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OECD ÜLKELERININ LOJISTIK PERFORMANS ENDEKSININ HIBRIT ÇKKV YÖNTEMLERINE GÖRE DEĞERLENDIRILMESI

Yıl 2025, Cilt: 47 Sayı: 1, 47 - 76, 14.04.2025
https://doi.org/10.14780/muiibd.1469898

Öz

Bu çalışma, entegre MCDM yöntemlerini kullanarak OECD ülkelerinin lojistik performans endeksini (LPI) değerlendirmeyi amaçlamaktadır. Bu çalışma için veriler “Rekabete Bağlanmak 2023 - LPI” raporundan elde edilmiştir. İlk olarak, kriterlerin ağırlıkları SD, CRITIC, LOPCOW ve MEREC gibi çeşitli yöntemlerle belirlenmiştir. Daha sonra, farklı yöntemlerden elde edilen kriter ağırlıkları, Toplu Ağırlıklandırma Yöntemi (AWM) ile birleştirilmiştir. OECD ülkelerinin LPI'leri CRADIS yöntemi kullanılarak sıralanmıştır. AWM sonuçları, izleme ve takip ile lojistik yeterliliği ve kalitenin sırasıyla en önemli ve en az önemli kriterler olduğunu göstermiştir. CRADIS yönteminin sonuçları Finlandiya'nın OECD ülkeleri arasında en iyi lojistik performansına sahip ülke olduğunu, Kosta Rika'nın ise en kötü lojistik performansına sahip olduğunu ortaya koymuştur. Buna ilaveten, önerilen modelin sağlamlığı ve geçerliliği duyarlılık analizi ve karşılaştırmalı analizle test edilmiştir.

Kaynakça

  • Arıkan Kargı, V. S. (2022). Evaluation of Logistics Performance of The OECD Member Countries with Integrated ENTROPY and WASPAS Method. Journal of Management and Economics, 29(4), 801-811.
  • Arvis, J. F., Ojala, L., Shepherd, B., Ulybina, D., & Wiederer, C. (2023). Connecting to Compete 2023: Trade Logistics in an Uncertain Global Economy-The Logistics Performance Index and Its Indicators. The World Bank. https://lpi.worldbank.org/international/global
  • Beysenbaev, R., & Dus, Y. (2020). Proposals for improving the logistics performance index. The Asian Journal of Shipping and Logistics, 36(1), 34-42.
  • Biswas, S., & Anand, O. P. (2020). Logistics Competitiveness Index-Based Comparison of BRICS and G7 Countries: An Integrated PSI-PIV Approach. IUP Journal of Supply Chain Management, 17(2).
  • Çakır, S. (2017). Measuring logistics performance of OECD countries via fuzzy linear regression. Journal of Multi‐Criteria Decision Analysis, 24(3-4), 177-186.
  • Çalık, A., Erdebilli, B., & Özdemir, Y. S. (2023). Novel Integrated Hybrid Multi-Criteria Decision-Making Approach for Logistics Performance Index. Transportation Research Record, 2677(2), 1392-1400.
  • Das, P. P., & Chakraborty, S. (2023). A comparative assessment of multicriteria parametric optimization methods for plasma arc cutting processes. Decision Analytics Journal, 6, 100190.
  • Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research, 22(7), 763-770.
  • Ecer, F., & Pamucar, D. (2022). A novel LOPCOW‐DOBI multi‐criteria sustainability performance assessment methodology: An application in developing country banking sector. Omega, 112, 102690.
  • Göçer, A., Özpeynirci, Ö., & Semiz, M. (2022). Logistics performance index-driven policy development: An application to Turkey. Transport policy, 124, 20-32.
  • Gogoneata, B. (2008). An analysis of explanatory factors of logistics performance of a country. The Amfiteatru Economic Journal, 10(24), 143-156.
  • Gök Kısa, C. ve Ayçin, E. (2019). OECD ülkelerinin lojistik performanslarının SWARA tabanlı EDAS yöntemi ile değerlendirilmesi. Çankırı Karatekin Üniversitesi İİBF Dergisi. 9 (1), 301-325.
  • Gürler, H. E., Özçalıcı, M., & Pamucar, D. (2024). Determining criteria weights with genetic algorithms for multi-criteria decision making methods: The case of logistics performance index rankings of European Union countries. Socio-Economic Planning Sciences, 91, 101758.
  • Isik, O., Aydin, Y., & 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.
  • Kara, K., Bentyn, Z., & Yalçın, G. C. (2022). Determining the logistics market performance of developing countries by entropy and MABAC methods. LogForum, 18(4).
  • Keleş, N. (2023). Lopcow ve Cradis yöntemleriyle G7 ülkelerinin ve Türkiye’nin yaşanabilir güç merkezi şehirlerinin değerlendirilmesi. Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 16(3), 727-747.
  • Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2021). Determination of objective weights using a new method based on the removal effects of criteria (MEREC). Symmetry, 13(4), 525.
  • 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 in international trade. Applied economics, 46(24), 2982-2992.
  • Mešić, A., Miškić, S., Stević, Ž., & Mastilo, Z. (2022). Hybrid MCDM solutions for evaluation of the logistics performance index of the Western Balkan countries. Economics, 10(1), 13-34.
  • Miškić, S., Stević, Ž., Tadić, S., Alkhayyat, A., & 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.
  • Oğuz, S., Alkan, G., & Yılmaz, B. (2019). Evaluation of Logistics Performance of Selected Asian Countries’ by TOPSIS Method. IBAD Journal of Social Sciences,(Special Issue), 497-507.
  • Orhan, M. (2019). Türkiye ile Avrupa Birliği ülkelerinin lojistik performanslarının Entropi ağırlıklı EDAS yöntemiyle karşılaştırılması. Avrupa Bilim ve Teknoloji Dergisi, (17), 1222-1238.
  • Özbek, H. E., & Özekenci, E. K. (2023). Investigation of Digital Logistics Market Performance in Developing Countries with Hybrid MCDM Methods. JOEEP: Journal of Emerging Economies and Policy, 8(2), 559- 576.
  • Özekenci, E. K. (2023). Assessing the Logistics Market Performance of Developing Countries By SWARACRITIC Based CoCoSo Methods. LogForum, 19(3).
  • Özekenci, E. K. (2024). Assessment of the Logistics Performance Index of OPEC Countries with ENTROPY, CRITIC and LOPCOW-based EDAS Methods. Journal of Transportation and Logistics, 9(2), 260-279.
  • Pamučar, D. S., Božanić, D., & Ranđelović, A. (2017). Multi-criteria decision making: An example of sensitivity analysis. Serbian journal of management, 12(1), 1-27.
  • Puška, A., Stević, Ž., & Pamučar, D. (2021). Evaluation and selection of healthcare waste incinerators using extended sustainability criteria and multi-criteria analysis methods. Environment, Development and Sustainability, 1-31.
  • Rao, R. V., & Patel, B. K. (2010). A subjective and objective integrated multiple attribute decision making method for material selection. Materials & Design, 31(10), 4738-4747.
  • Rao, R. V., Patel, B. K., & Parnichkun, M. (2011). Industrial robot selection using a novel decision making method considering objective and subjective preferences. Robotics and Autonomous Systems, 59(6), 367- 375.
  • Rashidi, K., & Cullinane, K. (2019). Evaluating the sustainability of national logistics performance using Data Envelopment Analysis. Transport Policy, 74, 35-46.
  • Rezaei, J., van Roekel, W. S., & Tavasszy, L. (2018). Measuring the relative importance of the logistics performance index indicators using Best Worst Method. Transport Policy, 68, 158-169.
  • Ulutaş, A., & Karaköy, Ç. (2019a). The Measurement of Logistics Performance Index of G-20 Countries with Multi-Criteria Decision-Making Model. Cumhuriyet University Journal of Economic and Administrative Sciences, 20(2), 71-84.
  • Ulutaş, A., & Karaköy, Ç. (2019b). An analysis of the logistics performance index of EU countries with an integrated MCDM model. Economics and Business Review, 5(4), 49-69.
  • Yalçin, B., & Ayvaz, B. (2020). Çok Kriterli Karar Verme Teknikleri ile Lojistik Performansin Değerlendirilmesi. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 19(38), 117-138.
  • Yildirim, B. F., & Adiguzel Mercangoz, B. (2020). Evaluating the logistics performance of OECD countries by using fuzzy AHP and ARAS-G. Eurasian Economic Review, 10(1), 27-45.
  • Yuan, J., Chen, Z., & Wu, M. (2023). A Novel Distance Measure and CRADIS Method in Picture Fuzzy Environment. International Journal of Computational Intelligence Systems, 16(1), 1-16.
  • Yürüyen, A. A., Ulutaş, A., & Özdağoğlu, A. (2023). Lojistik işletmelerinin performansının bir hibrit ÇKKV modeli ile değerlendirilmesi. Business & Management Studies: An International Journal, 11(3), 731-751.

EVALUATION OF THE LOGISTICS PERFORMANCE INDEX OF OECD COUNTRIES BASED ON HYBRID MCDM METHODS

Yıl 2025, Cilt: 47 Sayı: 1, 47 - 76, 14.04.2025
https://doi.org/10.14780/muiibd.1469898

Öz

This study aims to assess the logistics performance index (LPI) of OECD countries using the integrated MCDM methods. For this investigation, the data was obtained from the “Connecting to Compete 2023 - LPI” report. At first, the weight of criteria was determined by several methods such as SD, CRITIC, LOPCOW and MEREC. Then, criteria weights obtained from different methods were combined with the Aggregate Weighting Method (AWM). The LPI of OECD countries were ranked using the CRADIS method. The results of the AWM showed that tracking and tracing, and logistics competence and quality were the most and least important criteria, respectively. The results of the CRADIS method revealed that Finland was the best-ranked logistics performance, while Costa Rica was the worst-ranked logistics performance in the OECD countries. Additionally, the robustness and validity of the proposed model was tested by sensitivity analysis and comparative analysis.

Kaynakça

  • Arıkan Kargı, V. S. (2022). Evaluation of Logistics Performance of The OECD Member Countries with Integrated ENTROPY and WASPAS Method. Journal of Management and Economics, 29(4), 801-811.
  • Arvis, J. F., Ojala, L., Shepherd, B., Ulybina, D., & Wiederer, C. (2023). Connecting to Compete 2023: Trade Logistics in an Uncertain Global Economy-The Logistics Performance Index and Its Indicators. The World Bank. https://lpi.worldbank.org/international/global
  • Beysenbaev, R., & Dus, Y. (2020). Proposals for improving the logistics performance index. The Asian Journal of Shipping and Logistics, 36(1), 34-42.
  • Biswas, S., & Anand, O. P. (2020). Logistics Competitiveness Index-Based Comparison of BRICS and G7 Countries: An Integrated PSI-PIV Approach. IUP Journal of Supply Chain Management, 17(2).
  • Çakır, S. (2017). Measuring logistics performance of OECD countries via fuzzy linear regression. Journal of Multi‐Criteria Decision Analysis, 24(3-4), 177-186.
  • Çalık, A., Erdebilli, B., & Özdemir, Y. S. (2023). Novel Integrated Hybrid Multi-Criteria Decision-Making Approach for Logistics Performance Index. Transportation Research Record, 2677(2), 1392-1400.
  • Das, P. P., & Chakraborty, S. (2023). A comparative assessment of multicriteria parametric optimization methods for plasma arc cutting processes. Decision Analytics Journal, 6, 100190.
  • Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research, 22(7), 763-770.
  • Ecer, F., & Pamucar, D. (2022). A novel LOPCOW‐DOBI multi‐criteria sustainability performance assessment methodology: An application in developing country banking sector. Omega, 112, 102690.
  • Göçer, A., Özpeynirci, Ö., & Semiz, M. (2022). Logistics performance index-driven policy development: An application to Turkey. Transport policy, 124, 20-32.
  • Gogoneata, B. (2008). An analysis of explanatory factors of logistics performance of a country. The Amfiteatru Economic Journal, 10(24), 143-156.
  • Gök Kısa, C. ve Ayçin, E. (2019). OECD ülkelerinin lojistik performanslarının SWARA tabanlı EDAS yöntemi ile değerlendirilmesi. Çankırı Karatekin Üniversitesi İİBF Dergisi. 9 (1), 301-325.
  • Gürler, H. E., Özçalıcı, M., & Pamucar, D. (2024). Determining criteria weights with genetic algorithms for multi-criteria decision making methods: The case of logistics performance index rankings of European Union countries. Socio-Economic Planning Sciences, 91, 101758.
  • Isik, O., Aydin, Y., & 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.
  • Kara, K., Bentyn, Z., & Yalçın, G. C. (2022). Determining the logistics market performance of developing countries by entropy and MABAC methods. LogForum, 18(4).
  • Keleş, N. (2023). Lopcow ve Cradis yöntemleriyle G7 ülkelerinin ve Türkiye’nin yaşanabilir güç merkezi şehirlerinin değerlendirilmesi. Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 16(3), 727-747.
  • Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2021). Determination of objective weights using a new method based on the removal effects of criteria (MEREC). Symmetry, 13(4), 525.
  • 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 in international trade. Applied economics, 46(24), 2982-2992.
  • Mešić, A., Miškić, S., Stević, Ž., & Mastilo, Z. (2022). Hybrid MCDM solutions for evaluation of the logistics performance index of the Western Balkan countries. Economics, 10(1), 13-34.
  • Miškić, S., Stević, Ž., Tadić, S., Alkhayyat, A., & 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.
  • Oğuz, S., Alkan, G., & Yılmaz, B. (2019). Evaluation of Logistics Performance of Selected Asian Countries’ by TOPSIS Method. IBAD Journal of Social Sciences,(Special Issue), 497-507.
  • Orhan, M. (2019). Türkiye ile Avrupa Birliği ülkelerinin lojistik performanslarının Entropi ağırlıklı EDAS yöntemiyle karşılaştırılması. Avrupa Bilim ve Teknoloji Dergisi, (17), 1222-1238.
  • Özbek, H. E., & Özekenci, E. K. (2023). Investigation of Digital Logistics Market Performance in Developing Countries with Hybrid MCDM Methods. JOEEP: Journal of Emerging Economies and Policy, 8(2), 559- 576.
  • Özekenci, E. K. (2023). Assessing the Logistics Market Performance of Developing Countries By SWARACRITIC Based CoCoSo Methods. LogForum, 19(3).
  • Özekenci, E. K. (2024). Assessment of the Logistics Performance Index of OPEC Countries with ENTROPY, CRITIC and LOPCOW-based EDAS Methods. Journal of Transportation and Logistics, 9(2), 260-279.
  • Pamučar, D. S., Božanić, D., & Ranđelović, A. (2017). Multi-criteria decision making: An example of sensitivity analysis. Serbian journal of management, 12(1), 1-27.
  • Puška, A., Stević, Ž., & Pamučar, D. (2021). Evaluation and selection of healthcare waste incinerators using extended sustainability criteria and multi-criteria analysis methods. Environment, Development and Sustainability, 1-31.
  • Rao, R. V., & Patel, B. K. (2010). A subjective and objective integrated multiple attribute decision making method for material selection. Materials & Design, 31(10), 4738-4747.
  • Rao, R. V., Patel, B. K., & Parnichkun, M. (2011). Industrial robot selection using a novel decision making method considering objective and subjective preferences. Robotics and Autonomous Systems, 59(6), 367- 375.
  • Rashidi, K., & Cullinane, K. (2019). Evaluating the sustainability of national logistics performance using Data Envelopment Analysis. Transport Policy, 74, 35-46.
  • Rezaei, J., van Roekel, W. S., & Tavasszy, L. (2018). Measuring the relative importance of the logistics performance index indicators using Best Worst Method. Transport Policy, 68, 158-169.
  • Ulutaş, A., & Karaköy, Ç. (2019a). The Measurement of Logistics Performance Index of G-20 Countries with Multi-Criteria Decision-Making Model. Cumhuriyet University Journal of Economic and Administrative Sciences, 20(2), 71-84.
  • Ulutaş, A., & Karaköy, Ç. (2019b). An analysis of the logistics performance index of EU countries with an integrated MCDM model. Economics and Business Review, 5(4), 49-69.
  • Yalçin, B., & Ayvaz, B. (2020). Çok Kriterli Karar Verme Teknikleri ile Lojistik Performansin Değerlendirilmesi. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 19(38), 117-138.
  • Yildirim, B. F., & Adiguzel Mercangoz, B. (2020). Evaluating the logistics performance of OECD countries by using fuzzy AHP and ARAS-G. Eurasian Economic Review, 10(1), 27-45.
  • Yuan, J., Chen, Z., & Wu, M. (2023). A Novel Distance Measure and CRADIS Method in Picture Fuzzy Environment. International Journal of Computational Intelligence Systems, 16(1), 1-16.
  • Yürüyen, A. A., Ulutaş, A., & Özdağoğlu, A. (2023). Lojistik işletmelerinin performansının bir hibrit ÇKKV modeli ile değerlendirilmesi. Business & Management Studies: An International Journal, 11(3), 731-751.
Toplam 38 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İşletme
Bölüm Makaleler
Yazarlar

Emre Kadir Özekenci 0000-0001-6669-0006

Yayımlanma Tarihi 14 Nisan 2025
Gönderilme Tarihi 17 Nisan 2024
Kabul Tarihi 11 Şubat 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 47 Sayı: 1

Kaynak Göster

APA Özekenci, E. K. (2025). EVALUATION OF THE LOGISTICS PERFORMANCE INDEX OF OECD COUNTRIES BASED ON HYBRID MCDM METHODS. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi, 47(1), 47-76. https://doi.org/10.14780/muiibd.1469898
AMA Özekenci EK. EVALUATION OF THE LOGISTICS PERFORMANCE INDEX OF OECD COUNTRIES BASED ON HYBRID MCDM METHODS. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi. Nisan 2025;47(1):47-76. doi:10.14780/muiibd.1469898
Chicago Özekenci, Emre Kadir. “EVALUATION OF THE LOGISTICS PERFORMANCE INDEX OF OECD COUNTRIES BASED ON HYBRID MCDM METHODS”. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi 47, sy. 1 (Nisan 2025): 47-76. https://doi.org/10.14780/muiibd.1469898.
EndNote Özekenci EK (01 Nisan 2025) EVALUATION OF THE LOGISTICS PERFORMANCE INDEX OF OECD COUNTRIES BASED ON HYBRID MCDM METHODS. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi 47 1 47–76.
IEEE E. K. Özekenci, “EVALUATION OF THE LOGISTICS PERFORMANCE INDEX OF OECD COUNTRIES BASED ON HYBRID MCDM METHODS”, Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi, c. 47, sy. 1, ss. 47–76, 2025, doi: 10.14780/muiibd.1469898.
ISNAD Özekenci, Emre Kadir. “EVALUATION OF THE LOGISTICS PERFORMANCE INDEX OF OECD COUNTRIES BASED ON HYBRID MCDM METHODS”. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi 47/1 (Nisan2025), 47-76. https://doi.org/10.14780/muiibd.1469898.
JAMA Özekenci EK. EVALUATION OF THE LOGISTICS PERFORMANCE INDEX OF OECD COUNTRIES BASED ON HYBRID MCDM METHODS. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi. 2025;47:47–76.
MLA Özekenci, Emre Kadir. “EVALUATION OF THE LOGISTICS PERFORMANCE INDEX OF OECD COUNTRIES BASED ON HYBRID MCDM METHODS”. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi, c. 47, sy. 1, 2025, ss. 47-76, doi:10.14780/muiibd.1469898.
Vancouver Özekenci EK. EVALUATION OF THE LOGISTICS PERFORMANCE INDEX OF OECD COUNTRIES BASED ON HYBRID MCDM METHODS. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi. 2025;47(1):47-76.