Research on Supply Chain safety Inventory forecast based on GA-BP Neural Network

An efficient supply chain will bring huge benefits to enterprises, such as integrating resources, reducing logistics costs, improving logistics efficiency, and improving the overall service level. Supply chain management can significant decrease the costs of the stocks and the physical distribution wealth flows, so as to improve customer satisfaction and improve the capacity of enterprises. Inventory management is an important part of supply chain management. Safety inventory is to reduce the loss caused by inventory shortage in the supply chain. Setting up safety inventory is an important part of inventory management. In this paper, back propagation neural network (BPNN) is used to study the safety inventory prediction problem of supply chain with uncertainty. And to address the defects of BP neural network, using genetic algorithm to enhance it. Firstly, a BP predictive neural network trained by genetic algorithm (GA) is proposed to predict the future inventory changes of external suppliers. Secondly, through data analysis, several groups of data factors which have great influence on inventory are selected, in addition, the network is also trained, thereby the future inventory variation tendency can be obtained by GA-BP neural network Finally, a company's inventory data is introduced, the effectiveness of the method is verified by simulation results.