Vol. 11 No. 3 (2023): Business & Management Studies: An International Journal
Articles

The evaluation of the performance of logistics companies with a hybrid MCDM model

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  • Ali Aygün Yürüyen,
  • Alptekin Ulutaş,
  • Aşkın Özdağoğlu
Ali Aygün Yürüyen
Lect., Ardahan University, Nihat Delibalta Göle Vocational School, Ardahan, Türkiye
Alptekin Ulutaş
Assoc. Prof. Dr., Inonu University, Malatya, Türkiye
Aşkın Özdağoğlu
Prof. Dr., Dokuz Eylül University, Izmir, Türkiye

Published 2023-09-24

Keywords

  • Lojistik Performans Değerlendirme, SV, MEREC, CRITIC, LOPCOW, MACONT
  • Logistics Performance Evaluation, SV, MEREC, CRITIC, LOPCOW, MACONT

How to Cite

The evaluation of the performance of logistics companies with a hybrid MCDM model. (2023). Business & Management Studies: An International Journal, 11(3), 731-751. https://doi.org/10.15295/bmij.v11i3.2245

Copyright (c) 2023 Ali Aygün Yürüyen- Alptekin Ulutaş- Aşkın Özdağoğlu

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

The evaluation of the performance of logistics companies with a hybrid MCDM model. (2023). Business & Management Studies: An International Journal, 11(3), 731-751. https://doi.org/10.15295/bmij.v11i3.2245

Abstract

The logistics sector plays a very significant role in today's global business environment owing to the significance of cost and time for achieving supply chains. The performance evaluation process for developing logistics enterprises is an essential element of the logistics management structure and an important basis for enterprises to evaluate their economic benefits further. Formulating a scientific and effective basic performance measurement system is a complex problem. This study aims to evaluate the performance of the logistics enterprises in the “Fortune 500 Turkey” website for 2021 by integrating MCDM methods. SV, MEREC, CRITIC and LOPCOW methods were applied to determine the objective weights of the criteria. The alternatives were ranked using the MACONT method based on the determined objective criteria weights. According to the study results, the logistics company with the best performance was Lİ7, and the logistics company with the lowest was Lİ3.

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References

  1. Agrawal, S., Singh, R. K., & Murtaza, Q. (2016). Prioritizing critical success factors for reverse logistics implementation using fuzzy-TOPSIS methodology. Journal of Industrial Engineering International, 12, 15-27.
  2. Aguezzoul, A. (2014). Third-party logistics selection problem: A literature review on criteria and methods. Omega, 49, 69-78.
  3. Akbulut, O. Y. (2022). Hayat Dışı Sigorta Sektöründe Finansal Performansın Bütünleşik AHP-SV ve MAIRCA Yöntemleri ile Analizi. Üçüncü Sektör Sosyal Ekonomi.
  4. Akman, G., & Baynal, K. (2014). Logistics service provider selection through an integrated fuzzy multi-criteria decision making approach. Journal of industrial Engineering, 2014.
  5. Aksakal, B., Ulutaş, A., Balo, F., & Karabasevic, D. (2022). A New Hybrid MCDM Model for Insulation Material Evaluation for Healthier Environment. Buildings, 12(5), 655.
  6. Alaca, D., & Ulutaş, A. Bütünleşik Çok Kriterli Karar Verme Modeli ile Lojistik Firmalarının Performanslarının Ölçümü. Gümüşhane Üniversitesi Sosyal Bilimler Dergisi, 13(3), 1027-1045.
  7. Alkhatib, S. F., Darlington, R., Yang, Z., & Nguyen, T. T. (2015). A novel technique for evaluating and selecting logistics service providers based on the logistics resource view. Expert systems with applications, 42(20), 6976-6989.
  8. Altıntaş, F. F. (2021). Avrupa Birliği Ülkelerinin Lojistik Performanslarının CRITIC Tabanlı WASPAS ve COPRAS Teknikleri İle Analizi. Türkiye Sosyal Araştırmalar Dergisi, 25(1), 117-146.
  9. Ayaydın, H., Durmuş, S., & Pala, F. (2017). Gri İlişkisel Analiz Yöntemiyle Türk Lojistik Firmalarında Performans Ölçümü. Gümüshane University Electronic Journal of the Institute Of Social Science/Gümüshane Üniversitesi Sosyal Bilimler Enstitüsü Elektronik Dergisi, 8(21).
  10. Aydın, Y. (2021). Bütünleşik Bir ÇKKV Modeli ile Sigorta Şirketlerinin Piyasa Performansının Analizi. Uluslararası İktisadi ve İdari İncelemeler Dergisi, (32), 53-66.
  11. Bektas, S. (2022). Evaluating the Performance of the Turkish Insurance Sector for the Period 2002-2021 with MEREC, LOPCOW, COCOSO, EDAS CKKV Methods. Journal of BRSA Banking and Financial Markets, 16(2), 247-283.
  12. Bianchini, A. (2018). 3PL provider selection by AHP and TOPSIS methodology. Benchmarking: An International Journal, 25(1), 235-252.
  13. Boakai, S., & Samanlioglu, F. (2023). An MCDM approach to third party logistics provider selection. International Journal of Logistics Systems and Management, 44(3), 283-299.
  14. Candan, G. (2019). Lojistik performans değerlendirmesi için bulanık AHP ve Gri İlişkisel Analiz yöntemleri ile bütünleşik bir yaklaşım. Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi, 7(5), 277-286.
  15. Çakır, S. (2017). Measuring logistics performance of OECD countries via fuzzy linear regression. Journal of Multi‐Criteria Decision Analysis, 24(3-4), 177-186.
  16. Çakır, S., & Perçin, S. (2013). Çok kriterli karar verme teknikleriyle lojistik firmalarinda performans ölçümü/Performance measurement of logistics firms with multi-criteria decision making methods. Ege Akademik Bakis, 13(4), 449.
  17. Çalık, A., Erdebilli, B., & Özdemir, Y. S. (2023). Novel Integrated Hybrid Multi-Criteria Decision-Making Approach for Logistics Performance Index. Transportation Research Record, 2677(2), 1392-1400.
  18. Dadashpour, I., & Bozorgi-Amiri, A. (2020). Evaluation and ranking of sustainable third-party logistics providers using the D-analytic hierarchy process. International Journal of Engineering, 33(11), 2233-2244.
  19. Deveci, M., & Torkayesh, A. E. (2021). Charging type selection for electric buses using interval-valued neutrosophic decision support model. IEEE Transactions on Engineering Management.
  20. Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems
  21. Ecer, F., & Pamucar, D. (2022). A novel LOPCOW‐DOBI multi‐criteria sustainability performance assessment methodology: An application in developing country banking sector. Omega, 112, 102690.
  22. Ecer, F., & Torkayesh, A. E. (2022). A stratified fuzzy decision-making approach for sustainable circular supplier selection. IEEE Transactions on Engineering Management.
  23. Emovon, I., & Samuel, O. D. (2017). An integrated Statistical Variance and VIKOR methods for prioritising power generation problems in Nigeria. Journal of Engineering and Technology, 8(1), 92-104.
  24. Ersoy, Y., & Tehci, A. (2020). Lojistik Pazarlama: Lojistik hizmetleri alanında faaliyette bulunan işletmelerde veri zarflama analizi ile etkinlik ölçümü. The Journal of International Scientific Researches, 5(1), 1-9.
  25. “Fortune Türkiye 500”, https://www.fortuneturkey.com/fortune500, ( Erişim Tarihi: 01.04.2023).
  26. Fu, H. P., Chang, T. H., Lin, A., Du, Z. J., & Hsu, K. Y. (2015). Key factors for the adoption of RFID in the logistics industry in Taiwan. The International Journal of Logistics Management.
  27. Gergin, R. E., & Baki, B. (2015). Türkiye'deki bölgelerin lojistik performanslarinin bütünlestirilmis AHS ve TOPSIS yöntemiyle Degerlendirilmesi/Evaluation by integrated AHP and TOPSIS Method of Logistics Performance in Turkey's Regions. Business and economics research Journal, 6(4), 115.
  28. Goswami, S. S., Mohanty, S. K., & Behera, D. K. (2022). Selection of a green renewable energy source in India with the help of MEREC integrated PIV MCDM tool. Materials today: proceedings, 52, 1153-1160.
  29. Govindan, K., & Chaudhuri, A. (2016). Interrelationships of risks faced by third party logistics service providers: A DEMATEL based approach. Transportation research part E: logistics and transportation review, 90, 177-195.
  30. Gülençer, I., & Türkoglu, S. P. (2020). Gelişmekte olan Asya ve Avrupa ülkelerinin finansal gelişmişlik performansının istatistiksel varyans prosedürü temelli OCRA yöntemiyle analizi. Third Sector Social Economic Review, 55(2), 1330-1344.
  31. 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.
  32. Işık, Ö. (2022). Gri Entropi, FUCOM ve EDAS-M Yöntemleriyle Türk Lojistik Firmalarının Çok Kriterli Performans Analizi. Yaşar Üniversitesi E-Dergisi, 17(66), 472-489.
  33. Jahan, A., Mustapha, F., Sapuan, S. M., Ismail, M. Y., & Bahraminasab, M. (2012). A framework for weighting of criteria in ranking stage of material selection process. The International Journal of Advanced Manufacturing Technology, 58(1), 411-420.
  34. Jovčić, S., & Průša, P. (2021). A hybrid MCDM approach in third-party logistics (3PL) provider selection. Mathematics, 9(21), 2729.
  35. Jovčić, S., Průša, P., Dobrodolac, M., & Švadlenka, L. (2019b). A proposal for a decision-making tool in third-party logistics (3PL) provider selection based on multi-criteria analysis and the fuzzy approach. Sustainability, 11(15), 4236.
  36. Jovčić, S., Průša, P., Samson, J., & Lazarević, D. (2019a). A fuzzy-AHP approach to evaluate the criteria of third-party logistics (3PL) service provider. IJTTE: International Journal for Traffic and Transport Engineering, volume 9, issue: 3, 280-289.
  37. Keshavarz-Ghorabaee, M. (2021). Assessment of distribution center locations using a multi-expert subjective–objective decision-making approach. Scientific Reports, 11(1), 19461. https://doi.org/10.1038/s41598-021-98698-y
  38. 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.
  39. 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.
  40. Korucuk, S., Turpcu, E., & Akyurt, H. (2018). Bütünleşik DEMATEL ve GİA yöntemleri ile seyahat acentalarında lojistik performans unsurlarının ölçülmesi ve en ideal seyahat acentesi seçimi: Giresun ili örneği. İşletme Araştırmaları Dergisi, 10(4), 820-842.
  41. Krishankumar, R., Saranya, R., Nethra, R. P., Ravichandran, K. S., & Kar, S. (2019). A decision-making framework under probabilistic linguistic term set for multi-criteria group decision-making problem. Journal of Intelligent & Fuzzy Systems, 36(6), 5783-5795.
  42. Li, X. (2020). Discussion on Enterprise Logistics Management Performance Evaluation System.
  43. 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.
  44. Liu, H. C., Liu, L., & Wu, J. (2013). Material selection using an interval 2-tuple linguistic VIKOR method considering subjective and objective weights. Materials & Design (1980-2015), 52, 158-167.
  45. Liu, Y., Zhou, P., Li, L., & Zhu, F. (2020). An interactive decision-making method for third-party logistics provider selection under hybrid multi-criteria. Symmetry, 12(5), 729.
  46. Lun, Y. V., Lai, K. H., Wong, C. W., & Cheng, T. C. E. (2015). Greening propensity and performance implications for logistics service providers. Transportation Research Part E: Logistics and Transportation Review, 74, 50-62.
  47. Madić, M., & Radovanović, M. (2015). Ranking of some most commonly used nontraditional machining processes using ROV and CRITIC methods. UPB Sci. Bull., Series D, 77(2), 193-204.
  48. 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.
  49. Narkhede, B. E., Raut, R., Gardas, B., Luong, H. T., & Jha, M. (2017). Selection and evaluation of third party logistics service provider (3PLSP) by using an interpretive ranking process (IRP). Benchmarking: An International Journal, 24(6), 1597-1648.
  50. Nguyen, N. A. T., Wang, C. N., Dang, L. T. H., Dang, L. T. T., & Dang, T. T. (2022). Selection of cold chain logistics service providers based on a grey AHP and grey COPRAS framework: a case study in Vietnam. Axioms, 11(4), 154.
  51. Oğuz, S., Alkan, G., & Yılmaz, B. (2019). Seçilmiş Asya ülkelerinin lojistik performanslarının TOPSİS yöntemi ile değerlendirilmesi. IBAD Sosyal Bilimler Dergisi, 497-507.
  52. Özceylan, E., Çetinkaya, C., Erbaş, M., & Kabak, M. (2016). Logistic performance evaluation of provinces in Turkey: A GIS-based multi-criteria decision analysis. Transportation Research Part A: Policy and Practice, 94, 323-337.
  53. Pamucar, D., Chatterjee, K., & Zavadskas, E. K. (2019). Assessment of third-party logistics provider using multi-criteria decision-making approach based on interval rough numbers. Computers & Industrial Engineering, 127, 383-407.
  54. Pamucar, D., Žižović, M., Biswas, S., & Božanić, D. (2021). A new logarithm methodology of additive weights (LMAW) for multi-criteria decision-making: Application in logistics.
  55. Radović, D., Stević, Ž., Pamučar, D., Zavadskas, E. K., Badi, I., Antuchevičiene, J., & Turskis, Z. (2018). Measuring performance in transportation companies in developing countries: a novel rough ARAS model. Symmetry, 10(10), 434.
  56. Rashidi, K., & Cullinane, K. (2019). Evaluating the sustainability of national logistics performance using Data Envelopment Analysis. Transport Policy, 74, 35-46.
  57. Rao, R. V., & Patel, B. K. (2010). A subjective and objective integrated multiple attribute decision making method for material selection. Materials & Design, 31(10), 4738 4747.
  58. Rao, R. V., Patel, B. K., & Parnichkun, M. (2011). Industrial robot selection using a novel decision making method considering objective and subjective preferences. Robotics and Autonomous Systems, 59(6), 367-375.
  59. 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.
  60. Riaz, M., & Farid, H. M. A. (2022). Picture fuzzy aggregation approach with application to third-party logistic provider selection process. Reports in Mechanical Engineering, 3(1), 227-236.
  61. Roy, J., Pamučar, D., & Kar, S. (2020). Evaluation and selection of third party logistics provider under sustainability perspectives: an interval valued fuzzy-rough approach. Annals of Operations Research, 293, 669-714.
  62. Sharma, S. K., Mahapatra, S. S., & Parappagoudar, M. B. (2016). Benchmarking of product recovery alternatives in reverse logistics. Benchmarking: An International Journal, 23(2), 406-424.
  63. Sahu, N. K., Datta, S., & Mahapatra, S. S. (2013). Decision making for selecting 3PL service provider using three parameter interval grey numbers. International Journal of Logistics Systems and Management, 14(3), 261-297.
  64. Sahu, N. K., Sahu, A. K., & Sahu, A. K. (2015). Appraisement and benchmarking of third-party logistic service provider by exploration of risk-based approach. Cogent Business & Management, 2(1), 1121637.
  65. Sinani, F., Erceg, Z., & Vasiljević, M. (2020). An evaluation of a third-party logistics provider: The application of the rough Dombi-Hamy mean operator. Decision Making: Applications in Management and Engineering, 3(1), 92-107.
  66. Sremac, S., Stević, Ž., Pamučar, D., Arsić, M., & Matić, B. (2018). Evaluation of a third-party logistics (3PL) provider using a rough SWARA–WASPAS model based on a new rough dombi aggregator. Symmetry, 10(8), 305.
  67. Stević, Ž., Tanackov, I., Puška, A., Jovanov, G., Vasiljević, J., & Lojaničić, D. (2021). Development of modified SERVQUAL–MCDM model for quality determination in reverse logistics. Sustainability, 13(10), 5734.
  68. Stojanović, I., & Puška, A. (2021). Logistics performances of gulf cooperation council’s countries in global supply chains. Decision Making: Applications in Management and Engineering, 4(1), 174-193.
  69. 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. DOI: 10.31590/ejosat.1041106)
  70. Toslak, M., Ulutaş, A., Üre, S., & Stević, Ž. (2023). Selection of peanut butter machine by the integrated PSI-SV-MARCOS method. International Journal of Knowledge-based and Intelligent Engineering Systems, (Preprint), 1-14.
  71. Türkoğlu, M., & Duran, G. (2023). Çok Kriterli Karar Verme Yöntemleri ile Bölgesel Kapsamlı Ekonomik Ortaklık (Rcep) Ülkelerinin Lojistik Performanslarının Değerlendirilmesi. Ekonomi Bilimleri Dergisi, 15(1), 45-69.
  72. Ulutaş, A. (2022) A Fuzzy Hybrid MCDM Model To Solving The Transportation Company Selection Problem. An international serial publication for theory and practice of Management Science.
  73. Ulutaş, A., Balo, F., & Topal, A. (2023). Identifying the Most Efficient Natural Fibre for Common Commercial Building Insulation Materials with an Integrated PSI, MEREC, LOPCOW and MCRAT Model. Polymers, 15(6), 1500.
  74. Ulutaş, A., & Cengiz, E. (2018). CRITIC ve EVAMIX Yöntemleri ile Bir İşletme için Dizüstü Bilgisayar Seçimi. Journal of International Social Research, 11(55).
  75. Ulutaş, A., & Karaköy, Ç. (2019a). An analysis of the logistics performance index of EU countries with an integrated MCDM model. Economics and Business Review, 5(4), 49-69.
  76. Ulutaş, A., & Karaköy, Ç. (2019b). G-20 Ülkelerinin lojistik performans endeksinin çok kriterli karar verme modeli ile ölçümü. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, 20(2), 71-84.
  77. Ulutaş, A., Stanujkic, D., Karabasevic, D., Popovic, G., & Novaković, S. (2022). Pallet truck selection with MEREC and WISP-S methods. Strategic Management-International Journal of Strategic Management and Decision Support Systems in Strategic Management.
  78. Wen, Z., Liao, H., Kazimieras Zavadskas, E., & Al-Barakati, A. (2019). Selection third-party logistics service providers in supply chain finance by a hesitant fuzzy linguistic combined compromise solution method. Economic research-Ekonomska istraživanja, 32(1), 4033-4058.
  79. Wen, Z., & Liao, H. (2021). Pension service institution selection by a personalized quantifier-based MACONT method. International Journal of Strategic Property Management, 25(6), 446-458.
  80. Wen, Z., Liao, H., & Zavadskas, E. K. (2020). MACONT: Mixed aggregation by comprehensive normalization technique for multi-criteria analysis. Informatica, 31(4), 857-880.
  81. Wu, H. W., Zhen, J., & Zhang, J. (2020). Urban rail transit operation safety evaluation based on an improved CRITIC method and cloud model. Journal of Rail Transport Planning & Management, 16, 100206.
  82. Vazifehdan, M. N., & Darestani, S. A. (2019). Green logistics outsourcing employing multi criteria decision making and quality function deployment in the petrochemical industry. The Asian Journal of Shipping and Logistics, 35(4), 243-254.

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