Key performance parameter thresholds for the Giant Magellan Telescope

被引:0
|
作者
Sitarski, Breann N. [1 ]
Angeli, George Z. [2 ]
Gajadhar, Sarah [3 ]
Conan, Rodolphe [1 ]
Bouchez, Antonin H. [1 ]
Goodrich, Robert [1 ]
Walls, Brian [1 ]
机构
[1] GMTO Corp, 465 N Halstead St,Suite 250, Pasadena, CA 91107 USA
[2] GAAS Corp, 6925 N Camino De Las Candelas, Tucson, AZ 85718 USA
[3] SG Consulting Inc, 2258 Wascana Greens, Regina, SK, Canada
来源
MODELING, SYSTEMS ENGINEERING, AND PROJECT MANAGEMENT FOR ASTRONOMY X | 2022年 / 12187卷
关键词
systems engineering; key performance parameters; compliance monitoring; ground-based observatories; threshold estimation;
D O I
10.1117/12.2630324
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The Giant Magellan Telescope project established Key Performance Parameters (KPPs) for measuring, tracking, and managing the evolution of expected observatory performance through construction and commissioning. The KPPs are inherently statistical variables. The performance of the as-built observatory depends on environmental and operational parameters. Just as importantly, its performance also depends on not fully predictable programmatic processes and technical uncertainties associated with design and construction. Mitigation of technical risks and management response to realized technical risks have critical impact on achieved performance through decisions allocating project resources to remedy potentially impaired performance. While the requirements capture the objective values of KPPs, relaxed threshold values represent the minimum acceptable performance that must be achieved in support of the scientific objectives of the observatory. Properly defined threshold values may reduce project risk exposure and aid project management in building and delivering the observatory on time and on budget. This paper reports our statistical approach to determine the KPP threshold values the project can accomplish with high confidence level in the face of programmatic and technical uncertainties. The method identifies the technical risks threatening KPPs and carefully characterizes them to ensure their suitability for further evaluation. This paper also demonstrates a Monte Carlo approach to using recognized project and subsystem risks to bound the probability density functions for performance, cost, and schedule impacts, which are in turn applied to each KPP error budget to determine threshold values. The analysis is integrated into the project's statistical risk and contingency estimation framework.
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收藏
页数:17
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