<p> Leading indicators can be utilized to monitor a system and detect if a risk is present or increasing over time during key development phases such as integration and test. However, no leading indicator is perfect, and each contains inherent holes that can miss signals of risk. While the Swiss cheese model is a well-known framework for conceptualizing the propagation of risks through holes in system defenses, research is lacking on characterizing these holes. There are many choices for leading indicators, and to select an appropriate indicator for a system, engineering managers need to know how well the indicator will detect a signal of risk and what it can miss. A methodology was developed to quantify holes in proposed leading indicator methods and determine the impact to system performance if the methods miss detecting the risk. The methodology was executed through a case study that empirically evaluated two different techniques for detecting and monitoring molecular contamination risk to space system hardware performance during systems integration: visual inspections and portable Raman spectroscopy. Performance model results showed the impact the presence of a contaminant film had on space system surfaces, and how each indicator method missing the detection of a film could impact the system. Results from the methodology provide an understanding of the limitations in the risk detection and monitoring techniques of leading indicators to aid engineering managers in effectively selecting, deploying, and improving these techniques.</p><p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:10751853 |
| Date | 25 April 2018 |
| Creators | Seasly, Elaine Ellen |
| Publisher | The George Washington University |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
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