关键词: 物流企业绩效, 评价指标, 组合赋权法, BP神经网络

Abstract: Based on the performance evaluation index system of Japanese logistics companies and the SASAC's “Standard Values for Enterprise Performance Evaluation in 2019”, this paper organizes three criteria layers including business activities, logistics activities and the internal and external environment of the company by combing domestic and foreign literature. An evaluation system of 24 evaluation indicators such as employee turnover is established. Taking the 2018 data of 25 5A-level logistics companies as the research object, we combine two weighting methods (G1 method and BP neural network method) through a combined weighting model to establish an index system suitable for the performance evaluation of logistics companies in China. The research results in this paper show that the evaluation results of the performance evaluation index system of China's logistics enterprises are basically consistent with the development status of logistics enterprises. At present, insufficient R&D investment and weak talent attraction are the main reasons why traditional transportation and storage logistics companies lag behind comprehensive service logistics enterprises. The difference in asset return capability and customer service level is an important indicator of the gap between China's 5A-level logistics companies.

Key words: logistics enterprise performance, evaluation index, comprehensive weight model, BP neural network