Research on the optimization of the supplier intelligent management system for cross-border e-commerce platforms based on machine learning

  • Ying YangEmail author
Original Article


At present, with the continuous development of the intelligent system, it is used in many industries. In e-commerce industry, the intelligent system has also been used, especially in supplier management. Based on the machine learning theory, this paper studies the optimization of the supplier management intelligent system of cross-border e-commerce platforms. Based on the wisdom algorithm and machine learning perspective, the optimization of cross-border e-commerce platform supplier credit system is studied in this paper. Firstly, the calculation of the traditional supplier credit evaluation is optimized by introducing the decision matrix algorithm of the difference matrix and the cloud model evaluation method. Then a multi-objective joint decision model of supplier selection and order allocation is established, and the multi-objective evolutionary algorithm combined with actual examples is applied to verify the effectiveness and feasibility of the algorithm and model. Finally, the decision makers’ preferences are integrated into the intelligent decision-making, and the cloud model evaluation method is adopted. The rough set and gray relational analysis mathematical tools are used to construct the procurement supply evaluation system. The research results show that the comparison of the three general indicators of the procurement supply chain can be obtained through the cloud model evaluation calculation, which indirectly reflects the preference decision weights of the three objective functions of the cross-border e-commerce supplier selection and order allocation multi-objective optimization model. This indicates that the procurement supply evaluation system constructed in this paper has achieved the purpose of scientific evaluation and selection of suppliers, and has played a theoretical reference role for supplier management of cross-border e-commerce platform.


Machine learning Cross-border e-commerce System optimization 



  1. Ahmed RR (2015) Supply chain management: milk collection & distribution system in Pakistan. Eur J Sci Res 39(4):130–142Google Scholar
  2. Feng X, Liu Y, Gao Y (2015) Food safety system construction based on supply chain management. Bus Econ 10(9):78–85Google Scholar
  3. Gan ST (2017) Optimization and establishment of sustainable supply chain performance evaluation model for cross-border e-commerce. Value Eng 64(32):369–372Google Scholar
  4. Ghose A (2009) Internet exchanges for used goods: an empirical analysis of trade patterns and adverse selection. MIS Q 33(2):263–291CrossRefGoogle Scholar
  5. Kim DJ (2012) An investigation of the effect of online consumer trust on expectation, satisfaction, and post-expectation. IseB 10(2):219–240CrossRefGoogle Scholar
  6. Lamba K, Singh SP (2017) Big data in operations and supply chain management: current trends and future perspectives. Prod Plan Control 28(11–12):877–890CrossRefGoogle Scholar
  7. Lin F-T, Wu H-Y, Tran TNN (2015) Internet banking adoption in a developing country: an empirical study in Vietnam. IseB 13(2):267–287CrossRefGoogle Scholar
  8. Luo B, Lin Z (2013) A decision tree model for herd behavior and empirical evidence from the online P2P lending market. IseB 11(1):141–160CrossRefGoogle Scholar
  9. Pappas IO, Mikalef P, Giannakos MN et al (2018) Big data and business analytics ecosystems: paving the way towards digital transformation and sustainable societies. IseB 16(3):479–491CrossRefGoogle Scholar
  10. Rong DI, Zhao YJ, Hong-Xin YU et al (2017) Research on the supply chain management optimization in pharmaceutical circulation enterprises. Technoeconomics Manag Res 7(3):14–18Google Scholar
  11. Sanderson J, Lonsdale C, Mannion R et al (2015) Towards a framework for enhancing procurement and supply chain management practice in the NHS: lessons for managers and clinicians from a synthesis of the theoretical and empirical literature. Health Technol Assess 3(18):12–16Google Scholar
  12. Shan SN (2017) Research on the personnel training mode on cross-border e-commerce based on structural reform of the supply front. J Hubei Corresp Univ 36(7):230–256Google Scholar
  13. Vaidya K, Campbell J (2016) Multidisciplinary approach to defining public e-procurement and evaluating its impact on procurement efficiency. Inf Syst Front 18(2):1–16CrossRefGoogle Scholar
  14. Wang YY (2018) Research on the training mode of innovative talents in cross-border e-commerce from the perspective of “supply-side structural reform”. J Heilongjiang Vocat Inst Ecol Eng 10(4):21–25Google Scholar
  15. Yang LJ (2016) The supply-side reform of circulation enterprises based on supply chain optimization. China Bus Mark 5(1):42–46Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Northeast Asian Studies College of Jilin UniversityChangchunChina

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