Supply Chain Revenue Management: Mitigating Profit-at-Risk in Manufacturing and Distribution Networks

供应链收益管理：降低制造和分销网络的风险利润

• 批准号: 0621097
• 负责人: Sila Cetinkaya
• 金额: --
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-08-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0621097&HistoricalAwards=false
• 关键词: Supply; Chain; Revenue; Management; Mitigating

This grant provides funding for the development of a quantitative decision making framework for Supply Chain Revenue Management (SCRM): Application of revenue management (RM) in the world of physical goods under multiple resources that can be utilized flexibly for satisfying the market differentiated stochastic demands for products with short life cycles. The particular settings of interest include, but are not restricted to, the custom (contract) manufacturing and e-commerce industries. Complex features of the underlying supply chains in these industries"such as geographically dispersed multiple suppliers/facilities and customers, stochastic delivery/production lead times, and economies of scale inherent in transportation and production"impose a barrier against the immediate use of existing RM techniques. Hence, the goal is to develop quantitative models for dynamic resource allocation and pricing decisions in order to mitigate profit-at-risk by aligning demand and supply in realistic supply chain settings. The methodological research focuses on stochastic dynamic optimization models and effective solution algorithms.If successful, this research will i) deliver analytical models for dynamic resource allocation and pricing problems characterized by multiple limited resource capacities over finite selling horizons and market differentiated stochastic demands for products; ii) identify optimal SCRM policies and develop computational procedures for their implementation that will serve as online decision support tools; iii) quantify the benefits of SCRM and, hence, the use of advanced information technology enabling effective SCRM practices; and iv) enrich the theory of RM as applied to realistic supply chain settings for efficient industry implementation and economic impact.

该资助项目为供应链收益管理（SCRM）量化决策框架的开发提供资金：收益管理（RM）在实物商品领域的应用，可灵活运用多种资源，满足市场对短生命周期产品的差异化随机需求。感兴趣的特定环境包括但不限于定制（合同）制造和电子商务行业。这些行业基础供应链的复杂特征“例如地理上分散的多个供应商/设施和客户、随机的交付/生产提前期以及运输和生产固有的规模经济”对立即使用现有的 RM 技术造成了障碍。因此，我们的目标是开发动态资源分配和定价决策的定量模型，以便通过在现实供应链环境中调整需求和供应来降低风险利润。该方法研究的重点是随机动态优化模型和有效的求解算法。如果成功，本研究将 i) 提供动态资源分配和定价问题的分析模型，其特征是有限销售范围内的多个有限资源容量和市场差异化的产品随机需求； ii) 确定最佳的 SCRM 政策并为其实施开发计算程序，作为在线决策支持工具； iii) 量化 SCRM 的效益，从而量化先进信息技术的使用，以实现有效的 SCRM 实践； iv) 丰富 RM 理论，将其应用于现实的供应链环境，以实现高效的行业实施和经济影响。