FORTUNE 500 LİSTESİNDE YER ALAN LOJİSTİK FİRMALARIN DEĞERLENDİRİLMESİ

p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica; min-height: 14.0px} p.p2 {margin: 0.0px 0.0px 0.0px 0.0px; font: 9.0px Helvetica} span.s1 {font: 12.0px Helvetica} Bu çalışma ile 2017 Fortune listesinde yer alan ve lojistik sektöründe ulusal ve uluslararası faaliyette bulunan 8 adet firmanın çok kriterli karar verme (ÇKKV) yöntemleri ile performans ölçümü için bir model geliştirilmiştir. Modelde; işletmelerin performanslarının değerlendirilmesinde son yıllarda çokça kullanılan ÇKKV yöntemlerinden SWARA, COPRAS, GİA ve TOPSIS yaklaşımları bütünleşik olarak kullanılmıştır. Modelin ilk aşamasında değerlendirme kriterleri tespit edilmiştir. Daha sonra, beş farklı uzman tarafından SWARA yöntemine göre kriterlerin ağırlıkları belirlenmiştir. Modelin son kısmında ise, COPRAS, GİA ve TOPSIS yöntemlerine göre belirlenen kriterler dikkate alınarak firmaların performansları ölçülmüştür. Modelin uygulanması neticesinde en önemli kriterin 0,176 ile Net Satış (K1) ve performansı en yüksek olan firmanın ise her üç yönteme göre “Netlog” olduğu ortaya konmuştur.

Anahtar Kelimeler:

Çok Kriterli Karar Verme, ÇKKV, Lojistik, Gri İlişkisel Analiz, GİA, SWARA, COPRAS, TOPSIS

EVALUATION OF THE LOGISTICS COMPANIES ON THE LIST OF FORTUNE 500

With this study, a model has been developed by Multi-Criteria Decision Making Methods (MCDM) to measure the performance of 8 companies that were listed on Fortune 2017 and operating in land and field logistics both nationally and internationally. In the model, SWARA, COPRAS, GIA, and TOPSIS approaches, some of the most frequently used MCDM methods recently to evaluate the performance of companies, were used. Evaluation criteria were determined in the first part of the study. Then the ratio of the criteria were assigned according to SWARA method by five different experts. In the last part of the study, the performance of the companies was measured considering the criteria that were determined according to COPRAS, GIA, and TOPSIS. The study revealed that the most important criterion was Net Sales (K1) by 0,176; and the company with the highest performance was “Netlog” according to the three methods. p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica; min-height: 14.0px} p.p2 {margin: 0.0px 0.0px 0.0px 0.0px; font: 9.0px Helvetica} span.s1 {font: 12.0px Helvetica}

Keywords:

Multi Criteria Decision Making, Logistics, Gray Relational Analysis, SWARA, COPRAS, TOPSIS,

PDF

___

Alfaro, J., Ortiz, A. ve Poler, R. (2007), “Performance Measurement System for Business Processes”, Production Planning Control, 18(8), 641-654.
Başdeğirmen, A. ve Tunca, M. Z. (2017), “Lojistik Sektöründe Faaliyet Gösteren İşletmelerin Finansal Performanslarının Gri İlişkisel Analiz İle Değerlendirilmesi”, SDÜ, İİBF Dergisi, 22(2), 327-340.
Çakir, S. ve Perçin, S. (2013), “Çok Kriterli Karar Verme Teknikleriyle Lojistik Firmalarında Performans Ölçümü”, Ege Akademik Bakış, 13(4), 449-459.
Cheng, S., Chan, C. W. ve Huang G. H. (2002), “Using Multiple Criteria Decision Analysis for Supporting Decisions of Solid Waste Management”, Journal of Environment Science Health, Part A, 37(6), 975-990.
Fortune (2017), http://www.fortuneturkey.com/fortune500, (17.10.2017).
Jharkharia, S. ve Shankar, R. (2007), “Selection of logistics service provider: An analytic network Process (ANP) approach”, International Journal of Management Science, 35, 274 – 289.
Kaklauskas, A., Zavadskas, E. K. ve Raslanas, S. (2005), “Multivariant design and multiple criteria analysis of building refurbishments”, Energy and Buildings, 37(4), 361-372.
Keršulienė, V. ve Turskis, Z. (2011), “Integrated fuzzy multiple criteria decision making model for architect selection”, Technological and Economic Development of Economy, 17(4), 645-666.
Keršulienė, V., Zavadskas, E. K. ve Turskis, Z. (2010), “Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (SWARA)”, Journal of Business Economics and Management, 11(2), 243-258.
Lambert, D. M., Stock, J. R. ve Ellram, L. M. (1998), Fundamentals Of Logistics Management, USA: Irwin -Mc Graw Hill.
Meade, L. ve Sarkis, J. (2002), “A conceptual model for selecting and evaluating third-party reverse logistics providers”, Supply Chain Management: An International Journal, 7(5), 283-295.
Min, H. ve Joo, S. J. (2006), “Benchmarking the operational efficiency of third party logistics using data envelopment Analysis”, Supply Chain Management, 11(3), 259–265.
Özbek, A. (2017), Çok Kriterli Karar Verme Yöntemleri ve Excel İle Problem Çözümü. 1. Baskı, Ankara: Seçkin Yayıncılık.
Özbek, A. ve Eren, T. (2012), “Üçüncü Parti Lojistik (3PL) Firmanın Analitik Hiyerarşi Süreciyle (AHS) Belirlenmesi”, International Journal of Engineering Research and Development, 4(2), 46-54.
Özbek, A. ve Eren, T. (2013a), “Çok Ölçütlü Karar Verme Teknikleri İle Hizmet Sağlayıcı Seçimi”, Akademik Bakış Dergisi, 36, 1-22.
Özbek, A. ve Eren, T. (2013b), “Analitik Ağ Süreci Yaklaşımıyla Üçüncü Parti Lojistik (3PL) Firma Seçimi”, Atatürk Üniversitesi İktisadi Ve İdari Bilimler Dergisi, 27(1), 95-113.
Peters, M. L. ve Zelewski, S. (2007), “TOPSIS als Technik zur Effiziensanalyse”, Zeitschrift für Ausbildung und Hochschulkontakt, 1-9.
Qureshi, M. N., Kumar, D. ve Kumar, P. (2007), “Performance Evaluation of 3PL Services Provider Using AHP and TOPSIS: A Case Study”, The Icfai Journal of Supply Chain Management, 4 (3), 20-38
Ruzgys, A., Volvačiovas, R., Ignatavičius, Č. ve Turskis, Z. (2014), “Integrated evaluation of external wall insulation in residential buildings using SWARA-TODIM MCDM method”, Journal of Civil Engineering and Management, 20(1), 103-110.
Stanujkic, D., Karabašević, D. ve Zavadskas, E. K. (2015), “A framework for the selection of a packaging design based on the SWARA method”, Inzinerine Ekonomika - Engineering Economics, 26(2), 181-187.
Sun, C., Pan, Y. ve Bi, R. (2010), “Study on third-party logistics service provider selection evaluation indices system based on analytic network process with BOCR”, Logistics Systems and Intelligent Management, International Conference on, 1013–1017.
TDK (2017), Türk Dil Kurumu, http://www.tdk.gov.tr, (30.09.2017).
Triantaphyllou, E. (2000), Multi-Criteria Decision Making Methods: A Comparative Study, Netherlands: Kluwer Academic Publishers.
Üstünışık, N. Z. (2007), “Türkiye'deki iller ve Bölgeler Bazında Sosyo-Ekonomik Gelişmişlik Sıralaması Araştırması: Gri ilişkisel Analiz Yöntemi ve Uygulaması”, Yüksek Lisans Tezi, Gazi Üniversitesi Fen Bilimleri Enstitüsü, Ankara.
Vijayvargiya, A. ve Dey, A. K. (2010), “An analytical approach for selection of a logistics provider”, Management Decision, 48(3), 403 – 418.
Wang, Y. M. ve Elhag T. M. S. (2005), “Fuzzy TOPSIS Method Based on Alpha Level Sets with An Application to Bridge Risk Assessment”, Expert Systems with Applications, 31(2), 309-319.
Wu, H. H. (2002), “A Comparative Study Of Using Grey Relational Analysis in Multiple Attribute Decision Making Problems”, QualityEngineering, 15(2), 209-217.
Yıldırım, B. F., Önder, E. (2014), Çok Kriterli Karar Verme Yöntemleri, 1. Baskı, Bursa: Dora Yayıncılık.
Zavadskas, E.K., Kaklauskas, A., Banaitis, A. ve Kvederyte, N. (2004), “Housing credit access model: The case for Lithuania”, European Journal of Operational Research, 155 (2), 335-352
Zolfani, S. H., Zavadskas, E. K. ve Turskis, Z. (2013), “Design of products with both International and Local perspectives based on Yin-Yang balance theory and SWARA method”, Economic Research-Ekonomska Istraživanja, 26(2), 153-166.