Third-party logistics (3Pls) provider selection via Fuzzy AHP and EDAS integrated model
Abstract
In the global competitive environment, companies not only improve the quality of service and increase the efficiency, they also decrease the cost by means of third-party logistics (3PLs). 3PLs, therefore, is an important strategy for companies desiring to gain a competitive advantage and 3PLs provider selection plays a critical role for the success of outsourcing. Nevertheless, the level of uncertainty in the selection process is relatively high and need to be carefully considered. Hence, in order to select a proper 3PLs provider, integration of the Fuzzy AHP and Evaluation based on Distance from Average Solution (EDAS) has offered a novel integrated model, in which Fuzzy AHP is used for calculating priority weights of each criteria and EDAS is employed to achieve the final ranking of 3PLs providers. Besides, in order to demonstrate the applicability of the proposed model, it is validated by a case study. Cost together with quality, and professionalism are found to be the most important factors for 3PLs provider selection. Consequently, the advantage of this model is that it is simple to apprehend and easy to apply. The use of the proposed model leads to the selection of suitable alternative successfully in other selection problems.
First published online: 23 Apr 2017
Keyword : 3PLs, Fuzzy AHP, EDAS, logistics outsourcing, provider selection
How to Cite
Ecer, F. (2018). Third-party logistics (3Pls) provider selection via Fuzzy AHP and EDAS integrated model. Technological and Economic Development of Economy, 24(2), 615–634. https://doi.org/10.3846/20294913.2016.1213207
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Balezentis, A.; Balezentis, T.; Misiunas, A. 2012. An integrated assessment of Lithuanian economic sectors based on financial ratios and fuzzy MCDM methods, Technological and Economic Development of Economy 18(1): 34–53. https://doi.org/10.3846/20294913.2012.656151
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Banomyong, R.; Supatn, N. 2011. Selecting logistics providers in Thailand: a shippers’ perspective, European Journal of Marketing 45(3): 419–437. https://doi.org/10.1108/03090561111107258
Bottani, E.; Rizzi, A. 2006. A fuzzy TOPSIS methodology to support outsourcing of logistics services, Supply Chain Management: an International Journal 11(4): 294–308. https://doi.org/10.1108/13598540610671743
Büyüközkan, G.; Feyzioğlu, O.; Nebol, E. 2008. Selection of the strategic alliance partner in logistics value chain, International Journal of Production Economics 113(1): 148–158. https://doi.org/10.1016/j.ijpe.2007.01.016
Chang, D. Y. 1992. Extent analysis and synthetic decision, Optimization Techniques and Applications 1(1): 352–355.
Chen, L. Y.; Wang, T. C. 2009. Optimizing partners’ choice in IS/IT outsourcing projects: the strategic decision of fuzzy VIKOR, International Journal of Production Economics 120(1): 233–242. https://doi.org/10.1016/j.ijpe.2008.07.022
Cheng, A. C. 2013. A fuzzy multiple criteria comparison of technology valuation methods for the new materials development, Technological and Economic Development of Economy 19(3): 397–408. https://doi.org/10.3846/20294913.2013.821687
Chou, W. C.; Cheng, Y. P. 2012. A hybrid fuzzy MCDM approach for evaluating website quality of professional accounting firms, Expert Systems with Applications 39(3): 2783–2793. https://doi.org/10.1016/j.eswa.2011.08.138
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Choy, K. L.; Chow, H. K.; Tan, K. H.; Chan, C. K.; Mok, E. C.; Wang, Q. 2008. Leveraging the supply chain flexibility of third party logistics-hybrid knowledge-based system approach, Expert Systems with Applications 35(4): 1998–2016. https://doi.org/10.1016/j.eswa.2007.08.084
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Ecer, F. 2014. A hybrid banking websites quality evaluation model using AHP and COPRAS-G, Technological and Economic Development of Economy 20(4): 758–782. https://doi.org/10.3846/20294913.2014.915596
Ecer, F. 2015. Performance evaluation of internet banking branches via a hybrid MCDM model under fuzzy environment, Economic Computation and Economic Cybernetics Studies and Research 49(2): 211–230.
Efendigil, T.; Önüt, S.; Kongar, E. 2008. A holistic approach for selecting a third-party reverse logistics provider in the presence of vagueness, Computers and Industrial Engineering 54(2): 269–287. https://doi.org/10.1016/j.cie.2007.07.009
Falsini, D.; Fondi, F.; Schiraldi, M. M. 2012. A logistics provider evaluation and selection methodology based on AHP, DEA and linear programming integration, International Journal of Production Research 50(17): 4822–4829. https://doi.org/10.1080/00207543.2012.657969
Gao, L.; Hailu, A. 2013. Identifying preferred management options: an integrated agent-based recreational fishing simulation model with an AHP-TOPSIS evaluation method, Ecological Modelling 249: 75–83. https://doi.org/10.1016/j.ecolmodel.2012.07.002
Gumus, A. T. 2009. Evaluation of hazardous waste transportation firms by using a two–step fuzzy-AHP and TOPSIS methodology, Expert Systems with Applications 36: 4067–4074. https://doi.org/10.1016/j.eswa.2008.03.013
Hashemkhani Zolfani, S.; Sedaghat, M.; Zavadskas, E. K. 2012. Performance evaluating of rural ICT centers (telecenters), applying fuzzy AHP, SAW-G and TOPSIS Grey: a case study in Iran, Technological and Economic Development of Economy 18(2): 364–387. https://doi.org/10.3846/20294913.2012.685110
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: 573–584. https://doi.org/10.1016/j.ijproman.2004.01.002
Ho, W.; He, T.; Lee, C. K. M.; Emrouznejad, A. 2012. Strategic logistics outsourcing: an integrated QFD and fuzzy AHP approach, Expert Systems with Applications 39(12): 10841–10850. https://doi.org/10.1016/j.eswa.2012.03.009
Hsu, C. C.; Liou, J. J.; Chuang, Y. C. 2013. Integrating DANP and modified grey relation theory for the selection of an outsourcing provider, Expert Systems with Applications 40(6): 2297–2304. https://doi.org/10.1016/j.eswa.2012.10.040
Işıklar, G.; Alptekin, E.; Büyüközkan, G. 2007. Application of a hybrid intelligent decision support model in logistics outsourcing, Computers and Operations Research 34(12): 3701–3714. https://doi.org/10.1016/j.cor.2006.01.011
Jarzemskis, A. 2006. Determination and evaluation of the factors of outsourcing logistics, Transport 21(1): 44–47.
Jharkharia, S.; Shankar, R. 2007. Selection of logistics service provider: an analytic network process (ANP) approach, Omega 35(3): 274–289. https://doi.org/10.1016/j.omega.2005.06.005
Kabir, G. 2012. Third party logistic service provider selection using fuzzy AHP and TOPSIS method, International Journal for Quality Research 6(1): 71–79.
Kahraman, C.; Suder, A.; Kaya, I. 2014. Fuzzy multicriteria evaluation of health research investments, Technological and Economic Development of Economy 20(2): 210–226. https://doi.org/10.3846/20294913.2013.876560
Kannan, G.; Pokharel, S.; Kumar, P. S. 2009. A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider, Resources, Conservation and Recycling 54(1): 28–36. https://doi.org/10.1016/j.resconrec.2009.06.004
Keshavarz Ghorabaee, M.; Zavadskas, E. K.; Olfat, L.; Turskis, Z. 2015. Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS), Informatica 26(3): 435–451. https://doi.org/10.15388/Informatica.2015.57
Lehmusvaara, A.; Tuominen, M.; Korpela, J. 1999. An integrated approach for truck carrier selection, International Journal of Logistics Research and Applications 2(1): 5–20. https://doi.org/10.1080/13675569908901569
Liou, J. J.; Chuang, Y. T. 2010. Developing a hybrid multi-criteria model for selection of outsourcing providers, Expert Systems with Applications 37(5): 3755–3761. https://doi.org/10.1016/j.eswa.2009.11.048
Mardani, A.; Jusoh, A.; Zavadskas, E. K. 2015. Fuzzy multiple criteria decision-making techniques and applications – two decades review from 1994 to 2014, Expert Systems with Applications 42(8): 4126–4148. https://doi.org/10.1016/j.eswa.2015.01.003
Perçin, S.; Min, H. 2013. A hybrid quality function deployment and fuzzy decision-making method¬ology for the optimal selection of third-party logistics service providers, International Journal of Logistics Research and Applications 16(5): 380–397. https://doi.org/10.1080/13675567.2013.815696
Saaty, T. L. 1980. The Analytic Hierarchy Process. New York: McGraw Hill, 287 p.
Safaei Ghadikolaei, A.; Khalili Esbouei, S.; Antucheviciene, J. 2014. Applying fuzzy MCDM for financial performance evaluation of Iranian companies, Technological and Economic Development of Economy 20(2): 274–291. https://doi.org/10.3846/20294913.2014.913274
Sharma, S. K.; Kumar, V. 2015. Optimal selection of third-party logistics service providers using qual¬ity function deployment and Taguchi loss function, Benchmarking: An International Journal 22(7): 1281–1300. https://doi.org/10.1108/BIJ-02-2014-0016
Thakkar, J.; Deshmukh, S. G.; Gupta, A. D.; Shankar, R. 2005. Selection of third-party logistics (3PL): a hybrid approach using interpretive structural modeling (ISM) and analytic network process (ANP), Supply Chain Forum: An International Journal 6(1): 32–46.
Vasiliauskas, A. V.; Jakubauskas, G. 2007. Principle and benefits of third party logistics approach when managing logistics supply chain, Transport 22(2): 68–72.
World Bank. 2015 [online], [cited 11 November 2015]. Available from Internet: http://lpi.worldbank.org/international/global/2014
Yayla, A. Y.; Oztekin, A.; Gümüş, A. T.; Gunasekaran, A. 2015. A hybrid data analytic methodology for 3PL transportation provider evaluation using fuzzy multi-criteria decision making, International Journal of Production Research 53(20): 6097–6113. https://doi.org/10.1080/00207543.2015.1022266
Zadeh, L. A. 1965. Fuzzy set, Information Control 18(2): 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Zhang, G.; Shang, J.; Li, W. 2012. An information granulation entropy-based model for third-party logistics providers evaluation, International Journal of Production Research 50(1): 177–190. https://doi.org/10.1080/00207543.2011.571453
Zhang, Y.; Zhang, R. 2010. Study on the third party logistics service providers’ performance evaluation based on the weighted entropy and analysis process of grey relation, in Proceedings of the 17th International Conference on Management Science and Engineering, 24–26 November 2010. IEEE, Melbourne, Australia, 582–587. https://doi.org/10.1109/ICMSE.2010.5719861
Aguezzoul, A. 2014. Third-party logistics selection problem: a literature review on criteria and methods, Omega 49: 69–78. https://doi.org/10.1016/j.omega.2014.05.009
Akman, G.; Baynal, K. 2014. Logistics service provider selection through an integrated fuzzy multicriteria decision making approach, Journal of Industrial Engineering 2014: 1–16. https://doi.org/10.1155/2014/794918
Alkhatib, S. F.; Darlington, R.; Yang, Z.; Nguyen, T. T. 2015. A novel technique for evaluating and selecting logistics service providers based on the logistics resource view, Expert Systems with Applications 42: 6976–6989. https://doi.org/10.1016/j.eswa.2015.05.010
Almeida, A. T. 2007. Multicriteria decision model for outsourcing contracts selection based on utility function and ELECTRE method, Computers and Operations Research 34(12): 3569–3574. https://doi.org/10.1016/j.cor.2006.01.003
Arslan, G.; Aydin, Ö. 2009. A new software development for fuzzy multicriteria decision‐making, Technological and Economic Development of Economy 15(2): 197–212. https://doi.org/10.3846/1392-8619.2009.15.197-212
Balezentis, A.; Balezentis, T.; Misiunas, A. 2012. An integrated assessment of Lithuanian economic sectors based on financial ratios and fuzzy MCDM methods, Technological and Economic Development of Economy 18(1): 34–53. https://doi.org/10.3846/20294913.2012.656151
Balin, A.; Baraçli, H. 2015. A fuzzy multi-criteria decision making methodology based upon the interval type-2 fuzzy sets for evaluating renewable energy alternatives in Turkey, Technological and Economic Development of Economy (in press). https://doi.org/10.3846/20294913.2015.1056276
Banomyong, R.; Supatn, N. 2011. Selecting logistics providers in Thailand: a shippers’ perspective, European Journal of Marketing 45(3): 419–437. https://doi.org/10.1108/03090561111107258
Bottani, E.; Rizzi, A. 2006. A fuzzy TOPSIS methodology to support outsourcing of logistics services, Supply Chain Management: an International Journal 11(4): 294–308. https://doi.org/10.1108/13598540610671743
Büyüközkan, G.; Feyzioğlu, O.; Nebol, E. 2008. Selection of the strategic alliance partner in logistics value chain, International Journal of Production Economics 113(1): 148–158. https://doi.org/10.1016/j.ijpe.2007.01.016
Chang, D. Y. 1992. Extent analysis and synthetic decision, Optimization Techniques and Applications 1(1): 352–355.
Chen, L. Y.; Wang, T. C. 2009. Optimizing partners’ choice in IS/IT outsourcing projects: the strategic decision of fuzzy VIKOR, International Journal of Production Economics 120(1): 233–242. https://doi.org/10.1016/j.ijpe.2008.07.022
Cheng, A. C. 2013. A fuzzy multiple criteria comparison of technology valuation methods for the new materials development, Technological and Economic Development of Economy 19(3): 397–408. https://doi.org/10.3846/20294913.2013.821687
Chou, W. C.; Cheng, Y. P. 2012. A hybrid fuzzy MCDM approach for evaluating website quality of professional accounting firms, Expert Systems with Applications 39(3): 2783–2793. https://doi.org/10.1016/j.eswa.2011.08.138
Chow, H. K.; Choy, K. L.; Lee, W. B.; Chan, F. T. 2005. Design of a knowledge–based logistics strategy system, Expert Systems with Applications 29(2): 272–290. https://doi.org/10.1016/j.eswa.2005.04.001
Choy, K. L.; Chow, H. K.; Tan, K. H.; Chan, C. K.; Mok, E. C.; Wang, Q. 2008. Leveraging the supply chain flexibility of third party logistics-hybrid knowledge-based system approach, Expert Systems with Applications 35(4): 1998–2016. https://doi.org/10.1016/j.eswa.2007.08.084
Degraeve, Z.; Labro, E.; Roodhooft, F. 2004. Total cost of ownership purchasing of a service: the case of airline selection at Alcatel Bell, European Journal of Operational Research 156(1): 23–40. https://doi.org/10.1016/j.ejor.2003.08.002
Ecer, F. 2014. A hybrid banking websites quality evaluation model using AHP and COPRAS-G, Technological and Economic Development of Economy 20(4): 758–782. https://doi.org/10.3846/20294913.2014.915596
Ecer, F. 2015. Performance evaluation of internet banking branches via a hybrid MCDM model under fuzzy environment, Economic Computation and Economic Cybernetics Studies and Research 49(2): 211–230.
Efendigil, T.; Önüt, S.; Kongar, E. 2008. A holistic approach for selecting a third-party reverse logistics provider in the presence of vagueness, Computers and Industrial Engineering 54(2): 269–287. https://doi.org/10.1016/j.cie.2007.07.009
Falsini, D.; Fondi, F.; Schiraldi, M. M. 2012. A logistics provider evaluation and selection methodology based on AHP, DEA and linear programming integration, International Journal of Production Research 50(17): 4822–4829. https://doi.org/10.1080/00207543.2012.657969
Gao, L.; Hailu, A. 2013. Identifying preferred management options: an integrated agent-based recreational fishing simulation model with an AHP-TOPSIS evaluation method, Ecological Modelling 249: 75–83. https://doi.org/10.1016/j.ecolmodel.2012.07.002
Gumus, A. T. 2009. Evaluation of hazardous waste transportation firms by using a two–step fuzzy-AHP and TOPSIS methodology, Expert Systems with Applications 36: 4067–4074. https://doi.org/10.1016/j.eswa.2008.03.013
Hashemkhani Zolfani, S.; Sedaghat, M.; Zavadskas, E. K. 2012. Performance evaluating of rural ICT centers (telecenters), applying fuzzy AHP, SAW-G and TOPSIS Grey: a case study in Iran, Technological and Economic Development of Economy 18(2): 364–387. https://doi.org/10.3846/20294913.2012.685110
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: 573–584. https://doi.org/10.1016/j.ijproman.2004.01.002
Ho, W.; He, T.; Lee, C. K. M.; Emrouznejad, A. 2012. Strategic logistics outsourcing: an integrated QFD and fuzzy AHP approach, Expert Systems with Applications 39(12): 10841–10850. https://doi.org/10.1016/j.eswa.2012.03.009
Hsu, C. C.; Liou, J. J.; Chuang, Y. C. 2013. Integrating DANP and modified grey relation theory for the selection of an outsourcing provider, Expert Systems with Applications 40(6): 2297–2304. https://doi.org/10.1016/j.eswa.2012.10.040
Işıklar, G.; Alptekin, E.; Büyüközkan, G. 2007. Application of a hybrid intelligent decision support model in logistics outsourcing, Computers and Operations Research 34(12): 3701–3714. https://doi.org/10.1016/j.cor.2006.01.011
Jarzemskis, A. 2006. Determination and evaluation of the factors of outsourcing logistics, Transport 21(1): 44–47.
Jharkharia, S.; Shankar, R. 2007. Selection of logistics service provider: an analytic network process (ANP) approach, Omega 35(3): 274–289. https://doi.org/10.1016/j.omega.2005.06.005
Kabir, G. 2012. Third party logistic service provider selection using fuzzy AHP and TOPSIS method, International Journal for Quality Research 6(1): 71–79.
Kahraman, C.; Suder, A.; Kaya, I. 2014. Fuzzy multicriteria evaluation of health research investments, Technological and Economic Development of Economy 20(2): 210–226. https://doi.org/10.3846/20294913.2013.876560
Kannan, G.; Pokharel, S.; Kumar, P. S. 2009. A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider, Resources, Conservation and Recycling 54(1): 28–36. https://doi.org/10.1016/j.resconrec.2009.06.004
Keshavarz Ghorabaee, M.; Zavadskas, E. K.; Olfat, L.; Turskis, Z. 2015. Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS), Informatica 26(3): 435–451. https://doi.org/10.15388/Informatica.2015.57
Lehmusvaara, A.; Tuominen, M.; Korpela, J. 1999. An integrated approach for truck carrier selection, International Journal of Logistics Research and Applications 2(1): 5–20. https://doi.org/10.1080/13675569908901569
Liou, J. J.; Chuang, Y. T. 2010. Developing a hybrid multi-criteria model for selection of outsourcing providers, Expert Systems with Applications 37(5): 3755–3761. https://doi.org/10.1016/j.eswa.2009.11.048
Mardani, A.; Jusoh, A.; Zavadskas, E. K. 2015. Fuzzy multiple criteria decision-making techniques and applications – two decades review from 1994 to 2014, Expert Systems with Applications 42(8): 4126–4148. https://doi.org/10.1016/j.eswa.2015.01.003
Perçin, S.; Min, H. 2013. A hybrid quality function deployment and fuzzy decision-making method¬ology for the optimal selection of third-party logistics service providers, International Journal of Logistics Research and Applications 16(5): 380–397. https://doi.org/10.1080/13675567.2013.815696
Saaty, T. L. 1980. The Analytic Hierarchy Process. New York: McGraw Hill, 287 p.
Safaei Ghadikolaei, A.; Khalili Esbouei, S.; Antucheviciene, J. 2014. Applying fuzzy MCDM for financial performance evaluation of Iranian companies, Technological and Economic Development of Economy 20(2): 274–291. https://doi.org/10.3846/20294913.2014.913274
Sharma, S. K.; Kumar, V. 2015. Optimal selection of third-party logistics service providers using qual¬ity function deployment and Taguchi loss function, Benchmarking: An International Journal 22(7): 1281–1300. https://doi.org/10.1108/BIJ-02-2014-0016
Thakkar, J.; Deshmukh, S. G.; Gupta, A. D.; Shankar, R. 2005. Selection of third-party logistics (3PL): a hybrid approach using interpretive structural modeling (ISM) and analytic network process (ANP), Supply Chain Forum: An International Journal 6(1): 32–46.
Vasiliauskas, A. V.; Jakubauskas, G. 2007. Principle and benefits of third party logistics approach when managing logistics supply chain, Transport 22(2): 68–72.
World Bank. 2015 [online], [cited 11 November 2015]. Available from Internet: http://lpi.worldbank.org/international/global/2014
Yayla, A. Y.; Oztekin, A.; Gümüş, A. T.; Gunasekaran, A. 2015. A hybrid data analytic methodology for 3PL transportation provider evaluation using fuzzy multi-criteria decision making, International Journal of Production Research 53(20): 6097–6113. https://doi.org/10.1080/00207543.2015.1022266
Zadeh, L. A. 1965. Fuzzy set, Information Control 18(2): 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Zhang, G.; Shang, J.; Li, W. 2012. An information granulation entropy-based model for third-party logistics providers evaluation, International Journal of Production Research 50(1): 177–190. https://doi.org/10.1080/00207543.2011.571453
Zhang, Y.; Zhang, R. 2010. Study on the third party logistics service providers’ performance evaluation based on the weighted entropy and analysis process of grey relation, in Proceedings of the 17th International Conference on Management Science and Engineering, 24–26 November 2010. IEEE, Melbourne, Australia, 582–587. https://doi.org/10.1109/ICMSE.2010.5719861