Warehouse Business Management Of Auto Manufacturer Based On VMI And Research On Safe Stock Prediction

With the development of the modern management ideas and information technology, supply chain management has become a focus of manage theories and practice. Inventory management is an important part of the supply chain management, which is the key both to exert collaboration and to perform the integrated management. Therefore, further studies on the collaborative strategies have theoretical and realistic significance.The warehouse management in H Corp is vendor managed inventory. VMI is a new inventory management model in supply chain management environment. It reflects a integrated management thinking. By taking the supplies pull system of Shanghai H Corp as a background, this research subject elaborates the integral solution of the supplies pull system and introduces the application patterns and structure design of the system roughly. With the demand of supplies warehouse system, this thesis analyzes the business and business process of the supplies warehouse system, designs the architecture model of the supplies warehouse function part, sequence bans function part and so on. According to the function, it designs the database conceptual model and logistic model; furthermore, a detailed description of the realization in detail for the main function module, realize the function of the supplies warehouse system.With the further implementation of the supplies pull system, to support the enterprise decision, the enterprise wants to find a way to win the knowledge behind the invalid parts information. Using the methods of statistic and query can only provide the knowledge of surface. With the demand of enterprise, to support the enterprise decision, this dissertation uses the data mining technology to win the knowledge. Using BP Neural Networks Algorithm, this research subject analyses the factors of safe stock prediction in supplies first, and realizes the stock-prediction system with.NET technology last. With validated, the use of neural network model can predict the safe stock accurately. So this model has great practical significance.