Abstract
Supply chain finance (SCF) is a financial service that provides convenient loan transactions for small- and medium-sized enterprises (SMEs) upstream and downstream of the supply chain. SCF can help to smooth the capital flow of many SMEs. However, it is difficult for agricultural SMEs to participate in SCF because this kind of SME usually has various risks. Objectively, evaluating the credit rating of agricultural SMEs is difficult for commercial banks. Furthermore, unlike general manufacturing enterprises, agricultural enterprises produce directly based on nature. Sustainable development based on the natural environment is very important for agricultural enterprises. In this paper, considering sustainability, we construct a criteria system to evaluate the credit ratings of agricultural SMEs for SCF. Moreover, we study a method for processing the hybrid heterogeneous evaluation information of SMEs. A co-decision method is proposed to classify the credit ratings of agricultural SMEs with the help of three-way decisions. Agricultural SMEs are evaluated by both criteria evaluation information and misclassification loss. Finally, a credit rating evaluation example is presented to demonstrate the application of our method. The results show that our proposed method can be used to fully evaluate agricultural SMEs with fine classification effects. It can also provide a reference for commercial banks to determine the credit of agricultural SMEs with a low decision risk.
Similar content being viewed by others
References
Abdel-Basset, M., Mohamed, R., Sallam, K., & Elhoseny, M. (2020). A novel decision-making model for sustainable supply chain finance under uncertainty environment. Journal of Cleaner Production, 269, 122324.
Alsawafi, A., Lemke, F., & Yang, Y. (2021). The impacts of internal quality management relations on the triple bottom line: A dynamic capability perspective. International Journal of Production Economics, 232, 122324.
Anderson, J. C., Hakansson, H., & Johanson, J. (1994). Dyadic business relationships within a business network context. Journal of Marketing, 58(4), 1–15.
Bai, C. G., Dallasega, P., Orzes, G., & Sarkis, J. (2020). Industry 4.0 technologies assessment: A sustainability perspective. International Journal of Production Economics, 229, 107776.
Bai, C. G., Kusi-Sarpong, S., Badri Ahmadi, H., & Sarkis, J. (2019). Social sustainable supplier evaluation and selection: A group decision-support approach. International Journal of Production Research, 57(22), 7046–7067.
Bell, D. E. (1982). Regret in decision making under uncertainty. Operations Research, 30(5), 961–981.
Borodin, V., Bourtembourg, J., Hnaien, F., & Labadie, N. (2014). A quality risk management problem: Case of annual crop harvest scheduling. International Journal of Production Research, 52(9), 2682–2695.
Cai, X., Qian, Y. F., Bai, Q. S., & Liu, W. (2020). Exploration on the financing risks of enterprise supply chain using Back Propagation neural network. Journal of Computational and Applied Mathematics, 367, 112457.
Campagner, A., Cabitza, F., Berjano, P., & Ciucci, D. (2021). Three-way decision and conformal prediction: Isomorphisms, differences and theoretical properties of cautious learning approaches. Information Sciences, 579, 347–367.
Chen, S., Zhang, Q. Q., & Zhou, Y. P. (2019). Impact of supply chain transparency on sustainability under NGO scrutiny. Production and Operations Management, 28(12), 3002–3022.
Chen, Y., Yan, Y. F., Zhao, C. W., Qi, Z. G., & Chen, Z. Y. (2020). GINI coefficient: An effective way to evaluate inflow profile equilibrium of horizontal wells in Shengli Oil Field. Journal of Petroleum Science and Engineering, 193, 107369.
Chorus, C. G. (2012). Regret theory-based route choices and traffic equilibria. Transportmetrica, 8(4), 291–305.
Deng, J., Zhan, J. M., & Wu, W. Z. (2021). A three-way decision methodology to multi-attribute decision-making in multi-scale decision information systems. Information Sciences, 568, 175–198.
Du, J. L., Liu, S. F., & Liu, Y. (2021). A novel grey multi-criteria three-way decisions model and its application. Computers & Industrial Engineering, 158, 107405.
Fang, Y., Gao, C., & Yao, Y. Y. (2020). Granularity-driven sequential three-way decisions: A cost-sensitive approach to classification. Information Sciences, 507, 644–664.
Fayyaz, M. R., Rasouli, M. R., & Amiri, B. (2020). A data-driven and network-aware approach for credit risk prediction in supply chain finance. Industrial Management & Data Systems. https://doi.org/10.1108/IMDS-01-2020-0052
Gao, C., Zhou, J., Miao, D. Q., Wen, J. J., & Yue, X. D. (2021). Three-way decision with co-training for partially labeled data. Information Sciences, 544, 500–518.
Gregory, A. J., & Jackson, M. C. (1992). Evaluation methodologies: A system for use. Journal of the Operational Research Society, 43(1), 19–28.
Hermoso-Orzáez, M. J., & Garzón-Moreno, J. (2021). Risk management methodology in the supply chain: A case study applied. Annals of Operations Research. https://doi.org/10.1007/s10479-021-04220-y
Hinrichsen, D. (1987). Report of the world commission on environment and development: Our common future. (Chapter 2: Towards Sustainable Development).
Karagiannis, G., & Paleologou, S. M. (2021). A regression-based improvement to the multiple criteria abc inventory classification analysis. Annals of Operations Research, 306, 369–382.
Keeys, L. A., & Huemann, M. (2017). Project benefits co-creation: Shaping sustainable development benefits. International Journal of Project Management, 35(6), 1196–1212.
Kouvelis, P., & Zhao, W. H. (2017). Who should finance the supply chain? Impact of credit ratings on supply chain decisions. Manufacturing & Service Operations Management, 20(1), 19035.
Lai, H., Liao, H. C., Saparauskas, J., Banaitis, A., Ferreira, F. A. F., & Al-Barakati, A. (2020). Sustainable cloud service provider development by a z-number-based DNMA method with Gini-coefficient-based weight determination. Sustainability, 12, 3410.
Li, X. N., Wang, X., Lang, G. M., & Yi, H. J. (2021). Conflict analysis based on three-way decision for triangular fuzzy information systems. International Journal of Approximate Reasoning, 132, 88–106.
Liang, X. D., Zhao, X. L., Wang, M., & Li, Z. (2018). Small and medium-sized enterprises sustainable supply chain financing decision based on triple bottom line theory. Sustainability, 10(11), 4242.
Liu, J. B., Li, H. X., Zhou, X. Z., Huang, B., & Wang, T. X. (2019). An optimization-based formulation for three-way decisions. Information Sciences, 495, 185–214.
Liu, Q., Chen, Y., Zhang, G. Q., & Wang, G. Y. (2021). A novel functional network based on three-way decision for link prediction in signed social networks. Cognitive Computation. https://doi.org/10.1007/s12559-021-09873-2
Loomes, G., & Sugden, R. (1982). Regret theory: An alternative theory of rational choice under uncertainty. The Economic Journal, 92(368), 805–824.
Lü, Z. H., Jin, H., Yuan, P. P., & Zou, D. Q. (2010). A fuzzy clustering algorithm for interval-valued data based on Gauss distribution functions. Acta Electronica Sinica, 38(2), 295–300.
Mehdizadeh, M. (2020). Integrating ABC analysis and rough set theory to control the inventories of distributor in the supply chain of auto spare parts. Computers & Industrial Engineering, 139, 105673.
Moretto, A., Grassi, L., Caniato, F., Giorgino, M., & Ronchi, S. (2019). Supply chain finance: From traditional to supply chain credit rating. Journal of Purchasing and Supply Management, 25(2), 197–217.
Nigro, G. L., Favara, G., & Abbate, L. (2021). Supply chain finance: The role of credit rating and retailer effort on optimal contracts. International Journal of Production Economics, 240, 108235.
Pang, Q., Wang, H., & Xu, Z. S. (2016). Probabilistic linguistic term sets in multi-attribute group decision making. Information Sciences, 369, 128–143.
Pfohl, H. C., & Gomm, M. (2009). Supply chain finance: Optimizing financial flows in supply chains. Logistics Research, 1, 149–161.
Quintero-Angel, M., & González-Acevedo, A. (2018). Tendencies and challenges for the assessment of agricultural sustainability. Agriculture, Ecosystems & Environment, 254, 273–281.
Sengupta, A., & Pal, T. K. (2000). On comparing interval numbers. European Journal of Operational Research, 127(1), 28–43.
Shen, Y., Li, Q. H., & Yang, J. (2020). Farmers’ cooperatives’ poverty-reducing roles in agricultural supply chain finance. China Economist, 15(3), 76–91.
Touboulic, A., Chicksand, D., & Walker, H. (2014). Managing imbalanced supply chain relationships for sustainability: A power perspective. Decision Sciences, 45(4), 577–619.
Tseng, M. L., Lim, M. K., & Wu, K. J. (2019). Improving the benefits and costs on sustainable supply chain finance under uncertainty. International Journal of Production Economics, 218, 308–321.
Tseng, M. L., Wu, K. J., Hu, J. Y., & Wang, C. H. (2018). Decision-making model for sustainable supply chain finance under uncertainties. International Journal of Production Economics, 205, 30–36.
Wang, J. J., Ma, X. L., Dai, J. H., & Zhan, J. M. (2021). A novel three-way decision approach under hesitant fuzzy information. Information Sciences, 578, 482–506.
Wang, M. W., Liang, D. C., Xu, Z. S., & Cao, W. (2021). Consensus reaching with the externality effect of social network for three-way group decisions. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03875-3
Wang, R., Tian, Y., & He, X. B. (2020). Technical efficiency characteristics and the policy sensitivity of environmental protection enterprises: Micro evidence from China. Journal of Cleaner Production, 256, 120752.
Wang, X. F., & Xiao, M. S. (2010). Approach of group decision making based on normal distribution interval number with incomplete information. Control & Decision, 25(10), 1494–1498.
Wang, Z. Y. (2019). Research on agricultural supply chain finance credit risk parity based on logistic model (Master dissertation). Kunming University of Science and Technology.
Wu, X. L., & Liao, H. C. (2020). Utility-based hybrid fuzzy axiomatic design and its application in supply chain finance decision making with credit risk assessments. Computers in Industry, 114, 103144.
Wu, Y. L., Li, X., Liu, Q. Q., & Tong, G. J. (2021). The analysis of credit risks in agricultural supply chain finance assessment model based on genetic algorithm and backpropagation neural network. Computational Economics. https://doi.org/10.1007/s10614-021-10137-2
Wu, Y. N., Xu, H., Xu, C. B., & Chen, K. F. (2016). Uncertain multi-attributes decision making method based on interval number with probability distribution weighted operators and stochastic dominance degree. Knowledge-Based Systems, 113(1), 199–209.
Xu, S. Q. (2020). Research on agricultural supply chain: Sources and preventions of financial credit risk. In The 3rd international conference on economy, management and entrepreneurship (ICOEME 2020). Atlantis Press.
Xu, X. H., Chen, X. F., Jia, F., Brown, S., Gong, Y., & Xu, Y. F. (2018). Supply chain finance: A systematic literature review and bibliometric analysis. International Journal of Production Economics, 204, 160–173.
Yao, Y. Y. (2009). Three-way decision: An interpretation of rules in rough set theory. In P. Wen (Ed.), Rough sets and knowledge technology (pp. 642–649). Springer.
Yao, Y. Y. (2021). Set-theoretic models of three-way decision. Granular Computing, 6, 133–148.
Yi, Z. L., Wang, Y. L., & Chen, Y. J. (2021). Financing an agricultural supply chain with a capital-constrained smallholder farmer in developing economies. Production and Operations Management. https://doi.org/10.1111/poms.13357
Yu, Z., & Khan, S. A. R. (2021). Evolutionary game analysis of green agricultural product supply chain financing system: Covid-19 pandemic. International Journal of Logistics Research and Applications. https://doi.org/10.1080/13675567.2021.1879752
Yue, X. D., Chen, Y. F., Yuan, B., & Lv, Y. (2021). Three-way image classification with evidential deep convolutional neural networks. Cognitive Computation. https://doi.org/10.1007/s12559-021-09869-y
Zhang, H., Shi, Y. X., Yang, X. R., & Zhou, R. L. (2021). A firefly algorithm modified support vector machine for the credit risk assessment of supply chain finance. Research in International Business and Finance, 58, 101482.
Zhang, L., Hu, H. Q., & Zhang, D. (2015). A credit risk assessment model based on SVM for small and medium enterprises in supply chain finance. Financial Innovation, 1(1), 14.
Zhang, M., Zhang, J. T., Ma, R. L., & Chen, X. D. (2019). Quantifying credit risk of supply chain finance: A Chinese automobile supply chain perspective. IEEE Access, 7, 144264–144279.
Zhang, Q., Fan, Z. P., & Pan, H. D. (1999). A ranking approach for interval numbers in uncertain multiple attribute decision making problems. Systems Engineering-Theory & Practice, 5, 129–133.
Zhao, X. D., Yeung, K. H., Huang, Q. P., & Song, X. (2015). Improving the predictability of business failure of supply chain finance clients by using external big dataset. Industrial Management & Data Systems, 115(9), 1683–1703.
Zhu, Y., Zhou, L., Xie, C., Wang, G. J., & Nguyen, T. V. (2019). Forecasting SME’s credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach. International Journal of Production Economics, 211, 22–33.
Acknowledgements
This work is partially supported by the National Natural Science Foundation of China (No. 72071030), the National Key R&D Program of China (No. 2020YFB1711900), the Planning Fund for the Humanities and Social Sciences of Ministry of Education of China (No. 19YJA630042) and the Social Science Planning Project of the Sichuan Province (No. SC20C007).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Liang, D., Cao, W. & Wang, M. Credit rating of sustainable agricultural supply chain finance by integrating heterogeneous evaluation information and misclassification risk. Ann Oper Res 331, 189–219 (2023). https://doi.org/10.1007/s10479-021-04453-x
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10479-021-04453-x