Assessing SME credit rating in supply chain finance with multi-phase QFD-based MULTIMOORA under uncertainty
Abstract
Presently, financial institutions have tentatively utilized supply chain finance as a means of assessing small and medium-sized enterprise (SME) credit rating. However, traditional techniques cannot satisfy the requirements of such assessments because financial institutions need to assess SME credit rating from the perspective of the supply chain and core enterprise rather than only from the perspective of SME. In this study, a hybrid technique with quantitative and qualitative criteria called multi-phase quality function deployment (QFD)-based MULTIMOORA under interval type-2 fuzzy set (IT2FS) is proposed to overcome the defects of traditional techniques. First, the quantitative values were converted into IT2FSs using the developed formulas. Second, a multi-phase QFD model is proposed to obtain the SME credit rating matrix by integrating the core enterprise credit rating matrix and the criterion relationship matrices among SME, core enterprises and supply chains. Third, IT2FS-MULTIMOORA is enhanced by considering the improved Borda Rule and extended reference point simultaneously to derive the final rankings; therefore, a weight-determining technique is presented based on the correlation coefficients. Finally, the proposed technique was applied to the SME credit rating assessment problem. Comparisons with other techniques and the sensitivity analysis results provide suggestions for financial institutions to provide loans to SMEs.
First published online 31 March 2025
Keyword : multi-phase QFD, MULTIMOORA, SME credit rating, supply chain finance
How to Cite
Gao, H., Zhang, H., & Liu, P. (2025). Assessing SME credit rating in supply chain finance with multi-phase QFD-based MULTIMOORA under uncertainty. Technological and Economic Development of Economy, 1-32. https://doi.org/10.3846/tede.2025.23153
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Brauers, W., & Zavadskas, E. (2006). The MOORA method and its application to privatization in a transition economy. Control and Cybernetics, 35(2), 445–469.
Chen, Y., Ran, Y., Huang, G., Xiao, L., & Zhang, G. (2021). A new integrated MCDM approach for improving QFD based on DEMATEL and extended MULTIMOORA under uncertainty environment. Applied Soft Computing, 105, Article 107222. https://doi.org/10.1016/j.asoc.2021.107222
Chen, C., Zhang, C., & Xu, Z. (2024). Online reviews-driven Kano-QFD method for service design. IEEE Transactions on Engineering Management, 71, 8153–8165. https://doi.org/10.1109/TEM.2024.3387579
Gai, L., Liu, H., Wang, Y., & Xing, Y. (2023). Green supplier selection and order allocation using linguistic Z-numbers MULTIMOORA method and bi-objective non-linear programming. Fuzzy Optimization and Decision Making, 22, 267–288. https://doi.org/10.1007/s10700-022-09392-1
Garg, H., & Rani, D. (2022). An efficient intuitionistic fuzzy MULTIMOORA approach based on novel aggregation operators for the assessment of solid waste management techniques. Applied Intelligence, 52, 4330–4363. https://doi.org/10.1007/s10489-021-02541-w
Ghorabaee, M. K., Zavadskas, E. K., Amri, M., & Esmaeili, A. (2016). Multi-criteria evaluation of green suppliers using an extended WASPAS method with interval type-2 fuzzy sets. Journal of Cleaner Production, 137, 213–229. https://doi.org/10.1016/j.jclepro.2016.07.031
Goldmann, L., Crook, J., & Calabrese, R. (2024). A new ordinal mixed-data sampling model with an application to corporate credit rating levels. European Journal of Operational Research, 314(3), 1111–1126. https://doi.org/10.1016/j.ejor.2023.10.017
Hernandez, P., Cubillo S., & Torres-Blanc, C. (2022). A complementary study on general interval Type-2 fuzzy sets. IEEE Transactions on Fuzzy Systems, 30(11), 5034–5043. https://doi.org/10.1109/TFUZZ.2022.3167140
Li, H., Dai, X., Zhou, L., & Yang, W. (2024). Multi-criteria constrained interval type-2 fuzzy decision-making: A space analysis perspective. Information Sciences, 669, Article 120581. https://doi.org/10.1016/j.ins.2024.120581
Lin, B., & Dong, X. (2024). Empirical study on the impact of digital finance on commercial credit allocation in SMEs. Finance Research Letters, 61, Article 105011. https://doi.org/10.1016/j.frl.2024.105011
Liu, H.-C., Shi, H., Li, Z. W., & Duan, C.-H. (2022). An integrated behavior decision-making approach for large group quality function deployment. Information Sciences, 582, 334–348. https://doi.org/10.1016/j.ins.2021.09.020
Liu, P., Atifeh, M., Khorshidnia, M., & Taheri, S. G. (2023). System dynamics: An approach to modeling supply chain performance measurement. Technological and Economic Development of Economy, 29(4), 1291–1317. https://doi.org/10.3846/tede.2023.19211
Liu, P., & Gao, H. (2021). A novel green supplier selection method based on the interval type-2 fuzzy prioritized choquet bonferroni means. IEEE/CAA Journal of Automatica Sinica, 8(9), 1549–1566. https://doi.org/10.1109/JAS.2020.1003444
Meniz, B., & Özkan, M., E. (2023). Vaccine selection for COVID-19 by AHP and novel VIKOR hybrid approach with interval type-2 fuzzy sets. Engineering Applications of Artificial Intelligence, 119, Article 105812. https://doi.org/10.1016/j.engappai.2022.105812
Nemati, E. (2024). Assessment of suppliers through the resiliency and sustainability paradigms using a new MCDM model under interval type-2 fuzzy sets. Soft Computing, 28, 7439–7453. https://doi.org/10.1007/s00500-023-09603-w
Pang, M., Wang, F., & Li, Z. (2024). Credit risk prediction based on an interpretable three-way decision method: Evidence from Chinese SMEs. Applied Soft Computing, 157, Article 111538. https://doi.org/10.1016/j.asoc.2024.111538
Qin, J., & Ma, X. (2022). An IT2FS-PT3 based emergency response plan evaluation with MULTIMOORA method in group decision making. Applied Soft Computing, 122, Article 108812. https://doi.org/10.1016/j.asoc.2022.108812
Santos, L. F. O. M., Osiro, L., & Lima, R. H. P. (2017). A model based on 2-tuple fuzzy linguistic representation and analytic hierarchy process for supplier segmentation using qualitative and quantitative criteria. Expert Systems with Applications, 79, 53–64. https://doi.org/10.1016/j.eswa.2017.02.032
Shaker, F., Shahin, A., & Jahanyan, S. (2019). Developing a two-phase QFD for improving FMEA: An integrative approach. International Journal of Quality & Reliability Management, 36(8), 1454–1474. https://doi.org/10.1108/IJQRM-07-2018-0195
Shang, Z., Yang, X., Barnes, D., & Wu, C. (2022). Supplier selection in sustainable supply chains: Using the integrated BWM, fuzzy Shannon entropy, and fuzzy MULTIMOORA methods. Expert Systems with Applications, 195, Article 116567. https://doi.org/10.1016/j.eswa.2022.116567
Triantaphyllou, E. (2000). A sensitivity analysis approach for MCDM methods. In Multicriteria decision making methods: A comparative study: Vol. 44. Applied optimization (pp. 131–175). Springer. https://doi.org/10.1007/978-1-4757-3157-6_8
Vaezi, A., Rabbani, E., & Yazdian, S. A. (2024). Blockchain-integrated sustainable supplier selection and order allocation: A hybrid BWM-MULTIMOORA and bi-objective programming approach. Journal of Cleaner Production, 444, Article 141216. https://doi.org/10.1016/j.jclepro.2024.141216
Wang, W., Liu, X., & Qin, J. (2019). Risk priorization for failure modes with extended MULTIMOORA method under interval type-2 fuzzy environment. Journal of Intelligent & Fuzzy Systems, 36(2), 1417–1429. https://doi.org/10.3233/JIFS-181007
Wang, P., Fu, Y., Liu, P., Zhu, B., Wang, F., & Pamucar, D. (2024). Evaluation of ecological governance in the Yellow River basin based on Uninorm combination weight and MULTIMOORA-Borda method. Expert Systems with Applications, 235, Article 121227. https://doi.org/10.1016/j.eswa.2023.121227
Wu, X., Liao, H., Xu, Z., Hafezalkotob, A., & Herrera, F. (2018). Probabilistic linguistic MULTIMOORA: A multi-criteria decision making method based on the probabilistic linguistic expectation function and the improved Borda rule. IEEE Transactions on Fuzzy Systems, 26(6), 3688–3702. https://doi.org/10.1109/TFUZZ.2018.2843330
Wu, Y., Liu, P., & Li, Y. (2024). An integrated QFD and FMEA method under the co-opetitional relationship for product upgrading. Information Sciences, 667, Article 120505. https://doi.org/10.1016/j.ins.2024.120505
Xu, Y., Kou, G., Peng, Y., Ding, K., Ergu, D., & Alotaibi, F. S. (2024). Profit- and risk-driven credit scoring under parameter uncertainty: A multiobjective approach. Omega, 125, Article 103004. https://doi.org/10.1016/j.omega.2023.103004
Yang, Q., Chan, C. Y. P., Chin, K.-S., & Li, Y.-L. (2021). A three phase QFD based framework for identifying key passenger needs to improve satisfaction with the seat of high speed rail in China. Transportation, 48, 2627–2662. https://doi.org/10.1007/s11116-020-10142-8
Yilmaz, I., & Polat, L. (2023). Celiac disease multi-purpose diet plan through integrated goal programming and Interval type-2 fuzzy TOPSIS method. Expert Systems with Applications, 218, Article 119618. https://doi.org/10.1016/j.eswa.2023.119618
Yucesan, M., Özkan, B., Mete, S., Gul, M., & Özceylan, E. (2024). Evaluating sustainability of urban mobility of Asian cities: An integrated approach of interval type-2 fuzzy best-worst method and MULTIMOORA. Engineering Applications of Artificial Intelligence, 127, Article 107266. https://doi.org/10.1016/j.engappai.2023.107266
Zadeh, L. (1975). The concept of a linguistic variable and its application to approximate reasoning–I. Information Sciences, 8(3), 199–249. https://doi.org/10.1016/0020-0255(75)90036-5
Zhang, H., Huang, G., Tian, G., Wang, W., & Li, Z. (2023a). A hybrid QFD-based human-centric decision-making approach of disassembly schemes under interval 2-Tuple q-Rung Orthopair fuzzy sets. IEEE Transactions on Automation Science and Engineering, 22, 724–735. https://doi.org/10.1109/TASE.2023.3301237
Zhang, H., Wang, H., Wei, G., & Chen, X. (2023b). An integrated decision support system for stock investment based on spherical fuzzy PT-EDAS method and MEREC. Technological and Economic Development of Economy, 29(4), 1353–1381. https://doi.org/10.3846/tede.2023.19123