Hilal H. ERDOĞAN, Gökhan KIRBAÇ

Financial Performance Measurement of Logistics Companies Based on Entropy and Waspas Methods

Purpose – The aim of this study is to measure financial performance of the logistics companies in the Fortune500 list. Companies' financials are used to enlighten the logistics sector and supply chain operations byrevealing the developments between 2015 and 2019. Design/Methodology/Approach – The performance of the companies is evaluated by utilizing multi-criteriadecision-making techniques. As the best performance criteria affecting the companies’ performance isdetermined by Entropy Method, the company with the best performance is determined by WASPASMethod. Selected financial measurements are net sales, earnings before interest and taxes (EBIT), total assets, totalequity, and the amount of export. Findings – It can be reached from the results that Export was determined as the best performance criteriaaffecting the logistics companies’ performance for all the years between 2015 and 2019. On the other hand,while Netlog is the best alternative in the year of 2015, 2016 and 2019, Borusan is the best alternative in theyear of 2017. Discussion – Supply chain and logistics processes have critical importance in delivering the goods andservices of businesses to the final consumers. Hence, logistics companies need to determine performancemeasurements while performing these services. It is believed that this study will be useful for the companieswho trade in different sectors and will contribute to the finance literature as a reference for further studies.In this context, because of their quite comprehensive natures, both Entropy and WASPAS methods can beconducted to any manufacturing-related decision-making processes.

PDF

___

Akçakanat, Ö., Eren, H., Aksoy, E. and Ömürbek, V. (2018) .Bankacılık Sektöründe Entropi ve WASPAS Yöntemleri ile Performans Değerlendirmesi, Suleyman Demirel University the Journal of Faculty of Economics and Administrative Sciences, 22(2), 285-300.
Ayçin, E. and Güçlü, P. (2020). BIST Ticaret Endeksinde Yer Alan İşletmelerin Finansal Performanslarının Entropi ve MAIRCA Yöntemleri ile Değerlendirilmesi, The Journal of Accounting and Finance, 85, 287-312.
Bağcı, H. and Kaygın, C. Y. (2020). The Financial Performance Measurement of the Companies Listed in the BIST Holding and Investment Index by the MCDM Methods, The Journal of Accounting and Finance, 87, 301-324.
Bağcı, H. and Yiğiter, Ş. Y. (2019). BİST’te Yer Alan Enerji Şirketlerinin Finansal Performansının SD ve WASPAS Yöntemleriyle Ölçülmesi, Bingöl University Journal of Social Sciences Institute, 9(18), 877-898.
Beamon, B. M. (1999). Measuring Supply Chain Performance. International Journal of Operations and Production Management, 19(3), 275–292.
Chakraborty, S. and Zavadskas, E. K. (2014). Applications of WASPAS Method in Manufacturing Decision Making, Informatica, 25(1), 1-20.
Christopher, M. (2011). Logistics and Supply Chain Management: Creating Value-Adding Networks, 4 ed., Pearson UK.
Ghorabaee, M. K., Amiri, M., Zavadskas, E. K. and Antuchevičienė, J. (2017). Assessment of Third-Party Logistics Providers Using a CRITIC–WASPAS Approach with Interval Type-2 Fuzzy Sets, Transport, 32(1), 66-78.
Gunasekaran, A., Patel, C., & Tirtiroglu, E. (2001). Performance Measures and Metrics in a Supply Chain Environment. International Journal of Operations and Production Management, 21(1–2), 71– 87.
Gunasekaran, A. and Kobu, B. (2007). Performance Measures and Metrics in Logistics and Supply Chain Management: A Review of Recent Literature (1995–2004) for Research and Applications, International Journal of Production Research, 45(12), 2819–2840.
Özaydın, G., Kayahan Karakul, A. (2021). Entropi Tabanlı MAUT, SAW ve EDAS Yöntemleri ile Finansal Performans Değerlendirmesi, Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 26 (1), 13-29.
Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27, 379-423.
Skowron-Grabowska, B. (2009). Performance Measurement in Logistics Centers, Advanced Logistic Systems, 3(1), 213-218.
Strouhal, J., Stamfestova, P., Kljucnikov, A. and Vincurova, Z. (2018). Different Approaches to the EBIT Construction and Their Impact on Corporate Financial Performance based on the Return on Assets: Some Evidence from Czech Top100 Companies, Journal of Competitiveness, 10(1), 144-154.
Ulutaş, A. (2019). Entropi Tabanlı EDAS Yöntemi ile Lojistik Firnalarının Performans Analizi, International Journal of Economics and Administrative Studies, 23, 53-66.
Ural, M., Demireli, E. and Özçalık, S.G. (2018). Kamu Bankalarında Performans Analizi: Entropi ve WASPAS Yöntemleri ile Bir Uygulama, Pamukkale University Journal of Social Sciences Institute,31, 129-141.
Wu, J., Sun, J., Liang, L. and Zha, Y. (2011). Determination of Weights for Ultimate Cross Efficiency Using Shannon Entropy, Expert Systems with Applications, 38, 5162-5165.
Xiong, L., Zhong, S., Liu, S., Zhang, X. and Yanfeng, L.(2020). An Approach for Resilient-Green Supplier Selection Based on WASPAS, BWM, and TOPSIS under Intuitionistic Fuzzy Sets, Mathematical Problems in Engineering, 2020, 1-18.
Zavadskas, E.K., Turskis, Z. and Antucheviciene, J. (2012). Optimization of Weighted Aggregated Sum Product Assessment, Electronics and Electrical Engineering, 6(122), 3-6.
Zolfani, S. H., Aghdaie, M. H., Derakhti, A., Zavadskas, E. K. and Varzandeh, M. H. M. (2013). Decision Making on Business Issues with Foresight Perspective; An Application of New Hybrid MCDM Model in Shopping Mall Locating, Expert Systems with Applications, 40(17), 7111-7121.