In the process of supply chain optimization, data has characteristics such as multi-source heterogeneity and unstructuredness. Traditional supply chain optimization methods that rely on structured data and statistical analysis cannot achieve ideal goals. Therefore, this article studies the digital and intelligent optimization mechanism of the entire supply chain link based on generative artificial intelligence. The entire supply chain link is divided into product design and process links, product raw material procurement links, product production and manufacturing management links, product delivery links, and product retirement and recycling links. For each link, the ChatGPT large language model in generative artificial intelligence adopts a neural semantic analysis method based on an encoding–decoding architecture. For the multi-source heterogeneous general knowledge within the entire supply chain link, general corpus training, expert annotation, and special corpus training are carried out, and the semantic analysis of the general knowledge of the entire supply chain link is realized through in-depth mining and understanding. Based on the semantic analysis results, the generative adversarial network in generative artificial intelligence is used to predict complex patterns or solutions such as product design, transportation routes, and sales methods in each link of the entire supply chain, making the prediction results more accurate and more in line with the actual supply chain business. The experimental results show that this mechanism can accurately analyze the semantics of the general knowledge of the entire supply chain link, improve the accuracy of the prediction of each function of the entire supply chain link, and significantly improve the economic benefits of enterprises.
Zhao et al. (Sat,) studied this question.
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