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KÜMELEME ANALİZİ İLE OECD ÜLKELERİ VE SEÇİLMİŞ ÜYE OLMAYAN ÜLKELERİN LOJİSTİK FAALİYETLERİNE DAYALI TAŞIMACILIK SINIFLANDIRILMASI - CLASSIFICATION OF LOGISTICS-BASED TRANSPORTATION ACTIVITIES IN OECD COUNTRIES AND SELECTED NON-MEMBER COUNTRIES THROUGH CLUSTER ANALYSIS

Year 2020, Volume: 7 Issue: 2, 473 - 496, 30.07.2020
https://doi.org/10.30798/makuiibf.720768

Abstract

Lojistik, ürünün yalnızca taşınması değil, ürünün üretiminden başlayarak tüketiciye hatasız bir şekilde ulaştırılmasını sağlayan tüm faaliyetleri kapsamaktadır. Lojistik, süreç temelli olup taşıma faaliyeti, bu sürecin temelini oluşturmaktadır. Bu çalışmada, ülkelerin ekonomik gelişmişliğinde önemli rol oynayan taşımacılık faaliyeti göstergeleri temel alınarak “İktisadi İşbirliği ve Gelişme Teşkilatı” olan OECD ülkeleri ve seçilmiş üye olmayan ülkelerin yer aldığı OECD istatistiklerinden yararlanılarak 47 ülke hiyerarşik ve bulanık kümeleme yardımıyla sınıflandırılmaktır. Çalışmada taşımacılık göstergeleri açısından OECD istatistiklerinde yer alan ülkelerin benzeştiği ve farklılaşma gösterdiği kümeler bulunmuş ve Türkiye’nin ait olduğu küme ortaya konulmuştur.

References

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  • Aldenderfer, M.S. ve Blashfield R.K. (1984). Cluster Analysis, Sage Publications.
  • Alpar, R., (2011). Uygulamalı Çok Değişkenli İstatistiksel Yöntemler, Detay Yayıncılık, Ankara.
  • Bezdek J.C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, NewYork.
  • Bowersox, D.j., Closs, D.J, Cooper, M.B. (2002). Supply Chain Logistics Management, The McGraw Hill/Irwim Series, Ninth Edition, New York.
  • Çancı, M. ve Güngören, M. (2013). “İktisadi Yaşamda Taşımacılık Sektörü”. Elektronik Sosyal Bilimler Dergisi, 12 (45), pp.198-213.
  • Çekerol, G.S. (2019). Türkiye’de Lojistik Stratejileri ve Pratikler, Nisan Kitabevi, Eskişehir. pp.10.
  • Dibb, S. (1998). Market Segmentation: Strategies for success, Marketing Intelligence&Planning, 16/7, pp. 394–406.
  • Döring, C., Lesot, M.J., & Kruse, R. (2006). Data analysis with fuzzy clustering methods. Computational Statistics & Data Analysis, pp. 192 – 214.
  • Everitt, B. (1974). Cluster Analysis, London: Heinemann Educational Books Ltd. Fu, P.H., Yin, H.B., (2012). “Logistics Enterprise Evaluation Model Based On Fuzzy Clustering Analysis”, Physics Procedia 24, pp.1583 – 1587.
  • Hair, J.F., Anderson, R.E., Tatham, R.L. ve Black, W.C. (1998). Multivariate data analysis. (Fifth Edition). Prentice-Hall.
  • Hirschinger, M., Spickermann, A., Hartmann, E., Gracht, H.V.D., Darkow, I.L., (2015). “The Future of Logistics in Emerging Markets—Fuzzy Clustering Scenarios Grounded in Institutional and Factor‐Market Rivalry Theory”, Journal of Supply Chain Management Volume 51, Issue 4.
  • Höppner, F., Klawonn, F., Rudolf, K., Runkler, T. (1999). Fuzzy Cluster Analysis: Methods for Classification Data Analysis and Image Recognition. John Wiley & Sons, pp. 5-75. Johnson, R. A. And D. W. Wichern (1988). Applied Multivariate Statistical Analysis:(2nd Ed.) Prentice Hall, Englewood Cliffs, New Jersey.
  • Keskin, M.H. (2015). Lojistik El Kitabı, Küresel Aktörlerin Lojistik Pratikleri, 2. Basım, Nobel Yayıncılık, Ankara, pp. 65.
  • Korinek, J. ve Sourdin, P. (2011). To what extent are high- quality logistics services trade facilitating? OECD Trade Policy Papers, No. 108, Paris.
  • Koyuncugil, A. S. (2006). Bulanık veri madenciliği ve sermaye piyasalarına uygulanması, Ankara Üniversitesi Fen Bilimleri Enstitüsü İstatistik Anabilim Dalı, Doktora Tezi, Ankara.
  • Lima, F.S., Oliveira, D., Gonçalves, M.B., Samed, M.M.A. (2014). “Humanitarian Logistics: A Clustering Methodology for Assisting Humanitarian Operations”, Journal of Technology Management & Innovation vol.9 no.2 Santiago jul.
  • Malhotra, N. (2007). Marketing Research- An Applied Orientation Upper Saddle River. Prentice Hall.
  • Mansoori, E.G. (2011). FRBC: A Fuzzy Rule-Based Clustering Algorithm. IEEE Transactıons on Fuzzy Systems, 19 (5), 960-971.
  • Meng, W. (2007), “Optimize E-Commerce Supply Chain Based on the Fuzzy Clustering Analysis”, 2007 IEEE International Conference on Automation and Logistics, I.E.E.E.
  • Murtagh, F., Contreras, P., 2017, “Algorithms for hierarchical clustering: an overview II”, WIREs Data Mining and Knowledge Discovery, (7), Number:6, pp. 1-16.
  • Naes T., Mevik T.H., (1999). The Flexibility of Fuzzy Clustering Illustred By Examples, Journal Of Chemo Metrics.
  • Özdamar, K. (2004). Paket programlar ile İstatistiksel Veri Analizi (Çok değişkenli analizler). (5. Baskı). Eskişehir: Kaan Kitabevi.
  • Özdamar, K., (2010). Paket Programlar ile İstatistiksel Veri Analizi Çok Değişkenli Analizler – 2. (7. Baskı). Eskişehir: Kaan Kitabevi.
  • Rafsanjani, M.K., Varzaneh, Z.A., Chukanlo, N.E. (2012). “A survey of hierarchical clustering algorithms”, The Journal of Mathematics and Computer Science, Cilt 5, Sayı 3, pp. 229-240.
  • Ren,Y.C., Xing, T., Quan, Q., Zhao, G.Q. (2010). “Fuzzy Cluster Analysis of Regional City Multi-level Logistics Distribution Center Location Plan”, Quantitative Logic and Soft Computing 2010, pp. 499-508.
  • Ruspini, E.H. (1973). New experimental results in fuzzy clustering. Information Science, 6, pp. 273-284.
  • Siepermann, C. (2003). ‘Logistikkosten’, WiSu-Das Wirtschaftsstudium, Heft:7, 2003, s. 879.
  • Şen, Z., (2004). Mühendislikte Bulanık Mantık ile Modelleme Prensipleri, Su Vakfı Yayınları, İstanbul.
  • Taşkın, E. ve Durmaz, Y. (2012). Lojistik Faaliyetler. Detay Yayıncılık, Ankara.
  • Trappey, C., Trappey, A.J.C., Chang, A.C., Huang, A.Y.L. (2010). “Clustering analysis prioritization of automobile logistics services”, Industrial Management&Data Systems, Vol. 110, No.5, pp.731-743.
  • Wang, L., Wei, L. (2016). “Clustering analysis of dangerous goods transportation of logistics platform based on improved K-means algorithm”, 13th International Conference on Service Systems and Service Management (ICSSSM), Kunming, China.
  • Council of Higher Education, Thesis Center. Retrieved from https://tez.yok.gov.tr/UlusalTezMerkezi/tezSorguSonucYeni.jsp (Accessed 10.01.2020) Council of Supply Chain management Proffesional, https://cscmp.org/ (Accessed 19.12.2019)
  • Repuclic of Turkey, Ministry of Foreign Affair, http://www.mfa.gov.tr/ (Accessed 11.12.2019) Web of Science Group, https://mjl.clarivate.com (Accessed 07.01.2020)
  • Search for peer-review journals, articles, book chapter and open access content, www.sciencedirect.com (Accessed 07.01.2020)
  • Organization for Economic Co-Operation and Development, https://stats.oecd.org/ (Accessed 12.12.2019)

CLASSIFICATION OF LOGISTICS-BASED TRANSPORTATION ACTIVITIES IN OECD COUNTRIES AND SELECTED NON-MEMBER COUNTRIES THROUGH CLUSTER ANALYSIS - KÜMELEME ANALİZİ İLE OECD ÜLKELERİ VE SEÇİLMİŞ ÜYE OLMAYAN ÜLKELERİN LOJİSTİK FAALİYETLERİNE DAYALI TAŞIMACILIK SINIFLANDIRILMASI

Year 2020, Volume: 7 Issue: 2, 473 - 496, 30.07.2020
https://doi.org/10.30798/makuiibf.720768

Abstract

Logistics involves not only transportation of products, but also all the procedures that make it possible to deliver products to customers without any problems starting from production phase. Transport is the basic component of logistics, which is a process-based activity. This study aims to classify 47 countries according to the statistics about OECD (Organization for Economic Cooperation and Development) countries and selected non-member countries through hierarchical clustering and fuzzy clustering in terms of transportation activity indicators, which play an important role in countries' economic development. The study found that there are clusters in which the countries available in OECD statistics are similar or different in terms of transport indicators. The cluster which Turkey belongs to was also presented within the scope of the study.

References

  • Aksoy, Y., Özkan, E.M., Karanfil, S. (2014). Bulanık Mantığa Giriş, 2.Baskı, Yıldız Teknik Üniversitesi Basım Yayın Merkezi, İstanbul.
  • Aldenderfer, M.S. ve Blashfield R.K. (1984). Cluster Analysis, Sage Publications.
  • Alpar, R., (2011). Uygulamalı Çok Değişkenli İstatistiksel Yöntemler, Detay Yayıncılık, Ankara.
  • Bezdek J.C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, NewYork.
  • Bowersox, D.j., Closs, D.J, Cooper, M.B. (2002). Supply Chain Logistics Management, The McGraw Hill/Irwim Series, Ninth Edition, New York.
  • Çancı, M. ve Güngören, M. (2013). “İktisadi Yaşamda Taşımacılık Sektörü”. Elektronik Sosyal Bilimler Dergisi, 12 (45), pp.198-213.
  • Çekerol, G.S. (2019). Türkiye’de Lojistik Stratejileri ve Pratikler, Nisan Kitabevi, Eskişehir. pp.10.
  • Dibb, S. (1998). Market Segmentation: Strategies for success, Marketing Intelligence&Planning, 16/7, pp. 394–406.
  • Döring, C., Lesot, M.J., & Kruse, R. (2006). Data analysis with fuzzy clustering methods. Computational Statistics & Data Analysis, pp. 192 – 214.
  • Everitt, B. (1974). Cluster Analysis, London: Heinemann Educational Books Ltd. Fu, P.H., Yin, H.B., (2012). “Logistics Enterprise Evaluation Model Based On Fuzzy Clustering Analysis”, Physics Procedia 24, pp.1583 – 1587.
  • Hair, J.F., Anderson, R.E., Tatham, R.L. ve Black, W.C. (1998). Multivariate data analysis. (Fifth Edition). Prentice-Hall.
  • Hirschinger, M., Spickermann, A., Hartmann, E., Gracht, H.V.D., Darkow, I.L., (2015). “The Future of Logistics in Emerging Markets—Fuzzy Clustering Scenarios Grounded in Institutional and Factor‐Market Rivalry Theory”, Journal of Supply Chain Management Volume 51, Issue 4.
  • Höppner, F., Klawonn, F., Rudolf, K., Runkler, T. (1999). Fuzzy Cluster Analysis: Methods for Classification Data Analysis and Image Recognition. John Wiley & Sons, pp. 5-75. Johnson, R. A. And D. W. Wichern (1988). Applied Multivariate Statistical Analysis:(2nd Ed.) Prentice Hall, Englewood Cliffs, New Jersey.
  • Keskin, M.H. (2015). Lojistik El Kitabı, Küresel Aktörlerin Lojistik Pratikleri, 2. Basım, Nobel Yayıncılık, Ankara, pp. 65.
  • Korinek, J. ve Sourdin, P. (2011). To what extent are high- quality logistics services trade facilitating? OECD Trade Policy Papers, No. 108, Paris.
  • Koyuncugil, A. S. (2006). Bulanık veri madenciliği ve sermaye piyasalarına uygulanması, Ankara Üniversitesi Fen Bilimleri Enstitüsü İstatistik Anabilim Dalı, Doktora Tezi, Ankara.
  • Lima, F.S., Oliveira, D., Gonçalves, M.B., Samed, M.M.A. (2014). “Humanitarian Logistics: A Clustering Methodology for Assisting Humanitarian Operations”, Journal of Technology Management & Innovation vol.9 no.2 Santiago jul.
  • Malhotra, N. (2007). Marketing Research- An Applied Orientation Upper Saddle River. Prentice Hall.
  • Mansoori, E.G. (2011). FRBC: A Fuzzy Rule-Based Clustering Algorithm. IEEE Transactıons on Fuzzy Systems, 19 (5), 960-971.
  • Meng, W. (2007), “Optimize E-Commerce Supply Chain Based on the Fuzzy Clustering Analysis”, 2007 IEEE International Conference on Automation and Logistics, I.E.E.E.
  • Murtagh, F., Contreras, P., 2017, “Algorithms for hierarchical clustering: an overview II”, WIREs Data Mining and Knowledge Discovery, (7), Number:6, pp. 1-16.
  • Naes T., Mevik T.H., (1999). The Flexibility of Fuzzy Clustering Illustred By Examples, Journal Of Chemo Metrics.
  • Özdamar, K. (2004). Paket programlar ile İstatistiksel Veri Analizi (Çok değişkenli analizler). (5. Baskı). Eskişehir: Kaan Kitabevi.
  • Özdamar, K., (2010). Paket Programlar ile İstatistiksel Veri Analizi Çok Değişkenli Analizler – 2. (7. Baskı). Eskişehir: Kaan Kitabevi.
  • Rafsanjani, M.K., Varzaneh, Z.A., Chukanlo, N.E. (2012). “A survey of hierarchical clustering algorithms”, The Journal of Mathematics and Computer Science, Cilt 5, Sayı 3, pp. 229-240.
  • Ren,Y.C., Xing, T., Quan, Q., Zhao, G.Q. (2010). “Fuzzy Cluster Analysis of Regional City Multi-level Logistics Distribution Center Location Plan”, Quantitative Logic and Soft Computing 2010, pp. 499-508.
  • Ruspini, E.H. (1973). New experimental results in fuzzy clustering. Information Science, 6, pp. 273-284.
  • Siepermann, C. (2003). ‘Logistikkosten’, WiSu-Das Wirtschaftsstudium, Heft:7, 2003, s. 879.
  • Şen, Z., (2004). Mühendislikte Bulanık Mantık ile Modelleme Prensipleri, Su Vakfı Yayınları, İstanbul.
  • Taşkın, E. ve Durmaz, Y. (2012). Lojistik Faaliyetler. Detay Yayıncılık, Ankara.
  • Trappey, C., Trappey, A.J.C., Chang, A.C., Huang, A.Y.L. (2010). “Clustering analysis prioritization of automobile logistics services”, Industrial Management&Data Systems, Vol. 110, No.5, pp.731-743.
  • Wang, L., Wei, L. (2016). “Clustering analysis of dangerous goods transportation of logistics platform based on improved K-means algorithm”, 13th International Conference on Service Systems and Service Management (ICSSSM), Kunming, China.
  • Council of Higher Education, Thesis Center. Retrieved from https://tez.yok.gov.tr/UlusalTezMerkezi/tezSorguSonucYeni.jsp (Accessed 10.01.2020) Council of Supply Chain management Proffesional, https://cscmp.org/ (Accessed 19.12.2019)
  • Repuclic of Turkey, Ministry of Foreign Affair, http://www.mfa.gov.tr/ (Accessed 11.12.2019) Web of Science Group, https://mjl.clarivate.com (Accessed 07.01.2020)
  • Search for peer-review journals, articles, book chapter and open access content, www.sciencedirect.com (Accessed 07.01.2020)
  • Organization for Economic Co-Operation and Development, https://stats.oecd.org/ (Accessed 12.12.2019)
There are 36 citations in total.

Details

Primary Language English
Journal Section Research Articles
Authors

Gülsen Serap Çekerol 0000-0003-0391-2489

Publication Date July 30, 2020
Submission Date April 15, 2020
Published in Issue Year 2020 Volume: 7 Issue: 2

Cite

APA Çekerol, G. S. (2020). CLASSIFICATION OF LOGISTICS-BASED TRANSPORTATION ACTIVITIES IN OECD COUNTRIES AND SELECTED NON-MEMBER COUNTRIES THROUGH CLUSTER ANALYSIS - KÜMELEME ANALİZİ İLE OECD ÜLKELERİ VE SEÇİLMİŞ ÜYE OLMAYAN ÜLKELERİN LOJİSTİK FAALİYETLERİNE DAYALI TAŞIMACILIK SINIFLANDIRILMASI. Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 7(2), 473-496. https://doi.org/10.30798/makuiibf.720768

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