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

这笔赠款为供应链收入管理(SCRM)量化决策框架的开发提供资金：收入管理(RM)在多种资源下在实物商品世界中的应用，这些资源可以灵活利用，以满足市场对短生命周期产品的差异化随机需求。感兴趣的特定设置包括但不限于定制(合同)制造和电子商务行业。这些行业的基础供应链的复杂特征“如地理上分散的多个供应商/设施和客户、随机交付/生产提前期以及运输和生产中固有的规模经济”对现有的RM技术的立即使用构成了障碍。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降低风险利润。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，本研究将提供动态资源分配和定价问题的分析模型，其特征是在有限的销售范围内具有多个有限的资源能力和对产品的市场差异化随机需求；ii)确定最优的供应链管理政策并开发其实施的计算程序，作为在线决策支持工具；iii)量化供应链管理的好处，从而利用先进的信息技术实现有效的供应链管理实践；以及iv)丰富供应链管理的理论应用于现实的供应链环境，以有效地实施行业和经济影响。

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.