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MEREC ve WEDBA Yöntemleri ile Bir Lojistik Firmasının Yıllara Göre Performansının Değerlendirilmesi

Year 2022, Issue: 33, 363 - 372, 31.01.2022
https://doi.org/10.31590/ejosat.1041106

Abstract

Lojistik şirketlerinin performans değerlendirilmesinin yapılması ile bu şirketlerin faaliyetlerini gerçekleştirirken ne kadar verimli olduğu tespit edilebilir. Ayrıca bu şirketler performans değerlendirme sonucuna istinaden şirket hedeflerini ne derece gerçekleştirebildiğini görmüş olacaklardır ve performanslarını rakip firmalar ile kıyaslayarak güçlü ve zayıf yönlerini daha iyi fark edeceklerdir. Bu çalışmada bir lojistik işletmesinin yıllara göre performansı değerlendirilecektir. Değerlendirmede MEREC ve WEDBA yöntemleri kullanılacaktır. MEREC yöntemi ile kriter ağırlıkları bulunurken WEDBA yöntemi ile yıllar sıralanacaktır. Bu çalışma ile MEREC yöntemi yerel literatüre tanıtılıcaktır. Ayrıca MEREC ve WEDBA yöntemleri ilk defa birlikte kullanılacaktır. Böylece literatüre katkıda bulunulacaktır.

References

  • Aguezzoul, A., & Pires, S. (2016). 3PL performance evaluation and selection: a MCDM method. In Supply Chain Forum: An International Journal, 17(2), 87-94.
  • Al-Hawari, T., Naji, A., Alshraideh, H., & Bataineh, O. (2019). Extending the WEDBA to the fuzzy multi-criteria decision-making environment. International Journal of Computer Applications in Technology, 59(4), 330-346.
  • Ayaydın, H., Durmuş, S., & Pala, F. (2017). Gri İlişkisel Analiz Yöntemiyle Türk Lojistik Firmalarında Performans Ölçümü. Gümüshane Üniversitesi Sosyal Bilimler Enstitüsü Elektronik Dergisi, 8(21), 76-94.
  • Aydın, S., Yörükoğlu, M., & Kabak, M.(2021). Fourth party logistics firm assessment using a novel neutrosophic MCDM. Journal of Intelligent & Fuzzy Systems, (Yayın Aşamasında), 1-11.
  • Basar, M., & Tolga, A. C. (2020, July). Smart System Evaluation in Vertical Farming via Fuzzy WEDBA Method. In International Conference on Intelligent and Fuzzy Systems (pp. 534-542). Springer, Cham.
  • Chen, C. T., Pai, P. F., & Hung, W. Z. (2010). An integrated methodology using linguistic PROMETHEE and maximum deviation method for third-party logistics supplier selection. International Journal of Computational Intelligence Systems, 3(4), 438-451.
  • Çakır, S., & Perçin, S. (2013). Çok Kriterli Karar Verme Teknikleriyle Lojistik Firmalarında Performans Ölçümü. Ege Akademik Bakış, 13(4), 449-459.
  • Demir, G. (2021). Vakıf Üniversitelerinde Akademik Performans Analizi: CRITIC-WEDBA Bütünleşik Model Uygulaması. Uluslararası İktisadi ve İdari Akademik Araştırmalar Dergisi, 1(1), 39-50.
  • Eren, T., & Gür, S. (2017). Selection of 3PL company for online shopping sites with AHP and TOPSIS method. Hitit University Journal of Social Sciences Institute, 10(2), 819-834.
  • Fawcett, S. E., & Cooper, M. B. (1998). Logistics performance measurement and customer success. Industrial Marketing Management, 27(4), 341-357.
  • Garg, R. (2017). Optimal selection of E‐learning websites using multiattribute decision‐making approaches. Journal of Multi‐Criteria Decision Analysis, 24(3-4), 187-196.
  • Ghorabaee, M. K., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2021). Determination of Objective Weights Using a New Method Based on the Removal Effects of Criteria (MEREC). Symmetry, 13(4), 525.
  • Ghorabaee, M. K. (2021). Assessment of distribution center locations using a multi-expert subjective–objective decision-making approach. Scientific Reports, 11(1), 1-19.
  • Goswami, S. S., Mohanty, S. K., & Behera, D. K. (2021). Selection of a green renewable energy source in India with the help of MEREC integrated PIV MCDM tool. Materials Today: Proceedings, (Yayın Aşamasında).
  • Görener, A. (2009). Kesici Takım Tedarikçisi Seçiminde Analitik Ağ Sürecinin Kullanımı. Journal of Aeronautics and Space Technologies, 4(1), 99-110.
  • https://www.ekol.com/tr/kurumsal/rakamlarla-ekol-lojistik/ciro/ (Erişim Zamanı: 20/12/2021)
  • https://www.fortuneturkey.com/fortune500 (Erişim Zamanı: 20/10/2021)
  • Ishizaka, A. (2014). Comparison of fuzzy logic, AHP, FAHP and hybrid fuzzy AHP for new supplier selection and its performance analysis. International Journal of Integrated Supply Management, 9(1-2), 1-22.
  • Jain, V., & Ajmera, P. (2019). Application of MADM methods as MOORA and WEDBA for ranking of FMS flexibility. International Journal of Data and Network Science, 3(2), 119-136.
  • Jayant, A., & Singh, P. (2015). Application of AHP-VIKOR hybrid MCDM approach for 3PL selection: a case study. International Journal of Computer Applications (IJCA), 125(5), 4-11.
  • Jovčić, S., & Průša, P. (2021). A Hybrid MCDM Approach in Third-Party Logistics (3PL) Provider Selection. Mathematics, 9(21), 2729.
  • Khan, S. A., Ahmed, W., & Ubaid, A. (2020, October). A Decision Support System for Logistics Performance Evaluation of Courier Company. In 2020 5th International Conference on Logistics Operations Management (GOL) (pp. 1-5). IEEE.
  • Kısa, A. C. G., & Ayçin, E. (2019). OECD Ülkelerinin Lojistik Performanslarının SWARA Tabanlı EDAS Yöntemi ile Değerlendirilmesi. Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 9(1), 301-326.
  • Li, Y. L., Ying, C. S., Chin, K. S., Yang, H. T., & Xu, J. (2018). Third-party reverse logistics provider selection approach based on hybrid-information MCDM and cumulative prospect theory. Journal of Cleaner Production, 195, 573-584.
  • Mercangoz, B. A., Yildirim, B. F., & Yildirim, S. K. (2020). Time period based COPRAS-G method: application on the Logistics Performance Index. LogForum, 16(2).
  • Özbek, A., & Eren, T. (2012). Üçüncü parti lojistik (3PL) firmanın analitik hiyerarşi süreciyle (AHS) belirlenmesi. International Journal of Engineering Research and Development, 4(2), 46-54.
  • Rao, R. V., & Singh, D. (2011). Evaluating flexible manufacturing systems using Euclidean distance-based integrated approach. International Journal of Decision Sciences, Risk and Management, 3(1-2), 32-53.
  • Rezaei, J., van Roekel, W. S., & Tavasszy, L. (2018). Measuring the relative importance of the logistics performance index indicators using Best Worst Method. Transport Policy, 68, 158-169.
  • Senir, G. (2021). Comparison of Domestic Logistics Performances of Turkey and European Union Countries in 2018 With an Integrated Model. LogForum, 17(2), 193-204.
  • Ulutaş, A. (2020). Stacker Selection with PSI and WEDBA Methods. International Journal of Contemporary Economics and Administrative Sciences, 10(2), 493-504.
  • Ulutaş, A. (2019). Entropi Tabanlı EDAS Yöntemi ile Lojistik Firmalarının Performans Analizi. Uluslararası İktisadi ve İdari İncelemeler Dergisi, (23), 53-66.
  • Yalçın, B., & Ayvaz, B. (2020). Çok Kriterli Karar Verme Teknikleri ile Lojistik Performansın Değerlendirilmesi. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 19(38), 117-138.

The Evaluation of the Performance of a Logistics Company by Years with MEREC and WEDBA Methods

Year 2022, Issue: 33, 363 - 372, 31.01.2022
https://doi.org/10.31590/ejosat.1041106

Abstract

With the performance evaluation of logistics companies, it can be determined how efficient these companies are while performing their activities. In addition, these companies will have seen to what extent they have achieved the company's goals based on the performance evaluation result, and they will be able to better realize their strengths and weaknesses by comparing their performance with rival companies. In this study, the performance of a logistics company according to years will be evaluated. MEREC and WEDBA methods will be used in the evaluation. While the criteria weights are found with the MEREC method, the years will be sorted with the WEDBA method. With this study, the MEREC method will be introduced to the local literature. In addition, MEREC and WEDBA methods will be used together for the first time. Thus, it will contribute to the literature.

References

  • Aguezzoul, A., & Pires, S. (2016). 3PL performance evaluation and selection: a MCDM method. In Supply Chain Forum: An International Journal, 17(2), 87-94.
  • Al-Hawari, T., Naji, A., Alshraideh, H., & Bataineh, O. (2019). Extending the WEDBA to the fuzzy multi-criteria decision-making environment. International Journal of Computer Applications in Technology, 59(4), 330-346.
  • Ayaydın, H., Durmuş, S., & Pala, F. (2017). Gri İlişkisel Analiz Yöntemiyle Türk Lojistik Firmalarında Performans Ölçümü. Gümüshane Üniversitesi Sosyal Bilimler Enstitüsü Elektronik Dergisi, 8(21), 76-94.
  • Aydın, S., Yörükoğlu, M., & Kabak, M.(2021). Fourth party logistics firm assessment using a novel neutrosophic MCDM. Journal of Intelligent & Fuzzy Systems, (Yayın Aşamasında), 1-11.
  • Basar, M., & Tolga, A. C. (2020, July). Smart System Evaluation in Vertical Farming via Fuzzy WEDBA Method. In International Conference on Intelligent and Fuzzy Systems (pp. 534-542). Springer, Cham.
  • Chen, C. T., Pai, P. F., & Hung, W. Z. (2010). An integrated methodology using linguistic PROMETHEE and maximum deviation method for third-party logistics supplier selection. International Journal of Computational Intelligence Systems, 3(4), 438-451.
  • Çakır, S., & Perçin, S. (2013). Çok Kriterli Karar Verme Teknikleriyle Lojistik Firmalarında Performans Ölçümü. Ege Akademik Bakış, 13(4), 449-459.
  • Demir, G. (2021). Vakıf Üniversitelerinde Akademik Performans Analizi: CRITIC-WEDBA Bütünleşik Model Uygulaması. Uluslararası İktisadi ve İdari Akademik Araştırmalar Dergisi, 1(1), 39-50.
  • Eren, T., & Gür, S. (2017). Selection of 3PL company for online shopping sites with AHP and TOPSIS method. Hitit University Journal of Social Sciences Institute, 10(2), 819-834.
  • Fawcett, S. E., & Cooper, M. B. (1998). Logistics performance measurement and customer success. Industrial Marketing Management, 27(4), 341-357.
  • Garg, R. (2017). Optimal selection of E‐learning websites using multiattribute decision‐making approaches. Journal of Multi‐Criteria Decision Analysis, 24(3-4), 187-196.
  • Ghorabaee, M. K., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2021). Determination of Objective Weights Using a New Method Based on the Removal Effects of Criteria (MEREC). Symmetry, 13(4), 525.
  • Ghorabaee, M. K. (2021). Assessment of distribution center locations using a multi-expert subjective–objective decision-making approach. Scientific Reports, 11(1), 1-19.
  • Goswami, S. S., Mohanty, S. K., & Behera, D. K. (2021). Selection of a green renewable energy source in India with the help of MEREC integrated PIV MCDM tool. Materials Today: Proceedings, (Yayın Aşamasında).
  • Görener, A. (2009). Kesici Takım Tedarikçisi Seçiminde Analitik Ağ Sürecinin Kullanımı. Journal of Aeronautics and Space Technologies, 4(1), 99-110.
  • https://www.ekol.com/tr/kurumsal/rakamlarla-ekol-lojistik/ciro/ (Erişim Zamanı: 20/12/2021)
  • https://www.fortuneturkey.com/fortune500 (Erişim Zamanı: 20/10/2021)
  • Ishizaka, A. (2014). Comparison of fuzzy logic, AHP, FAHP and hybrid fuzzy AHP for new supplier selection and its performance analysis. International Journal of Integrated Supply Management, 9(1-2), 1-22.
  • Jain, V., & Ajmera, P. (2019). Application of MADM methods as MOORA and WEDBA for ranking of FMS flexibility. International Journal of Data and Network Science, 3(2), 119-136.
  • Jayant, A., & Singh, P. (2015). Application of AHP-VIKOR hybrid MCDM approach for 3PL selection: a case study. International Journal of Computer Applications (IJCA), 125(5), 4-11.
  • Jovčić, S., & Průša, P. (2021). A Hybrid MCDM Approach in Third-Party Logistics (3PL) Provider Selection. Mathematics, 9(21), 2729.
  • Khan, S. A., Ahmed, W., & Ubaid, A. (2020, October). A Decision Support System for Logistics Performance Evaluation of Courier Company. In 2020 5th International Conference on Logistics Operations Management (GOL) (pp. 1-5). IEEE.
  • Kısa, A. C. G., & Ayçin, E. (2019). OECD Ülkelerinin Lojistik Performanslarının SWARA Tabanlı EDAS Yöntemi ile Değerlendirilmesi. Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 9(1), 301-326.
  • Li, Y. L., Ying, C. S., Chin, K. S., Yang, H. T., & Xu, J. (2018). Third-party reverse logistics provider selection approach based on hybrid-information MCDM and cumulative prospect theory. Journal of Cleaner Production, 195, 573-584.
  • Mercangoz, B. A., Yildirim, B. F., & Yildirim, S. K. (2020). Time period based COPRAS-G method: application on the Logistics Performance Index. LogForum, 16(2).
  • Özbek, A., & Eren, T. (2012). Üçüncü parti lojistik (3PL) firmanın analitik hiyerarşi süreciyle (AHS) belirlenmesi. International Journal of Engineering Research and Development, 4(2), 46-54.
  • Rao, R. V., & Singh, D. (2011). Evaluating flexible manufacturing systems using Euclidean distance-based integrated approach. International Journal of Decision Sciences, Risk and Management, 3(1-2), 32-53.
  • Rezaei, J., van Roekel, W. S., & Tavasszy, L. (2018). Measuring the relative importance of the logistics performance index indicators using Best Worst Method. Transport Policy, 68, 158-169.
  • Senir, G. (2021). Comparison of Domestic Logistics Performances of Turkey and European Union Countries in 2018 With an Integrated Model. LogForum, 17(2), 193-204.
  • Ulutaş, A. (2020). Stacker Selection with PSI and WEDBA Methods. International Journal of Contemporary Economics and Administrative Sciences, 10(2), 493-504.
  • Ulutaş, A. (2019). Entropi Tabanlı EDAS Yöntemi ile Lojistik Firmalarının Performans Analizi. Uluslararası İktisadi ve İdari İncelemeler Dergisi, (23), 53-66.
  • Yalçın, B., & Ayvaz, B. (2020). Çok Kriterli Karar Verme Teknikleri ile Lojistik Performansın Değerlendirilmesi. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 19(38), 117-138.
There are 32 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Melike Toslak 0000-0002-0417-2796

Beyza Aktürk 0000-0002-1172-2127

Alptekin Ulutaş 0000-0002-8130-1301

Early Pub Date January 30, 2022
Publication Date January 31, 2022
Published in Issue Year 2022 Issue: 33

Cite

APA Toslak, M., Aktürk, B., & Ulutaş, A. (2022). MEREC ve WEDBA Yöntemleri ile Bir Lojistik Firmasının Yıllara Göre Performansının Değerlendirilmesi. Avrupa Bilim Ve Teknoloji Dergisi(33), 363-372. https://doi.org/10.31590/ejosat.1041106

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