Research On Design Of Material Pulling Mode And Forecast In Supply Chain Under C2B Customization Mode

In the era of Internet economy,the B2C consumption mode used by the automobile industry is gradually being broken by the new consumption idea of customer driven and direct interaction of all value chains.In the mode of large-scale user selection,the number of product varieties and the types of raw materials are increasing rapidly,leading to an exponential increase in the complexity of logistics operations.This is mainly reflected in the fact that manufacturers can neither support response through regular stock preparation,nor avoid the longer response cycle caused by production according to orders after demand locking.Based on the analysis of the economic benefits of the automobile enterprises’implementation of the C2B customization mode and the challenges faced by the manufacturing supply chain,this paper focuses on the characteristics of the core driving force of users under the C2B customization mode,and relies on big data algorithms to carry out the research on the design of the material pull mode of the supply chain under the customization mode and the prediction of material demand:mining the business value contained in the customer demand data based on Kano model and feedforward neural network calculation,To guide the business development of the enterprise;The quantitative model is used to capture and identify customer value needs and map them to the technical solution domain.Reduce prediction cost through inspiration,and formulate enterprise strategy through prediction.Using the importance and frequency analysis model of customer value demand and the two analysis methods of the House of Quality,we can realize the personalized module design directly driven by demand,realize the personalized automobile design that customers participate in,optimize products and businesses through reasonable evaluation indicators,and reduce enterprise research and development costs.Combined with the premise of product modular design and the pain points of supply mode under C2B customization mode,the modular pull mode of materials is designed,which greatly reduces the inventory of this material and the cost waste caused by stock preparation.Based on ARIMA algorithm,optimize the material prediction algorithm,improve the accuracy of material demand prediction,and reduce supply risk.Through the design,research and system implementation of material modular pull and configuration level part demand forecast under C2B customization mode,the overall inventory of the enterprise supply chain has decreased by nearly 50%,greatly reducing the physical space in the field and at the line.and reducing the cost of supply chain management.Through use tracking,the overall supply chain responsiveness has been improved.Under the real-time demand transmission mode,the total order delivery time has been reduced to 16 days.The inventory decreased by nearly 50%.saving 700W of inventory capital every year,greatly improving the operation efficiency of logistics.