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Supplier Evaluation in Supply Chain Environment Based on Radial Basis Function Neural Network

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  • Shilin Liu

    (Beijing University of Posts and Telecommunications, China)

  • Guangbin Yu

    (Beijing Zhongtianruiheng Technology Co. Ltd., China)

  • Youngchul Kim

    (Hanseo University, South Korea)

Abstract

The comprehensive evaluation and selection of suppliers under the environment of supply chain management has become a key factor affecting the success of supply chain. How to select suppliers and the strategic partnership between suppliers under the environment of supply chain management has become an important challenge. To solve this problem, this paper takes the supplier evaluation and selection of Guangzhou Automobile Toyota Company as the research object, constructs the index system of supplier comprehensive evaluation and selection, uses the RBF neural network algorithm to establish the supplier evaluation and selection model, and makes an experimental study. The results show that radial basis function neural network is a local approximation network, which has a unique and definite solution to the problem, and there is no local minimum problem in BP network. It is a method that enables enterprises and suppliers to have a clear understanding and seek further promotion together. The research provides theoretical data support for enterprise managers to make decisions.

Suggested Citation

  • Shilin Liu & Guangbin Yu & Youngchul Kim, 2024. "Supplier Evaluation in Supply Chain Environment Based on Radial Basis Function Neural Network," International Journal of Information Technology and Web Engineering (IJITWE), IGI Global, vol. 19(1), pages 1-18, January.
  • Handle: RePEc:igg:jitwe0:v:19:y:2024:i:1:p:1-18
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