Supplier default risk prediction method

Abstract

The invention discloses a supplier default risk prediction method. The supplier default risk prediction method provided by the invention comprises the following steps: constructing a supplier knowledge graph by using a knowledge graph technology, restoring partial missing information in the knowledge graph with time sequence characteristics by using a knowledge graph prediction link technology, comprehensively considering cross-domain data, and predicting the supplier default risk by adopting the neural network. Compared with a single-domain data prediction mode using historical sales volume or user feedback evaluation and the like in traditional supplier default risk prediction, the supplier supply risk in the prediction period can be effectively judged, and the method is helpful for analyzing the selection of future suppliers and ensuring the safe operation of a supply chain, and reduces the operation risk of an enterprise.

Description

technical field [0001] The invention belongs to the technical field of manufacturing supply chain management, and in particular relates to a supplier default risk prediction method. Background technique [0002] Compared with other industries, manufacturing and assembly procedures are numerous and complex, and a single enterprise cannot perform all the work. At present, most manufacturing enterprises outsource their non-core business and manufacture by purchasing parts. Parts suppliers have more and more initiative and technical power, so in an increasingly competitive environment, improving supply chain efficiency is a business strategy for companies to achieve rapid development. As the front link of the supply chain, the supplier plays a very important role in the supply chain. The interruption or suspension of any link in the industrial supply chain may cause the entire supply chain to stop. Scientific management of the supply chain is a key factor for enterprises to ach...

AI Technical Summary

Problems solved by technology

[0006] In view of the lack of research on supplier default risk prediction in the current manufacturing industry and the single data dimension, this invention proposes a supplier default risk prediction method based on knowledge graphs and RLSTM algorithm technology to improve the stability of the enterprise supply chain system. Judge the risk of supplier default as soon as possible, and the technical effect of reducing the economic and time loss of the enterprise

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