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新产品供应链的VMI寄售返利契约模型
引用本文:王菲,于辉,孙彩虹.新产品供应链的VMI寄售返利契约模型[J].系统工程理论与实践,2013,33(11):2804-2810.
作者姓名:王菲  于辉  孙彩虹
作者单位:1. 重庆大学 经济与工商管理学院, 重庆 400030;2. 重庆大学 现代物流重庆市重点实验室, 重庆 400030;3. 重庆工商大学 电子商务及供应链系统重庆市重点实验室, 重庆 400067
基金项目:国家自然科学基金(70871126,90924009,71101160);教育部新世纪优秀人才支持计划(NCET-10-0843);中央高校基本科研业务费(CDJSK100211,CDJXS12020005);重庆市社会科学规划项目(2011YBGL115)
摘    要:市场份额和销售额是衡量新产品上市成功的重要指标,然而市场需求的严重不确定性是新产品供应链决策面临的主要困难. 文章利用部分需求信息刻画新产品需求特征, 设计了基于VMI的寄售返利契约的新产品推出方式并模型化. 引入鲁棒博弈方法, 分析了新产品供应链在"VMI+寄售+返利"模式下的合作改进效益. 通过比较研究发现: 该模式能有效控制新产品上市风险, 提高市场份额, 增强企业效益, 从而有效支持新产品入市.

关 键 词:新产品供应链  供应商管理库存  寄售  返利  鲁棒优化  
收稿时间:2011-08-31

The model of new product supply chain under vendor-managed inventory with consignment and sales rebate
WANG Fei,YU Hui,SUN Cai-hong.The model of new product supply chain under vendor-managed inventory with consignment and sales rebate[J].Systems Engineering —Theory & Practice,2013,33(11):2804-2810.
Authors:WANG Fei  YU Hui  SUN Cai-hong
Institution:1. School of Economics and Business Administration, Chongqing University, Chongqing 400030, China;2. Chongqing Key Laboratory of Logistics, Chongqing University, Chongqing 400030, China;3. Chongqing Key Laboratory of Electronic Commerce and Supply Chain System, Chongqing Technology and Business University, Chongqing 400067, China
Abstract:Market share and sales are important indexes to measure the success of new product launches; however, serious uncertainty of new product demand is the main difficulty to make decision for enterprise. Considering this, we describe the demand of new product with partial information, and design a new contract based on VMI, consignment and sales rebate. By means of the worst-case robust optimization approach, the improvement of corporation with the new contract is analyzed. The result shows that the new contract can control the risk of new product launches, improve market share, enhance enterprise benefit, and all of these would effectively support the new product launches.
Keywords:new product supply chain  VMI  consignment  sales rebate  robust optimization  
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