A Risk-Based Scalable Methodology to Support Strategic Design of An Enterprise Infrastructure

产能中断是对全球化企业风险影响最深远的因素之一。这笔赠款提供资金，用于建立全面的分析框架，以捕捉面临随机容量中断的企业网络中可用容量和需求的综合动态。定义网络容量位置的许多因素将被纳入模型中，包括但不限于容量降级和恢复的各种模式以及网络拓扑的影响。这个数学框架将为开发计算上可行的方法提供基础，以支持可持续和敏捷的企业基础设施的设计。将创建一个具有动态数据库驱动分析引擎和3D可视化模块的软件原型，以促进涉及网络设计的决策。该赠款还将支持研究的教育部分，重点是培养一批来自学术和种族背景的专业干部，他们可以作为战略决策者，有效地减轻全球化生产和服务中固有的不确定性。一个跨学科的企业风险分析课程将开发，这将独特地整合传统的不确定性下的决策与基于风险的设计和分析capacitated networks.The预期的好处，这项研究包括开发新的数学模型，这将提供基础模型的拓扑结构的网络风险大的复杂性。如果成功的话，这些基础模型和方法将提高国内制造企业倡导精益制造理念的应变能力和竞争力。与此同时，研究和教育任务将有助于现有和未来的方法，这些方法适合于国家关键基础设施系统固有的模型风险。

Capacity disruption is one of the most profound influence on risk for globalized enterprises. This grant provides funding for building comprehensive analytical framework for capturing the integrative dynamics of available capacity and demand in enterprise networks exposed to random capacity disruptions. A number of factors defining the network capacity position will be incorporated into the models including, but are not limited to, various modes of capacity degradation and recovery as well as impact of network topology. This mathematical framework will provide a basis for developing a computationally viable methodology to support design of a sustainable and agile enterprise infrastructure. A software prototype with a dynamic database driven analytical engine and a 3D visualization module will be created to facilitate decision making involving network design. The grant will also support the educational component of the research focused on fostering a professional cadre from a spectrum of academic and ethnic backgrounds who can serve as strategic decision makers to effectively mitigate the uncertainties inherent in globalized production and services. An interdisciplinary enterprise risk analysis course will be developed which will uniquely integrate traditional decision making under uncertainty with risk-based design and analysis of capacitated networks.The expected benefits of this research include development of novel mathematical models that will provide foundations to model network risk for topologies of large complexity. If successful, these underlying models and the methodology will enhance resilience and competitiveness of domestic manufacturing enterprises advocating lean manufacturing philosophy. At the same time, the research and educational tasks will contribute to existing and future methodologies that are tailored to model risk inherent in national critical infrastructure systems.