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Article type: Research Article
Affiliations: School of Economics, Beijing Wuzi University, Beijing, China
Correspondence: [*] Corresponding author. Ling Xin, School of Economics, Beijing Wuzi University, Beijing, China. E-mail: [email protected].
Abstract: In the era of digital economy, the optimization of enterprise supply chain networks has become a key challenge, while the problems of traditional supply chains, including information asymmetry and lack of trust, seriously hinder the development of enterprise supply chain networks. This paper will use the blockchain distributed technology and the digital economy background to explore how to use the blockchain distributed technology to optimize the existing problems. Firstly, study the supply chain information sharing to develop resources to reduce costs, then use the application of block chain technology and smart contract to establish information sharing mechanism to help the supply chain information more transparent and improve trust; secondly, use the block chain technology decentralized storage model to realize the decentralized supply chain research, and finally use the consensus method to improve the privacy protection of information, to avoid information asymmetry among users. Through experiments, it could be found that the optimization method of enterprise supply chain network based on blockchain distributed technology had a traceability accuracy of over 92.35% for the extracted products, with an average traceability accuracy of 93.791% for 10 products. Research on the transparency of different supply chain information was above 89.73% . By utilizing blockchain distributed technology, information protection in enterprise supply chains could be effectively improved; trust mechanisms could be better established; risk control effectiveness could be improved; optimization of enterprise supply chain networks could be better assisted.
Keywords: Enterprise supply chain network optimization, blockchain distributed technology, digital economy, smart contracts, deep learning
DOI: 10.3233/JIFS-234664
Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
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