Abstract
Organizations have adopted information methods for Supply Chain Risk Management (SCRM) as a consequence of enhanced hazard levels of exposure, advancements in technology, and also the increasing info overload in supply chain operations. Data Mining (DM) uses a variety of analysis methods to make a smart and informed decision; its promise for SCRM has yet to be fully realized. The goal of this study would be to create a DM-based tool for determining, assessing, and mitigating various types of supply chain risks. A holistic methodology combines DM and risk assessment operations into a coherent system for efficient analysis. A case study was based on semi-structured conversations, conversations, and perhaps focused study sessions validate the concept. The research shows how a DM can help you find hidden and appropriate data in unorganized data information so you can make good risk management choices.
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References
Kosasih, E.E., Brintrup, A.: A machine learning approach for predicting hidden links in the supply chain with graph neural networks. Int. J. Prod. Res. 1–14 (2021)
Liou, J.J., Chang, M.H., Lo, H.W., Hsu, M.H.: Application of an MCDM model with data mining techniques for green supplier evaluation and selection. Appl. Soft Comput. 109, 107534 (2021)
Kong, J., Yang, C., Wang, J., Wang, X., Zuo, M., Jin, X., Lin, S.: Deep-stacking network approach by multisource data mining for hazardous risk identification in IoT-based intelligent food management systems. Comput. Intell. Neurosci. (2021)
Belhadi, A., Kamble, S.S., Mani, V., Benkhati, I., Touriki, F.E.: An ensemble machine learning approach for forecasting credit risk of agricultural SMEs’ investments in agriculture 4.0 through supply chain finance. Ann. Oper. Res. 1–29 (2021)
Shah, S.M., Lütjen, M., Freitag, M.: Text mining for supply chain risk management in the apparel industry. Appl. Sci. 11(5), 2323 (2021)
Qu, Q., Liu, C., Bao, X.: E-commerce enterprise supply chain financing risk assessment based on linked data mining and edge computing. Mobile Inf. Syst. (2021)
Jia, X., Zhang, D.: Prediction of maritime logistics service risks applying soft set-based association rule: an early warning model. Reliab. Eng. Syst. Saf. 207, 107339 (2021)
Liu, Y., Zhang, S., Chen, M., Wu, Y., Chen, Z.: The sustainable development of financial topic detection and trend prediction by data mining. Sustainability 13(14), 7585 (2021)
Tirkolaee, E.B., Sadeghi, S., Mooseloo, F.M., Vandchali, H.R., Aeini, S.: Application of machine learning in supply chain management: a comprehensive overview of the main areas. Math. Probl. Eng. (2021)
Garikapati, P., Balamurugan, K., Latchoumi, T.P., Malkapuram, R.: A cluster-profile comparative study on machining AlSi7/63% of SiC hybrid composite using agglomerative hierarchical clustering and k-means. SILICON 13(4), 961–972 (2021)
Latchoumi, T.P., Parthiban, L.: Quasi oppositional dragonfly algorithm for load balancing in cloud computing environment. Wirel. Pers. Commun. 1–18 (2021)
Arunkarthikeyan, K., Balamurugan, K.: Performance improvement of cryo treated insert on turning studies of AISI 1018 steel using multi-objective optimization. In: 2020 International Conference on Computational Intelligence for Smart Power System and Sustainable Energy (CISPSSE). IEEE, July 2020, pp. 1–4 (2020)
Alipour-Vaezi, M., Aghsami, A., Rabbani, M.: Introducing a novel revenue-sharing contract in media supply chain management using data mining and multi-criteria decision-making methods. Soft Comput. 1–18 (2022)
Chinnamahammad Bhasha, A., Balamurugan, K.: Fabrication and property evaluation of Al 6061+x% (RHA+TiC) hybrid metal matrix composite. SN Appl. Sci. 1(9), 1–9 (2019)
Bakhtadze, N., Suleykin, A.: Industrial digital ecosystems: predictive models and architecture development issues. Ann. Rev. Control. 51, 56–64 (2021)
Cheng, K.C., Huang, M.J., Fu, C.K., Wang, K.H., Wang, H.M., Lin, L.H.: Establishing a multiple-criteria decision-making model for stock investment decisions using data mining techniques. Sustainability 13(6), 3100 (2021)
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Kumar Raja, D.R., Hemanth Kumar, G., Lakshmi Sagar, P. (2022). Data Mining Approach for Prediction of Various Risk Factors in Supply Chain Management. In: Satyanarayana, C., Gao, XZ., Ting, CY., Muppalaneni, N.B. (eds) Proceedings of the International Conference on Computer Vision, High Performance Computing, Smart Devices and Networks. Advanced Technologies and Societal Change. Springer, Singapore. https://doi.org/10.1007/978-981-19-4044-6_18
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DOI: https://doi.org/10.1007/978-981-19-4044-6_18
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