants.

There are location considerations for different specialized participants to establishtheir bases, such as regional distribution or reprocessing centers, in specific regionsto provide various logistics services. Hence, in order to develop a regional GLH, thegovernors of many locations have made efforts to strengthen their infrastructure andeconomic ability to attract MNCs of specialized logistics participants (Tao and Park2004; Sheu 2004).

Integrating these logistics activities of functional/primary/supporting (transporta-tion/manufacturing) and cargo flows (export and transshipment), the GLH types (aslisted in Table2) were proposed as the foundation for analyzing the issue of compe-titive position for location developing a GLH.

3.1.1 Transshipment type GLH

Focusing on Kaoshiung city in southern Taiwan, this city has the largest port in Tai-wan, which was ranked sixth among the worlds container ports in 2005 (Kaohsiung
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volume, which measures 4.60 million TEU and comprises 52% of gross handlingvolume (Containerization International 2004), and it is one of the most importanthub-ports in the PacificAsia region.

3.1.2 Re-export type GLH

By providing this type of logistics service, local manufacturing industries (such asscience-based industrial parks and industrial parks), distribution centers, and ports canbe integrated into the functional activities. As an illustration, let us consider re-exporttype GLH typically used by Taiwan information manufacturing firms: the MNCs orderfrom the OEM manufacturers in Taiwan (David 2001); the OEM manufacturers inTaiwan import some of the parts from several international markets, reprocess them

in Taiwan, and finally export them to international consumer markets. The OEMmanufacturers in Taiwan create the value of reprocessing.

3.2 Target sample collection

We developed a structured questionnaire based on the seven stages outlined byChurchill (1991).The information to be sought was first specified, and then the fol-lowing were determined: type of questionnaire and its method of administration,content of individual questions, form of response to and wordings of each question,sequence of questions, and physical characteristics of the questionnaire. The ques-

tionnaire was pretested and revised wherever necessary. The content validity of thequestionnaire was tested through a theoretical review and pilot test, i.e., questions inthe questionnaire were based on previous studies and discussion with a number oflogistics executives and experts.

The sample firms operate in a variety of industries including international manu-facturing firms (such as apparel, computer, electronics, machinery, office supplies, andpharmaceuticals industries), numbers of International Logistics Association, shippingcompanies, and freight forwarders industries. Due to the limitations in finance andtime, the eight-page questionnaire survey was sent to the managers of internationalmanufacturers (200) from the List of Leading Firms in 2004 with Good Export andImport Performance published by the Board of Foreign Trade of the Ministry ofEconomic Affairs in Taiwan, shipping companies (9), freight forwarders (26), and themembership of Taiwan International Association (40).

The revised questionnaire was sent to a manager in each of our target samplefirms by post-mail, email or interview. In order to encourage potential respondentsparticipation, we offered some incentives such as to provide a respondent with theresults of our research upon completion and gifts to respondents once receivinghis/her answered questionnaire. After deleting parts of questionnaires that are notreasonable, finally there were 49 questionnaires responding with the valid recovery

rate of 17.8%. The sample consists of 49 MNCs-based in a various industries, as shownin Table3.


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Internal

factors

External

factors

Transshipment

GLH

Political/economic/society stability

( 1I )

Ext-TR Convenience (I2)

Information abilities (I3)

Reprocessing tax (I11)

Zero custom tax (I12)

Reprocessing time (I13)

Reprocessing facilities (I14)

Indus. Environ. legal Guarantee (I15)

Products original Certificate (I16)

Reprocessing cost (I17)

Manpower quality (I18)

Industrial cluster environment (I19)

Re-proc. Ext. transportation (I20)

Financing deregulation (I21)

I

Location resistance (E1)

Density of shipping line (E2)

Regional industrial competition (E6)

Parts cost (E7)

Shanghai (L1)

Busan (L2)

Kaohsiung (L3)

Shenzhen (L4)

HK (L5)

Singapore (L6)

Type Environment Indicators Locations

Fig. 3 Hierarchical structure of re-export type GLH

SW

O

T

KaohsiungSingapore

HK

Busan

ShanghaiShenzhen

Fig. 4 The competitive position of transshipment type GLH in PacificAsia region. Remark: S:Strength; W: Weakness; O: opportune; T: Threaten

The total weight score (as shown in Tables7,8) of transshipment and re-exporttypes GLH can be obtained by multiplying the weights with indicators performance

after defuzziness by the graded mean integration representation. The benchmarks canbe obtained by average value of all locations weight score and the coordinate valuescan be obtained by weight score of the location subtract benchmark. Eventually, the


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Table 4 The indicators weights of two types GLH

Criteria Weight

Transshipment type (1) Re-export type (2)

Internal criteriaPolitical, economic, society stability 0.099 0.076Ext-TR convenience 0.068 0.038Information abilities 0.011 0.055

Port rate 0.206

One stop service 0.056

Transshipment time 0.117

Port and warehouse facilities 0.109

Port operation system 0.089

Port operation legal guarantee 0.078

IM/EX volume 0.077Reprocessing tax 0.091

Zero custom tax 0.084

Reprocessing time 0.077

Reprocessing facilities 0.057

Reprocessing deregulation

Indus. environ. legal guarantee 0.060

Products original certificate 0.041

Reprocessing cost 0.081

Re-processing manpower quality 0.081

Industrial cluster environment 0.105

Re-proc. ext. transportation 0.041

Financing deregulation 0.065R&D cost 0.048

Summary 1 1

External criteria

Location resistance 0.260 0.127

Density of shipping line 0.232 0.231

Regional port competition 0.192

Port alliance/internationalize 0.113

Transshipment volume

Regional industrial competition 0.203 0.389

Parts cost 0.253

Summary 1 1

transshipment type (as shown in Fig. 4) shows that Singapore, Hong Kong, Kaohsiung,and Busan locate in the SO quadrant, so have external opportunities for development

and internal competing strength, thus are in the best position for facing competition.Although Kaohsiung and Busan are in the first quadrant, there is a gap between themand Singapore and Hong Kong. Shanghai and Shenzhen locate in the WT quadrant,


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Table 5 The qualified performance of locations developing GLH in the East Asia region

Evaluative indicators Unit Locations

L1 L2 L3 L4 L5 L6

Internal indicatorsI10Containers handlingvolume

ThousandTEU

14,557 11,403 9,715 13,655 21,932 21,310

External indicatorsE1Location resistance Miles 7,288 8,784 5,401 6,421 6,356 17,199E2Density of shipping line Lines 106 130 109 112 215 336

Locations: Shanghai (L1); Busan (L2); Kaohsiung (L3); Shenzhen (L4); HK (L5); Singapore (L6)Source: Containerisation International Yearbook 2005

Table 6 Normalize the qualified performance of locations developing GLH in the East Asia region

Evaluative indicators Unit Locations

L1 L2 L3 L4 L5 L6

Internal indicatorsI10Containers handlingvolume

ThousandTEU

0.6637 0.5199 0.4439 0.6231 1.0000 0.9716

External indicatorsE1Location resistance Miles 0.7411 0.6149 1.0000 0.8411 0.8497 0.3140E2Density of shipping line Lines 0.3155 0.3869 0.3244 0.3333 0.6399 1.0000

Locations: Shanghai (L1); Busan (L2); Kaohsiung (L3); Shenzhen (L4); HK (L5); Singapore (L6)

Table 7 The benchmarks and coordinate values of transshipment type GLH

Environment Coordinatevalue

L1 L2 L3 L4 L5 L6 Benchmark

Internal Weightedscore (SW)

0.5869 0.6394 0.6466 0.5088 0.6994 0.7557 0.6395

Coordinatevalue (SW)

0.0526 0.0001 0.0071 0.1307 0.0599 0.1162

External Weighted

score (OT)

0.6067 0.6898 0.6652 0.5358 0.7313 0.6580 0.6478

Coordinatevalue (OT)

0.0411 0.0420 0.0174 0.1120 0.0835 0.0102

Remark: Locations: Shanghai (L1); Busan (L2); Kaohsiung (L3); Shenzhen (L4); HK (L5); Singapore(L6)Coordinate value = Weighted average valueBenchmark

On analyzing the locations developing the re-export type GLH from the rela-tionship of competitive position and conditions, we can find that Shenzhen, Busan,and Kaohsiung locate in the SO quadrant due to the competitiveness on the keyindicators of high-tech industrial environment. As far as the re-export type GLH do

not lay major emphasis on the port condition but on high-tech industrial conditions,so HK, Busan, and Singapore locate in WT quadrant. Since the Shanghai activelyimprove infrastructure (where includes Great Yangshan Island and Little Yangshan


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Table 8 The benchmarks and coordinate values of re-export mode GLH

Environment Coordinatevalue

L1 L2 L3 L4 L5 L6 Benchmark

Internal Weightedaveragevalue (SW)

0.7082 0.7523 0.7491 0.7859 0.7223 0.6833 0.7344

Coordinatevalue (SW)

0.0262 0.0179 0.0147 0.0515 0.0121 0.0511

External Weightedaveragevalue (OT)

0.6799 0.7259 0.6940 0.7079 0.6697 0.6550 0.6887

Coordinatevalue (OT)

0.0088 0.0372 0.0053 0.0192 0.0190 0.0337

Remark: Locations: Shanghai (L1); Busan (L2); Kaohsiung (L3); Shenzhen (L4); HK (L5); Singapore(L

6)Coordinate value = Weighted average valueBenchmark

Kaohsiung SW

O

T

Shenzhen

Busan

HK

Shanghai

Singapore

Fig. 5 The competitive position of re-export mode GLH in Pacific-Asia region. Remark: S: Strength;W: Weakness; O: opportune; T: Threaten

4 Discussion and implication

Quantified SWOT in this study not only shows the competitive relation of locationsdeveloping GLH, but also but also has a reference for developing strategies on thebasis of the Grand Strategy Matrix (GSM) (Chou et al. 2003). Just as in the GSM,the enterprises are parked in the four quadrants of the coordinate according to theircategories (as shown in Fig. 6). However, there is a reversal in that the ordinate standsfor the external environment (opportunities, threats) while the abscissa stands for theinternal environment (strengths, weaknesses).

The meaning of the four quadrants is(Chang and Huang 2006): the first quadrantstands for the enterprises strengths and market opportunities. Enterprises in this

quadrant can use their strengths to adopt strategies, such as market penetration,market development, and product development to form competitive strength. If theenterprise in the first quadrant has extra resources, forward, backward, and horizontal


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Rapid market growth

Quadrant IMarket development

Market penetration

Product development

Forward integrationBackward integration

Horizontal integration

Concentric diversification

Quadrant IIMarket development

Market penetration

Product development

Horizontal integrationDivestiture

Liquidation

Quadrant IIIRetrenchment

Concentric diversification

Horizontal diversification

Conglomerate DiversificationDivestiture

Liquidation

Quadrant IVConcentric diversification

Horizontal diversification

Conglomerate

Diversification

Joint ventures

Slow market growth

Strong

Competitive

Position

Weak

Competitive

Position

Fig. 6 The grand strategy matrix. Source: Adapted from R.Christensen et al. (1976)

strength through joint venture or horizontal merger strategies. Enterprises in the thirdquadrant are of low-competitive strength and facing threats from other competitors.Defensive strategies, such as focusing on the most favored markets, can be adoptedto avoid threats. Divestiture or liquidation should be adopted if these strategies fail.

Enterprises in the fourth quadrant are those possessing competition strength butfacing greater threats than opportunities. Diversification or joint venture should beadopted to reduce threats.

In case of Kaohsiung, several strategies is a brief illustration of developing re-exporttype GLH (see Table9) depending on the SO quadrants dimensions of market deve-lopment, market penetration, product development, forward integration, backwardintegration, horizontal integration, concentric diversification.

Locations will be judged on their environmental ability to find ways for developingthe suitable mode GLH depending on the evaluation of competitive position. Locationgovernor may implement suitable strategies in an effort to improve environmental

conditions of GLH according the competitive position.

5 Conclusion

SWOT analysis is very important in the process of strategy formulation. In thisstudy, a quantified SWOT procedure, that integrates the MCDM concept and fuzzyAHP method, was proposed to help decision makers assess the competitive po-sition of location developing a GLH. The method shows similarities to the GSM

concept, so could be combined with the GSM for strategy formulation and locationselection.We analysis the position of locations developing GLH in Pacific Asia region was


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102 K-L. Lee et al.

Table 9 SO strategies of location developing re-export type GLH

Strategy Description Example

Market development Expanding new market

to increase the export

Signing the free trade agreements to expand

the economic hinterland and to exploit newconsumption market and supplying market(manufacturing market), establishing long-term relations with other nations and orga-nizations (such as signing Asian and PacificRegional Trade Agreement (RTA))

Market penetration Finding out newconsumption marketand supplying market(manufacturing market

In the existing industrial environment, esta-blishing a high-tech strategic union with theinternational enterprises so as to expand theconsumption market and the manufacturingmarket (signing the Free Trade Agreement(FTA) for the USA, Taiwan, and the Main-

land)Productdevelopment

Developing new pro-duction and improvingtraditional production

Providing better industry-conglomeratingenvironment, adding values to humanresources and to deep-level taxation andits inducements (including sales tax, subsi-dies for exporting and importing talents andfor talents researches), constructing betterhigh-tech industrial environment

Forward integration Integrating upstreammarket of supply andmanufacture side

Signing the union agreement with the ma-nufacturing (production) hinterland to sup-ply the raw materials, semi finished productsand key parts for manufacturing

Backwardintegration

Integrating downstreammarket of consumptionside

Signing the free trade agreement with thehinterland of the manufacturing market(the supplier) and that of the consumptionmarket (the consumer)

Horizontalintegration

Integrating the advan-tageous resources tojointly design and deve-lop new products

Integrating the existing high-tech establish-ment and technology, transferring or sup-porting the industrial parks at home andabroad to upgrade (such as integrating high-tech industrial resources of Taiwan)

Concentricdiversification

Increasing the commontechnology and marketof new production in the

existing porduction

Providing financial service, information cen-ter service, and other functional services(such as the liberalization of the financial

system, Asian and Pacific direct sales (auc-tion) center)

the weights are obtained precisely. The performance value for qualitative indicatorsare often imprecisely defined for decision-makers and it is not easy to precisely quan-tify the rating. Hence, the fuzzy AHP method is used to integrate various linguisticassessments and weights to evaluate the location suitability and determine the best

selection.Depending on the coordinate value of the qualified SWOT analysis of the locationstheir position in the competition can be clearly realized, this helps location governors


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Appendix A. The description of competitive indicators of GLH

Indicators Description

Internal indicators

Political, economic, society stability The internal environmental stability of location will affectthe investment of MNCsExt-TR Convenience The convenience of extension transportation between port

and reprocessing will affect the efficiency of time and costInformation abilities It provides the convenience of MNCs in information service

requirement on logistics, commerce, financing activitiesPort rate (terminal rate) It affect the transportation cost of MNCsOne stop service It provides the convenience of MNCs in administration and

operational serviceTransshipment time It means the operation time of cargos from import to export

at portPort and warehouse facilities The excellent facilities is necessary attracting shipping com-

pany and forwarderPort operation system The systems such as public, priority, rent system, will affectthe operation efficiency at port

Port operation legal guarantee The legalization of port operation will attract MNCs of ship-ping companies

IM/EX volume It affect the transshipment cost of cargos through the effectof economic scale

Reprocessing tax It will affect the reprocessing cost of cargosZero custom tax It will affect the transshipment and reprocessing cost of car-

gosReprocessing time It provides the ability of time performanceReprocessing facilities It means the manufacturing facilities providing deep repro-

cessing abilities

Reprocessing deregulation The deregulation of deep reprocessing activities will attractMNCs

Indus. environ. legal guarantee The legalization of reprocessing environment will attractMNCs of manufacturing companies

Products original certificate It affect the brand of products, such as made in Taiwan(MIT), design in Taiwan (DIT)

Reprocessing cost It includes the cost such as facility, manpower, operation,etc., cost

Re-processing manpower quality It affect the quality of product value-addedIndustrial cluster environment The cluster ability of vertical and horizontal industries will

affect the efficiency of deep reprocessingFinancing deregulation It will affect the investment of foreign MNCsR&D cost The R&D cost affect the deep reprocessing cost of cargos

External indicatorsLocation resistance It means the distance from location to main consumer mar-

ket, will affect the distribution cost and timeDensity of shipping line The frequency of shipping line from locations port to main

marketplace

Regional port competition The port competition scenario among competitive locationswill affect the selecting of GLH

Port alliance/internationalize The port internationalization affect the competitive ability

of port, and affect the selecting of GLHTransshipment volume It affect the reprocessing cost of cargos through the effect

of economic scale


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104 K-L. Lee et al.

Appendix B. The fuzziness evaluation value of transshipment type GLH

Indicators Weight Unit Fuzziness preference value

L1 L2 L3 L4 L5 L6

Internal indicatorsPolitical,economic, so-ciety stability

0.099 5 scales (0.5377,0.7453,0.9434)

(0.4623,0.6698,0.8679)

(0.4434,0.6509,0.8585)

(0.5377,0.7453,0.9434)

(0.6509,0.8585,1.0000)

(0.6509,0.8585,1.0000)

Ext-TRconvenience

0.068 5 scales (0.5000,0.7245,0.9388)

(0.4796,0.7041,0.9286)

(0.5612,0.7857,1.0000)

(0.5408,0.7653,0.9694)

(0.3367,0.5612,0.7857)

(0.3776,0.6020,0.8265)

Informationabilities

0.011 5 scales (0.2621,0.4757,0.6893)

(0.4757,0.6893,0.9029)

(0.4757,0.6893,0.9029)

(0.3592,0.5728,0.7864)

(0.6117,0.8252,1.0000)

(0.6117,0.8252,0.9903)

Port rate (ter-minal rate)

0.206 5 scales (0.4128,0.6147,0.8165)

(0.5046,0.7064,0.9083)

(0.4679,0.6697,0.8716)

(0.1560,0.3578,0.5596)

(0.4495,0.6514,0.8532)

(0.6881,0.8899,1.0000)

One stop ser-vice

0.056 5 scales (0.4020,0.6176,0.8333)

(0.5000,0.7157,09314)

(0.5392,0.7549,0.9510)

(0.3824,0.5980,0.8137)

(0.5588,0.7745,0.9706)

(0.6176,0.8333,1.0000)

Transshipmenttime

0.117 5 scales (0.5354,0.7576,0.9798)

(0.5354,0.7576,0.9798)

(0.5556,0.7778,1.0000)

(0.3333,0.5556,0.7778)

(0.5556,0.7778,1.0000)

(0.5556,0.7778,1.0000)

Port and wa-rehouse faci-lities

0.109 5 scales (0.3679,0.5755,0.7830)

(0.5189,0.7264,0.9340)

(0.4434,0.6509,0.8491)

(0.3302,0.5377,0.7453)

(0.5943,0.8019,0.9717)

(0.6509,0.8585,1.0000)

Port opera-tion system

0.089 5 scales (0.3592,0.5728,0.7864)

(0.5340,0.7476,0.9612)

(0.5534,0.7670,0.9709)

(0.3592,0.5728,0.7864)

(0.6311,0.8447,1.0000)

(0.6117,0.8252,0.9903)

Port opera-tion legal gua-rantee

0.078 5 scales (0.3558,0.5673,0.7788)

(0.5288,0.7404,0.9519)

(0.5481,0.7596,0.9519)

(0.3942,0.6058,0.8173)

(0.5865,0.7981,0.9808)

(0.6250,0.8365,1.0000)

Containershandlingvolume

0.077 ThousandTEU

(0.6637,0.6637,0.6637)

(0.5199,0.5199,0.5199)

(0.4439,0.4439,0.4439)

(0.6231,0.6231,0.6231)

(1.0000,1.0000,1.0000)

(0.9716,0.9716,0.9716)

Weightedaverage value

1.000 (0.4112,0.5872,0.7615)

(0.4636,0.6395,0.8145)

(0.4534,0.6293,0.9091)

(0.3332,0.5091,0.6828)

(0.5267,0.7026,0.8590)

(0.587,0.7631,0.8950)

External indicatorsLocation

resistance

0.260 Miles (0.7411,

0.7411,0.7411)

(0.6149,

0.6149,0.6149)

(1.0000,

1.0000,1.0000)

(0.8411,

0.8411,0.8411)

(0.8497,

0.8497,0.8497)

(0.3140,

0.3140,0.3140)

Density ofshipping line

0.232 Lines (0.3155,0.3155,0.3155)

(0.6228,0.7399,0.7724)

(0.3244,0.3244,0.3244)

(0.3333,0.3333,0.3333)

(0.6399,0.6399,0.6399)

(1.0000,1.0000,1.0000)

Regional portcompetition

0.192 5 scales (0.3786,0.5922,0.8058)

(0.7005,0.8895,0.9791)

(0.4951,0.6087,0.8406)

(0.0000,0.1068,0.3204)

(0.5146,0.7282,0.9417)

(0.6117,0.8252,0.8995)

Transshipmentvolume

0.113 5 scales (0.3235,0.5392,0.7549)

(0.6708,0.8559,0.974)

(0.3824,0.598,0.7583)

(0.4216,0.6373,0.8529)

(0.3431,0.5588,0.7745)

(0.598,0.7116,0.9068)

Regional in-

dustrial com-petition

0.203 5 scales (0.6117,

0.8252,1.0000)

(0.7807,

0.8716,0.9666)

(0.5534,

0.7005,0.8652)

(0.4951,

0.7087,0.9223)

(0.5728,

0.7864,0.9806)

(0.3204,

0.534,0.7476)Weighted 1.000 (0.4993, (0.6184, (0.5858, (0.4442, (0.6232, (0.5637,


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Appendix C. The fuzziness evaluation value of re-export type GLH


L1 L2 L3 L4 L5 L6

Internal indicatorsPolitical eco-nomic societystability

0.076 5 scales (0.5625,0.7708,0.9583)

(0.5637,0.7129,0.8438)

(0.4375,0.6458,0.8542)

(0.5625,0.7708,0.9583)

(0.6042,0.8125,0.9792)

(0.6667,0.7804,0.9055)

Reprocessingcost

0.038 5 scales (0.6250,0.8333,0.9896)

(0.6105,0.7292,0.9375)

(0.3542,0.5625,0.7708)

(0.6458,0.8542,1.0000)

(0.4167,0.6250,0.8333)

(0.0938,0.2917,0.5000)

Zero customtax

0.055 5 scales (0.5376,0.7527,0.9677)

(0.5376,0.7816,0.9355)

(0.5591,0.7742,0.9785)

(0.6022,0.8172,1.0000)

(0.6022,0.8065,0.9892)

(0.6237,0.7009,0.9049)

Reprocessingtax

0.091 5 scales (0.5217,0.7391,0.9565)

(0.6558,0.8851,0.96740

(0.5870,0.7935,0.9783)

(0.5870,0.8043,1.0000)

(0.4565,0.6739,0.8804)

(0.4783,0.6957,0.9022)

Ext-TRconvenience

0.084 5 scales (0.5591,0.7742,0.9785)

(0.5376,0.7527,0.9677)

(0.5806,0.7849,0.9785)

(0.5376,0.7527,0.9677)

(0.6237,0.8280,1.0000)

(0.6237,0.7807,0.9892)

Reprocessingtime

0.077 5 scales (0.5106,0.7234,0.9362)

(0.5745,0.7872,0.9787)

(0.5957,0.8085,0.9894)

(0.6170,0.8298,1.0000)

(0.4468,0.6596,0.8723)

(0.4681,0.6809,0.8936)

Re-processinghuman qua-lity

0.057 5 scales (0.3226,0.5376,0.7527)

(0.5591,0.7742,0.9785)

(0.6022,0.8172,1.0000)

(0.6022,0.8172,1.0000)

(0.4731,0.6882,0.9032)

(0.3656,0.5806,0.7957)

Indus.environ. legalguarantee

0.060 5 scales (0.3736,0.5934,0.8132)

(0.5495,0.7692,0.9890)

(0.5714,0.7912,1.0000)

(0.5275,0.7473,0.9670)

(0.5495,0.7582,0.9451)

(0.5934,0.8022,0.9890)

Logistics Hubinformationabilities

0.041 5 scales (0.5319,0.7447,0.9574)

(0.5532,0.7660,0.9681)

(0.55320., 660,0.9681)

(0.5106,0.7234,0.9362)

(0.5957,0.7979,0.9574)

(0.6596,0.8617,1.0000)

Products ori-ginal certifi-cate

0.081 5 scales (0.4667,0.6889,0.9111)

(0.5333,0.7556,0.9778)

(0.5556,0.7778,1.0000)

(0.4444,0.6667,0.8889)

(0.5333,0.7556,0.9667)

(0.5333,0.7556,0.9667)

Industrial

clusterenviro.

0.081 5 scales (0.5053,

0.7158,0.9263)

(0.6316,

0.8316,0.9895)

(0.6526,

0.8421,0.9895)

(0.6316,

0.8421,1.0000)

(0.4211,

0.6316,0.316)

(0.3579,

0.5684,0.7684)

Reprocessingfacilities

0.105 5 scales (0.5053,0.7053,0.8947)

(0.6105,0.8105,0.9684)

(0.5895,0.7895,0.9684)

(0.6526,0.8526,1.0000)

(0.5053,0.7158,0.9158)

(0.4000,0.6105,0.8105)

Re-proc. ext.transporta-tion

0.041 5 scales (0.3191,0.5319,0.7447)

(0.5106,0.234,0.9362)

(0.4681,0.6809,0.8936)

(0.6170,0.8298,1.0000)

(0.3830,0.5957,0.8085)

(0.5106,0.7234,0.9362)

Financingderegulation

0.065 5 scales (0.4894,0.7021,0.9149)

(0.5532,0.7660,0.9681)

(0.4681,0.6809,0.8936)

(0.5319,0.7447,0.9574)

(0.6383,0.8404,1.0000)

(0.5957,0.7979,0. 681)

R&D cost 0.048 5 scales (0.5652,0.7826,0 9891)

(0.5000,0.7174,0 9348)

(0.3913,0.6087,0 8261)

(0.5870,0.8043,1 0000)

(0.5435,0.7609,0 9783)

(0.4348,0.6522,0 8696)


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106 K-L. Lee et al.

Appendix C. continued


L1 L2 L3 L4 L5 L6

External indicatorsLocationresistance

0.127 Miles (0.7411,0.7411,0.7411)

(0.6149,0.6149,0.6149)

(1.0000,1.0000,1.0000)

(0.8411,0.8411,0.8411)

(0.8497,0.8497,0.8497)

(0.3140,0.3140,0.3140)

Density ofshipping line

0.231 Lines (0.3155,0.3155,0.3155)

(0.3869,0.3869,0.3869)

(0.3244,0.3244,0.3244)

(0.3333,0.3333,0.3333)

(0.6399,0.6399,0.6399)

(1.0000,1.0000,1.0000)

Regionalindustrialcompetition

0.389 5 scales (0.6905,0.7986,0.9617)

(0.6965,0.877,0.9559)

(0.5208,0.7798,0.9375)

(0.5208,0.7292,0.9375)

(0.5625,0.6006,0.9583)

(0.5458,0.6107,0.7633)

Parts cost 0.253 5 scales (0.6081,0.8005,0.9011)

(0.7541,0.8916,0.989)

(0.5055,0.7952,0.9451)

(0.5714,0.7912,1.0000)

(0.4615,0.6059,0.9011)

(0.4512,0.5663,0.6255)

Weightedaverage value

1.000 (0.5895,0.6802,0.7691)

(0.6292,0.7342,0.7895)

(0.5324,0.7065,0.8057)

(0.5310,0.6676,0.8015)

(0.5913,0.6427,0.8565)

(0.5973,0.6517,0.7261)

Remark: Locations: Shanghai (L1);Busan(L2); Kaohsiung (L3); Shenzhen (L4); H K (L5); Singapore(L6)

References

Bellman, RE., Zadel, L.A.: Decision-makingin a fuzzy environment.Manag. Sci.17(4), 141164 (1970)Bortolan, G., Degani, R.: A review of some method for ranking fuzzy subsets. Fuzzy Sets Syst. 15,

119 (1985)Bowersox, D.J., Closs, D.J.: Logistical managementthe Integrated Supply Chain Process. McGraw-

Hill, New York (1996)Chang, H.H., Huang,W.C.: Application of a quantification SWOT analytical method. J. Math. Comput.

Model.43, 158169 (2006)Chen, S.J., Huang, C.L., Huang, F.P.: Fuzzy Multiple Attribute Decision Making Methods and Appli-

cations. Springer, Berlin Heidelberg New York (1992)Chen, S.H., Hsieh, C.H.: Representation, ranking, distance, and similarity of L-R type fuzzy number

and application. Aust. J. Intell. Process. Syst. 6(4), 217229 (2000)Chou, C.C., Chu, C.W., Liang, G.S.: Competitiveness analysis of major ports in Eastern Asia. J. East.

Asia Soc. Transportation Stud.5, 682697 (2003)Christensen, R., Berg, N., Salter, M.: Policy Formulation and Administration, Ill.Richard D. Irwin, Ho-

mewood (1976)Churchill, G.A.: Marketing research: methodological foundation, 5th edn. The Dryden Press, New

York (1991)Containerization International, 2004. http:/www.c1-online.co.ukDavid, F.R.: Strategic Management, Concepts and Cases, 8th edn. Prentice Hall, Englwood Cliffs,

NJ (2001)David, F.R.: Strategic Management, 7th edn. Prentice-Hall, New Jersey (1998)Dubois, D., Prade H.: Operations on fuzzy number. Int. J. Syst. Sci. 9, 613626 (1978)

Dornier, P.P., Ernst, R., Fender, M., Kouveilis, P.: Global Operation and Logistics. Wiley, NewYork (1998)

Kaohsiung Port Authority, 2005. http://www.khb.gov.tw


21/22


Lin, S.C., Liang, G.S., Lee, K.L.: Applying fuzzy analytic hierarchy process in location mode ofinternational logistics on airports competition evaluation. J. Mar. Sci. Technol. 14(1), 2535 (2006)

Lou, G. S.: The competitive strategy of global logistics system of international port, A thesis for themaster degree of department of harbor and river. National Taiwan Ocean University(2004)

Luis, M. de C.I., Antonio, G.M.: A subjective approach for ranking fuzzy numbers. Fuzzy Sets

Syst.29, 145153 (1989)Lu, C.S.: Market segment evaluation and international distribution centers. Transportation Res. Part

E39, 4960 (2003)Saaty, T.L.: The Analytic Hierarchy Process. McGraw-Hill, NY (1980)Saaty, T.L., Vargas, L.G.: The Logic of Priorities. Kluwer-Nijhoff, Boston, MA (1982)Sheu, J.B.: A hybrid fuzzy-based approach for identifying global logistics strategies. Transportation

Res. Part E40, 3961 (2004)Stewart, R.A., Mohamed, S., Daet, R.: Strategic implementation of IT/IS projects in construction:

a case study. Autom. Construction11, 681694 (2002)Tao, H.O., Park, J.H.: Multinational firms location preference for regional distribution centers: focus

on the Northeast Asian region. Transportation Res. Part E 40, 101121 (2004)Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338353 (1965)

Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning, Part I,II and III. Inf. Sci.8, 199249, 301357;9, 4380 (1975, 1976)


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