The study of analytic network process based quantitative swot method

UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA DOCTORAL DISSERTATION SWOT ANP The Study of Analytic Network Process Based Quantitative SWOT Method 201113110104 NGUYEN THI PHUONG GIANG UDC S WOT ANP NGUYEN THI PHUONG GIANG 2014 UDC THE STUDY OF ANALYTIC NETWORK PROCESS BASED QUANTITATIVE SWOT METHOD A Dissertation Submitted to University of Electronic Science and Technology of China Major: Author: Advisor: School: Management Science and Engineering NGUYEN THI PHUONG GIANG Prof GANG KOU School of Management and Economic (SWOT) (ANP) SWOT SWOT SWOT SWOT SWOT SWOT AHP ANP AHP AHP ANP AHP ANP SWOT ANP SWOT ANP SWOT ANP AHP SWOT SWOT SWOT SWOT ANP SWOT ANP SWOT ANP SWOT SWOT ANP SWOT Bitexco IUH SWOT I SWOT SWOT SWOT SWOT ANP SWOT SWOT ANP ANP SWOT SWOT ANP SWOT SWOT , , SWOT II SWOT-ANP Abstract ABSTRACT The primary research target of this dissertation is to develop a new method focusing on analysing Strengths, Weaknesses, Opportunities and Threats (SWOT) position as a function of the evaluation criteria The Analytic Network Process (ANP) technique is utilized in this work for measuring inter-factor dependencies by transforming all the factors from the SWOT analysis and the alternative strategies The research is constructive and based on quantitative SWOT criteria with case study methodology SWOT analysis is a generally used tool which examines strengths and weaknesses of organizations or industrial companies as well as opportunities and threats of the marketplace environment SWOT analysis provides the basic outline which performs analysis of decision situations In this study, due to the lack of determination of the importance ranking for the SWOT factors, we proposed to enhance SWOT analysis with MCDM techniques called AHP and ANP approach AHP makes pair-wise comparisons among factors or criteria in order to prioritize them at each level of the hierarchy using the eigen-value calculation In addition to AHP, ANP technique is a general form that allows interdependencies, outerdependencies, and feedbacks among decision elements in the hierarchical or non-hierarchical structures The main purpose of this study is to explain how to use the AHP and ANP methods in order to prioritize SWOT factors and compare them The ANP method which is capable of handling interdependences among the evaluation criteria has been applied widely recently SWOT analysis, one of the tools in the strategy formulation process, is used in strategic issues for internal and external analysis The author explains how to use the ANP method to prioritize SWOT factors Conducted in Industrial University of Ho Chi Minh city (IUH), this research proposed to enhance SWOT analysis combining with ANP technique The AHP method resolves some weaknesses of the SWOT technique but it lacks dependency between SWOT factors, which mays inefficient to obtain alternative strategy Therefore, combining the dependence of the SWOT and ANP method would be considers getting bester results III Abstract The first theoretical of this research to management field and management strategy, as well as building a new method by analyzing SWOT characteristics in an organization The dependence among SWOT factors is the success of analyzing quantitative SWOT combining ANP method The method used in this research is to build SWOT- oriented criteria, to study strengths of ANP method The theoretical background was built from analyzing SWOT calculating the ANP, and combining these two methods We also paid particular attention to the previous methods, as well as oriented a new method and identified the approach to the case study This is the theoretical analysis and synthesis used to create a framework for analyzing new methods The framework analysis method was created to experiment with the materials of the two case study SWOT 'drawn from publicly report annually Case studies are Bitexco Group and especially the IUH (Industry University of Ho Chi Minh City) The experimental results showed that the SWOT analysis framework can generate new information about the case, and detect changes in the position to compare the pair perfectly The new method constitutes a firm toolbox analysis, in which the analysis tools can be used individually, or in groups of the second matrix level The full set of results can also be adopted The second theoretical of this research to existing literature by providing a new method The most influential internal and external elements were detected with the help of the techniques of strategy formulation Using the SWOT matrix is one of the most important tools for formulating strategies This research uses the ANP, which allows the quantitative analysis of SWOT and measurement of the dependences among factors The dependences among the SWOT factors are observed to affect strategies and sub-factor weights, as well as to change the strategy priorities Therefore, the quantatitive SWOT analysis as a decision tool is formulated on strategic plans for selected problems To the best of our knowledge, no one has applied the combination ANP method and SWOT analysis in selection problems in Vietnam Using the concept of ANP theory and values of MCDM model based on SWOT analysis are proposed to deal with the selection problems Pair-wise comparison used in the model makes the obtained weights of criteria more precise Rather than, the author proposed to enhance Fuzzy-ANP method has been integrated with SWOT analysis to deal with imprecision of human thought IV Abstract Theoretical significance of this thesis is the result of combining the views of the SWOT analysis and ANP weight assessment method, thereby creating the analytical methods the new strategic direction The SWOT analysis method builds a toolbox analysis can be beneficial to the business of strategic analysis for detecting strategic focus and change it as well as creating a new method combining SWOT analysis and ANP method for method bank in the MCDM Thesis contributes to the development of new methods, and is the result of combining the perspective adopted in the framework of SWOT analysis with the MCDM, thereby creating the analytical methods and new orientation Areas for further research include successful businesses, e-commerce and some other areas Keywords: MCDM, ANP, SWOT analysis, SWOT-ANP method V Table of Contents Table of Contents List of Figures List of Tables List of Abbreviations and Definitions Chapter- INTRODUCTION 1.1 Research Background and Contribution 1.1.1 Background of the Research 1.1.2 Contribution of the Research 1.2 Problems of the Research 1.3 The Aim and Objectives of the Research 12 1.4 Motivation 12 1.5 Methodology and Data Used 13 1.6 Outline of the Dissertation 16 Chapter- LITERATURE REVIEWS 17 2.1 Literature of the SWOT 17 2.2 Literature of the ANP 19 2.3 Literature of the Analytical Hierarchy Process (AHP)- SWOT 20 2.4 Limitations of Analytical Hierarchy Process (AHP)-SWOT 22 2.5 Conclusion 24 Chapter- METHODOLOGY 25 3.1 Introduction 25 3.2 The Analytic Hierarchy Process (AHP) Method 25 VI Doctoral Dissertation of University of Electronic Science and Technology of China S13 ST SO WT WO Local weights S16 ST SO WT WO Local weights ST 1.000 1.548 0.701 1.111 0.256 ST 1.000 0.951 0.281 1.533 0.175 1.000 0.569 1.032 0.193 SO 1.000 0.351 1.632 0.192 1.000 1.265 0.327 WT 1.000 2.333 0.489 1.000 0.224 WO 1.000 0.144 SO WT WO CR=0.01 CR=0.04 S14 ST SO WT WO Local weights S17 ST SO WT WO Local weights ST 1.000 1.111 0.222 0.749 0.143 ST 1.000 1.151 1.082 1.732 0.299 1.000 0.282 0.843 0.149 SO 1.000 1.023 1.361 0.258 1.000 0.880 0.440 WT 1.000 1.052 0.245 1.000 0.268 WO 1.000 0.144 SO WT WO CR=0.08 CR=0.01 S15 ST SO WT WO Local weights S18 ST SO WT WO Local weights ST 1.000 1.312 0.889 1.333 0.274 ST 1.000 1.732 0.941 1.101 0.289 1.000 0.733 1.111 0.218 SO 1.000 0.911 1.261 0.226 1.000 1.722 0.312 WT 1.000 1.000 0.256 1.000 0.195 WO 1.000 0.229 SO WT WO CR=0 CR=0.02 S19 ST SO WT WO Local weights ST 1.000 0.850 0.620 0.430 0.164 1.000 0.910 0.640 0.215 1.000 0.270 0.226 1.000 0.395 SO WT WO CR=0.01 111 Appendix APPENDIX- D I) Pairwise Comparison Matrix and Computations: (Evaluation Criteria):- Detail steps of solution A) Original Matrix A = [1,2,4,3,3;1/2,1,3,3,2;1/4,1/3,1,2,4;1/3,1/3,1/2,1,3;1/3,1/2,1/4,1/3,1] A= 1.0000 2.0000 4.0000 3.0000 3.0000 0.5000 1.0000 3.0000 3.0000 2.0000 0.2500 0.3333 1.0000 2.0000 4.0000 0.3333 0.3333 0.5000 1.0000 3.0000 0.3333 0.5000 0.2500 0.3333 1.0000 d= eigs(A) [eigs = Eigen value] d= 5.4894 -0.1414 - 1.6070i -0.1414 + 1.6070i -0.1033 - 0.1222i -0.1033 + 0.1222i λmax = 5.4894 > λmax thrd (5.444) CI = (5.4894 - 5)/ (5 - 1) = 0.12235 CR = 0.12235/1.11 = 0.11> 0.1 This is inconsistent matrix Now identify inconsistency matrix from A 112 Doctoral Dissertation of University of Electronic Science and Technology of China a) Inconsistency Identification C = A ! AT = (cij ) = (n n Õa ik akj × a ji ) = U if aik akj = aij k =1 ổ A = (aij )nn = ỗ n ỗ ố n ếa k =1 ik akj ÷ = L*R ÷ ø n´n C = L ´ R ! AT = (cij ) = (n n Õ aik × n k =1 n Õa kj × a ji ) = U if aik akj = aij k =1 A= 1.0000 0.5000 0.2500 0.3333 0.3333 L1 = " 2.0000 1.0000 0.3333 0.3333 0.5000 4.0000 3.0000 1.0000 0.5000 0.2500 3.0000 3.0000 2.0000 1.0000 0.3333 3.0000 2.0000 4.0000 3.0000 1.0000 (1 ∗ ∗ ∗ ∗ 3) = 2.35 similarly other L = [2.35,1.55,0.92,0.69,0.425]' R1 = " ) ) ) ) * + , , (1 ∗ ∗ ∗ ∗ ) = 0.425 similarly other R = [0.425,0.64,1.08,1.43,2.35] 113 Appendix A=L*R = 0.9988 1.5040 2.5380 3.3605 0.6587 0.9920 1.6740 2.2165 0.3910 0.5888 0.9936 1.3156 0.2932 0.4416 0.7452 0.9867 0.1806 0.2720 0.4590 0.6078 T A = A' = 1.0000 0.5000 0.2500 0.3333 2.0000 1.0000 0.3333 0.3333 4.0000 3.0000 1.0000 0.5000 3.0000 3.0000 2.0000 1.0000 3.0000 2.0000 4.0000 3.0000 T C = L*R*A = 1.0000 0.7520 0.6345 1.1202 1.3174 1.0000 0.5580 0.7390 1.5640 1.7664 1.0000 0.6578 0.8796 1.3248 1.4900 1.0000 0.5418 0.5440 1.8360 1.8230 max C15 = 1.841 (maximum value deviating from 1) b) Inconsistency Adjustment 5.5225 3.6425 2.1620 1.6215 0.9988 0.3333 0.5000 0.2500 0.3333 1.0000 1.8410 1.8210 0.5405 0.5405 1.0000 a~ij = n - n Õa a = n-2 ik kj k =1, ¹ i , j ổ a n-2 a = aij ỗ ij ữ ỗa ữ a ố ij ứ n ij ij 114 Doctoral Dissertation of University of Electronic Science and Technology of China a15 = "12 ( -.-**-" , ^* ) = 8.295 = approximately Now, Replacing the value of A by replacing the inconsistent elements a15 and a51 with and 1/8 respectively A= 1.0000 2.0000 4.0000 3.0000 8.0000 0.5000 1.0000 3.0000 3.0000 2.0000 0.2500 0.3333 1.0000 2.0000 4.0000 0.3333 0.3333 0.5000 1.0000 3.0000 0.1250 0.5000 0.2500 0.3333 1.0000 Column total = 2.208 4.167 8.75 9.33 18 eigs (A) ans = 5.3636 -0.1430 - 1.3251i -0.1430 + 1.3251i -0.0387 - 0.3871i -0.0387 + 0.3871i λmax = 5.3636 < λmax thrd (5.444) CI = (5.3636 – 5)/ (5-1) = 0.0909 CR = 0.0909 / 1.11 = 0.0819 < 0.1 (so, now it is consistent) II) Pairwise Comparison Matrices and Priorities (Detail Solution) A) With respect to Quality Original Matrix:A = [1,5,6,1/3,1/6;1/5,1,2,1/6,1/8;1/6,1/2,1,1/8,1/7;3,6,8,1,1/3;6,8,7,3,1] 115 Appendix A= 1.0000 5.0000 6.0000 0.3333 0.1667 0.2000 1.0000 2.0000 0.1667 0.1250 0.1667 0.5000 1.0000 0.1250 0.1429 3.0000 6.0000 8.0000 1.0000 0.3333 6.0000 8.0000 7.0000 3.0000 1.0000 Column total = 10.37 20.5 24 4.63 1.77 d=eigs(A) d= 5.3879 -0.0515 - 1.4105i -0.0515 + 1.4105i -0.1424 - 0.2197i -0.1424 + 0.2197i λmax = 5.3879 < λmax thrd (5.444) CI = (5.3879 – 5)/ (5-1) = 0.097 CR = 0.097 / 1.11 = 0.087 < 0.1 (so, now it is consistent) B) With Respect to Price Original Matrix :A = [1,1/3,5,6,7;3,1,7,5,8;1/5,1/7,1,2,3;1/6,1/5,1/2,1,4;1/7,1/8,1/3,1/4,1] A= 1.0000 0.3333 5.0000 6.0000 7.0000 3.0000 1.0000 7.0000 5.0000 8.0000 0.2000 0.1429 1.0000 2.0000 3.0000 0.1667 0.2000 0.5000 1.0000 4.0000 0.1429 0.1250 0.3333 0.2500 1.0000 116 Doctoral Dissertation of University of Electronic Science and Technology of China Column Total= 4.5 1.8 13.83 14.25 23 >> d= eigs (A) d = 5.3818 0.0251 - 1.4159i 0.0251 + 1.4159i -0.2160 - 0.1555i -0.2160 + 0.1555i λmax = 5.3818 < λmax thrd (5.444) CI = (5.3818 – 5)/ (5-1) = 0.09545 CR = 0.09545 / 1.11 = 0.0859 < 0.1 (so, now it is consistent) C) With Respect to Service Original Matrix:A = [1,5,4,8,7;1/5,1,1/2,4,5;1/4,2,1,5,7;1/8,1/4,1/5,1,3;1/7,1/5,1/7,1/3,1] A= 1.0000 5.0000 4.0000 8.0000 7.0000 0.2000 1.0000 0.5000 4.0000 5.0000 0.2500 2.0000 1.0000 5.0000 7.0000 0.1250 0.2500 0.2000 1.0000 3.0000 0.1429 0.2000 0.1429 0.3333 1.0000 Column total= 1.72 8.45 5.843 18.33 23 >> d=eigs(A) d= 5.3599 0.0132 - 1.3604i 117 Appendix 0.0132 + 1.3604i -0.1932 - 0.2260i -0.1932 + 0.2260i λmax = 5.3599 < λmax thrd (5.444) CI = (5.3599 – 5)/ (5-1) = 0.0899 CR = 0.0899 / 1.11 = 0.081 < 0.1 (so, now it is consistent) D) With Respect to Delivery Original Matrix:A = [1,3,1/5,1,3;1/3,1,1/8,1/3,4;5,8,1,1/5,4;1,3,5,1,6;1/3,1/4,1/4,1/6,1] A= 1.0000 3.0000 0.2000 1.0000 3.0000 0.3333 1.0000 0.1250 0.3333 4.0000 5.0000 8.0000 1.0000 0.2000 4.0000 1.0000 3.0000 5.0000 1.0000 6.0000 0.3333 0.2500 0.2500 0.1667 1.0000 >> d=eigs(A) d= 6.2627 -0.5793 - 2.5811i -0.5793 + 2.5811i -0.0520 - 0.8872i -0.0520 + 0.8872i λmax =6.2627 < λmax thrd (5.444) CI = (6.2627 – 5)/ (5-1) = 0.3157 118 Doctoral Dissertation of University of Electronic Science and Technology of China CR = 0.3157 / 1.11 = 0.284 < 0.1 (This is inconsistent matrix Now identify inconsistency matrix from above matrix A ) a) Inconsistency Identification :- C = A ! AT = (cij ) = (n n Õa ik akj × a ji ) = U aik akj = aij if k =1 æ A = (aij )nn = ỗ n ỗ ố n ếa k =1 ik akj ÷ = L*R ÷ ø n´n C = L ´ R ! AT = (cij ) = (n n Õ aik × n k =1 n Õa kj × a ji ) = U if aik akj = aij k =1 A=[1,3,1/5,1,3;1/3,1,1/8,1/3,4;5,8,1,2,4;1,3,1/2,1,6;1/3,1/4,1/4,1/6,1] A= 1.0000 3.0000 0.2000 1.0000 3.0000 0.3333 1.0000 0.1250 0.3333 4.0000 5.0000 8.0000 1.0000 2.0000 4.0000 1.0000 3.0000 0.5000 1.0000 6.0000 0.3333 0.2500 0.2500 0.1667 1.0000 L1 = " ) (1 ∗ ∗ ∗ ∗ 3) = 1.124 similarly other - L=[1.24,.56,3.17,1.55,.322]' R1 = " ) ) , , (1 ∗ ∗ ∗ ∗ ) = 0.889 similarly other R=[.889,1.78,.315,.64,3.10] 119 Appendix A=L*R = 1.1024 2.2072 0.3906 0.7936 0.4978 0.9968 0.1764 0.3584 2.8181 5.6426 0.9985 2.0288 1.3780 2.7590 0.4883 0.9920 0.2863 0.5732 0.1014 0.2061 T A = A' = 1.0000 0.3333 5.0000 1.0000 3.0000 1.0000 8.0000 3.0000 0.2000 0.1250 1.0000 0.5000 1.0000 0.3333 2.0000 1.0000 3.0000 4.0000 4.0000 6.0000 T C = L*R*A = 1.0000 0.7360 1.9500 0.7900 1.4900 1.0000 1.4100 1.0800 0.5600 0.7050 1.0000 1.0150 1.3800 0.9200 0.8900 1.0000 0.8700 2.2800 0.4040 1.2400 max C35 = 2.45 (maximum value deviating from 1) 3.8440 1.7360 9.8270 4.8050 0.9982 0.3333 0.2500 0.2500 0.1667 1.0000 1.2800 0.4350 2.4500 0.8000 1.0000 120 Doctoral Dissertation of University of Electronic Science and Technology of China b) Inconsistency Adjustment:2 n a~ij = n - Õa a = n-2 ik kj k =1, ¹ i , j a35 = "12 ( 4.5-" ) +2 ổ a n-2 a = aij ỗ ij ữ ỗa ữ a ố ij ứ n ij ij = 17.8 = approximately 18 Now, Replacing the value of A by replacing the inconsistent elements a35 and a53 with 18 and 1/18 respectively A=[1,3,1/5,1,3;1/3,1,1/8,1/3,4;5,8,1,2,18;1,3,1/2,1,6;1/3,1/4,1/18,1/6,1] A= 1.0000 3.0000 0.2000 1.0000 3.0000 0.3333 1.0000 0.1250 0.3333 4.0000 5.0000 8.0000 1.0000 2.0000 18.0000 1.0000 3.0000 0.5000 1.0000 6.0000 0.3333 0.2500 0.0556 0.1667 1.0000 >> d=eigs(A) d= 5.1932 -0.1022 - 0.9006i -0.1022 + 0.9006i 0.0056 - 0.4290i 0.0056 + 0.4290i λmax =5.1932 < λmax thrd (5.444) CI = (5.1932 – 5)/ (5-1) = 0.0483 121 Appendix CR = 0.0483 / 1.11 = 0.04 < 0.1 (This is inconsistent matrix Now identify inconsistency matrix from above matrix A ) c) Inconsistency Identification :- C = A ! AT = (cij ) = (n n Õa ik akj × a ji ) = U aik akj = aij if k =1 æ A = (aij )nn = ỗ n ỗ ố n ếa k =1 ik akj ÷ = L*R ÷ ø n´n C = L ´ R ! AT = (cij ) = (n n Õa ik k =1 ×n n Õa kj × a ji ) = U if aik akj = aij k =1 A = [1,3,1/5,1,3;1/3,1,1/8,1/3,4;5,8,1,1/5,4;1,3,5,1,6;1/3,1/4,1/4,1/6,1] A= 1.0000 3.0000 0.2000 1.0000 3.0000 0.3333 1.0000 0.1250 0.3333 4.0000 5.0000 8.0000 1.0000 0.2000 4.0000 1.0000 3.0000 5.0000 1.0000 6.0000 0.3333 0.2500 0.2500 0.1667 1.0000 L1 = " ) (1 ∗ ∗ ∗ ∗ 3) = 1.124 similarly other - L=[1.124,.56,2,2.46,.322]' R1 = " ) ) , , (1 ∗ ∗ ∗ ∗ ) = 0.889 similarly other R=[.889,1.78,.5,.41,3.10] 122 Doctoral Dissertation of University of Electronic Science and Technology of China A=L*R = 0.9992 2.0007 0.5620 0.4608 0.4978 0.9968 0.2800 0.2296 1.7780 3.5600 1.0000 0.8200 2.1869 4.3788 1.2300 1.0086 0.2863 0.5732 0.1610 0.1320 T A = A' = 1.0000 0.3333 5.0000 1.0000 3.0000 1.0000 8.0000 3.0000 0.2000 0.1250 1.0000 5.0000 1.0000 0.3333 0.2000 1.0000 3.0000 4.0000 4.0000 6.0000 T C = L*R*A = 1.0000 0.6700 2.8000 0.4600 1.4940 1.0000 2.2400 0.6900 0.3560 0.4450 1.0000 4.1000 2.1870 1.4600 0.2500 1.0000 0.8580 2.2900 0.6400 0.7900 max C34 = 4.1 (maximum value deviating from 1) 3.4844 1.7360 6.2000 7.6260 0.9982 0.3333 0.2500 0.2500 0.1667 1.0000 1.1600 0.4350 1.5500 1.2700 1.0000 c) Inconsistency Adjustment:2 a~ij = n - n Õa a = n-2 ik kj k =1, ¹ i , j ổ a n-2 a = aij ỗ ij ữ ỗa ữ a ố ij ứ n ij ij 123 Appendix a34 = "12 ( 6.5*" " ) = 2.1 = approximately Now, Replacing the value of A by replacing the inconsistent elements a34 and a43 with and 1/8 respectively A=[1,3,1/5,1,3;1/3,1,1/8,1/3,4;5,8,1,2,4;1,3,1/2,1,6;1/3,1/4,1/4,1/6,1] A= 1.0000 0.3333 5.0000 1.0000 0.3333 Column total= 7.67 >> d=eigs(A) d= 5.4923 -0.1068 - 1.6055i -0.1068 + 1.6055i -0.1394 - 0.1892i -0.1394 + 0.1892i 3.0000 1.0000 8.0000 3.0000 0.2500 15.25 0.2000 0.1250 1.0000 0.5000 0.2500 1.88 1.0000 0.3333 2.0000 1.0000 0.1667 4.5 3.0000 4.0000 4.0000 6.0000 1.0000 32 λmax =5.4923 < λmax thrd (5.444) CI = (5.4923 – 5)/ (5-1) = 0.1225 CR = 0.1225 / 1.11 = 0.11 < 0.1 (so, now it is consistent) 124 Doctoral Dissertation of University of Electronic Science and Technology of China E) With Respect to Flexibility:Original Matrix:A=[1,3,5,6,4;1/3,1,2,5,6;1/5,1/2,1,3,3;1/6,1/5,1/3,1,3;1/4,1/6,1/3,1/3,1] A= 1.0000 3.0000 5.0000 6.0000 4.0000 0.3333 1.0000 2.0000 5.0000 6.0000 0.2000 0.5000 1.0000 3.0000 3.0000 0.1667 0.2000 0.3333 1.0000 3.0000 0.2500 0.1667 0.3333 0.3333 1.0000 Column total= 1.95 4.87 8.67 15.33 17 >> d=eigs(A) d= 5.4248 -0.0057 - 1.5082i -0.0057 + 1.5082i -0.3391 -0.0744 λmax =5.4248 < λmax thrd (5.444) CI = (5.4248 – 5)/ (5-1) = 0.1062 CR = 0.1062 / 1.11 = 0.096 < 0.1 (so, now it is consistent) 125 ... GIANG Prof GANG KOU School of Management and Economic (SWOT) (ANP) SWOT SWOT SWOT SWOT SWOT SWOT AHP ANP AHP AHP ANP AHP ANP SWOT ANP SWOT ANP SWOT ANP AHP SWOT SWOT SWOT SWOT ANP SWOT ANP SWOT. .. SWOT ANP SWOT SWOT ANP SWOT Bitexco IUH SWOT I SWOT SWOT SWOT SWOT ANP SWOT SWOT ANP ANP SWOT SWOT ANP SWOT SWOT , , SWOT II SWOT- ANP Abstract ABSTRACT The primary research target of this dissertation... new method combining SWOT analysis and ANP method for method bank in the MCDM Thesis contributes to the development of new methods, and is the result of combining the perspective adopted in the