Date of Award
2025
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Systems Engineering
Committee Chair
Bryan Mesmer
Committee Member
Susan Ferreira
Committee Member
Sampson Gholston
Committee Member
Hanumanthrao Kannan
Committee Member
Ana Wooley
Research Advisor
Bryan Mesmer
Subject(s)
Systems engineering, Large scale systems, Measurement, System analysis
Abstract
Technical measurement is foundational in large-scale complex engineered systems (LSCES) design and contract awards due to the state of practice of systems engineering. Although technical measurement is ubiquitous in systems engineering practice, the selection and assessment of the impact of a set of technical measures is often heuristics-based, rather than based on empirical evidence or theory. Current technical measure set selection has been observed to be subject to many challenges which, in turn, can degrade system outcomes. Two main thrusts comprise the research: selection and impact. Motivated by the potential impacts of a poor set of technical measures, a significant body of literature has been published containing guidance on selecting technical measure sets. This dissertation seeks to foster improved selection practices for technical measure sets, establish that omissions of technical measures can impact systems, and provide evidence-based assessments of technical measure set completeness operationalizations. The dissertation first analyzes the identified guidance for technical measure selection in systems engineering literature for inconsistencies and areas of need. While it is generally understood that omissions of technical measures from a set can impact the resulting system alternative, little prior research has given evidence for the impacts of omissions. The impacts of omission are modeled in a LSCES case study using the NASA Human Landing System (HLS), comparing two systems engineering decision-making frameworks. It is shown that regardless of framework, omitting technical measures can change the system alternative chosen and the system acceptability. From the identified guidance, four operationalizations of completeness for technical measures sets are established. Rather than attempting to define completeness, the research demonstrates how the operationalization of completeness can be assessed. The assessments demonstrate how a set of technical measures that is complete for one operationalization is not necessarily complete for the remaining. This dissertation moves forward the body of knowledge on the topics of technical measure set selection and assessment. The findings move the foundational practice of technical measure selection towards one based on empirical evidence. The findings enable both the directive for “completeness” and the consequences of not achieving completeness in technical measure sets to be understood and assessed.
Recommended Citation
Eaton, Casey, "Assessing the selection and impact of completeness in technical measure sets in systems engineering" (2025). Dissertations. 445.
https://louis.uah.edu/uah-dissertations/445