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Veri zarflama analizi ile Avrupa geçiş ekonomilerinin lojistik performans endeksi kullanılarak değerlendirilmesi

Yıl 2023, Cilt: 12 Sayı: 1, 30 - 51, 30.06.2023

Kadir Kaan Göncü Duygu Yücel

https://doi.org/10.47934/tife.12.01.02

Öz

Rekabetin her geçen gün daha yoğun yaşandığı günümüz kapitalizminde, performans ile onun unsurları olan etkinlik ve verimlilik artan önemini korumaktadır. Üretim sisteminde yüksek bir performans gerçekleştirmek için tüm süreçte optimal kaynak kullanımının sağlanması, maliyetlerin düşürülmesi gerekmektedir. Malzeme arzı, malzeme tedariki ve lojistik süreci boyunca tüm aşama optimum uyum içinde gerçekleştirilmelidir. Dolayısıyla performans artışında, lojistik sektörlerinin etkinliğinin ve verimliliğinin sağlanması önem kazanmaktadır. Bu çalışmada, Avrupa Geçiş Ekonomisi Ülkeleri içinde geçişi tamamlamış olan 11 ülkenin etkinlik ve verimlilik analizi, Lojistik Performans Endeksi alt boyutlarına ait veriler kullanılarak Girdi Odaklı Ölçeğe Göre Sabit Getiri (CCR) ve Girdi Odaklı Ölçeğe Göre Değişken Getiri (BCC) Modellerine göre yapılmıştır. Lojistik Performans Endeksinin altı alt boyutundan üçü (gümrük, altyapı ve lojistik kalite) girdi olarak kullanılırken, diğer üçü ise (uluslararası gönderiler, izleme-takip ve zamanında teslimat) çıktı olarak ele alınmıştır. Veri Zarflama Analizi ile yapılan çalışmada “EMS Paket Programı” kullanılarak etkinlik değerlendirilmesi yapılmıştır. Analiz bulgularından elde edilen sonuçlara göre etkin ve verimli olan ülkeler bulunmuştur. Aynı zamanda etkinsiz ve verimsiz olan ülkeler de bulunarak, etkin olmayan ülkelerin ideal etkinlik düzeyine çıkabilmesi için girdi değişkenlerin iyileştirme oranları hesaplanmıştır.

Anahtar Kelimeler

Geçiş Ekonomileri Lojistik Performans Endeksi Veri Zarflama Analizi

Kaynakça

  • Acar, M. F. (2021). Lojistik performans indeks: Türkiye-Avrupa Birliği karşılaştırması. International Journal of Advances in Engineering and Pure Sciences, 33(3), 422-428.
  • Acer, A. (2021). Lojistik faaliyetlerde antrepoların etkinliğinin veri zarflama analizi ile belirlenmesi. İşletme Araştırmaları Dergisi, 13(4), 2976-2989.
  • Aigner, D. J. ve Chu, S. F. (1968). On estimating the industry production function. The American Economic Review, 58(4), 826-839.
  • Altıntaş, F. F. (2022, Ocak) . G7 ülkelerinin lojistik etkinlik ve verimlilik performanslarının değerlendirilmesi. Verimlilik Dergisi, (1), 78-93.
  • Altuğ, F. N. (2005). Ekonomide devletin yeri. Toprak İşveren (Türkiye Toprak, Seramik, Çimento ve Cam Sanayii İşverenleri Sendikası Yayın Organı), 68, 11-19.
  • Asker, V. (2018). Veri zarflama analizi ile finansal ve operasyonel etkinlik ölçümü: geleneksel havayolu işletmelerinde bir uygulama. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 18(1), 153-172.
  • Bakırcı, F. (2006). Sektörel bazda bir etkinlik ölçümü: VZA ile bir analiz. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 20(2), 199-217.
  • Baležentis, A., & Baležentis, T. (2011). Assessing the efficiency of Lithuanian transport sector by applying the methods of MULTIMOORA and data envelopment analysis. Transport, 26(3), 263-270.
  • Banker, R. D., Charnes, A. ve Cooper W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis management science. Management Science, 30(9), 1078-1092.
  • Bayrak, R., & Bahar, O. (2018). Economic efficiency analysis of tourism sector in OECD countries: An emprical study with DEA. Uluslararası İktisadi ve İdari İncelemeler Dergisi, (20), 83-100.
  • Biloslavo, R., Bagnoli, C. ve Figelj, R. R. (2013). Managing dualities for efficiency and effectiveness of organisations. Industrial Management ve Data Systems, 113(3), 423-442. Doi: 10.1108/02635571311312695
  • Charnes, A., Cooper, W.W. ve Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444. Doi: 10.1016/0377-2217(78)90138-8
  • Chen, X., Miao, Z., Wang, K., & Sun, C. (2020). Assessing eco-performance of transport sector: Approach framework, static efficiency and dynamic evolution. Transportation Research Part D: Transport and Environment, 85, 102414.
  • Coelli, T., Estache, A., Perelman, S. ve Trujillo, L. (2003). A primer on efficiency measurement for utilities and transport regulators. WBI Development Studies.
  • Coelli, T. (1996). A guide to DEAP version 2.1: a data envelopment analysis (computer) program. Centre for Efficiency and Productivity Analysis, University of New England, Australia, 96(08), 1-49.
  • Cook, W. D. ve Seiford, L. M. (2009). Data envelopment analysis (DEA)-thirty years on. European Journal of Operational Research, 192(1), 1-17. Doi:10.1016/j.ejor.2008.01.032
  • Çemberci, M, Civelek, M.E. ve Canbolat, N. (2015). The moderator effect of global competitiveness index on dimensions of logistics performance index. Procedia-Social and Behavioral Sciences, 195, 1514–1524.
  • Daştan, H. (2018). Türkiye şeker sanayinin etkinlik ve verimlilik analizi. Gazi Üniversitesi Sosyal Bilimler Dergisi, 5(14), 478-498.
  • De Melo, M., Denizer, C., Gelb, A., ve Tenev, S. (2001). Circumstance and choice: The role of initial conditions and policies in transition economies. The World Bank Economic Review, 15(1), 1-31.
  • Debreu, G. (1951). The coefficient of resource utilization. Econometrica, 19(3), 273-292.
  • Erturan, M. B., & Merdivenci, F. (2021). LPI based two stage network DEA model to measure logistics efficiency: An application on OECD countries. İşletme Araştırmaları Dergisi, 13(2), 1187-1199.
  • Fanchon, P. (2003). Variable selection for dynamic measures of efficiency in the computer industry. International Advances in Economic Research, 9(3), 175-188.
  • Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, Series A (General), 120 (3), 253-290.
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  • Florensa, J. P. ve Simar, L. (2005). Parametric approximations of nonparametric frontiers. Journal of Econometrics, 124(1), 91-116. Doi:10.1016/j.jeconom.2004.02.012
  • Gattoufi, S., Wang, Y., Reisman, A. ve Oral, M. (2007). An interpretation of the technical efficiency as the "best possible deviation" from the conditions defined by the weak axiom of profit maximization. International Business ve Economics Research Journal, 6(2), 49-58.
  • Gökgöz, F. (2009). Veri zarflama analizi ve finans alanına uygulanması. Ankara Üniversitesi Siyasal Bilgiler Fakültesi Yayını, (597).
  • Greene, W. H. (1990). A gamma-distributed stochastic frontier model. Journal of Econometrics, 46(1-2), 141-163.
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  • Jiang, C. (2010, April). Research on logistics network ınfrastructure based on HCA and DEA-PCA approach. Journal of Computers, 5(4), 533-540.
  • Jiang, C. & Fu, P. (2009, October). Evaluating efficiency and effectiveness of logistics infrastructure based on PCA-DEA approach in China. In 2009 Second International Conference on Intelligent Computation Technology and Automation, 3, (pp. 62-66). IEEE.
  • Karahan, A. & Özgür, E. (2009). Hastanelerde Performans Yönetim Sistemi ve Veri Zarflama Analizi (Birinci Baskı). İstanbul: Nobel Yayınevi, 36.
  • Kıyıldı, R. K. ve Karaşahin, M. (2006). Türkiye’deki hava alanlarının veri zarflama analizi ile altyapı performansının değerlendirilmesi. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 10(3), 391-397.
  • Koopmans, T. C. (1951), An Analysis of production as an efficient combination of activities. T.C. Koopmans (Ed.). Activity Analysis of Production and Allocation, Cowles Commission for Research in Economics, Monograph No. 13, (ss. 33-98). London: John Wiley and Sons Inc.
  • Kuah, C. T., Wong, K. Y. ve Behrouzi, F. (2010). A review on data envelopment analysis (DEA). Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation, IEEE, (ss. 168-173). Doi: 10.1109/AMS.2010.45
  • Kumar, S., & Gulati, R. (2008). An examination of technical, pure technical, and scale efficiencies in Indian public sector banks using data envelopment analysis. Eurasian Journal of Business and Economics, 1(2), 33-69.
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  • Kutlar, A. ve Kartal, M. (2004). Cumhuriyet Üniversitesinin verimlilik analizi: fakülteler düzeyinde veri zarflama yöntemiyle bir uygulama. Kocaeli Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (8), 49-79.
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Evaluation of European transition economies using the logistics performance index with data envelopment analysis

Yıl 2023, Cilt: 12 Sayı: 1, 30 - 51, 30.06.2023

Kadir Kaan Göncü Duygu Yücel

https://doi.org/10.47934/tife.12.01.02

Öz

In today's capitalism, where competition is more intense day by day, performance and its elements, efficiency, and productivity, remain of increasing importance. In order to realise a high performance in the production system, it is necessary to ensure optimal resource utilisation in the whole process and to reduce costs. The entire stage during the material supply, material procurement and logistics process should be carried out in optimal harmony. Therefore, ensuring the activity and efficiency of logistics sectors has come into prominence in performance improvement. In this study, the efficiency and productivity analysis of 11 countries that have completed the transition within the European Transition Economy Countries were made according to the Input-Oriented Constant Return to Scale Model and Input-Oriented Variable Returns to Scale Model by using the data belonging to the Logistics Performance Index sub-dimensions. Three of the six sub-dimensions of the Logistics Performance Index (customs, infrastructure, and logistics quality) were used as inputs, while the other three (international shipments, tracking / tracking, and on-time delivery) were considered as outputs. In the study conducted with Data Envelopment Analysis, the efficiency evaluation was made by using the "EMS Package Program". According to the results acquired from the analysis findings, efficient and productive countries were found. At the same time, ineffective and nonproductive countries were found and the improvement rates of the input variables have been calculated so that the inefficient countries can reach the ideal efficiency level.

Anahtar Kelimeler

Transition Economies Logistics Performance Index Data Envelopment Analysis

Kaynakça

  • Acar, M. F. (2021). Lojistik performans indeks: Türkiye-Avrupa Birliği karşılaştırması. International Journal of Advances in Engineering and Pure Sciences, 33(3), 422-428.
  • Acer, A. (2021). Lojistik faaliyetlerde antrepoların etkinliğinin veri zarflama analizi ile belirlenmesi. İşletme Araştırmaları Dergisi, 13(4), 2976-2989.
  • Aigner, D. J. ve Chu, S. F. (1968). On estimating the industry production function. The American Economic Review, 58(4), 826-839.
  • Altıntaş, F. F. (2022, Ocak) . G7 ülkelerinin lojistik etkinlik ve verimlilik performanslarının değerlendirilmesi. Verimlilik Dergisi, (1), 78-93.
  • Altuğ, F. N. (2005). Ekonomide devletin yeri. Toprak İşveren (Türkiye Toprak, Seramik, Çimento ve Cam Sanayii İşverenleri Sendikası Yayın Organı), 68, 11-19.
  • Asker, V. (2018). Veri zarflama analizi ile finansal ve operasyonel etkinlik ölçümü: geleneksel havayolu işletmelerinde bir uygulama. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 18(1), 153-172.
  • Bakırcı, F. (2006). Sektörel bazda bir etkinlik ölçümü: VZA ile bir analiz. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 20(2), 199-217.
  • Baležentis, A., & Baležentis, T. (2011). Assessing the efficiency of Lithuanian transport sector by applying the methods of MULTIMOORA and data envelopment analysis. Transport, 26(3), 263-270.
  • Banker, R. D., Charnes, A. ve Cooper W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis management science. Management Science, 30(9), 1078-1092.
  • Bayrak, R., & Bahar, O. (2018). Economic efficiency analysis of tourism sector in OECD countries: An emprical study with DEA. Uluslararası İktisadi ve İdari İncelemeler Dergisi, (20), 83-100.
  • Biloslavo, R., Bagnoli, C. ve Figelj, R. R. (2013). Managing dualities for efficiency and effectiveness of organisations. Industrial Management ve Data Systems, 113(3), 423-442. Doi: 10.1108/02635571311312695
  • Charnes, A., Cooper, W.W. ve Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444. Doi: 10.1016/0377-2217(78)90138-8
  • Chen, X., Miao, Z., Wang, K., & Sun, C. (2020). Assessing eco-performance of transport sector: Approach framework, static efficiency and dynamic evolution. Transportation Research Part D: Transport and Environment, 85, 102414.
  • Coelli, T., Estache, A., Perelman, S. ve Trujillo, L. (2003). A primer on efficiency measurement for utilities and transport regulators. WBI Development Studies.
  • Coelli, T. (1996). A guide to DEAP version 2.1: a data envelopment analysis (computer) program. Centre for Efficiency and Productivity Analysis, University of New England, Australia, 96(08), 1-49.
  • Cook, W. D. ve Seiford, L. M. (2009). Data envelopment analysis (DEA)-thirty years on. European Journal of Operational Research, 192(1), 1-17. Doi:10.1016/j.ejor.2008.01.032
  • Çemberci, M, Civelek, M.E. ve Canbolat, N. (2015). The moderator effect of global competitiveness index on dimensions of logistics performance index. Procedia-Social and Behavioral Sciences, 195, 1514–1524.
  • Daştan, H. (2018). Türkiye şeker sanayinin etkinlik ve verimlilik analizi. Gazi Üniversitesi Sosyal Bilimler Dergisi, 5(14), 478-498.
  • De Melo, M., Denizer, C., Gelb, A., ve Tenev, S. (2001). Circumstance and choice: The role of initial conditions and policies in transition economies. The World Bank Economic Review, 15(1), 1-31.
  • Debreu, G. (1951). The coefficient of resource utilization. Econometrica, 19(3), 273-292.
  • Erturan, M. B., & Merdivenci, F. (2021). LPI based two stage network DEA model to measure logistics efficiency: An application on OECD countries. İşletme Araştırmaları Dergisi, 13(2), 1187-1199.
  • Fanchon, P. (2003). Variable selection for dynamic measures of efficiency in the computer industry. International Advances in Economic Research, 9(3), 175-188.
  • Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, Series A (General), 120 (3), 253-290.
  • Fischer, S. ve Sahay, R. (2004). Transition economies: the role of institutions and initial conditions. Calvo Conference-April 1-4.
  • Florensa, J. P. ve Simar, L. (2005). Parametric approximations of nonparametric frontiers. Journal of Econometrics, 124(1), 91-116. Doi:10.1016/j.jeconom.2004.02.012
  • Gattoufi, S., Wang, Y., Reisman, A. ve Oral, M. (2007). An interpretation of the technical efficiency as the "best possible deviation" from the conditions defined by the weak axiom of profit maximization. International Business ve Economics Research Journal, 6(2), 49-58.
  • Gökgöz, F. (2009). Veri zarflama analizi ve finans alanına uygulanması. Ankara Üniversitesi Siyasal Bilgiler Fakültesi Yayını, (597).
  • Greene, W. H. (1990). A gamma-distributed stochastic frontier model. Journal of Econometrics, 46(1-2), 141-163.
  • IMF. (September 2000). World Economic Outlook: Focus on Transition Economies. Erişim tarihi: 28.01.2022, https://www.imf.org/en/Search#q=transition%20economiesvesort=relevancy
  • IMF. (2000, November 3. Retrieved 2009, March 9). Transition Economies: An IMF Perspective on Progress and Prospects. Erişim tarihi: 28.01.2022, https://www.imf.org/external/np/exr/ib/2000/110300.htm
  • Jiang, C. (2010, April). Research on logistics network ınfrastructure based on HCA and DEA-PCA approach. Journal of Computers, 5(4), 533-540.
  • Jiang, C. & Fu, P. (2009, October). Evaluating efficiency and effectiveness of logistics infrastructure based on PCA-DEA approach in China. In 2009 Second International Conference on Intelligent Computation Technology and Automation, 3, (pp. 62-66). IEEE.
  • Karahan, A. & Özgür, E. (2009). Hastanelerde Performans Yönetim Sistemi ve Veri Zarflama Analizi (Birinci Baskı). İstanbul: Nobel Yayınevi, 36.
  • Kıyıldı, R. K. ve Karaşahin, M. (2006). Türkiye’deki hava alanlarının veri zarflama analizi ile altyapı performansının değerlendirilmesi. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 10(3), 391-397.
  • Koopmans, T. C. (1951), An Analysis of production as an efficient combination of activities. T.C. Koopmans (Ed.). Activity Analysis of Production and Allocation, Cowles Commission for Research in Economics, Monograph No. 13, (ss. 33-98). London: John Wiley and Sons Inc.
  • Kuah, C. T., Wong, K. Y. ve Behrouzi, F. (2010). A review on data envelopment analysis (DEA). Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation, IEEE, (ss. 168-173). Doi: 10.1109/AMS.2010.45
  • Kumar, S., & Gulati, R. (2008). An examination of technical, pure technical, and scale efficiencies in Indian public sector banks using data envelopment analysis. Eurasian Journal of Business and Economics, 1(2), 33-69.
  • Kutlar, A. & Babacan, A. (2008). Türkiye’deki kamu üniversitelerinde CCR etkinliği-ölçek etkinliği analizi: DEA tekniği uygulaması. Kocaeli Üniversitesi Sosyal Bilimler Dergisi, (15), 148-172.
  • Kutlar, A. ve Kartal, M. (2004). Cumhuriyet Üniversitesinin verimlilik analizi: fakülteler düzeyinde veri zarflama yöntemiyle bir uygulama. Kocaeli Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (8), 49-79.
  • Lame, G. (2019). Systematic literature reviews: An introduction. In Proceedings of the 22nd International Conference on Engineering Design (ICED19), Delft, The Netherlands, 5-8 August 2019. 1633-1642. doi:10.1017/ dsi.2019.169
  • Lee, H. L. ve Billington, C. (1992). Managing supply chain inventory: pitfalls and opportunities. MIT Sloan Management Review, 33(3), 65-73.
  • Lieberman, I. W., Kopf, D. J. (Ed.). (2008). Privatization in transition economies : The ongoing story. Series: contemporary studies in economic and financial analysis, 90. Amsterdam: JAI Press Inc.
  • Machado, L. K. C. & dos Santos, A. C. (2021). Índice de Desempenho Logístico (LPI): uma análise da eficiência logística e da importância relativa dos seus indicadores/Logistic Performance Index (LPI): an analysis of logistical efficiency and the relative importance of its indicators. Revista de Ciências da Administração-RCA. 23(60), 53-72.
  • Markovits-Somogyi, R., & Bokor, Z. (2014). Assessing the logistics efficiency of European countries by using the DEA-PC methodology. Transport, 29(2), 137-145.
  • Martí, L., Martín, J.C. & Puertas, R. (2017, May). A dea-logistics performance index. Journal of Applied Economics, XX(1), 169-192.
  • Miranda, R. D.C., Montevechi, J. A. B., Silva, A. F. D., & Marins, F. A. S. (2014). A New Approach to Reducing Search Space and Increasing Efficiency in Simulation Optimization Problems via the Fuzzy-DEA-BCC. Mathematical Problems in Engineering, 2014, 1-15.
  • OECD. (2001). Measuring productivity: measurement of aggregate and ındustry-level productivity growth, France.
  • Okursoy, A. & Özdemir, M. (2015). Veri Zarflama Analizinde homojen olmayan karar verme birimi problemi için kümeleme analizi yaklaşımı. Ege Academic Review, 15(1), 81-90.
  • Olesen, O. B., Petersen, N. C. ve Podinovski, V. V. (2015). Efficiency analysis with ratio measures. European Journal of Operational Research, 245(2), 446-462. Doi: 10.1016/j.ejor.2015.03.013
  • Özdemir, A. İ. (2004, Temmuz-Aralık). Tedarik zinciri yönetiminin gelişimi, süreçleri ve yararları. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 23, 87-96.
  • Özden, A. (2010). Etkinlik kavramı ve ölçüm metotları. Türkiye IX. Tarım Ekonomisi Kongresi (ss. 740-747). Şanlıurfa.
  • Öztürk, D. (2016). Tedarik zinciri yönetimi süreçlerini etkileyen faktörler. International Journal of Social and Economic Sciences, 6(1), 17-24.
  • Redek, T. ve Sušjan, A. (2005). The impact of institutions on economic growth: the case of transition economies, Journal of Economic Issues, 39(4), 995-1027. Doi: 10.1080/00213624.2005.11506864
  • Remeikiene, R., Gaspareniene L., Fedajev, A., ve Vebraite, V. (2021). The role of ICT development in boosting economic growth in transition economies. Journal of International Studies, 14(4), 9-22. Doi: 10.14254/2071-8330.2022/14-4/1
  • Roghaniana, P., Raslia, A. ve Gheysari, H. (2012). Productivity through effectiveness and efficiency in the banking industry. Procedia-Social and Behavioral Sciences, 40, 550-556. Doi: 10.1016/j.sbspro.2012.03.229
  • Round, J. (2009). Transitional economies. International Encyclopedia of Human Geography, 355-360. 3
  • Schaffer, M. E. ve Turley, G. (2000, November). Effective versus statutory taxation: Measuring effective tax administration in transition economies. M. Cuddy ve R. Gekker (Ed.), Institutional Change In Transition Economies, New York: Routledge.
  • Shang, S. ve Mao, X. (2009). Data envelopment analysis on efficiency evaluation of irrigationfertilization schemes for winter wheat in North China. D. Li, Z. Chunjiang (Ed.). IFIP International Federation for Information Processing, Volume 293, Computer and Computing Technologies in Agriculture II, Volume 1. (ss. 39-48).
  • Sherman, H.D. (1984). Hospital efficiency measurement and evaluation: empirical test of a new technique. Medical Care, 22(10), 922-938.
  • Sternad, M., Skrucany, T., & Jereb, B. (2018). International logistics performance based on the DEA analysis. Communications-Scientific Letters of the University of Zilina, 20(4), 10-15.
  • Svejnar, J. (Winter 2002). Transition economies: performance and challenges. Journal of Economic Perspectives, 16(1), 3-28.
  • Talluri, S. (2000). Data envelopment analysis: models and extensions. Decision Line, 31(3), 8-11.
  • Tarım A. (2001). Veri zarflama analizi: matematiksel programlama tabanlı göreli etkinlik ölçüm yaklaşımı., Ankara: T.C. Sayıştay Bakanlığı.
  • Tranfield, D., Denyer, D. & Smart, P. (2003). Towards a methodology for developing evidence-ınformed management knowledge by means of systematic review. British Journal of Management, 14, 207-222.
  • World Bank (2018). Connecting to Compete 2018 Trade Logistics in the Global Economy. The Logistics Performance Index and Its Indicators. Washington, DC. Erişim tarihi: 08.02.2022, www.worldbank.org
  • World Bank (2018). International LPI. Erişim tarihi: 08.02.2022. https://lpi.worldbank.org/international/scorecard/radar/254/C/CZE/2018/C/POL/2007/C/HUN/2018/C/SVN/2018/C/EST/2018/C/ROM/2018/C/HRV/2018/C/BGR/2018/C/SVK/2018/C/LVA/2018/C/LTU/2018?sort=asc&order=Country#datatable
  • World Bank. LPI. https://lpi.worldbank.org/about (23.05.2022).
  • Yu, M. M. & Hsiao, B. (2016). Measuring the technology gap and logistics performance of individual countries by using a meta-DEA–AR model. Maritime Policy & Management, 43(1), 98-120.
  • Yükçü, S. ve Atağan, G. (2009). Etkinlik, etkililik ve verimlilik kavramlarının yarattığı karışıklık. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 23(4), 1-13.
Toplam 69 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Uluslararası Lojistik
Bölüm Araştırma Makalesi
Yazarlar

Kadir Kaan Göncü 0000-0002-4810-6336

Duygu Yücel 0000-0002-2665-6732

Erken Görünüm Tarihi 15 Haziran 2023
Yayımlanma Tarihi 30 Haziran 2023
Gönderilme Tarihi 10 Mart 2022
Yayımlandığı Sayı Yıl 2023 Cilt: 12 Sayı: 1

Kaynak Göster

APA Göncü, K. K., & Yücel, D. (2023). Veri zarflama analizi ile Avrupa geçiş ekonomilerinin lojistik performans endeksi kullanılarak değerlendirilmesi. Trakya Üniversitesi İktisadi Ve İdari Bilimler Fakültesi E-Dergi, 12(1), 30-51. https://doi.org/10.47934/tife.12.01.02
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