Research On VMI Supply Chain Inventory Decision Model Based On CVaR

With the continuous development of economic globalization,the relationship between supply chains has become complicated and interlaced.In addition to facing fierce market competition,enterprises must also face the inevitable supply chain risk.Especially in the automobile manufacturing industry,the complex process flow and coordination between various component manufacturers and vehicle assemblers are difficulties that every enterprise must face and a challenge to the supply chain risk management system..In recent years,the vendor-managed inventory(VMI)model has played an important role in supply chain management.Based on realistic problems,this paper studies the inventory decision model of VMI supply chain based on CVa R,and extends the research to assembly systems.By transforming realistic problems into mathematical models for analysis and research,the aim is to provide manufacturing enterprises with optimal decisions to minimize their own risks when facing supply chain risks from the perspective of quantitative analysis,while also enriching supply chain risk management.research content.This paper first builds a two-tier supply chain system consisting of a supplier and a retailer.By analyzing the decision model of supplier risk avoidance under VMI mode and retailer risk avoidance under RMI mode,the optimal decision is solved and discussed.The introduction of replenishment strategy to explore its impact on the operation of the supply chain.Later,the research was extended to an assembly system consisting of two suppliers and one assembler,and to analyze the impact of a single supplier's replenishment strategy on the production decisions of the two suppliers when the risk avoidance of a single supplier was avoided.Through research,it is found that the implementation of replenishment strategies can improve the benefits of supply chain members under the two modes.The size of replenishment costs and the degree of risk aversion will affect the optimal decisions of supply chain members.Our research findings are explained in detail using numerical analysis.