Capital University of Economics and Business, Beijing 100070, China.
Comput Intell Neurosci. 2022 Aug 23;2022:7977335. doi: 10.1155/2022/7977335. eCollection 2022.
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.
近年来，随着新技术的涌现，大数据、人工智能等技术对供应链管理产生了更大的影响。其中，大数据分析能力作为供应链企业应具备的重要能力之一，对供应链弹性管理有着特别显著的影响。从绩效管理的角度出发，基于供应链弹性理论，分析供应链绩效的管理水平、供应链协作等供应链弹性要素以及大数据分析能力与供应链绩效之间的关系，可以研究大数据分析能力对不同规模企业的供应链绩效的影响。研究了大数据分析能力对供应链绩效水平的影响。本文基于 LMBP 算法研究了供应链性能评估与优化问题，为供应链性能评估与优化提供了一定的参考。
