Application of improved BP neural network based on e-commerce supply chain network data in the forecast of aquatic product export volume

2019 ◽  
Vol 57 ◽  
pp. 228-235 ◽  
Author(s):  
Yizhuo Zhang
Keyword(s):  
Neural Network ◽  
Supply Chain ◽  
Bp Neural Network ◽  
Network Data ◽  
Supply Chain Network ◽  
Aquatic Product ◽  
Export Volume

scholarly journals An Inventory Controlled Supply Chain Model Based on Improved BP Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Wei He
Keyword(s):  
Neural Network ◽  
Supply Chain ◽  
Convergence Rate ◽  
Inventory Control ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Inventory Level ◽  
Bp Neural Network Model

Inventory control is a key factor for reducing supply chain cost and increasing customer satisfaction. However, prediction of inventory level is a challenging task for managers. As one of the widely used techniques for inventory control, standard BP neural network has such problems as low convergence rate and poor prediction accuracy. Aiming at these problems, a new fast convergent BP neural network model for predicting inventory level is developed in this paper. By adding an error offset, this paper deduces the new chain propagation rule and the new weight formula. This paper also applies the improved BP neural network model to predict the inventory level of an automotive parts company. The results show that the improved algorithm not only significantly exceeds the standard algorithm but also outperforms some other improved BP algorithms both on convergence rate and prediction accuracy.

Integration performance statistics of green suppliers based on fuzzy mathematics and BP neural network

2020 ◽  
pp. 1-12
Author(s):  
Yijie Wang ◽  
Peihua Fu
Keyword(s):  
Neural Network ◽  
Supply Chain ◽  
Bp Neural Network ◽  
Strategic Alliances ◽  
Environmental Sustainability ◽  
Enterprise Management ◽  
Fuzzy Mathematics ◽  
Green Logistics ◽  
Performance Statistics ◽  
Green Suppliers

With the enhancement of people’s environmental awareness, improving environmental performance has become an important way for manufacturing enterprises to achieve sustainable development. At present, one of the key challenges facing enterprises in terms of environmental sustainability is to extend it to other supply chain members. In this paper, the author analyzes the integration performance statistics of green suppliers based on fuzzy mathematics and BP neural network. Supply chain integration represents the company’s ability to formulate strategic alliances, integrate resources, establish seamless processes and share information. The empirical results show that it is feasible to evaluate the performance of green logistics enterprise integration. Through reasonable calculation method and model, the effective and reasonable evaluation results can be obtained. Enterprise managers can reasonably evaluate the key links of enterprise management, allocate enterprise resources correctly, completely and reasonably, minimize costs and maximize profits. Therefore, for the green logistics enterprises, should pay attention to the enterprise’s own performance evaluation, timely adjust their own development direction, plan and goal.

A Model on Supplier Selection in Green Supply Chain Based on BP Neural Network

2011 ◽  
Vol 143-144 ◽  
pp. 312-316 ◽  
Author(s):  
Dao Guo Li ◽  
Zhong Yuan Zhou ◽  
Chen Yang
Keyword(s):  
Neural Network ◽  
Supply Chain ◽  
Bp Neural Network ◽  
Supplier Selection ◽  
Simulation Experiment ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Green Supply Chain ◽  
Evaluation Indexes ◽  
Bp Neural Network Model

Based on the complexity of the evaluation index for supplier selection in green supply chain, this paper puts forward the evaluation index system in green supply chain and presents BP neural network model to select supplier with evaluation indexes as BP neural network's input and the outcome of DEA/AHP model as BP neural network's expected output. It can be proved that the model can effectively select suppliers in green supply chain by simulation experiment with LIINGO 9.0 and MATLAB7.0.

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