Supplier Flexibility And Selection Research Under Uncertain Demand Conditions

With the rapid development of social economy, the market competition has become more fierce, product life cycles have been shortened, customers demand have become more discriminating, and the time has become more important. It increases uncertainty and dynamics of operation, causes the enterprises to face the stern challenge, and sets a higher request to supply chain management. Recently, out-sourcing the peripheral business is popular in a lot of corporate to simplify Supply Chain. Although it does reduce the cost of Supply Chain, It also increases the risk and complexity of commercial relationship, because it generates a lot of interest principals. The reaction of Supply Chain decreases due to the increasing complexity. In order to achieve the overall operating efficiency, all participants in the chain must consistently react to the change of the outside uncertainty. We are facing a lot of challenges on how to effectively manage the supply chain.The goal of the supply chain management is enabling the enterprises to meet the fierce changeable market requirements. However, the flexibility was considered as an effective strategy to respond to the uncertainty.This thesis focuses on study of Supplier Flexibility and Selection Model under demand uncertainty. In the paper, analyze the concept of supply chain and supply chain management systematically, focus on the source and performance about uncertainty in supply chain, and the dynamic characteristic of the flexibly of supply chain, make a qualitative research and provide a evaluation model for the flexibility of supply chain. Sum up the models and method about suppliers selection and make a conclusion. A supplier selection model is developed under stochastic demand conditions. Stochastic supplier selection is determined with simultaneous consideration of the total cost, the quality rejection rate, the late delivery rate and the flexibility rate, involving constraints of demand satisfaction and capacity. Using a problem specific genetic algorithm, results are presented.