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Exploring the Application and Optimization Strategy of the LMBP Algorithm in Supply Chain Performance Evaluation

In recent years, with the emergence of new technologies, big data, artificial intelligence, and other technologies have had a greater impact on supply chain management. Among them, big data analysis capability, as one of the important capabilities that supply chain enterprises should have, has a par...

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Detalles Bibliográficos
Autor principal: Gu, Fei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9427211/
https://www.ncbi.nlm.nih.gov/pubmed/36052047
http://dx.doi.org/10.1155/2022/7977335
Descripción
Sumario:In recent years, with the emergence of new technologies, big data, artificial intelligence, and other technologies have had a greater impact on supply chain management. Among them, big data analysis capability, as one of the important capabilities that supply chain enterprises should have, has a particularly significant impact on supply chain resilience management. From the perspective of performance management, based on supply chain resilience theory, the relationship between the supply chain performance management level, supply chain collaboration, and other supply chain resilience elements, as well as big data analysis capability and supply chain performance can be analyzed to study the impact of big data analysis capability on supply chain performance of enterprises of different scales. The impact on the level of supply chain performance is being studied. This paper investigates the problem of supply chain performance evaluation and optimization based on the LMBP algorithm and provides some references for supply chain performance evaluation and optimization.