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

该赠款为供应链收入管理（SCRM）的定量决策框架的开发提供资金：收入管理（RM）在多种资源下的实物商品世界中的应用，这些资源可以灵活地用于满足市场差异化的短生命周期产品的随机需求。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。这些行业的基本供应链具有复杂的特点，“如地理上分散的多个供应商/设施和客户、随机的交货/生产提前期以及运输和生产固有的规模经济“，这对立即使用现有的记录管理技术构成了障碍。因此，目标是为动态资源分配和定价决策开发量化模型，以便通过在现实的供应链环境中调整需求和供应来减轻风险利润。方法论研究的重点是随机动态优化模型和有效的求解算法，如果成功的话，本研究将：i）给出有限销售期内多个有限资源容量和市场差异化随机产品需求的动态资源分配和定价问题的分析模型; ii）确定最佳的SCRM政策，并开发用于其实施的计算程序，作为在线决策支持工具; iii）量化SCRM的好处，因此，使用先进的信息技术，使有效的SCRM实践;和iv）丰富RM的理论，适用于现实的供应链设置，以实现有效的行业实施和经济影响。

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.