A Combined Fuzzy Decision Making Approach to Supply Chain Risk Assessment
Many firms implemented various initiatives such as outsourced manufacturing which could make a supply chain (SC) more vulnerable to various types of disruptions. So managing risk has become a critical component of SC management. Different types of SC vulnerability management methodologies have been proposed for managing SC risk, most offer only point-based solutions that deal with a limited set of risks. This research aims to reinforce SC risk management by proposing an integrated approach. SC risks are identified and a risk index classification structure is created. Then we develop a SC risk assessment approach based on the analytic network process (ANP) and the VIKOR methods under the fuzzy environment where the vagueness and subjectivity are handled with linguistic terms parameterized by triangular fuzzy numbers. By using FANP, risks weights are calculated and then inserted to the FVIKOR to rank the SC members and find the most risky partner.
[1] Barry J (2004) Supply chain risk in an uncertain global supply chain
environment. International Journal of Physical Distribution & Logistics
Management. 34/9:695-697
[2] Bayazita, O., & Karpak, B. (2007). An analytical network processbased
framework for successful total quality management (TQM): An assessment of Turkish manufacturing industry readiness. International
Journal of Production Economics, 105(1), 79-96.
[3] Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy
environment. Management Science, 17(4), 141-164.
[4] Blackhurst, J. & Craighead, C. & Elkins, D. & Handfield, R., (2005).
An empirically derived agenda of critical research issues for managing
supply-chain disruptions. International Journal of Production Research
43 (19), 4067-4081.
[5] Buyukozkan, G. & Ruan, D. (2008), Evaluation of software
development projects using a fuzzy multi-criteria decision approach,
Mathematics and Computers in Simulation 77, 464-475.
[6] Chen, C. T. & Lin, C. T. & Huang, S. F. (2006). A fuzzy approach for
supplier evaluation and selection in supply chain management.
International Journal of Production Economics, 102(2), 289-301.
[7] Chen, S. J. & Hwang, C. L. & Hwang, F. P. (1992). Fuzzy multiple
attribute decision making. Lecture Notes in Economics and
Mathematical System, 375.
[8] Chen, L. Y. & Wang, T. C. (2008). Optimizing partners- choice in
IS/IT outsourcing projects: The strategic decision of fuzzy VIKOR, Int.
J. Production Economics.
[9] Chiclana, F. & Herrera, F. & Herrera-Viedman, E. (1998) Integrating
three representation models in fuzzy multipurpose decision making
based on fuzzy preference relations, Fuzzy Sets Syst. 97, 33-48.
[10] Chiou, H. K., & Tzeng, G. H. (2002). Fuzzy multiple-criteria decisionmaking
approach for industrial green engineering. Environment
Management, 30(6), 816-830.
[11] Chiou, H. K. & Tzeng, G. H., & Cheng, D. C. (2005). Evaluating
sustainable fishing development strategies using fuzzy MCDM
approach. Omega, 33(3), 223-234.
[12] Chopra S. & Sodhi M. (2004) Managing Risk to Avoid Supply Chain
Breakdown. MIT Sloan Management Review 46/1:53-61
[13] Chung, S. H. & Lee, A. H. & Pearn, W. L. (2005). Product mix
optimization for semiconductor manufacturing based on AHP and ANP
analysis. International Journal of Advanced Manufacturing
Technology, 25, 1144-1156.
[14] Cui, X. & Wang, F. & Li, X. & Zhang, Y. & Yu, S. (2007). A Fuzzy-
ANP Approach to Cooperation Risk Evaluation of Virtual Logistics
Enterprise, International Conference on Automation and Logistics.
[15] Dan, W. & Zan, Y. (2007), Risk Management of Global Supply Chain,
International Conference on Automation and Logistics.
[16] Dikmen, M. T. & Birgonul & Han, S. (2007) "Using fuzzy risk
assessment to rate cost overrun risk in international construction
projects," International Journal of Project Management, vol. 25, pp.
494-505.
[17] Dubois, D., & Prade, H. (1978). Operations on fuzzy numbers.
International Journal ofSystem Sciences, 9(6), 613-626.
[18] Duncan, R.B., (1972). Characteristics of organizational environments
and perceived environmental uncertainty. Administrative Science
Quarterly, 17(3).
[19] Engelhardt-Nowitzki C. & Zsifkovits H. (2006) Complexity-Induced
Supply Chain Risks - Interdependencies Between Supply Chain Risk
and Complexity Management. In: Kersten W, Blecker T (eds)
Managing Risks in Supply Chains: How to Build Reliable
Collaboration in Logistics, pp. 37-56, Berlin
[20] Fisher, M., (1997). What is the right supply chain for your product?
Harvard Business Review 75, 105-117.
[21] Hendricks, K.B. & Singhal, V.R. (2005). An empirical analysis of the
effects of supply chain disruptions on long-run stock price performance
and equity risk of the firm, Production Operations Management, 14(1).
[22] Henke M. & Kurzhals R. & Jahns C. (2006) Enterprise and Supply
Risk Management from the Perspective of Internat and External
Auditors. In: Kersten W, Blecker T (eds) Managing Risks in Supply
Chains: How to Build Reliable Collaboration in Logistics. pp. 97- 109,
Berlin
[23] Herrera, F. & Herrera-Viedma, E. & Chiclana, F. (2001) Multiperson
decision-making based on multiplicative preference relations, Eur. J.
Oper. Res. 129, 372-385.
[24] Hsieh, T. Y. & Lu, S. T. & Tzeng, G. H. (2004). Fuzzy MCDM
approach for planning and design tenders selection in public office
buildings. International Journal of Project Management, 22(7), 573-
584.
[25] Huang, M. & Yang, H.-M. & Wang, X.-W. (2004) "Fuzzy synthetic
evaluation based risk evaluation for virtual enterprise," Mathematics in
Practice and Theory, vol. 34, no.6, pp. 45-51.
[26] Jahns C. & Hartmann E. & Moder M. (2006) Managing Supply Risks:
A System Theory Approach to Supply Early Warning Systems. In:
Kersten W, Blecker T (eds) Managing Risks in Supply Chains: How to
Build Reliable Collaboration in Logistics. pp. 195- 212, Berlin
[27] Jahns C. & Henke M. (2004) Supply Risk Management. Managementund
Überwachungssystem nach KonTraG zur systematischen
Risikobeherrschung, Beschaffung aktuell, 4:38-44
[28] Jung JY. & Blau G. & Pekny J. & Reklaitis G. & Eversdyk D. (2004)
A simulation based optimization approach to supply chain management
under demand uncertainty; Computer and Chemical Engineering
28/10:2087-2106
[29] J├╝ttner U. (2005) Supply chain risk management: Understanding the
business requirements from a practitioner perspective. The
International Journal of Logistics Management 16/1:120-141
[30] Kersten W. & Böger M. & Hohrath P. & Sp├ñth H (2006) Supply Chain
Risk Management: Development of a Theoretical and Empirical
Framework. In: Kersten W, Blecker T (eds) Managing Risks in Supply
Chains: How to Build Reliable Collaboration in Logistics, pp. 3-17,
Berlin
[31] Lee, A. H. I. & Chen, W. C. & Chang, C. J. (2008). A fuzzy AHP and
BSC approach for evaluating performance of IT department in the
manufacturing industry in Taiwan. Expert Systems with Applications,
34(1), 96-107.
[32] Lee, H., (2002). Aligning supply chain strategies with product
uncertainties. California Management Review 44 (3), 105-119.
[33] Lee, J.W. & Kim, S.H. (2000) Using analytic network process and goal
programming for interdependent information system project selection,
Comput. Oper. Res. 27, 367-382.
[34] Li, Y. & Liao, X.-W. (2007) "Decision support for risk analysis on
dynamic alliance," Decision Support Systems, vol. 42, pp. 2043-32059.
[35] March JP. & Shapira Z. (1987) Managerial Perspectives on Risk and
Risk Taking, Management Science, 33:1404
[36] Mikhailov L. & Singh, M. G. (2003) "Fuzzy analytic network process
and its application to the development of decision support systems,"
IEEE Transactions on Systems, Man, and Cybernetics-Part C:
Applications and Reviews, vol. 33, no. 1, pp. 33-41, February.
[37] Miles, R.E. & Snow, C.C., (1978). Organization Strategy, Structure,
and Process. McGraw-Hill, New York.
[38] Mohanty, R. P. & Agarwal, R. & Choudhury, A. K., & Tiwari, M. K.
(2005). A fuzzy ANPbased approach to R&D project selection: A case
study. International Journal of Production Research, 43(24), 5199-
5216.
[39] M├╝ssigmann N (2006) Mitigating Risk during Strategic Supply
Network Modeling. In: Kersten W, Blecker T (eds) Managing Risks in
Supply Chains: How to Build Reliable Collaboration in Logistics, pp.
213-226, Berlin
[40] Onut, S. & Kara, S. S. & Isik, E. (2009), Long term supplier selection
using a combined fuzzy MCDM approach: A case study for a
telecommunication company, Expert Systems with Applications 36,
3887-3895.
[41] Opricovic, S. (1998) Multicriteria Optimization of Civil Engineering
Systems, Faculty of Civil Engineering, Belgrade.
[42] Opricovic, S., & Tzeng, G. H. (2003). Defuzzification within a fuzzy
multicriteria decision model. International Journal of Uncertainty,
Fuzziness and Knowledgebased Systems, 11(5), 635-652.
[43] Opricovic, S. & Tzeng, G.H. (2004) Compromise solution by MCDM
methods: a comparative analysis of VIKOR and TOPSIS, Eur. J. Oper.
Res. 156, 445-455.
[44] Pepiot, G. & Cheikhrouhou, N. & Furbringer, J.-M., & Glardon, R.
(2008). A fuzzy approach for the evaluation of competences.
International Journal of Production Economics, 112(1), 336-353.
[45] Ramik, J. (2006). A decision system using ANP and fuzzy inputs. In
12th international conference on the foundations and applications of
utility, risk and decision theory, Roma.
[46] Ritchie B. & Brindley C. (2007) Supply chain risk management and
performance: A guiding framework for future development.
International Journal of Operations & Production Management
27/3:303-322
[47] Saaty, R. W. (2003). Decision making in complex environment: The
analytic hierarchy process (AHP) for decision making and the analytic
network process (ANP) for decision making with dependence and
feedback. <www.Superdecisions.com></www>.
[48] Saaty, T. L. (1999). Fundamentals of the analytic network process.
ISAHP 1999. Kobe, Japan.
[49] Saaty, T. L., & Vargas, L. G. (1998). Diagnosis with dependent
symptoms: Bayes theorem and the analytic network process.
Operations Research, 46(4), 491-502.
[50] Saaty, T.L. (1996) Decision making with dependence and feedback: the
analytic network process, RWS Publications, Pittsburgh,.
[51] Sanayei, A. & Mousavi S. F. & Yazdankhah, A. (2009), Group
decision making process for supplier selection with VIKOR under
fuzzy environment, Expert Systems with Applications.
[52] Smeltzer L. & Siferd S. (1998) Proactive supply management: the
management of risk, Journal of Supply Chain Management 34/1:38-45
[53] Spartalis, S. & Iliadis, L. & Maris, F. (2007) "An innovative risk
evaluation system estimating its own fuzzy entropy," Mathematical
and Computer Modelling, vol. 46, pp. 260-267.
[54] Spekman RE. & Davis EW. (2004) Risky business: expanding the
discussion on risk and the extended enterprise. International Journal of
Physical Distribution & Logistics Management 34/5:414-433
[55] Teufel S. & Erat A. (2001) Sicherheitsmanagement im Electronic
Business. In: Meier A (eds) Internet & Electronic Business, Z├╝rich.
[56] Teuteberg, F. Supply Chain Risk Management: A Neural Network
Approach, E-Business and Information Systems & Research Center for
Information Systems in Project and Innovation Networks.
[57] Tuzkaya, U. R., & Onut, S. (2008). A fuzzy analytic network process
based approach to transportation-mode selection between Turkey and
Germany: A case study. Information Sciences, 178, 3133-3146.
[58] Tsay, A. & Nahmias, S. & Agrawal, N. (1999) "Modeling supply chain
contracts: A review, in: S. Tayur, R. Ganeshan, M. Magazine (Eds.),
Quantitative Models for Supply Chain Management," Kluwer
Academic Publishers, Norwell, MA, , pp. 299_336.
[59] Tzeng, G.H. & Lin, C.W. & Opricovic, S. (2005) Multi-criteria
analysis of alternative-fuel buses for public transportation, Energy
Policy 33, 1373-1383.
[60] van Wyk J. & Baerwaldt W. (2005) External Risks and the Global
Supply Chain in the Chemicals Industry. Supply Chain Forum: An
International Journal 6/1:1-15.
[61] Venkatraman, N. & Camillus, J. (1984). Exploring the concept of ÔÇÿÔÇÿfit--
in strategic management. The Academy of Management Review 9 (3),
513-525.
[62] Wagner, S.M. & Bode, C., (2008). An empirical examination of supply
chain performance along several dimensions of risk. Journal of
Business Logistics, 29(1).
[63] Wang, T. C., & Chang, T. H. (2007). Application of TOPSIS in
evaluating initial training aircraft under a fuzzy environment. Expert
Systems with Applications, 33(4), 870-880.
[64] Wen, L. & Xi, Z. (2007). Supply Chain Risk Evaluation Based on
Fuzzy Multi-Criteria Lattice-Order Decision-Making, International
Conference on Automation and Logistics.
[65] Wu, H. Y. & Tzeng, G. H. & Chen, Y. H. (2009). A fuzzy MCDM
approach for evaluating banking performance based on Balanced
Scorecard, Expert Systems with Applications 36, 10135-10147.
[66] Wu, W. W., & Lee, Y. T. (2007). Selecting knowledge management
strategies by using the analytic network process. Expert Systems with
Applications, 32(3), 841-847.
[67] Y├╝ksel, I., & Dagdeviren, M. (2007). Using the analytic network
process (ANP) in a SWOT analysis - A case study for a textile firm.
Information Sciences, 177(16), 3364-3382.
[68] Yu, P.L., (1973). A class of solutions for group decision problems.
Management Science 19 (8), 936-946.
[69] Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-
353.
[70] Zadeh, L. A. (1975). The concept of a linguistic variable and its
application to approximate reasoning I. Information Science, 8(3), 199-
249.
[71] Zeleny, M., (1974). The concept of compromise solutions and the
method of the displaced ideal. Computers & Operations Research 1 (4),
479-496.
[1] Barry J (2004) Supply chain risk in an uncertain global supply chain
environment. International Journal of Physical Distribution & Logistics
Management. 34/9:695-697
[2] Bayazita, O., & Karpak, B. (2007). An analytical network processbased
framework for successful total quality management (TQM): An assessment of Turkish manufacturing industry readiness. International
Journal of Production Economics, 105(1), 79-96.
[3] Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy
environment. Management Science, 17(4), 141-164.
[4] Blackhurst, J. & Craighead, C. & Elkins, D. & Handfield, R., (2005).
An empirically derived agenda of critical research issues for managing
supply-chain disruptions. International Journal of Production Research
43 (19), 4067-4081.
[5] Buyukozkan, G. & Ruan, D. (2008), Evaluation of software
development projects using a fuzzy multi-criteria decision approach,
Mathematics and Computers in Simulation 77, 464-475.
[6] Chen, C. T. & Lin, C. T. & Huang, S. F. (2006). A fuzzy approach for
supplier evaluation and selection in supply chain management.
International Journal of Production Economics, 102(2), 289-301.
[7] Chen, S. J. & Hwang, C. L. & Hwang, F. P. (1992). Fuzzy multiple
attribute decision making. Lecture Notes in Economics and
Mathematical System, 375.
[8] Chen, L. Y. & Wang, T. C. (2008). Optimizing partners- choice in
IS/IT outsourcing projects: The strategic decision of fuzzy VIKOR, Int.
J. Production Economics.
[9] Chiclana, F. & Herrera, F. & Herrera-Viedman, E. (1998) Integrating
three representation models in fuzzy multipurpose decision making
based on fuzzy preference relations, Fuzzy Sets Syst. 97, 33-48.
[10] Chiou, H. K., & Tzeng, G. H. (2002). Fuzzy multiple-criteria decisionmaking
approach for industrial green engineering. Environment
Management, 30(6), 816-830.
[11] Chiou, H. K. & Tzeng, G. H., & Cheng, D. C. (2005). Evaluating
sustainable fishing development strategies using fuzzy MCDM
approach. Omega, 33(3), 223-234.
[12] Chopra S. & Sodhi M. (2004) Managing Risk to Avoid Supply Chain
Breakdown. MIT Sloan Management Review 46/1:53-61
[13] Chung, S. H. & Lee, A. H. & Pearn, W. L. (2005). Product mix
optimization for semiconductor manufacturing based on AHP and ANP
analysis. International Journal of Advanced Manufacturing
Technology, 25, 1144-1156.
[14] Cui, X. & Wang, F. & Li, X. & Zhang, Y. & Yu, S. (2007). A Fuzzy-
ANP Approach to Cooperation Risk Evaluation of Virtual Logistics
Enterprise, International Conference on Automation and Logistics.
[15] Dan, W. & Zan, Y. (2007), Risk Management of Global Supply Chain,
International Conference on Automation and Logistics.
[16] Dikmen, M. T. & Birgonul & Han, S. (2007) "Using fuzzy risk
assessment to rate cost overrun risk in international construction
projects," International Journal of Project Management, vol. 25, pp.
494-505.
[17] Dubois, D., & Prade, H. (1978). Operations on fuzzy numbers.
International Journal ofSystem Sciences, 9(6), 613-626.
[18] Duncan, R.B., (1972). Characteristics of organizational environments
and perceived environmental uncertainty. Administrative Science
Quarterly, 17(3).
[19] Engelhardt-Nowitzki C. & Zsifkovits H. (2006) Complexity-Induced
Supply Chain Risks - Interdependencies Between Supply Chain Risk
and Complexity Management. In: Kersten W, Blecker T (eds)
Managing Risks in Supply Chains: How to Build Reliable
Collaboration in Logistics, pp. 37-56, Berlin
[20] Fisher, M., (1997). What is the right supply chain for your product?
Harvard Business Review 75, 105-117.
[21] Hendricks, K.B. & Singhal, V.R. (2005). An empirical analysis of the
effects of supply chain disruptions on long-run stock price performance
and equity risk of the firm, Production Operations Management, 14(1).
[22] Henke M. & Kurzhals R. & Jahns C. (2006) Enterprise and Supply
Risk Management from the Perspective of Internat and External
Auditors. In: Kersten W, Blecker T (eds) Managing Risks in Supply
Chains: How to Build Reliable Collaboration in Logistics. pp. 97- 109,
Berlin
[23] Herrera, F. & Herrera-Viedma, E. & Chiclana, F. (2001) Multiperson
decision-making based on multiplicative preference relations, Eur. J.
Oper. Res. 129, 372-385.
[24] Hsieh, T. Y. & Lu, S. T. & Tzeng, G. H. (2004). Fuzzy MCDM
approach for planning and design tenders selection in public office
buildings. International Journal of Project Management, 22(7), 573-
584.
[25] Huang, M. & Yang, H.-M. & Wang, X.-W. (2004) "Fuzzy synthetic
evaluation based risk evaluation for virtual enterprise," Mathematics in
Practice and Theory, vol. 34, no.6, pp. 45-51.
[26] Jahns C. & Hartmann E. & Moder M. (2006) Managing Supply Risks:
A System Theory Approach to Supply Early Warning Systems. In:
Kersten W, Blecker T (eds) Managing Risks in Supply Chains: How to
Build Reliable Collaboration in Logistics. pp. 195- 212, Berlin
[27] Jahns C. & Henke M. (2004) Supply Risk Management. Managementund
Überwachungssystem nach KonTraG zur systematischen
Risikobeherrschung, Beschaffung aktuell, 4:38-44
[28] Jung JY. & Blau G. & Pekny J. & Reklaitis G. & Eversdyk D. (2004)
A simulation based optimization approach to supply chain management
under demand uncertainty; Computer and Chemical Engineering
28/10:2087-2106
[29] J├╝ttner U. (2005) Supply chain risk management: Understanding the
business requirements from a practitioner perspective. The
International Journal of Logistics Management 16/1:120-141
[30] Kersten W. & Böger M. & Hohrath P. & Sp├ñth H (2006) Supply Chain
Risk Management: Development of a Theoretical and Empirical
Framework. In: Kersten W, Blecker T (eds) Managing Risks in Supply
Chains: How to Build Reliable Collaboration in Logistics, pp. 3-17,
Berlin
[31] Lee, A. H. I. & Chen, W. C. & Chang, C. J. (2008). A fuzzy AHP and
BSC approach for evaluating performance of IT department in the
manufacturing industry in Taiwan. Expert Systems with Applications,
34(1), 96-107.
[32] Lee, H., (2002). Aligning supply chain strategies with product
uncertainties. California Management Review 44 (3), 105-119.
[33] Lee, J.W. & Kim, S.H. (2000) Using analytic network process and goal
programming for interdependent information system project selection,
Comput. Oper. Res. 27, 367-382.
[34] Li, Y. & Liao, X.-W. (2007) "Decision support for risk analysis on
dynamic alliance," Decision Support Systems, vol. 42, pp. 2043-32059.
[35] March JP. & Shapira Z. (1987) Managerial Perspectives on Risk and
Risk Taking, Management Science, 33:1404
[36] Mikhailov L. & Singh, M. G. (2003) "Fuzzy analytic network process
and its application to the development of decision support systems,"
IEEE Transactions on Systems, Man, and Cybernetics-Part C:
Applications and Reviews, vol. 33, no. 1, pp. 33-41, February.
[37] Miles, R.E. & Snow, C.C., (1978). Organization Strategy, Structure,
and Process. McGraw-Hill, New York.
[38] Mohanty, R. P. & Agarwal, R. & Choudhury, A. K., & Tiwari, M. K.
(2005). A fuzzy ANPbased approach to R&D project selection: A case
study. International Journal of Production Research, 43(24), 5199-
5216.
[39] M├╝ssigmann N (2006) Mitigating Risk during Strategic Supply
Network Modeling. In: Kersten W, Blecker T (eds) Managing Risks in
Supply Chains: How to Build Reliable Collaboration in Logistics, pp.
213-226, Berlin
[40] Onut, S. & Kara, S. S. & Isik, E. (2009), Long term supplier selection
using a combined fuzzy MCDM approach: A case study for a
telecommunication company, Expert Systems with Applications 36,
3887-3895.
[41] Opricovic, S. (1998) Multicriteria Optimization of Civil Engineering
Systems, Faculty of Civil Engineering, Belgrade.
[42] Opricovic, S., & Tzeng, G. H. (2003). Defuzzification within a fuzzy
multicriteria decision model. International Journal of Uncertainty,
Fuzziness and Knowledgebased Systems, 11(5), 635-652.
[43] Opricovic, S. & Tzeng, G.H. (2004) Compromise solution by MCDM
methods: a comparative analysis of VIKOR and TOPSIS, Eur. J. Oper.
Res. 156, 445-455.
[44] Pepiot, G. & Cheikhrouhou, N. & Furbringer, J.-M., & Glardon, R.
(2008). A fuzzy approach for the evaluation of competences.
International Journal of Production Economics, 112(1), 336-353.
[45] Ramik, J. (2006). A decision system using ANP and fuzzy inputs. In
12th international conference on the foundations and applications of
utility, risk and decision theory, Roma.
[46] Ritchie B. & Brindley C. (2007) Supply chain risk management and
performance: A guiding framework for future development.
International Journal of Operations & Production Management
27/3:303-322
[47] Saaty, R. W. (2003). Decision making in complex environment: The
analytic hierarchy process (AHP) for decision making and the analytic
network process (ANP) for decision making with dependence and
feedback. <www.Superdecisions.com></www>.
[48] Saaty, T. L. (1999). Fundamentals of the analytic network process.
ISAHP 1999. Kobe, Japan.
[49] Saaty, T. L., & Vargas, L. G. (1998). Diagnosis with dependent
symptoms: Bayes theorem and the analytic network process.
Operations Research, 46(4), 491-502.
[50] Saaty, T.L. (1996) Decision making with dependence and feedback: the
analytic network process, RWS Publications, Pittsburgh,.
[51] Sanayei, A. & Mousavi S. F. & Yazdankhah, A. (2009), Group
decision making process for supplier selection with VIKOR under
fuzzy environment, Expert Systems with Applications.
[52] Smeltzer L. & Siferd S. (1998) Proactive supply management: the
management of risk, Journal of Supply Chain Management 34/1:38-45
[53] Spartalis, S. & Iliadis, L. & Maris, F. (2007) "An innovative risk
evaluation system estimating its own fuzzy entropy," Mathematical
and Computer Modelling, vol. 46, pp. 260-267.
[54] Spekman RE. & Davis EW. (2004) Risky business: expanding the
discussion on risk and the extended enterprise. International Journal of
Physical Distribution & Logistics Management 34/5:414-433
[55] Teufel S. & Erat A. (2001) Sicherheitsmanagement im Electronic
Business. In: Meier A (eds) Internet & Electronic Business, Z├╝rich.
[56] Teuteberg, F. Supply Chain Risk Management: A Neural Network
Approach, E-Business and Information Systems & Research Center for
Information Systems in Project and Innovation Networks.
[57] Tuzkaya, U. R., & Onut, S. (2008). A fuzzy analytic network process
based approach to transportation-mode selection between Turkey and
Germany: A case study. Information Sciences, 178, 3133-3146.
[58] Tsay, A. & Nahmias, S. & Agrawal, N. (1999) "Modeling supply chain
contracts: A review, in: S. Tayur, R. Ganeshan, M. Magazine (Eds.),
Quantitative Models for Supply Chain Management," Kluwer
Academic Publishers, Norwell, MA, , pp. 299_336.
[59] Tzeng, G.H. & Lin, C.W. & Opricovic, S. (2005) Multi-criteria
analysis of alternative-fuel buses for public transportation, Energy
Policy 33, 1373-1383.
[60] van Wyk J. & Baerwaldt W. (2005) External Risks and the Global
Supply Chain in the Chemicals Industry. Supply Chain Forum: An
International Journal 6/1:1-15.
[61] Venkatraman, N. & Camillus, J. (1984). Exploring the concept of ÔÇÿÔÇÿfit--
in strategic management. The Academy of Management Review 9 (3),
513-525.
[62] Wagner, S.M. & Bode, C., (2008). An empirical examination of supply
chain performance along several dimensions of risk. Journal of
Business Logistics, 29(1).
[63] Wang, T. C., & Chang, T. H. (2007). Application of TOPSIS in
evaluating initial training aircraft under a fuzzy environment. Expert
Systems with Applications, 33(4), 870-880.
[64] Wen, L. & Xi, Z. (2007). Supply Chain Risk Evaluation Based on
Fuzzy Multi-Criteria Lattice-Order Decision-Making, International
Conference on Automation and Logistics.
[65] Wu, H. Y. & Tzeng, G. H. & Chen, Y. H. (2009). A fuzzy MCDM
approach for evaluating banking performance based on Balanced
Scorecard, Expert Systems with Applications 36, 10135-10147.
[66] Wu, W. W., & Lee, Y. T. (2007). Selecting knowledge management
strategies by using the analytic network process. Expert Systems with
Applications, 32(3), 841-847.
[67] Y├╝ksel, I., & Dagdeviren, M. (2007). Using the analytic network
process (ANP) in a SWOT analysis - A case study for a textile firm.
Information Sciences, 177(16), 3364-3382.
[68] Yu, P.L., (1973). A class of solutions for group decision problems.
Management Science 19 (8), 936-946.
[69] Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-
353.
[70] Zadeh, L. A. (1975). The concept of a linguistic variable and its
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@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:54301", author = "P. Moeinzadeh and A. Hajfathaliha", title = "A Combined Fuzzy Decision Making Approach to Supply Chain Risk Assessment", abstract = "Many firms implemented various initiatives such as outsourced manufacturing which could make a supply chain (SC) more vulnerable to various types of disruptions. So managing risk has become a critical component of SC management. Different types of SC vulnerability management methodologies have been proposed for managing SC risk, most offer only point-based solutions that deal with a limited set of risks. This research aims to reinforce SC risk management by proposing an integrated approach. SC risks are identified and a risk index classification structure is created. Then we develop a SC risk assessment approach based on the analytic network process (ANP) and the VIKOR methods under the fuzzy environment where the vagueness and subjectivity are handled with linguistic terms parameterized by triangular fuzzy numbers. By using FANP, risks weights are calculated and then inserted to the FVIKOR to rank the SC members and find the most risky partner.
", keywords = "Analytic network process (ANP), Fuzzy sets, Supply chain risk management (SCRM), VIšekriterijumsko KOmpromisno Rangiranje (VIKOR)", volume = "3", number = "12", pages = "1487-17", }
