JMI Inventory Management Of Material Based On 3PL And Research On Safe Stock Prediction

With the development of economic globalization and supply chain management, the competition of modern enterprises is becoming more and more international, and gradually transforms into the competition between enterprise supply chains. In the enterprise supply chain management, people gradually find inventory management is an important part of supply chain management, and inventory costs also account for a considerable proportion in enterprise costs. Therefore, the study of each node enterprise supply chain inventory management strategy has a strong theoretical and practical significance to the overall optimization of the supply chain.In this paper, H car factory using vendor-managed inventory strategies reduces logistics costs in a certain, which suppliers can independently manage materials inventory. However, in the existing inventory management, there is a lack of communication and consultation between manufactures and suppliers in the decision making process, and the control to material flow in the existing information system is not timely, accurate etc. To solve these problems, this paper proposes a solution, the joint inventory management based on third party logistics management. Joint inventory management pays more attention to the right balance of responsibility and risk sharing between the upstream and downstream enterprise nodes in the supply chain, which can effectively avoid the "bullwhip effect". The introduction of the third party logistics in the joint inventory management, outsourcing enterprise inventory management and logistics services, allows enterprises to focus on their core business and enhances the competitiveness of enterprises.Meanwhile, for the safety stock inventory management problem, this paper proposes LM-BP neural network algorithm optimized by genetic algorithm, and establishes an appropriate safety stock predictive models. For the problems of easily falling into local optimum and slow convergence in BP neural network, the model uses genetic algorithms and neural networks LM algorithm improved with the learning ability of neural network and genetic algorithm global search capability, and greatly improves the performance of BP neural network. Moreover, the feasibility and the performance are verified by Matlab simulation experiment of the algorithm, and the effect is obvious.Finally, the JMI material management system based on 3PL is designed and realized using.NET three layer frame structure, and the safety stock prediction algorithm implemented in C # is applied to this system.