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

Shilin Liu et al.

International Journal of Information Technology and Web Engineering2024https://doi.org/10.4018/ijitwe.339186article
ABDC C
Weight
0.46

What the paper says

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.

3 citations

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https://doi.org/https://doi.org/10.4018/ijitwe.339186

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@article{shilin2024,
  title        = {{Supplier Evaluation in Supply Chain Environment Based on Radial Basis Function Neural Network}},
  author       = {Shilin Liu et al.},
  journal      = {International Journal of Information Technology and Web Engineering},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.4018/ijitwe.339186},
}

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

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Evidence weight

0.46

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.38 × 0.4 = 0.15
M · momentum0.57 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.