Abstract
The supply chain management process in the Internet of Things and the information age needs to deal with massive amounts of data and several influencing factors. Therefore, traditional supply chain management models cannot cope with the needs of modern supply chain management. Based on this, this paper combines machine learning and human–computer interaction technology to construct a supply chain management model, and uses wireless sensor networks as the basis of machine learning and human–computer interaction supply chain models. Combine human–computer interaction algorithms with smart learning algorithms, use machine learning for wireless sensor network data processing, and combine smart learning for data analysis. Moreover, this paper combines the actual needs of the supply chain model to improve the wireless sensor network so that it can meet the operational needs of the supply chain model. In addition, this paper starts with the construction of multiple module functions from the aspects of supply chain management risk, supply chain logistics, supply chain management, and supply chain information transmission. Finally, this paper designs experiments to verify the performance of the system in this paper. The experimental research results show that the supply chain management model based on machine learning and human–computer interaction constructed in this paper has good results.
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Funding was provided by Scientific Research Project of Shanghai Science (Grant No. 19511102202).
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Yuan, G., Wu, S. & Wang, B. Supply chain management model based on machine learning. Neural Comput & Applic 35, 4319–4335 (2023). https://doi.org/10.1007/s00521-022-06986-z
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DOI: https://doi.org/10.1007/s00521-022-06986-z