Study On Core Competitiveness Of Middle And Small Logistics Enterprises

The generation of the modern logistics industry is based on the traditional logistics, combined with the objective needs of national economic development, through the extensive application of modern science and technology, constantly sum up logistics practice and integration of social logistics resources constantly. As a linkage industry between the related industries of the national economy, the development of modern logistics industry has not only become the primary means of changing the mode of economic growth in various sectors, but also has become a new economic growth point in the logistics activities, at the same time is also marks the development of the national economy to achieve a transition from extensive to intensive. Middle and small logistics enterprises are important part of the logistics enterprises in China, how to maintain the survival and development of middle and small logistics enterprises at the impact of the economic transformation? The effective way is to cultivate and enhance its core competitiveness.This thesis is used the method of combining theoretical research and case studies, and based on the research results of domestic and foreign scholars in this field. First, we define the middle and small logistics enterprises, as well as the core competitiveness connotation, characteristics and elements. Secondly, the current development of the middle and small logistics enterprises and the need to cultivate its core competitiveness and influencing factors are analyzed, and constructed a set of evaluation index system which is suitable for Chinese middle and small logistics enterprises core competitiveness. Then establishing the BP neural network evaluation model, and apply this model for evaluation of the core competitiveness of KJ Logistics Company. Finally, based on the evaluation results, propose the way to cultivate and enhance the core competitiveness of small and medium-sized logistics enterprises.Select a3-layer neural network to establish the BP neural network evaluation model, and determines16hidden layer neurons, activation function and training function are selected by instance validation, in order to ensure that the results of the evaluation authenticity and accuracy.