Programmatic Risk Analysis for Critical Engineering Systems Under Tight Resource Constraints

在资源紧张的情况下，关键工程系统的程序性风险分析CONSTRAINTSABSTRACT工程项目一直被期望用更少的资源完成更多的工作，并且这些项目之间的依赖性正在增加。例如，在NASA，更快-更好-更便宜(FBC)项目正在作为计划进行管理，以增强较小项目之间的协同效益，并降低单个大型任务的风险。在资源紧张的情况下，FBC项目经理必须平衡几种类型的失败风险，包括他们的项目表现对未来任务的影响。现有的风险分析模型一般侧重于技术风险或管理风险的量化。由于很难同时平衡某一项目的费用、进度和业绩及其对其他项目的影响，面临这些问题的管理人员可以从综合方案风险分析中受益。拟议研究的目标是开发一个理论框架，将技术风险和管理风险的分析结合起来。它的设计将量化这两种风险之间的权衡，并优化项目的技术设计和预算分配。然后，我们将把不同数量的测试和审查、部分任务失败以及程序中的项目相关性的影响合并到模型中。此外，由于当前的管理决策通常是基于直觉和隐含的奖励系统做出的，我们将在一个说明性的例子中捕捉这些偏好，并使用代表组织及其经理的目标的效用或估值函数。将开发用于建模方案风险的通用框架，以支持对不同行业方案中相互依赖的项目的管理。将根据对无人航天飞行任务和项目的管理以及NASA/JPL及其承包商提供的数据提供说明。

PROGRAMMATIC RISK ANALYSIS FOR CRITICAL ENGINEERING SYSTEMS UNDER TIGHT RESOURCE CONSTRAINTSABSTRACTEngineering projects are continually expected to accomplish more with fewer resources, and the dependencies among these projects are increasing. At NASA, for example, faster-better-cheaper (FBC) projects are being managed as programs to enhance the benefits of synergies among smaller projects, and to attenuate the risks of single large missions. Under tight resource constraints, FBC project managers must balance several types of failure risks including the effect of their project's performance on future missions. Existing risk analysis models generally focus on the quantification of either technical or management risks. Since it is difficult to balance simultaneously cost, schedule and performance of a given project as well as their effects on other projects, managers who face these problems can benefit from an integrated programmatic risk analysis. The goal of the proposed study is to develop a theoretical framework to integrate the analysis of both technical and management risks. It will be designed to quantify the tradeoffs among these two types of risk and to optimize a project's technical design and budget allocation. We will then incorporate into the model the effects of various amounts of testing and reviews, partial mission failures, and project dependencies within programs. Also, since current management decisions are often made based on intuition and an implicit reward system, we will capture these preferences in an illustrative example with a utility or valuation function that represents the objectives of the organization and its managers. This generic framework for modeling programmatic risks will be developed to support the management of interdependent projects within programs in different industries. An illustration will be provided based on the management of unmanned space missions and programs, and data available at NASA/JPL and its contractors.