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Optimization of supply chain efficiency management based on machine learning and neural network

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Abstract

Supply chain management is of great significance to business operations and socioeconomic development. However, the current supply chain efficiency management cannot effectively control the risk caused by the inefficient supply chain management. In order to study the improvement in supply chain efficiency management, supported by machine learning and neural network technology, this study builds a supply chain risk management model based on learning and neural network. Moreover, this study evaluates the risk indicator system based on the current status of supply chain management. In addition, the model simulation research is carried out in the MATLAB platform, and the validity analysis of the model is performed with examples. Finally, after training the data through the training model, the risk assessment value is output, and strategies for coping with the risk are given. The research shows that the model proposed in this paper has a certain practical effect and can be considered for application.

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Acknowledgements

The study was supported by China National Social Science Foundation Project (19CJY047), Heilongjiang Philosophy and Social Science Research Planning Project (18JYC259).

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  1. College of Economics, Harbin University of Commerce, Harbin, 150028, China

    Chaoliang Han & Qi Zhang

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  1. Chaoliang Han
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  2. Qi Zhang
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Correspondence to Qi Zhang.

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Han, C., Zhang, Q. Optimization of supply chain efficiency management based on machine learning and neural network. Neural Comput & Applic 33, 1419–1433 (2021). https://doi.org/10.1007/s00521-020-05023-1

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  • DOI: https://doi.org/10.1007/s00521-020-05023-1

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