Research On Data-driven Supply Chain Multi-level Inventory Resource Allocation Method

Multi-level inventory resource allocation is an important research content in multi-level inventory management,and it is also the guarantee for the continuous profit and normal operation of the multi-level inventory system in the supply chain.Demand forecast all levels of nodes in the supply chain,and inventory optimization control according to the forecast results.At each time period,lower-level nodes submit ordering orders to superiors based on predicted results,existing inventory,and inventory constraints,which has certain theoretical and practical significance to the data-driven multi-level inventory resource allocation problem.The current research on the method of multi-level inventory resource allocation mostly focuses on the vertical resource allocation between upper and lower nodes.With the continuous development of the material transportation tools and the information age,it is possible to carry out the same kind of goods distribution and transportation between the nodes at the same level.To increase the demand satisfaction rate of nodes and reduce the waste of resources in excess inventory nodes are the focus of supply chain optimization research.This paper studies the inventory resource allocation problem of a secondary system consisting of a distribution center and multiple node retailers.The main research contents include the following aspects:(1)Research on data-driven multi-level inventory vertical resource allocation method is studied.First,use the exponential smoothing method and ARIMA model to forecast the demand of each node at the distribution center.Second,according to the forecast results and inventory control conditions such as the beginning inventory amount,and consider the overall system profit and cost relationship,establish a multi-level inventory vertical resource allocation.The model makes vertical distribution of the overall inventory resources of the supply chain system to maximize the overall profit.Finally,the genetic algorithm is used to design and calculate the model solution.(2)The data-driven multi-level inventory collaborative resource allocation method is studied.Based on the study of vertical resource allocation methods,the horizontal resource allocation among secondary node retailers is considered in the study of inventory resource allocation.Vertical resource allocation and horizontal resource allocation are determined at the same time.The distribution center selects a resource allocation method and allocation amount that maximizes the overall profit of the system for each node to meet market demand,reduce the cost of out-of-stock losses and inventory costs,and use genetic algorithms to program through Matlab.Program the model implementation to illustrate the feasibility of the model and the effectiveness of the algorithm.(3)The application research of data-driven multi-level inventory resource allocation method for a large supermarket chain is proposed.Using historical sales data,the exponential smoothing method and ARIMA model are used to forecast demand,and the demand forecast results corresponding to the two forecasting methods on historical sales are obtained.The use of two forecasting results to study the application of multi-level inventory vertical resource allocation method and collaborative resource allocation method illustrates the impact of considering both price optimization and multi-level inventory collaborative resource allocation method on improving overall supply chain benefits.Based on the research of multi-level inventory vertical resource allocation,this paper considers the horizontal resource allocation method between peer nodes in the study of multilevel inventory resource allocation based on the actual situation.It studies the multi-level inventory collaborative resource allocation method to efficiently use inventory.Resources and improve overall profit,this research has practical application value for the operation of supply chain.