TÜRKİYE’DEKİ LOJİSTİK KÖYLERİN POTANSİYEL ETKİNLİKLERİNE GÖRE DEĞERLENDİRİLMESİ: MOORA YÖNTEMİ İLE BİR UYGULAMA

Yıl 2021, Cilt: 12 Sayı: 24, 1000 - 1026, 21.12.2021

Harun Dumlu Aysen Wolff

https://doi.org/10.36543/kauiibfd.2021.041

Cited By: 3

Öz

Uluslararası ticaretin artması ile dünya genelinde taşımacılık, depolama ve elleçleme gibi lojistik faaliyetler ve bu lojistik faaliyetleri gerçekleştiren firmalar da artmıştır. Ancak firmaların faaliyet gösterdikleri tüm bölgelerde fiziki alanlara, depolara ve ofis gibi ortamlara sahip olmaları yüksek maliyetlere neden olmaktadır. Bu nedenle lojistik firmalar ve işletmeler tarafından faaliyet gösterdikleri bazı bölgelerde mülkiyet edinmek yerine, daha düşük maliyetler sağlayacağı düşünülen kiralama işlemleri yapılmaktadır. Bu da lojistik köylerin kurulmasına neden olan başlıca durumlar arasında yer almaktadır. Ayrıca lojistik firmalarının ve işletmelerin lojistik köyleri tercih etmelerinde o lojistik köylerin sahip oldukları özellikleri ve potansiyelleri önemli rol oynamaktadır. Bu çalışmada, Türkiye Cumhuriyeti Devlet Demiryolları tarafından yapımı tamamlanan 11 lojistik köyün potansiyel etkinliklerine göre sıralamaları çok kriterli karar verme yöntemleri arasında yer alan MOORA yöntemi ile belirlenmiştir. Çalışmada lojistik köylerin potansiyel etkinliklerinin belirlenmesi için kapasite, toplam fiziki alan, dış ticaret hacmi, en yakın serbest bölge, en yakın karayolu, en yakın havalimanı, en yakın demiryolu ve en yakın liman kriterleri kullanılmıştır. Yapılan analizler sonucunda, en yüksek potansiyel etkinliğe sahip olan ilk üç lojistik köy sırasıyla Köseköy/İzmit, Halkalı/İstanbul ve Yenice/Mersin lojistik köyüdür. Palandöken/Erzurum lojistik köyü ise en düşük potansiyel etkinliğe sahip lojistik köy olarak belirlenmiştir.

Anahtar Kelimeler

Lojistik Köy, Potansiyel Etkinlik, Çok Kriterli Kara Verme

EVALUATION OF LOGISTICS VILLAGES IN TURKEY ACCORDING TO THE POTENTIAL EFFICIENCY: AN APPLICATION WITH THE MOORA METHOD

Öz

With the increase in international trade, logistics activities such as transportation, storage and handling and the companies performing these logistics activities have also increased. However, the fact that companies have physical areas, warehouses and environments such as offices in all regions where they operate causes high costs. For this reason, instead of acquiring property in some regions where they operate, leasing transactions, which are thought to provide lower costs, are carried out by logistics companies and businesses. This is located between the main situations that lead to the establishment of logistics villages. Also, the characteristics and potentials of those logistics villages play an important role in the choice of logistics companies and businesses. In this study, potential efficiencies of the completed 11 logistics villages of the built by the Republic of Turkey State Railways were determined by MOORA method of located between multiple-criteria decision analysis methods. Capacity, total physical area, foreign trade volume, closest free zone, nearest highway, nearest airport, nearest railway and nearest port criteria were used to determine the potential efficiencies of logistic villages. As a result of the analysis, the first three logistics villages with the highest potential efficiency are Köseköy/İzmit, Halkalı/Istanbul and Yenice/Mersin logistics village, respectively. Palandöken/Erzurum logistics village has been determined as the logistics village with the lowest potential efficiency.

Anahtar Kelimeler

Logistics Village, Potential Efficiency, Multi-Criteria Decision Making

Kaynakça

• Aloini, D., Benvenuti, G., Dulmin, R., Gloor, P. A., Guerrazzi, E., Mininno, V. & Stefanini, A. (2019). Exploring the impact of environmental and human factors on operational performance of a logistics hub. In Collaborative innovation networks conference of Digital Transformation of Collaboration (pp. 75-82). Springer, Cham.
• Aritua, B., Havenga, J., Simpson, Z. & Chiew, E.W.L. (2018). Unlocking India's logistics potential: The value of disaggregated macroscopic freight flow analysis. The World Bank. Policy Research Working Paper: 8337. doi: https://doi.org/10.1596/1813-9450-8337
• Aydın, G.T., & Öğüt, K.S. (2008). Avrupa ve Türkiye’de lojistik köyleri, 2. Uluslararası Demiryolu Sempozyumu, (ss. 1-11), İstanbul.
• Ballis, A. (2006). Freight villages: Warehouse design and rail link aspects. Transportation Research Record, 1966(1), 27–33. doi: https://doi.org/10.1177/0361198106196600104
• Bilovodska, O., Gryshchenko, O. & Syhyda, L. (2016). Specific features of regional logistics potential consideration in the process of the enterprise distribution system development. Economic Annals-XXI, 160(7-8), 105-110.
• Brauers, W.K.M. & Zavadskas, E.K. (2006). The MOORA method and its application to privatization in a transition economy. Control and Cybernetics, 35(2), 445-469.
• Brauers, W.K.M., Zavadskas, E.K., Peldschus, F. & Turskis, Z. (2008). Multi-objective decision-making for road design. Transport, 23(3): 183-193. doi: https://doi.org/10.3846/1648-4142.2008.23.183-193
• Brauers, W.K.M. & Zavadskas, E.K. (2010). Project management by multimoora as an instrument for transition economies. Technological and Economic Development of Economy, 16(1), 5-24.
• Brauers, W.K.M. & Zavadskas, E.K. (2011). MULTIMOORA optimization used to decide on a bank loan to buy property. Technological and Economic Development of Economy, 17(1), 174-188. doi: https://doi.org/10.3846/13928619.2011.560632
• Bulturbayevich, M.B., Saodat, S., Umida, J., Shakhnoza, N. & Feruza, S. (2020). Mechanisms of State Incentives for Logistics Centers to Ensure the Competitiveness of the Economy. International Engineering Journal For Research & Development, 5(5), 1-7.
• Burki, A.A., & Terrell, D. (1998). Measuring production efficiency of small firms in Pakistan. World Development, 26(1), 155-169. doi: https://doi.org/10.1016/S0305-750X(97)00122-8
• Chakraborty, S. (2011). Applications of the MOORA method for decision making in manufacturing environment. The International Journal of Advanced Manufacturing Technology, 54(9-12), 1155-1166. doi: https://doi.org/10.1007/s00170-010-2972-0
• Chen, K.H., Liao, C.N., & Wu, L.C. (2014). A selection model to logistic centers based on TOPSIS and MCGP methods: The case of airline industry. Journal of Applied Mathematics, 2014, 1-10. doi: https://doi.org/10.1155/2014/470128
• Chen, L. & Notteboom, T., (2012). Determinants for assigning value-added logistics services to logistics centers within a supply chain configuration. Journal of International Logistics and Trade, 10(1), 3-41.
• Davis, P.S., & Peri, T.L. (2002). Measuring organizational efficiency and effectiveness. Journal of Management Research, 2(2), 87-97.
• Gürbüz, E. & Dumlu, H. (2018). BİST sürdürülebilirlik endeksindeki şirketlerin etkinliklerinin ölçülmesi: veri zarflama analizi ile bir uygulama. İşletme Araştırmaları Dergisi, 10(2), 223-244. doi: http://doi.org/10.20491/isarder.2018.427
• Fernandes, C. & Rodrigues, G. (2009). Dubai’s potential as an ıntegrated logistics hub. Journal of Applied Business Research, 25(3), 77-92. doi: https://doi.org/10.19030/jabr.v25i3.1028
• Hamzaçebi, C., İmamoğlu, G. & Alçı, A. (2016). Selection of logistics center location with MOORA method for black sea region of Turkey. Journal of Economics Bibliography, 3(15), 74-82.
• Hamzeh, F.R., Tommelein, I.D., Ballard, G., & Kaminsky, P. (2007). Logistics centers to support project-based production in the construction industry. Proceedings of the 15th Annual Conference of the International Group for Lean Construction (pp. 181-191). doi: https://doi.org/10.13140/RG.2.1.4328.6563
• Hong, L. & Xiaohua, Z. (2011). Study on location selection of multi-objective emergency logistics center based on AHP. Procedia Engineering, 15, 2128-2132. doi: https://doi.org/10.1016/j.proeng.2011.08.398
• Kabadurmuş, Ö., Karaköprü, U. & Dördüncü, H. (2020). Aliağa ve İzmir Limanlarının lojistik potansiyeli ve bölgenin konteyner taşımaları açısından geleceği. KAÜİİBFD, 11(21), 159-181. doi: https://doi.org/10.36543/kauiibfd.2020.008
• Kara, M., Tayfur, L. & Basık, H. (2009). Küresel ticarette lojistik üslerin önemi ve Türkiye. Mustafa Kemal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 6(11), 69-84.
• Liu, J., Ding, F.Y. & Lall, V. (2000). Using data envelopment analysis to compare suppliers for supplier selection and performance improvement. Supply Chain Management: An International Journal, 5(3), 143-150.
• Mensah, Y.M., & Li, S.H. (1993). Measuring production efficiency in a not-for-profit setting: an extension. Accounting Review, 68(1), 66-88.
• Murti, B.P.S., Choi, Y.K. & Desai, P. (1997). Efficiency of mutual funds and portfolio performance measurement: A non-parametric approach. European Journal of Operational Research, 98(2), 408-418. doi: https://doi.org/10.1016/S0377-2217(96)00356-6
• Narasimhan, R., Talluri, S. & Mendez, D. (2006). Supplier evaluation and rationalization via data envelopment analysis: An empirical examination. Journal of Supply Chain Management, 37(2), 28-37. doi: https://doi.org/10.1111/j.1745-493X.2001.tb00103.x
• Okpanachi, J. (2011). Comparative analysis of the impact of mergers and acquisitions on Financial efficiency of banks in Nigeria. Journal of Accounting and Taxation, 3(1), 1-7. doi: https://doi.org/10.5897/JAT10.005
• Özdemir, S., Keskin, B., Eren, T. & Özcan, E. (2020). Türkiye’deki lojistik merkezleri yatırım önceliklerinin değerlendirilmesinde çok kriterli karar modeli önerisi. Demiryolu Mühendisliği. 12, 83-94. doi: https://doi.org/10.47072/demiryolu.722626
• Peker, İ., Baki, B., Ar, İ.M. & Tanyaş, M. (2011). Türkiye ve Avrupa lojistik köylerinin karşılaştırmalı analizi. XI. Üretim Araştırmaları Sempozyumu (ss. 526-535).
• Pham, T.Y., Ma, H.M. & Yeo, G.T. (2017). Application of fuzzy delphi TOPSIS to locate logistics centers in Vietnam: The Logisticians’ Perspective. The Asian Journal of Shipping and Logistics, 33(4), 211-219. doi: https://doi.org/10.1016/j.ajsl.2017.12.004
• Pires, H.M. & Fernandes, E. (2012). Malmquist financial efficiency analysis for airlines. Transportation Research Part E: Logistics and Transportation Review, 48(5), 1049-1055. doi: https://doi.org/10.1016/j.tre.2012.03.007
• Shahparvari, S., Nasirian, A., Mohammadi, A., Noori, S. & Chhetri, P. (2020). A GIS-LP ıntegrated approach for the logistics hub location problem. Computers & Industrial Engineering, 146, 106488, 1-17. doi: https://doi.org/10.1016/j.cie.2020.106488
• Tangen, S. (2005). Demystifying productivity and performance. International Journal of Productivity and Performance Management, 54(1), 34-46.
• TCDD, (2019). Demiryolu sektör raporu, Ankara: Strateji Geliştirme Dairesi Başkanlığı.
• Ticaret Bakanlığı, Serbest bölgeler haritası, 27 Ocak 2021 tarihinde https://www.sebatlas.gov.tr/index.aspx, adresinden erişildi.
• Tomić, V., Marinković, D. & Marković, D. (2014). The selection of logistic centers location using multi-criteria comparison: case study of the Balkan Peninsula. Acta Polytechnica Hungarica, 11(10), 97-113.
• Turğut, M., Şahi̇n, Z. & Şahi̇n, A., (2020). Zonguldak'in lojistik potansiyeli ve mevcut durum analizi. IBAD Sosyal Bilimler Dergisi, Özel Sayı, 269-289. doi: https://doi.org/10.21733/ibad.791966
• Turskis, Z. & Zavadskas, E.K. (2010). A new fuzzy additive ratio assessment method (ARAS‐F). case study: The analysis of fuzzy multiple criteria in order to select the logistic centers location. Transport, 25(4), 423-432. doi: https://doi.org/10.3846/transport.2010.52
• Türkiye İstatistik Kurumu, https://data.tuik.gov.tr, 26 Ocak 2021 tarihinde erişildi.
• Yavaş, V. & Özkan Özen, Y.D. (2020). Logistics centers in the new industrial era: A proposed framework for logistics center 4.0. Transportation Research Part E: Logistics and Transportation Review, 135, 101864, 1-18. doi: https://doi.org/10.1016/j.tre.2020.101864
• Yıldız, A. (2007). İmalat sanayi şirketlerinin etkinliklerinin ölçülmesi. Gazi Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 9(2), 91-103.
• Wang, S. & Liu, P. (2007, September). The evaluation study on location selection of logistics center based on fuzzy AHP and TOPSIS. 2007 International conference on wireless communications, networking and mobile computing, pp. 3779-3782. doi: https://doi.org/10.1109/WICOM.2007.935
• Zak, J. & Weglinski, S. (2014). The selection of the logistics center location based on MCDM/A methodology. Transportation Research Procedia, 3, 555-564. doi: https://doi.org/10.1016/j.trpro.2014.10.034