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MEREC, Veri Zarflama Analizi ve EATWIOS Yöntemlerinin Hibrit Kullanımı ile Afrika Ülkelerinin Lojistik Performanslarının Değerlendirilmesi

Yıl 2024, Cilt: 8 Sayı: 2, 1033 - 1071, 30.11.2024
https://doi.org/10.30561/sinopusd.1495650

Öz

Bu çalışmanın amacı, Afrika ülkelerinin lojistik etkinliklerinin Veri Zarflama Analizi (VZA), EATWIOS ve MEREC yöntemleri ile çok kriterli olarak değerlendirilmesidir. Ülkelerin etkinliklerinin analizi için literatür incelemesine dayalı olarak dört girdi (lojistik altyapı, konteyner liman trafiği, gümrükleme sürecinin verimliliği, uluslararası gönderi maliyetleri) ve beş çıktı değişkeni (gayri safi yurtiçi hasıla (GSYH), karbondioksit (CO2) emisyonu, zamanındalık, izleme ve takip, lojistik hizmetlerin yetkinliği ve kalitesi) belirlenmiştir. Elli dört Afrika ülkesi arasından, belirlenen girdi çıktı değişkenlerinde tam veriye sahip olan 18 ülke değerlendirmeye alınmıştır. Bu bağlamda, Afrika ülkelerinin lojistik performanslarına göre sıralamaları önce kriter ağırlıkları eşit varsayılarak daha sonra da kriterler MEREC yöntemi ile ağırlıklandırılarak boşluk tabanlı VZA ve EATWIOS hibrit yöntemi kullanılarak elde edilmiştir. MEREC analizi sonucunda lojistik performansının değerlendirilmesinde en önemli girdi değişkenlerinin konteyner liman trafiği skoru ile lojistik alt yapı olanaklarının olduğu; en önemli çıktı değişkenlerinin ise GSYİH ülke payı ve ulaşım ve lojistik kaynaklı CO2 emisyonu olduğu bulgulanmıştır. Nihai analiz sonuçlarına göre performansı en yüksek ülkenin Demokratik Kongo Cumhuriyeti, en düşük ülkenin Benin olduğu tespit edilmiştir.

Etik Beyan

Çalışmada etik kurul iznine ihtiyaç bulunmamaktadır.

Kaynakça

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Yıl 2024, Cilt: 8 Sayı: 2, 1033 - 1071, 30.11.2024
https://doi.org/10.30561/sinopusd.1495650

Öz

This study aims to evaluate the logistics efficiency of African countries using Data Envelopment Analysis (DEA), EATWIOS, and MEREC methods. Four input variables (logistics infrastructure, container port traffic, customs, international shipping) and five output variables (gross domestic product (GDP), carbon dioxide (CO2) emission, timeliness, tracking and tracing, competence, and quality of logistics services) were identified based on the literature review for the analysis of countries' efficiency. Among the fifty-four African countries, 18 countries with complete data on the input and output variables were included in the evaluation. In this context, the ranking of African countries according to their logistics performance was obtained using the slacks-based DEA and EATWIOS hybrid method by assuming equal criteria weights and then weighting the criteria with the MEREC method. As a result of the MEREC analysis, it is found that the most important input variables in the evaluation of logistics performance are container port traffic score and logistics infrastructure facilities, while the most important output variables are GDP country share and CO2 emissions from transportation and logistics. According to the final analysis results, the Democratic Republic of the Congo has the highest performance and Benin has the lowest.

Kaynakça

  • Abdoulkarim, H. T., Fatouma, S. H. ve Hassane, H. T. (2019). Assessment of dry port efficiency in Africa using data envelopment analysis. Journal of Transportation Technologies, 9(02), 193. https://doi.org/10.4236/jtts.2019.92012
  • Acar, M. F. (2021). Lojistik performans indeks: Türkiye-Avrupa Birliği karşılaştırması. International Journal of Advances in Engineering and Pure Sciences, 33(3), 422-428. https://doi.org/10.7240/jeps.845982
  • Acquah, D. A. (2018). Selection of gateway port for west African landlocked countries using data envelopment analysis. International Journal of Novel Research in Marketing Management and Economics, 5(2), 7-17. https://www.noveltyjournals.com/issue/IJNRMME/Issue-2-May-2018-August-2018
  • Adıgüzel Mercangöz, B., Yıldırım, B. F. ve Kuzu Yıldırım, S. (2020). Time period based COPRAS-G method: application on the Logistics Performance Index. Scientific Journal of Logistics, 16 (2), 239-250. http://doi.org/10.17270/J.LOG.2020.432
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  • Gök Kısa, A. C. ve Ayçin, E. (2019). OECD ülkelerinin lojistik performanslarının SWARA Tabanlı EDAS yöntemi ile değerlendirilmesi. Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Faskültesi Dergisi, 9 (1), 301-325. https://doi.org/10.18074/ckuiibfd.500320
  • Guo, P. ve Tanaka, H. (2001). Fuzzy DEA: a perceptual evaluation method. Fuzzy Sets and Systems, 119(1), 149-160. https://doi.org/10.1016/S0165-0114(99)00106-2
  • Hadžikadunic, A., Stevic, Ž., Badi, I., ve Roso, V. (2023). Evaluating the logistics performance index of European Union Countries: An integrated multi-criteria decision-making approach utilizing the Bonferroni Operator. International Journal of Knowledge and Innovation Studies, 1, 44-59. https://doi.org/10.56578/ijkis010104. Hemmati, M., Dalghandi, S. ve Nazari, H. (2013). Measuring relative performance of banking industry using a DEA and TOPSIS. Management Science Letters, 3(2), 499-504. https://doi.org/10.5267/j.msl.2012.12.025 Hua, Z. ve Bian, Y. (2007). DEA with undesirable factors. Modeling data irregularities and structural complexities in data envelopment analysis içinde (s.103-121), Springer Link.
  • Işık, O., Aydın, Y. ve Kosaroglu, S. M. (2020). The assessment of the logistics performance index of CEE countries with the new combination of SV and MABAC methods. LogForum, 16(4), 549-559. http://doi.org/10.17270/J.LOG.2020.504
  • Jahanshahloo, G. R., Lotfi, F. H., Shoja, N., Tohidi, G. ve Razavyan, S. (2005). Undesirable inputs and outputs in DEA models. Applied Mathematics and Computation, 169 (2), 917-925. https://doi.org/10.1016/j.amc.2004.09.069
  • Kara, M. Tayfur, L. ve Basık, H. (2009). Küresel ticarette lojistik üslerin önemi ve Türkiye. Mustafa Kemal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, Cilt: 6. Sayı: 11. ss. 69-84. https://dergipark.org.tr/tr/pub/mkusbed/issue/19558/208484
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  • Mešić, A., Miškić, S., Stević, Ž. ve Mastilo, Z. (2022). Hybrid MCDM solutions for evaluation of the logistics performance index of the Western Balkan countries. Economics, 10(1), 13-34. https://doi.org/10.2478/eoik-2022-0004
  • Miškić, S., Stević, Ž., Tadić, S., Alkhayyat, A. ve Krstić, M. (2023). Assessment of the LPI of the EU countries using MCDM model with an emphasis on the importance of criteria. World Review of Intermodal Transportation Research, 11(3), 258-279. https://doi.org/10.1504/WRITR.2023.132501
  • Mousavi-Nasab, S. H. ve Sotoudeh-Anvari, A. (2017). A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems. Materials & Design, 121, 237-253. https://doi.org/10.1016/j.matdes.2017.02.041
  • Munim, Z. H. ve Schramm, H. J. (2018). The impacts of port infrastructure and logistics performance on economic growth: the mediating role of seaborne trade. Journal of Shipping and Trade, 3(1), 1-19. https://doi.org/10.1186/s41072-018-0027-0
  • Ngangaji, M. M. F. (2019). An assessment of container terminal efficiency in East Africa ports using data envelopment analysis (DEA): the case of Dar es Salaam & Mombasa ports (Yayımlanmamış Yüksek Lisans Tezi) World Maritime University, Tanzanya.
  • Nguyen, C. D. T., Luong, B. T. ve Hoang, H. L. T. (2021). The impact of logistics and infrastructure on economic growth: Empirical evidence from Vietnam. The Journal of Asian Finance, Economics and Business, 8(6), 21-28. https://doi.org/10.13106/jafeb.2021.vol8.no6.0021
  • Odu, G. O. (2019). Weighting methods for multi-criteria decision making technique. Journal of Applied Sciences and Environmental Management, 23(8), 1449-1457. https://dx.doi.org/10.4314/jasem.v23i8.7
  • Ojala, L. ve Celebi, D. (2015). The World Bank’s Logistics Performance Index (LPI) and drivers of logistics performance. Proceeding of MAC-EMM, OECD, 3-30. https://www.semanticscholar.org/paper/The-World-Bank%27s-Logistics-Performance-Index-(LPI)-Ojala-%C3%87elebi/e9d3433e9e41914974e8f2f507ecbfcb962e0317
  • Ozmen, M. (2019). Logistics competitiveness of OECD countries using an improved TODIM method. Sādhanā, 44, 1-11. https://doi.org/10.1007/s12046-019-1088-y
  • Peters, M. L. ve Zelewski, S. (2006, April). Efficiency analysis under consideration of satisficing levels for output quantities. Proceedings of the 17th Annual Conference of the Production and Operations Management Society (POMS) içinde, 28 (1.05). https://www.researchgate.net/publication/331155463_Efficiency_Analysis_under_Consideration_of_Satisficing_Levels_for_Output_Quantities
  • Rashidi, K. ve Cullinane, K. (2019). Evaluating the sustainability of national logistics performance using Data Envelopment Analysis. Transport Policy, 74, 35-46. https://doi.org/10.1016/j.tranpol.2018.11.014
  • Rezaei, J., van Roekel, W. S. ve Tavasszy, L. (2018). Measuring the relative importance of the logistics performance index indicators using Best Worst Method. Transport Policy, 68, 158-169. https://doi.org/10.1016/j.tranpol.2018.05.007
  • Rizet, C. Browne, M. Cornelis, E. ve Leonardi, J. (2012). Assessing carbon footprint and energy efficiency in competing supply chains: Review – case studies and benchmarking. Transportation Research Part D: Transport And Environment, 17(4), 293–300. https://doi.org/10.1016/j.trd.2012.01.002
  • Sadjadi, S. J. ve Omrani, H. (2010). A bootstrapped robust data envelopment analysis model for efficiency estimating of telecommunication companies in Iran. Telecommunications Policy, 34(4), 221-232. https://doi.org/10.1016/j.telpol.2009.09.003
  • Sadjadi, S. J., Omrani, H., Abdollahzadeh, S., Alinaghian, M. ve Mohammadi, H. (2011). A robust super-efficiency data envelopment analysis model for ranking of provincial gas companies in Iran. Expert Systems with Applications, 38(9), 10875-10881. https://doi.org/10.1016/j.eswa.2011.02.120
  • Said, M. ve Fatima-Zahra, D. (2018). L’impact de la logistique et du transport sur la performance econmique. Laboratoire de Recherche en Management des Organisations (LАREMO), 2, 1-33. https://doi.org/10.48430/IMIST.PRSM/remac-n2.12344
  • Seiford, L. M. ve Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European journal of operational research, 142(1), 16-20. https://doi.org/10.1016/S0377-2217(01)00293-4
  • Selamzade, F., Ersoy, Y., Ozdemir, Y. ve Celik, M. Y. (2023). Health efficiency measurement of OECD countries against the COVID-19 pandemic by using DEA and MCDM methods. Arabian Journal for Science and Engineering, 48(11), 15695-15712. https://doi.org/10.1007/s13369-023-08114-y
  • Senir, G. (2021). Comparison of domestic logistics performances of Turkey and European Union countries in 2018 with an integrated model. LogForum, 17(2), 193-204. http://doi.org/10.17270/J.LOG.2021.576
  • Sezer, S. (2016). Lojistik sektörünün ekonomiye etkisi: OECD ülkeleri üzerine bir uygulama (Yayımlanmamış Doktora Tezi). Anadolu Üniversitesi Sosyal Bilimler Enstitüsü. Eskişehir.
  • Shamsuzzoha, A., Ehrs, M., Addo-Tenkorang, R., Nguyen, D. ve Helo, P. (2013). Performance Evaluation Of Tracking And Tracing For Operations. Int. J. Shipping and Transport Logistics, 5(1), 31-54. https://doi.org/10.1504/IJSTL.2013.050587
  • Shokouhi, A. H., Hatami-Marbini, A., Tavana, M., ve Saati, S. (2010). A robust optimization approach for imprecise data envelopment analysis. Computers & Industrial Engineering, 59(3), 387-397. https://doi.org/10.1016/j.cie.2010.05.011
  • Singh, S. (2023), Logistics Market Research Report Informatiın By Transportation Type (Airways, Waterways, Railways and Roadways), By Logistics Type (First Party, Second Party, and Third Party), By End User (Industrial and Manufacturing, Retail, Healthcare, and Oi l& Gas), And By Region (North America, Europe, Asia-Pacific, and Rest of the World)-Market Forecast Till 2030. https://www.marketresearchfuture.com/reports/logistics-market-5076
  • Sueyoshi, T. (2000). Stochastic DEA for restructure strategy: an application to a Japanese petroleum company. Omega, 28(4), 385-398. https://doi.org/10.1016/S0305-0483(99)00069-9
  • Toloo, M., Tone, K. ve Izadikhah, M. (2023). Selecting slacks-based data envelopment analysis models. European Journal of Operational Research, 308(3), 1302-1318. https://doi.org/10.1016/j.ejor.2022.12.032 Tone, K. (2001). A slacks- based measure of efficiency in data envelopment analysis. European Journal Of Operation Research, 130(3), 498-509. https://doi.org/10.1016/S0377-2217(99)00407-5
  • Tone, K. ve Tsutsui, M. (2009). Application of network DEA model to vertically integrated electric utilities. GRIPS Discussion Papers, pp. 07-03). National Graduate Institute for Policy Studies. https://ideas.repec.org/p/ngi/dpaper/07-03.html
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  • Uludağ, A. S., ve Şahin, E. (2023). Verimlilik analizlerinde tatmin seviyesi tayin edilmemiş EATWIOS mu? OCRA mı?: Sağlık turizmi üzerine bir araştırma. Verimlilik Dergisi, 57(2), 289-312. https://doi.org/10.51551/verimlilik.1155635
  • Ulutaş, A. ve Karaköy, Ç. (2019). An analysis of the logistics performance index of EU countries with an integrated MCDM model. Economics and Business Review, 5(4), 49-69. https://doi.org/10.18559/ebr.2019.4.3
  • Ulutaş, A. ve Karaköy, Ç. (2021). Evaluation of LPI values of transition economies countries with a grey MCDM model. Handbook of research on applied aı for ınternational business and marketing applications içinde (s. 499-511). IGI Global.
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  • Ustalı, N. K. ve Tosun, Ö. (2020). Investigation of logistic performance of G-20 countries using data envelopment analysis and malmquist total factor productivity analysis. Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 7(3), 755-781. https://doi.org/10.30798/makuiibf.792066 van Dyck, G. K. (2015). Assessment of port efficiency in West Africa using data envelopment analysis. American Journal of Industrial and Business Management, 5(04), 208. https://doi.org/10.4236/ajibm.2015.54023
  • Wang, C. N., Dang, T. T. ve Wang, J. W. (2022). A combined data envelopment analysis (DEA) and grey based multiple criteria decision making (G-MCDM) for solar PV power plants site selection: A case study in Vietnam. Energy Reports, 8, 1124-1142. https://doi.org/10.1016/j.egyr.2021.12.045
  • Yıldırım, M. ve Ayvaz, B. (2019). Ülkelerin lojistik performanslarının veri zarflama analizi ile ölçümü. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 18(35), 57-73. https://dergipark.org.tr/tr/pub/ticaretfbd/issue/55970/565306
  • Yıldırım, B. F. ve Adıgüzel Mercangöz, B. (2020). Evaluating the logistics performance of OECD countries by using fuzzy AHP and ARAS-G. Eurasian Economic Review, 10(1), 27-45. https://doi.org/10.1007/s40822-019-00131-3
  • Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X
  • Zheng, Z. (2021). Energy efficiency evaluation model based on DEA-SBM-Malmquist index. Energy Reports, 7, 397-409. https://doi.org/10.1016/j.egyr.2021.10.020
Toplam 79 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Sosyolojide Niceliksel Yöntemler
Bölüm Araştırma Makaleleri
Yazarlar

Pembe Güçlü 0000-0003-0395-7433

Mohamed Oudoum Mohamed 0000-0002-2643-1613

Yayımlanma Tarihi 30 Kasım 2024
Gönderilme Tarihi 4 Haziran 2024
Kabul Tarihi 24 Eylül 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 8 Sayı: 2

Kaynak Göster

APA Güçlü, P., & Oudoum Mohamed, M. (2024). MEREC, Veri Zarflama Analizi ve EATWIOS Yöntemlerinin Hibrit Kullanımı ile Afrika Ülkelerinin Lojistik Performanslarının Değerlendirilmesi. Sinop Üniversitesi Sosyal Bilimler Dergisi, 8(2), 1033-1071. https://doi.org/10.30561/sinopusd.1495650

                                                 

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