Vol. 12 No. 1 (2024): Business & Management Studies: An International Journal
Articles

Measuring the logistics performance of G20 countries with a hybrid MCDM model

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  • Ebru Acar Akbulut,
  • Alptekin Ulutaş,
  • Ali Aygün Yürüyen,
  • Saime Balalan
Ebru Acar Akbulut
Master of Education, Sivas, Türkiye
Alptekin Ulutaş
Assoc. Prof., İnönü Üniversitesi, Malatya, Türkiye
Ali Aygün Yürüyen
Lect.., Ardahan Üniversitesi, Ardahan, Türkiye
Saime Balalan
Master of Education, İnönü Üniversitesi, Malatya, Türkiye
DOI: https://doi.org/10.15295/bmij.v12i1.2300

Published 2024-03-25

Keywords

  • Lojistik Performans, ÇKKV, LPI, SD, PSI, MEREC, MARA
  • Logistics Performance, MCDM, LPI, SD, PSI, MEREC, MARA

How to Cite

Acar Akbulut, E., Ulutaş, A., Yürüyen, A. A., & Balalan, S. (2024). Measuring the logistics performance of G20 countries with a hybrid MCDM model . Business & Management Studies: An International Journal, 12(1), 1–21. https://doi.org/10.15295/bmij.v12i1.2300

Copyright (c) 2024 Ebru Acar Akbulut- Alptekin Ulutaş- Ali Aygün Yürüyen- Saime Balalan

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

Logistics is a system for creating a management decision to store a product or strategically control its flows within a certain plan to maximize investment profit. The Logistics Performance Index (LPI) is very important for countries to determine the situation in the field of logistics and to determine the areas that need improvement. This article evaluates the logistics performance of G20 countries with Multi-criteria decision-making (MCDM) methods. The data utilised in the study were taken from the LPI report published by the World Bank in 2018. MCDM methods can be used in any field where performance measurement and ranking are needed to select the best-performing alternative or the desired target. The MCDM methods used in the study have not been used together in any previous study. Therefore, this study is original. The study used three different methods (SD, PSI and MEREC) to evaluate the criteria weights. It is thought that more precise and reliable results can be obtained using these three methods together.

In the study, the MARA (Magnitude of the Area for the Ranking of Alternatives) method, seldom used in the literature, was used to rank the alternatives. There are no Turkish publications in the literature using the MARA method. The study aims to fill this gap in the literature. Germany was determined as the best-performing alternative in line with the methods applied and the results acquired.

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References

  1. Altıntaş, F.F. (2021). “Avrupa Birliği Ülkelerinin Lojistik Performanslarının Critic Tabanlı Waspas ve Copras Teknikleri ile Analizi”, Türkiye Sosyal Araştırmalar Dergisi, Yıl 25, Sayı:1 – Nisan 2021
  2. Andrejic, M., & Kilibarda, M. (2014). Global Logistics Efficiency Index. Center for Quality.
  3. Arvis, J. F., Ojala, L., Wiederer, C., Shepherd, B., Raj, A., Dairabayeva, K., & Kiiski, T. (2018). Connecting to compete 2018.
  4. Ayaydın, H., Durmuş, S., & Pala, F. (2017). Gri İlişkisel Analiz Yöntemiyle Türk Lojistik Firmalarında Performans Ölçümü. Gümüshane University Electronic Journal of the Institute Of Social Science/Gümüshane Üniversitesi Sosyal Bilimler Enstitüsü Elektronik Dergisi, 8(21).
  5. Aydın, Y. (2021). Bütünleşik Bir ÇKKV Modeli ile Sigorta Şirketlerinin Piyasa Performansının Analizi. Uluslararası İktisadi ve İdari İncelemeler Dergisi, (32), 53-66.
  6. Bayır, T., & Yılmaz, Z. (2017). AB Ülkelerinin Lojistik Performans Endekslerinin AHP ve Vikor Yöntemleri ile Değerlendirilmesi. Middle East Journal of Educations (meje), 3(2), 73-92.
  7. Bayraktutan, Y., & Özbilgin, M. (2015). Lojistik maliyetler ve lojistik performans ölçütleri. Maliye Araştırmaları Dergisi, 1(2), 95-112.
  8. Boakai, S., & Samanlioglu, F. (2023). An MCDM approach to third party logistics provider selection. International Journal of Logistics Systems and Management, 44(3), 283-299.
  9. Çakır, S. (2017). Measuring logistics performance of OECD countries via fuzzy linear regression. Journal of Multi‐Criteria Decision Analysis, 24(3-4), 177-186.
  10. Çalık, A., Erdebilli, B., & Özdemir, Y. S. (2022). Novel Integrated Hybrid Multi-Criteria Decision-Making Approach for Logistics Performance Index. Transportation Research Record, 03611981221113314.
  11. Demir, G., (2022). Hayat Dışı Sigorta Sektöründe Kurumsal Performansın PSI-SD Tabanlı MABAC Metodu ile Ölçülmesi: Anadolu Sigorta Örneği. Ekonomi Politika ve Finans Araştırmaları Dergisi, 7(1), 112-136.
  12. Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research, 22(7), 763-770.
  13. Dünya Bankası (2022). International LPI. https://lpi.worldbank.org/.
  14. Ersoy, N. (2022). OECD ve AB Üyesi Ülkelerin İnovasyon Performanslarının MEREC-MARCOS Bütünleşik Modeli ile Ölçümü. Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 24(3), 1039-1063.
  15. Gligorić, M., Gligorić, Z., Lutovac, S., Negovanović, M., & Langović, Z. (2022). Novel Hybrid MPSI–MARA Decision-Making Model for Support System Selection in an Underground Mine. Systems, 10(6), 248.
  16. Goswami, S. S., Mohanty, S. K., & Behera, D. K. (2022). Selection of a green renewable energy source in India with the help of MEREC integrated PIV MCDM tool.Materials today: proceedings,52, 1153-1160.
  17. Karakaş, A. (2022). Sürdürülebilir ve yılmaz tedarikçi seçimi probleminin MEREC tabanlı MAIRCA yöntemi ile çözümü. (Yayımlanmamış Yüksek Lisans Tezi). Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Enstitüsü, Eskişehir, Türkiye.
  18. Keshavarz-Ghorabaee, M. (2021). Assessment of distribution center locations using a multi-expert subjective–objective decision-making approach. Scientific Reports, 11(1), 19461.
  19. 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.
  20. Khorshidi, R., & Hassani, A. (2013). Comparative Analysis Between TOPSIS and PSI Methods of Materials Selection to Achieve a Desirable Combination of Strength and Workability in Al/SiC Composite. Materials and Design, 52, 999-1010.
  21. Krishankumar, R., Saranya, R., Nethra, R. P., Ravichandran, K. S., & Kar, S. (2019). A decision-making framework under probabilistic linguistic term set for multi-criteria group decision-making problem. Journal of Intelligent & Fuzzy Systems, 36(6), 5783-5795.
  22. Maniya, K., & Bhatt, M. G. (2010). A selection of material using a novel type decision-making method: Preference selection index method. Materials & Design, 31(4), 1785-1789.
  23. Maniya, K. D., & Bhatt, M. G. (2011). An alternative multiple attribute decision making methodology for solving optimal facility layout design selection problems. Computers & Industrial Engineering, 61(3), 542-549.
  24. 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.
  25. Nguyen, N. A. T., Wang, C. N., Dang, L. T. H., Dang, L. T. T., & Dang, T. T. (2022). Selection of cold chain logistics service providers based on a grey AHP and grey COPRAS framework: a case study in Vietnam. Axioms, 11(4), 154
  26. Oğuz, S., Alkan, G., & Yılmaz, B. (2019). ''Seçilmiş Asya Ülkelerinin Lojistik Performanslarının TOPSIS Yöntemi ile Değerlendirilmesi'', IBAD Sosyal Bilimler Dergisi, Özel Sayı, 497-507.
  27. Qiu, L. M., Sun, L. F., Liu, X. J., & Zhang, S. Y. (2013). Material selection combined with optimal structural design for mechanical parts. Journal of Zhejiang University SCIENCE A, 14(6), 383-392.
  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.
  29. Pamucar, D., Žižović, M., Biswas, S., & Božanić, D. (2021). A new logarithm methodology of additive weights (LMAW) for multi-criteria decision-making: Application in logistics.
  30. Parashkevova, L. (2007). Logistics Outsourcing-a Means of Assuring the Competitive Advantage for an Organization. Vadyba / Management, 15 (2). 29-38
  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.
  32. 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.
  33. Sarı, E. B. (2019). Measuring The performances of the machines via Preference Selection Index (PSI) method and comparing them with values of Overall Equipment Efficiency (OEE). İzmir İktisat Dergisi, 34(4), 573-581.
  34. Şen, İ. K. (2014). Lojistik faaliyetlerin yönetimi ve maliyetleme yaklaşımları. Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 4(1), 83-106.
  35. Stević, Ž., Tanackov, I., Puška, A., Jovanov, G., Vasiljević, J., & Lojaničić, D. (2021). Development of modified SERVQUAL–MCDM model for quality determination in reverse logistics. Sustainability, 13(10), 5734.
  36. Uca, N., Civelek, M. E., & Çemberci, M. (2015). Lojistik Performans Endeksi Bileşenlerinin Gayri Safi Milli Hasıla Üzerine Etkisi: Kavramsal Model Önerisi (The Effect of the Components of Logistics Performance Index on Gross Domestic Product: Conceptual Model Proposal). Eurasian Business & Economics Journal,
  37. Ulutaş, A. (2018). Entropi Tabanlı EDAS Yöntemi ile Lojistik Firmalarının Performans Analizi. Uluslararası İktisadi ve İdari İncelemeler Dergisi, (23), 53-66.
  38. Ulutaş, A., & Karaköy, Ç. (2019). G-20 Ülkelerinin Lojistik Performans Endeksinin Çok Kriterli Karar Verme Modeli ile Ölçümü. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, 20(2), 71-84.
  39. Ulutaş, A., & Karaköy, Ç. (2021). Evaluation of LPI values of transition economies countries with a grey MCDM model. In Handbook of Research on Applied AI for International Business and Marketing Applications (pp. 499-511). IGI Global.
  40. Ulutaş, A. (2020). Stacker selection with PSI and WEDBA Methods. International Journal of Contemporary Economics and Administrative Sciences, 10(2), 493-504.
  41. Ulutaş, A., Stanujkic, D., Karabasevic, D., Popovic, G., & Novaković, S. (2022). Pallet truck selection with MEREC and WISP-S methods. Strategic Management-International Journal of Strategic Management and Decision Support Systems in Strategic Management.
  42. Wen, Z., Liao, H., Kazimieras Zavadskas, E., & Al-Barakati, A. (2019). Selection third-party logistics service providers in supply chain finance by a hesitant fuzzy linguistic combined compromise solution method. Economic research-Ekonomska istraživanja, 32(1), 4033-4058.
  43. Wong, W.P., Tang, C.F., (2017), “The Major Determinants Of Logistic Performance In A Global Perspective: Evidence From Panel Data Analysis”, International Journal of Logistics Research and Applications, 21(4), 431-443.
  44. Vahdani, B., Mousavi, S. M., & Tavakkoli-Moghaddam, R. (2011). Group decision making based on novel fuzzy modified TOPSIS method. Applied Mathematical Modelling, 35(9), 4257-4269.
  45. Yalçin, B., & Ayvaz, B. (2020). Çok Kriterli Karar Verme Teknikleri ile Lojistik Performansın Değerlendirilmesi. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 19(38), 117-138.
  46. Yıldırım, B. F., & 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.
  47. Yürüyen, A. A., & Ulutaş, A. (2020). Bulanık AHP ve bulanık EDAS yöntemleri ile üçüncü parti lojistik firması seçimi. Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi, 8(İktisadi ve İdari Bilimler), 283-294. https://doi.org/10.18506/anemon.767354
  48. 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. https://doi.org/10.15295/bmij.v11i3.2245