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Performance Measurement of the Logistics Companies on the Fortune 500 by Swara and Merec-Based Cradis Methods Cover

Performance Measurement of the Logistics Companies on the Fortune 500 by Swara and Merec-Based Cradis Methods

Open Access
|Jan 2025

References

  1. Alaca, D., & Ulutaş, A. (2022). Measuring The Performance of Logistics Firms with an Integrated Multi-Criteria Decision Making Model. Gümüşhane University Journal of Social Sciences Institute, 13(3), 1027-1045.
  2. Aldalou, E., & Perçin, S. (2020). Application of integrated fuzzy MCDM approach for financial performance evaluation of Turkish technology sector. International Journal of Procurement Management, 13(1), 1-23.
  3. Ali, T., Chiu, Y. R., Aghaloo, K., Nahian, A. J., & Ma, H. (2020). Prioritizing the Existing Power Generation Technologies in Bangladesh’s Clean Energy Scheme Using A Hybrid Multi-Criteria Decision Making Model. Journal of Cleaner Production, 267, 121901.
  4. Alinezhad, A., & Khalili, J. (2019). New Methods and Applications in Multiple Attribute Decision Making (MADM) (Vol. 277). Cham: Springer.
  5. Avcı, M. C. (2019). Performance Analysis in Companies Operating in the Energy Sector with Multi-criteria Decision Making Methods. (Master Thesis, Marmara University, Turkey).
  6. Ayaydin, H., Durmuş, S., & Pala, F. (2017). Performance Measurement in Turkish Logistics Industry with Grade Relative Analysis Method. Gümüshane University Electronic Journal of the Institute of Social Science, 8(21).
  7. Bandono, A., & Nugroho, S. H. (2023). The Assessment of Company Performance Target Using Balanced Scorecard Methods. International Journal of Professional Business Review, 8(5), e01968-e01968.
  8. Bu, M. (2021). Performance evaluation of enterprise supply chain management based on the discrete hopfield neural network. Computational Intelligence and Neuroscience, 2021.
  9. Çakır, S. (2017). Measuring logistics performance of OECD countries via fuzzy linear regression. Journal of Multi-Criteria Decision Analysis, 24(3-4), 177-186.
  10. Çakir, S., & Perçin, S. (2013). Performance measurement of logistics firms with multi-criteria decision making methods. Ege Academic Review, 13(4), 449.
  11. Călinescu, G. (2022). The Applications of Blockchain and Artificial Intelligence in Logistics. Romanian Economic Journal, 25(84).
  12. Christopher, M. (2022). Logistics and supply chain management (6th Ed.). Pearson UK.
  13. Çınaroğlu, E. (2019). Evaluation of The Automotive Sector Companies in The Fortune 500 List wıth SWARA Supported COPRAS Method. Cankırı Karatekin University Journal of the Faculty of Economics and Administrative Sciences, 9(2), 593-611.
  14. Ersoy, N. (2023). Applying an integrated data-driven weighting system–CoCoSo approach for financial performance evaluation of Fortune 500 companies. E&M Economics and Management, 26(3), 92-108.
  15. Ersoy, Y., & Tehci, A. (2020). Logistics Marketing: Performance Evaluation in Companies Operating in Logistics Services with Data Envelopment Analysis. The Journal of International Scientific Researches, 5(1), 1-9.
  16. Farrokh, M., Heydari, H., & Janani, H. (2016). Two comparative MCDM approaches for evaluating the financial performance of Iranian basic metals companies.
  17. Ighravwe, D., & Babatunde, M. (2018). Selection of a Mini-grid Business Model for Developing Countries Using CRITIC-TOPSIS with Interval Type-2 Fuzzy Sets. Decision Science Letters, 7(4), 427-442.
  18. Iman, R. L., & Helton, J. C. (1988). An investigation of uncertainty and sensitivity analysis techniques for computer models. Risk analysis, 8(1), 71-90.
  19. Işık, Ö. (2022). A Multi-Criteria Performance Analysis of Turkish Logistics Firms Using Grey Entropy, FUCOM and EDAS-M Methods. Journal of Yasar University, 17(66), 472-489.
  20. Isik, O., Aydin, Y., & Kosaroglu, S. M. (2020). The assessment of the logistics performance index of CEE countries with the new combination of SV and MABAC methods. LogForum, 16(4), 549-559.
  21. Junior, F. R. L., Osiro, L., & Carpinetti, L. C. R. (2014). A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection. Applied soft computing, 21, 194-209.
  22. Kara, K., Bentyn, Z., & Yalçın, G. C. (2022). Determining the logistics market performance of developing countries by entropy and MABAC methods. LogForum, 18(4).
  23. Keršuliene, V., Zavadskas, E. K., & 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.
  24. Keshavarz Ghorabaee, M., Amiri, M., Kazimieras Zavadskas, E., & 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.
  25. Keshavarz-Ghorabaee, M., 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.
  26. Kotane, I., & Kuzmina-Merlino, I. (2012). Assessment of Financial Indicators for Evaluation f Business Performance. European integration studies, (6).
  27. Kou, G., Lu, Y., Peng, Y., & Shi, Y. (2012). Evaluation of classification algorithms using MCDM and rank correlation. International Journal of Information Technology & Decision Making, 11(01), 197-225.
  28. Maliene, V., Dixon-Gough, R., & Malys, N. (2018). Dispersion of relative importance values contributes to the ranking uncertainty: Sensitivity analysis of Multiple Criteria Decision-Making methods. Applied Soft Computing, 67, 286-298.
  29. Mashovic, A. (2018). Key financial and nonfinancial measures for performance evaluation of foreign subsidiaries. Journal оf Contemporary Economic аnd Business Issues, 5(2), 63-74.
  30. Md Saad, R., Ahmad, M. Z., Abu, M. S., & Jusoh, M. S. (2014). Hamming distance method with subjective and objective weights for personnel selection. The Scientific World Journal, 2014.
  31. Mešić, A., Miškić, S., Stević, Ž., & Mastilo, Z. (2022). Hybrid MCDM solutions for evaluation of the logistics performance index of the Western Balkan countries. Economics, 10(1), 13-34.
  32. Neely, A., Gregory, M., & Platts, K. (2005). Performance measurement system design: A literature review and research agenda. International journal of operations & production management, 25(12), 1228-1263.
  33. Ochego, M. C., & Wycliffe, A. (2020). Logistics strategy as a competitive tool for firm performance: The moderating effect of customer service effectiveness. Journal of Sustainable Development of Transport and Logistics, 5(1), 56-65.
  34. Özbek, A. (2018). Evaluation of The Logistics Companies on The List of Fortune 500. Afyon Kocatepe University Journal of Economics and Administrative Sciences,, 20(1), 13-26.
  35. Özbek, A., & Demirkol, İ. (2018). Performance Analysis of Companies in the Logistics Sector by SWARA and GRA Methods. Kırıkkale University Journal of Social Sciencesi, 8(1), 71-86.
  36. Özekenci, E. K. (2023). Assessing The Logistics Market Performance of Developing Countries By SWARA-CRITIC Based CoCoSo Method. LogForum, 19(3), 375-394.
  37. Paramanik, A. R., Sarkar, S., & Sarkar, B. (2022). OSWMI: An objective-subjective weighted method for minimizing inconsistency in multi-criteria decision making. Computers & Industrial Engineering, 169, 108138.
  38. Peng, Y., Kou, G., Wang, G., & Shi, Y. (2011). FAMCDM: A fusion approach of MCDM methods to rank multiclass classification algorithms. Omega, 39(6), 677-689.
  39. Puška, A., Božanić, D., Mastilo, Z., & Pamučar, D. (2023). Extension of MEREC-CRADIS methods with double normalization-case study selection of electric cars. Soft Computing, 27(11), 7097-7113.
  40. Puška, A., Stević, Ž., & Pamučar, D. (2021). Evaluation and selection of healthcare waste incinerators using extended sustainability criteria and multi-criteria analysis methods. Environment, Development and Sustainability, 1-31.
  41. 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.
  42. Toslak, M., Aktürk, B., & Ulutaş, A. (2022). The Evaluation of the Performance of a Logistics Company by Years with MEREC and WEDBA Methods. European Journal of Science and Technology, (33), 363-372.
  43. Triantaphyllou, E., & Sánchez, A. (1997). A sensitivity analysis approach for some deterministic multi-criteria decision-making methods. Decision sciences, 28(1), 151-194.
  44. Ulutaş, A. (2018). The Performance Analysis of Logistics Companies with Entropy Based Edas Method. International Journal of Economics and Administrative Studies, (23), 53-66.
  45. Ulutaş, A., & Karaköy, Ç. (2019). An analysis of the logistics performance index of EU countries with an integrated MCDM model. Economics and Business Review, 5 (4), 49-69.
  46. Vilko, J., & Hallikas, J. (2023). Impact of COVID-19 on logistics sector companies. International Journal of Industrial Engineering and Operations Management.
  47. Yagmahan, B., & Yılmaz, H. (2023). An integrated ranking approach based on group multi-criteria decision making and sensitivity analysis to evaluate charging stations under sustainability. Environment, Development and Sustainability, 25(1), 96-121.
  48. Yürüyen, A. A., Ulutaş, A., & Özdağoğlu, A. (2023). The evaluation of the performance of logistics companies with a hybrid MCDM model. Business & Management Studies: An International Journal, 11(3), 731-751.
DOI: https://doi.org/10.2478/sbe-2024-0051 | Journal eISSN: 2344-5416 | Journal ISSN: 1842-4120
Language: English
Page range: 191 - 212
Published on: Jan 22, 2025
Published by: Lucian Blaga University of Sibiu
In partnership with: Paradigm Publishing Services
Publication frequency: 3 issues per year

© 2025 Emre Kadir Özekenci, published by Lucian Blaga University of Sibiu
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.