The assessment of the logistics performance index of CEE countries with the new combination of SV and MABAC methods

• Autorzy: Isik Ozcan , Aydin Yuksel , Kosaroglu Serife Merve

Identyfikatory

DOI

10.17270/J.LOG.2020.504

Warianty tytułu

PL

Oszacowanie współczynnika działalności logistyczenej krajów Europy Środkowo-Wschodniej za pomocą nowej kombinacji metod SV oraz MABAC

• Języki publikacji: EN

Abstrakty

EN

Background: The increase in global trade has caused logistics activities to be an important tool in providing strategic competitive advantage on a global scale. The logistics industry, which helps to facilitate the activities related to the movement of goods in the supply chain, is one of the fastest-growing sectors and has important effects on the economic performance of the countries. Measuring and evaluating the logistics performance of countries can enable them to reach their goals of achieving sustainable competitive advantage by revealing the strengths and weaknesses of logistics services in the entire supply chain. In this regard, the purpose of this study is to analyze and rank logistics performance in terms of selected 11 Central and Eastern European Countries (CEECs). Methods: In this study, the SV (Statistical Variance) and the MABAC (Multi-Attributive Border Approximation area Comparison) methods are used to form a decision-making model in evaluating the logistic performance. In logistics performance evaluation, the SV method is used to weight the selected performance criteria, whereas the MABAC method is employed to evaluate and rank the logistics performance of CEECs. Results: The results obtained from the SV method demonstrates that timeliness and infrastructure are the most and least significant performance criteria, respectively. According to the performance ranking of the countries by the MABAC method, the countries in the top three rankings are the Czech Republic, Poland and Hungary, respectively. Conclusions: The fact that the ranking of the proposed hybrid model is the same as the original logistics performance index (LPI) ranking of the selected countries suggests that the proposed model is consistent.

PL

Wstęp: Wzrost globalnego handlu jest przyczyną wzrostu ważności działalności logistycznej jako narzędzia służącego do uzyskiwania przewagi konkurencyjnej na globalną skalę. Branża logistyczna, która wspomaga wszelkie czynności związane z przepływem towarów w obrębie łańcucha dostaw, jest jednym z najszybciej rosnących sektorów i ma istotny wpływ na ekonomiczne wyniki krajów. Pomiar oraz ocena sprawności logistycznej krajów umożliwia im osiągnięcie postawionych celów w uzyskaniu zrównoważonej przewagi konkurencyjnej poprzez ujawnienie słabych i mocnych stron swoich usług logistycznych w obrębie całego łańcucha dostaw. Celem pracy jest analiza i stworzenie rankingu działalności logistycznej wybranych 11 krajów Europy Środkowo-Wschodniej. Metody: W pracy zastosowano metody SV (Statistical Variance) oraz MABAC (Multi-Attributive Border Approximation area Comparison) dla zbudowania modelu podejmowania decyzji odnośnie oceny działalności logistycznej. Dla oceny działalności logistycznej, metoda SV została zastosowana do wyznaczenia wagi poszczególnych kryteriów oceny, podczas gdy metoda MABAC została używana do oceny i tworzenia rankingu działalności logisty stycznej krajów Europy Środkowo-Wschodniej. Wyniki: Wyniki uzyskane przy użyciu metody SV pokazują, że terminowość oraz infrastruktura jest najważniejszymi kryteriami oceny działalności. Zgodnie ze stworzonym rankingiem przy pomocy metody MABAC, najwyżej ocenionymi krajami były: Czechy, Polska i Węgry. Wnioski: Ranking uzyskany za pomocą opracowanej metody jest taki sam jak przy użyciu oryginalnego współczynnika działalności logistycznej (LPI), co dowodzi poprawności wypracowanego modelu.

Słowa kluczowe

EN

SV; MABAC; logistics performance; CEECs; multi criteria decision making

PL

SV; MABAC; działalność logistyczna; kraje Europy Środkowo-Wschodniej; wielokryterialne podejmowanie decyzji

• Wydawca: Wyższa Szkoła Logistyki
• Czasopismo: LogForum
• Rocznik: 2020
• Tom: Vol. 16, no. 4
• Strony: 549--559
• Opis fizyczny: Bibliogr. 30 poz., tab.

Twórcy

autor

Isik Ozcan

• Sivas Cumhuriyet University, Zara Veysel Dursun School of Applied Sciences, Department of Banking and Finance, Sivas 58700, Turkey

autor

Aydin Yuksel

• Sivas Cumhuriyet University, Faculty of Economics and Administrative Sciences, Department of Business Administration, Sivas 58140, Turkey

autor

Kosaroglu Serife Merve

• Sivas Cumhuriyet University, Cumhuriyet Social Sciences Vocational School, Department of Finance, Banking and Insurance, Sivas 58140, Turkey

Bibliografia

• 1. Bayır T., Yılmaz Z., 2017. Assesment of Logistic Performance Indexes of EU Countries by AHP and VIKOR Methods. Middle East Journal of Education, 3 (2), 73-92.
• 2. Biswas T. K., Das M. C., 2019. Selection of commercially available electric vehicle using fuzzy AHP-MABAC. Journal of The Institution of Engineers (India): Series C, 100 (3), 531-537, http://doi.org/10.1007/s40032-018-0481-3
• 3. Božanić D. A., Pamučar D. S., Karović S. M., 2016. Use of the fuzzy AHP-MABAC hybrid model in ranking potential locations for preparing laying-up positions, Vojnotehnički Glasnik / Military Technical Courier, 64 (3), 705-729, http://doi.org/10.5937/vojtehg64-9261
• 4. Çakır S., 2017. Measuring logistics performance of OECD countries via fuzzy linear regression. Journal of Multi‐Criteria Decision Analysis, 24 (3-4), 177-186, http://doi.org/10.1002/mcda.1601
• 5. Candan G., 2019. Lojistik Performans Değerlendirmesi İçin Bulanık AHP ve Gri İlişkisel Analiz Yöntemleri İle Bütünleşik Bir Yaklaşım / Integrated Approach of Fuzzy AHP and Grey Relational Analysis for Logistic Performance Evaluation. Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi / Journal of Social Sciences of Mus Alparslan University, 7 (5), 277-286, http://doi.org/10.18506/anemon.506769
• 6. Erkan B., 2014. The importance and determinants of logistics performance of selected countries. Journal of Emerging Issues in Economics, Finance and Banking, 3 (6), 1237-1254.
• 7. Gülençer İ., Türkoğlu S.P., 2020, Gelişmekte Olan Asya ve Avrupa Ülkelerinin Finansal Gelişmişlik Performansının İstatistiksel Varyans Prosedürü Temelli Ocra Yöntemiyle Analizi / Analysis of Financial Development Performance of Developing Asian and European Countries Using OCRA Method Based Statistical Variance Procedure, Üçüncü Sektör Sosyal Ekonomi Dergisi/Third Sector Social Economic Review, 55 (2), 1330-1344, http://doi.org/10.15659/3.sektor-sosyal-ekonomi.20.06.1375
• 8. 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, http://doi.org/doi.org/10.3233/JIFS-181633
• 9. Liu H. C., Liu L., Wu J., 2013. Material selection using an interval 2-tuple linguistic VIKOR method considering subjective and objective weights. Materials & Design (1980-2015), 52, 158-167, http://doi.org/10.1016/j.matdes.2013.05.054
• 10. Liu H. C., You J. X., Chen S., Chen Y. Z., 2016, An integrated failure mode and effect analysis approach for accurate risk assessment under uncertainty. IIE Transactions, 48 (11), 1027-1042, http://doi.org/10.1080/0740817X.2016.1172742
• 11. Luo S. Z., Xing L. N., 2019. A Hybrid Decision Making Framework for Personnel Selection Using BWM, MABAC and PROMETHEE. International Journal of Fuzzy Systems, 21 (8), 2421-2434, http://doi.org/10.1007/s40815-019-00745-4
• 12. Marti L., Martin J. C., Puertas R., 2017. A DEA-logistics performance index. Journal of Applied Economics, 20 (1), 169-192, http://doi.org/10.1016/S1514-0326(17)30008-9
• 13. Marti L., Puertas R., Garcia L., 2014, The importance of the Logistics Performance Index in international trade. Applied Economics, 46 (24), 2982-2992, http://doi.org/10.1080/00036846.2014.916394
• 14. Mercangoz B. A., Yildirim B., Yildirim S. K., 2020. Time Period Based COPRAS-G Method: Application on the Logistics Performance Index. LogForum, 16 (2), 239-250, http://doi.org/10.17270/J.LOG.2020.432
• 15. Mıhçı H., 2011. Human Development Performance of Transition Economies in the Post-Cold War Period, Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 29 (1), 21-42.
• 16. Milosavljević M., Bursać M., Tričković G., 2018. Selection of the railroad container terminal in Serbia based on multi criteria decision making methods. Decision Making: Applications in Management and Engineering, 1 (2), 1-15, http://doi.org/10.31181/dmame1802001m
• 17. Muravev D., Mijic N., 2020. A Novel Integrated Provider Selection Multicriteria Model: The BWM-MABAC Model. Decision Making: Applications in Management and Engineering, 3 (1), 60-78, http://doi.org/10.31181/dmame2003078m
• 18. 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, http://doi.org/10.31181/oresta19012010113n
• 19. Pamučar D., Ćirović G., 2015. The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert systems with applications, 42 (6), 3016-3028, http://doi.org/10.1016/j.eswa.2014.11.057
• 20. Pamučar D., Stević Ž., Zavadskas E. K., 2018. Integration of interval rough AHP and interval rough MABAC methods for evaluating university web pages. Applied Soft Computing, 67, 141-163, http://doi.org/10.1016/j.asoc.2018.02.057
• 21. Rahim N., Abdullah L., Yusoff B., 2020. A Border Approximation Area Approach Considering Bipolar Neutrosophic Linguistic Variable for Sustainable Energy Selection. Sustainability, 12 (10), 3971, http://doi.org/10.3390/su12103971
• 22. 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, http://doi.org/10.1016/j.matdes.2010.05.014
• 23. 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, http://doi.org/10.1016/j.robot.2011.01.005
• 24. Rashidi K., Cullinane K., 2019. Evaluating the sustainability of national logistics performance using Data Envelopment Analysis. Transport Policy, 74, 35-46, http://doi.org/10.1016/j.tranpol.2018.11.014
• 25. 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, http://doi.org/10.1016/j.tranpol.2018.05.007
• 26. Sharma H. K., Roy J., Kar S., Prentkovskis O., 2018. Multi criteria evaluation framework for prioritizing indian railway stations using modified rough AHP-MABAC method. Transport and telecommunication journal, 19 (2), 113-127, http://doi.org/10.2478/ttj-2018-0010
• 27. Sharma S. K., Mahapatra S. S., Parappagoudar M. B., 2016. Benchmarking of product recovery alternatives in reverse logistics. Benchmarking: An International Journal, 23 (2), 406-424, http://doi.org/10.1108/BIJ-01-2014-0002
• 28. Ulutaş A., 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, http://doi.org/10.18559/ebr.2019.4.3
• 29. Wei G., Wei C., Wu J., Wang H., 2019. Supplier selection of medical consumption products with a probabilistic linguistic MABAC method. International Journal of Environmental Research and Public Health, 16(24), 5082, http://doi.org/10.3390/ijerph16245082
• 30. Yildirim B. F., Mercangoz B. A., 2020, Evaluating the logistics performance of OECD countries by using fuzzy AHP and ARAS-G. Eurasian Economic Review, 10 (1), 27-45, http://doi.org/10.1007/s40822-019-00131-3

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).