A Novel Combined Fuzzy MCDM Approach Based on IMF-SWARA and F-CODAS for Consulting Firm Selection

Öz

In today's challenging industry conditions, where being good is not enough to be successful, companies trying to be the best, need consultancy in different fields. Consulting firms provides consultancy services to businesses, and they need to determine the most appropriate one for them. Fuzzy MCDM (Multi Criteria Decision Making) methods are appropriate to solve consulting firm selection problem. In this study, consulting firm selection problem of a textile company operating in Istanbul, Turkey is handled by using a novel combined fuzzy MCDM method based on IMF-SWARA (Improved Fuzzy Stepwise Weight Assessment Ratio Analysis) and F-CODAS (Fuzzy COmbinative Distance-based Assessment) methods. The importance weights of the criteria are calculated with IMF-SWARA method. Findings indicate that the top three important criteria are respectively, experience, references, and reliability. Then, F-CODAS method is used to rank the consulting firms and the best one is presented to the Human Resources department of the textile company. This study contributes to the existing literature in various aspects. It suggests a novel combined fuzzy MCDM method to solve consulting firm selection and a new Fuzzy CODAS based on TFNs is proposed. Moreover, HR managers can use the findings of this study to evaluate consulting firms.

Anahtar Kelimeler

• Fuzzy Multi Criteria Decision Making
• IMF-SWARA
• F-CODAS
• Consulting Firm Selection

Kaynakça

1. Avikal, S., Nigam, M., & Ram, M. (2022). A hybrid multi criteria decision making approach for consultant selection problem in ERP project. International Journal of System Assurance Engineering and Management, 1-10.
2. Bellman, R. E., & Zadeh, L.A. (1970). Decision making in a fuzzy environment. Management Sciences, 17, 141-164.
3. Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95, 649-655.
4. Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114(1), 1-9.
5. Ecer, F. (2015). Performance evaluation of internet banking branches via a hybrid MCDM model under fuzzy environment. Economic Computation & Economic Cybernetics Studies & Research, 49(2), 211-230.
6. El-Santawy, M. F., & El-Dean, R. A. Z. (2012). Selection of a consulting firm by using SDV-MOORA. Life Science Journal, 9(1s), 171-173.
7. Guo, S., & Zhao, H. (2017). Fuzzy Best-Worst multi-criteria decision-making method and its applications. Knowledge-Based Systems, 121, 23-31.
8. Hadi-Vencheh, A., & Mokhtarian, M. N. (2011). A new fuzzy MCDM approach based on centroid of fuzzy numbers. Expert Systems with Applications, 38(5), 5226-5230.

9. Kabir, G., & Sumi, R. S. (2014). Integrating fuzzy analytic hierarchy process with PROMETHEE method for total quality management consultant selection. Production & Manufacturing Research, 2(1), 380-399.
10. Kahraman, C., Cebeci, U., & Ruan, D. (2004). Multi-attribute comparison of catering service companies using fuzzy AHP: The case of Turkey. International Journal of Production Economics, 87, 171–184.
11. Katrancı, A., & Kundakcı, N. (2020). Evaluation of cryptocurrency investment alternatives with fuzzy CODAS Method. Afyon Kocatepe University Journal of Social Science, 22(4), 958-973.
12. Kersuliene, 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.
13. Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Hooshmand, R., & Antucheviciene, J. (2017). Fuzzy extension of the CODAS method for multi-criteria market segment evaluation. Journal of Business Economics and Management, 18(1), 1-19.
14. Keshavarz Ghorabaee, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2016). A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making. Economic Computation & Economic Cybernetics Studies & Research, 50(3), 25-44.
15. Mavi, R. K., Goh, M., & Zarbakhshnia, N. (2017). Sustainable third-party reverse logistic provider selection with fuzzy SWARA and fuzzy MOORA in plastic industry. International Journal of Advanced Manufacturing Technology, 91, 2401–2418.
16. Moslem, S., Stević, Ž., Tanackov, I., & Pilla, F. (2023). Sustainable development solutions of public transportation: An integrated IMF SWARA and Fuzzy Bonferroni operator. Sustainable Cities and Society, 93, 104530.
17. Musani, S., & Jemain, A. A. (2013, November). A fuzzy MCDM approach for evaluating school performance based on linguistic information. In AIP Conference Proceedings (Vol. 1571, No. 1, pp. 1006-1012). American Institute of Physics.
18. Nomir, M., & Hammad, A. (2023). Decision support system for selecting engineering consultants using Qualifications-Based Selection (QBS) and fuzzy TOPSIS. Canadian Journal of Civil Engineering. e-First https://doi.org/10.1139/cjce-2022-0076
19. Peker, B. N., & Görener, A. (2023). Facility location selection with improved fuzzy SWARA and fuzzy CODAS methods: An application in the manufacturing industry. Journal of Turkish Operations Management, 7(1), 1493-1512.
20. Perçin, S. (2019). An integrated fuzzy SWARA and fuzzy AD approach for outsourcing provider selection. Journal of Manufacturing Technology Management, 30(2), 531-552.
21. Puška, A., Nedeljković, M., Stojanović, I., & Božanić, D. (2023). Application of fuzzy TRUST CRADIS method for selection of sustainable suppliers in agribusiness. Sustainability, 15(3), 2578.
22. Razi, P. Z., Ramli, N. I., Ali, M. I., & Ramadhansyah, P. J. (2020). Selection of best consultant by using Analytical Hierarchy Process (AHP). IOP Conference Series: Materials Science and Engineering (712(1), pp. 012016). IOP Publishing.
23. Razi, P. Z., Ramli, N. I., Ali, M. I., & Ramadhansyah, P. J. (2020). Selection of best consultant by using analytical hierarchy Process (AHP). In IOP Conference Series: Materials Science and Engineering (Vol. 712, No. 1, p. 012016). IOP Publishing.
24. Roszkowska, E., & Wachowicz, T. (2015). Application of fuzzy TOPSIS to scoring the negotiation offers in ill-structured negotiation problems. European Journal of Operational Research, 242(3), 920-932.
25. Saremi, M., Mousavi, S. F., & Sanayei, A. (2009). TQM consultant selection in SMEs with TOPSIS under fuzzy environment. Expert Systems with Applications, 36(2), 2742-2749.
26. Sporrong, J. (2011). Criteria in consultant selection: public procurement of architectural and engineering services. Australasian Journal of Construction Economics and Building, 11(4), 59-76.
27. Stević, Ž., Subotić, M., Softić, E., & Božić, B. (2022). Multi-criteria decision-making model for evaluating safety of road sections. J. Intell. Manag. Decis, 1(2), 78-87.
28. Terzioğlu (Eds.), Advances in Econometrics, Operational Research, Data Science and Actuarial Studies. Contributions to Economics. (pp. 389-404) Springer, Cham. https://doi.org/10.1007/978-3-030-85254-2_24.
29. Tsai, W. H., Lin, T. W., Chen, S. P., & Hung, S. J. (2007). Users' service quality satisfaction and performance improvement of ERP consultant selections. International Journal of Business and Systems Research, 1(3), 280-301.
30. Tsaur, S. H., Chang, T. Y., & Yen, C. H. (2002). The evaluation of airline service quality by fuzzy MCDM. Tourism management, 23(2), 107-115.
31. Tuş Işık, A., & Aytaç Adalı, E. (2016). UTA method for the consulting firm selection problem. Journal of Engineering Science & Technology Review, 9(1), 56-60.
32. Ulutaş, A. (2021). Supplier Evaluation with BWM and Fuzzy CODAS Methods. Handbook of Research on Recent Perspectives on Management, International Trade, and Logistics (pp. 335-351). IGI Global.
33. Vrtagić, S., Softić, E., Subotić, M., Stević, Ž., Dordevic, M., & Ponjavic, M. (2021). Ranking road sections based on MCDM model: new improved fuzzy SWARA (IMF SWARA). Axioms, 10(2), 92.
34. Yalçın, N., & Yapıcı Pehlivan, N. (2019). Application of the fuzzy CODAS method based on fuzzy envelopes for hesitant fuzzy linguistic term sets: A case study on a personnel selection problem. Symmetry, 11(4), 493.
35. Yeni, F. B., & Özçelik, G. (2019). Interval-valued Atanassov intuitionistic fuzzy CODAS method for multi criteria group decision making problems. Group Decision and Negotiation, 28(2), 433-452.
36. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338-353.
37. Zolfani, S. H., Görçün, Ö. F., & Küçükönder, H. (2021). Evaluating logistics villages in Turkey using hybrid improved fuzzy SWARA (IMF SWARA) and fuzzy MABAC techniques. Technological and Economic Development of Economy, 27(6), 1582-1612.