Data mining has become an important research topic in the field of information technology and has received extensive attention. With the maturity of data mining technology, data mining has gradually been applied to the field of enterprise management. Therefore, the application of data mining technology to enterprise supply chain management has far-reaching significance for improving the core competitiveness of enterprises. This paper aims to study the procurement logistics optimization of supply chain management based on data mining technology. Based on the analysis of the functions of data mining, the role of procurement management and the application of data mining in supply chain management, company A is taken as an example. Purchasing logistics optimization goals and purchasing organization structure are described and improved. Finally, this article compares the effects before and after the procurement logistics optimization shows that the company's procurement efficiency has been significantly improved, the procurement cycle has been greatly shortened, and the procurement cost has been significantly reduced.

Bei Jiang. Procurement Logistics Optimization of Supply Chain Management Based on Data Mining Technology. International Journal of Business Management and Economics and Trade (2021), Vol. 2, Issue 1: 9-16. https://doi.org/10.38007/IJBMET.2021.020102.

[1] Lu H, Setiono R, Liu H. Effective Data Mining Using Neural Networks. Knowledge & Data Engineering IEEE Transactions on, 2016, 8(6):957-961. https://doi.org/10.1109/69.553163

[2] Helma C, Cramer T, Kramer S, et al. Data Mining and Machine Learning Techniques for the Identification of Mutagenicity Inducing Substructures and Structure Activity Relationships of Noncongeneric Compounds. J Chem Inf Comput, 2018, 35(4):1402-1411. https://doi.org/ 10.1021/ci034254q

[3] Adeniyi D A, Wei Z, Yongquan Y. Automated Web Usage Data Mining and Recommendation System Using K-Nearest Neighbor (KNN) classification method. Applied Computing & Informatics, 2016, 12(1):90-108. https://doi.org/10.1016/j.aci.2014.10.001

[4] Kavakiotis I, Tsave O, Salifoglou A, et al. Machine Learning and Data Mining Methods in Diabetes Research. Computational and Structural Biotechnology Journal, 2017, 15(C):104-116. https://doi.org/10.1016/j.csbj.2016.12.005

[5] Chaurasia V, Pal S. A Novel Approach for Breast Cancer Detection using Data Mining Techniques. Social Science Electronic Publishing, 2017, 3297(1):2320-9801.

[6] Buczak A, Guven E. A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection. IEEE Communications Surveys & Tutorials, 2017, 18(2):1153-1176. https://doi.org/10.1109/COMST.2015.2494502

[7] Croxton K L, SJ García‐Dastugue, Lambert D M, et al. The Supply Chain Management Processes. International Journal of Logistics Management, 2016, 12(2):13-36. https://doi.org/ 10.1108/09574090110806271

[8] Harsasi M, Minrohayati N A. The Impact of Supply Chain Management Practices on Competitive Advantage. International Journal of Economic Policy in Emerging Economies, 2017, 10(3):240-. https://doi.org/10.1504/IJEPEE.2017.086623

[9] Zhong R Y, Newman S T, Huang G Q, et al. Big Data for Supply Chain Management in the Service and Manufacturing Sectors: Challenges, Opportunities, and Future Perspectives. Computers & Industrial Engineering, 2016, 101(nov.):572-591. https://doi.org/10.1016/j.cie. 2016.07.013

[10] Ashenbaum B, Maltz A. Purchasing-Logistics Integration and Supplier Performance: An Information-Processing View. International Journal of Logistics Management, 2017, 28(2):379-397. https://doi.org/10.1108/IJLM-07-2014-0113

[11] Shin S Y, Pak M S. The Critical Factors for Korean Freight Forwarders' Purchasing Negotiation in International Logistics. The Asian Journal of Shipping and Logistics, 2016, 32(4):195-201. https://doi.org/10.1016/j.ajsl.2016.12.002