Evaluating the Logistics Performance of OECD Countries by Using Fuzzy AHP and ARAS-G

M25

Dr. Bahadır Fatih Yıldırım
İstanbul Üniversitesi Ulaştırma ve Lojistik Fakültesi, Lojistik Anabilim Dalı

Doç. Dr. Burcu Adıgüzel Mercangöz
İstanbul Üniversitesi Ulaştırma ve Lojistik Fakültesi, Lojistik Anabilim Dalı

Abstract

Logistics has become an important field as the volume of world commerce expands. The World Bank (WB) has been publishing the Logistics Performance Index (LPI) for most of the countries since 2007. LPI is accepted as an important indicator of logistical performance. In this study, a model is proposed to evaluate the LPI of the OECD countries within a specific time frame. With the proposed model, the logistical performance of OECD countries between the years 2010–2018 is analyzed and compared with the existing LPI rankings. The index is calculated using six indicators. Different from the WB survey, the fuzzy analytical hierarchy method is used to determine the weighting scores of these six indicators. The grey numbers give the researcher an opportunity to obtain the numerical expressions of a time period by showing minimum and maximum values. Thus, grey additive ratio assessment (ARAS-G) method is used to evaluate the logistics performances of OECD countries by years. The data created in this study refers to the logistics performances of the OECD countries between the years 2010 and 2018. Thus, OECD countries are ranked according to the logistics performances calculated by the ARAS-G method. The rankings calculated by ARAS-G are compared to the yearly rankings calculated by the WB. Spearman ρ and Kendall’s Tau correlation methods are used to investigate the relationships within the yearly rankings and the rankings calculated for the period between 2010 and 2018 by using ARAS-G. The results show that the rankings calculated by ARAS-G have the strongest relationship with years. Indeed, this study provides a different field of study for the ARAS-G method application.

Keywords: ARAS-G, Fuzzy AHP, Logistics Performance Index, MCDM, Non-parametric Correlation

Jel Sınıflandırma: C02

  • Aghdaie, M. H., & Behzadian, M. (2010). A hybrid fuzzy MCDM approach to thesis subject selection. Journal of Mathematics and Computer Science, 1(4), 355–365.
  • Arvis, J.-F., Ojala, L., Wiederer, C., Shepherd, B., Raj, A., Dairabayeva, K., & Kiiski, T. (2018). Connecting to Compete 2018: Trade logistics in the global economy. Washington, DC: World Bank. https://openknowledge.worldbank.org/handle/10986/29971. Accessed 21 Mar 2019. License: CC BY 3.0 IGO.
  • Badi, I., & Ballem, M. (2018). Supplier selection using the rough BWM-MAIRCA model: A case study in pharmaceutical supplying in Libya. Decision Making: Applications in Management and Engineering, 1(2), 16–33.
  • Bai, L., & Chen, X.R. (2010). Choice-making on distribution locations of logistics center based on fuzzy multi-criteria decision-making theory. In 2010 International Conference on Communications and Intelligence Information Security (pp. 17–22). IEEE.
  • Cakir, S. (2017). Measuring logistics performance of OECD countries via fuzzy linear regression. Multi-Criteria Decision Analysis, (Wiley Research Article), 2017(24), 177–186.
  • Cakir, S., & Perçin, S. (2013). Performance measurement in logistics companies by using multi criteria decision making techniques. Ege Academic Review, 13(4), 449–459.
  • Cemberci, M., Civelek, M. E., & Canbolat, N. (2015). The moderator effect of global competitiveness index on dimensions of Logistics Performance Index. Procedia Social and Behavioral Sciences, 195, 1514–1524.
  • Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649–655.
  • Chatterjee, N., & Bose, G. (2013). Selection of vendors for wind farm under fuzzy MCDM environment. International Journal of Industrial Engineering Computations, 4(4), 535–546.
  • Chatterjee, K., Zavadskas, E. K., Roy, J., & Kar, S. (2018). Performance evaluation of green supply chain management using the grey DEMATEL–ARAS Model. In S. Kar, U. Maulik, & X. Li (Eds.), Operations Research and Optimization. FOTA 2016. Springer Proceedings in Mathematics & Statistics (Vol. 225). Singapore: Springer.
  • Dahooie, H., Beheshti, J., Abadi, J., Vanaki, E., Firoozfar, A. S., & Reza, H. (2018). Competency-based IT personnel selection using a hybrid SWARA and ARAS-G methodology. Human Factors and Ergonomics in Manufacturing & Service Industries, 28(1), 5–16.
  • Deste, M., & Şimşek, Aİ. (2019). Comparison of logistics performance of airline companies bu using entropy and topology methods. Journal of Management and Economics Studies, 17(1), 395–411.
  • 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.
  • Esangbedo, M.O., & Che, A. (2016). Grey weighted sum model for evaluating business environment in West Africa. Mathematical Problems in Engineering, 2016 (Article ID 3824350).
  • Fazlollahtabar, H. (2018). Operations and inspection cost minimization for a reverse supply chain. Operational Research in Engineering Sciences: Theory and Applications, 1(1), 91–107.
  • Jhawar, A., Garg, S. K., & Khera, S. N. (2014). Analysis of the skilled work force effect on the logistics performance index—case study from India. Logistics Research, 7(1), 1–10.
  • Liu, F., Aiwu, G., Lukovac, V., & Vukic, M. (2018). A multicriteria model for the selection of the transport service provider: A single valued neutrosophic DEMATEL multicriteria model. Decision Making: Applications in Management and Engineering, 1(2), 121–130.
  • Martí, L., Martín, J. C., & Puertas, R. (2017). A DEA-Logistics Performance Index. Journal of Applied Economics, 20(1), 169–192.
  • Martí, L., Puertas, R., & García, L. (2014). The importance of the Logistics Performance Index. International Trade, Applied Economics, 46(24), 2982–2992. https://doi.org/10.1080/00036846.2014.916394
  • Nunic, Z. (2018). Evaluation and selection of manufacturer PVC carpentry using FUCOM-MABAC model. Operational Research in Engineering Sciences: Theory and Applications, 1(1), 13–28.
  • Pamucar, D., Chatterjee, K., & Zavadskas, E. K. (2019). Assessment of third-party logistics provider using multi-criteria decision-making approach based on interval rough numbers. Computers & Industrial Engineering, 127, 383–407.
  • Petrovic, M., Jeremic, V., & Bojkovic, N. (2017). Exploring Logistics Performance Index using I-distance statistical approach. In Proceedings of 3rd Logistics International Conferenc e, 25–27 May 2017, 160–165.
  • Petrovic, I., & Kankaras, M. (2018). DEMATEL-AHP multi-criteria decision making model for the selection and evaluation of criteria for selecting an aircraft for the protection of air traffic. Decision Making: Applications in Management and Engineering, 1(2), 93–110.
  • Pohekar, S. D., & Ramachandran, M. (2004). Application of multi criteria decision making to sustainable energy planning—A review. Renewable and Sustainable Energy Reviews, 8(4), 365–381.
  • Puertas, R., Martí, L., & García, L. (2014). Logistics performance and export competitiveness: European experience. Empirica, 41(3), 467–480.
  • Pumacar, D., Badi, I., & Sanja, K. (2018). A novel approach for the selection of power generation technology using an linguistic neutrosophic combinative distance-based assessment (CODAS) method: A case study in Libya. Energies, 11(9), 1–25. https://doi.org/10.3390/en11092489
  • Pupavac, D., & Drašković, M. (2017). Analysis of logistic performance in southeast European countries. Proceedings of International Scientific Conference Business Logistics in Modern Management, 4, 569–580.
  • Puska, A., Maksimovic, A., & Stojanovic, I. (2018). Improving organizational learning by sharing information through innovative supply chain in agro-food companies from Bosnia and Herzegovina. Operational Research in Engineering Sciences: Theory and Applications, 1(1), 76–90.
  • Sen, H. (2017a). Personnel selection with ARAS-G. The Eurasia Proceedings of Educational & Social Sciences (EPESS), 8, 73–79.
  • Sen, H. (2017b). Hospital location selection with Aras-G. The Eurasia Proceedings of Science, Technology, Engineering & Mathematics (EPSTEM). ICONTES2017: International Conference on Technology, Engineering and Science, 1, 359–365.
  • Senthil, S., Murugananthan, K., & Ramesh, A. (2018). Analysis and prioritisation of risks in a reverse logistics network using hybrid multi-criteria decision making methods. Journal of Cleaner Production, 179, 716–730.
  • Stanujkic, D., Djordjevic, B., & Karabasev, D. (2015). Selection of candidates in the process of recruitment and selection of personnel based on the SWARA and ARAS Methods. Timisoara, Quaestus Multidisciplinary Research Journal, 7, 53–64.
  • Triantaphyllou E. (2000). Multi-criteria decision making methods. In: Multi-criteria decision making methods: A comparative study. Applied optimization, vol. 44. Boston: Springer.
  • Turskis, Z., & Zavadskas, E. K. (2010). A novel method for multiple criteria analysis: Grey additive ratio assessment (ARAS-G) method. Informatica, 21(4), 597–610.
  • Turskis, Z., Zavadskas, E. K., & Kutut, V. (2013). A model based on ARAS-G and AHP methods for multiple criteria prioritizing of heritage value. International Journal of Information Technology & Decision Making, 12(01), 45–73.
  • Ulutaş, A., & Bayrakçil, A. O. (2017). Evaluation of Vegetable Suppliers for a Restaurant by using Grey AHS and ARAS-G Methods. Cumhuriyet University, Journal of Economics and Administrative Sciences, 18(2), 189–204.
  • Ulutas, A., Karakoy, C., Aric, K.H., & Cengiz, E. (2018). Determining the Location of Logistics Center with Multi Criteria Decision Making Methods, Siirt University.
  • Yaprakli, T. S., & Unalan, M. (2017). The global Logistics Performance Index and analysis of the last 10 years logistics performance of Turkey. Ataturk University Journal of Economics & Administrative Sciences, 31(3), 589–606.
  • Yildirim, B. F. (2015). ARAS Method for Multi Criteria Decision Making Problems. Kafkas University. Journal of Faculty of Economics and Administrative Sciences, 6(9), 285–296.
  • Karaköy, Ç., & Ölmez, U. (2019). Balkan Ülkelerinde Lojistik Performans Endeksi Değerlendirilmesi. SETSCI Conference Proceedings, 4 (8), 178-180. https://doi.org/10.36287/setsci.4.8.031
  • Ulutaş, A., & Karaköy, Ç. (2019). An analysis of the logistics performance index of EU countries with an integrated MCDM model . Economics and Business Review EBR 19(4), 49-69 DOI: 10.18559/ebr.2019.4.3
  • Ulutaş, A, Karaköy, Ç. (2019). G-20 Ülkelerinin Lojistik Performans Endeksinin Çok Kriterli Karar Verme Modeli İle Ölçümü. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi 20(2) 71-84.
  • Beysenbaev, R. & Dus, Y. (2019). Proposals for improving the Logistics Performance Index. The Asian Journal of Shipping and Logistics, 2019, https://doi.org/10.1016/j.ajsl.2019.10.001.

Yıldırım, B. F., Adıgüzel Mercangöz, B. (2019). "Evaluating the Logistics Performance of OECD Countries by Using Fuzzy AHP and ARAS-G". Eurasian Economic Review, , 1-19. https://doi.org/10.1007/s40822-019-00131-3

We thank the editors and two anonymous referees for insightful comments and suggestions.

  • Paylaş

Eurasian Economic Review

Eurasian Economic Review

Yıl 2019

ss. 1-19

https://doi.org/10.1007/s40822-019-00131-3

İndir [339,0 KB]

Oku [Tam Metin]