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Open Access

Performance evaluation model of cross border e-commerce supply chain based on LMBP feedback neural network

Department of E-Commerce, Zhejiang Business College, Hangzhou 310053, China
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Abstract

In recent years, with the support of national policies, Cross Border E-Commerce (CBEC) has developed rapidly. This business model not only brings significant benefits to the national economy, but also has many unique challenges, especially at the level of supply chain management. Therefore, to enable CBEC enterprises to develop sustainable supply chain, this study discusses the performance evaluation model of supply chain and proposes a CBEC Supply Chain Performance Evaluation Model (CBECSC-EM) based on the Levenberg–Marquardt Backpropagation (LMBP) algorithm. This experiment constructs performance evaluation indicators for the supply chain of CBEC enterprises. On this basis, the LMBP algorithm is introduced, and improved in the experiment to make the overall performance of the evaluation model more scientific and reasonable. In the verification set, the maximum F1 values of LMBP, DEA, SBM, and BP are 98.46%, 93.78%, 87.29%, and 78.95%, respectively. The MAPE value of LMBP model is 0.102%, which is lower than the other three methods (0.282%, 0.343%, and 0.385%) selected in the experiment. The maximum standard deviation rates of importance and operability of the evaluation indexes are 0.1346 and 0.1405, respectively, and there is a significant consistency between the expert scores. Therefore, the LMBP algorithm has broad application prospects in supply chain performance evaluation of CBEC enterprises.

Keywords

Levenberg–Marquardt Backpropagation (LMBP) algorithmCross Border E-Commerce (CBEC)supply chain performance evaluationevaluation indicatorsartificial fish swarm algorithm

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Intelligent and Converged Networks
Volume 4 Issue 2,
June 2023
Pages 168-180
DOI: 10.23919/ICN.2023.0013
Cite this article:
Tan L. Performance evaluation model of cross border e-commerce supply chain based on LMBP feedback neural network. Intelligent and Converged Networks, 2023, 4(2): 168-180. https://doi.org/10.23919/ICN.2023.0013

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Received: 29 April 2023
Revised: 23 May 2023
Accepted: 30 May 2023
Published: 30 June 2023
© All articles included in the journal are copyrighted to the ITU and TUP.

This work is available under the CC BY-NC-ND 3.0 IGO license:https://creativecommons.org/licenses/by-nc-nd/3.0/igo/
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