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What, When, Where, and How Many? A Systematic Review of Technical Measure Selection Guidance in Systems Engineering Literature.

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Title: What, When, Where, and How Many? A Systematic Review of Technical Measure Selection Guidance in Systems Engineering Literature.
Authors: Eaton, Casey E.1 (AUTHOR) cee0009@uah.edu, Campo, Kelly X.1 (AUTHOR), Longhurst, August1 (AUTHOR), Mesmer, Bryan1 (AUTHOR), Ferreira, Susan2 (AUTHOR)
Source: Systems Engineering. May2025, Vol. 28 Issue 3, p343-362. 20p.
Subjects: Systems engineering, Systems development, Choice (Psychology), Stakeholder analysis, Technical literature, Units of measurement
Abstract: The decisions that direct system development are based on assessments of a system, referred to as technical measures. Systems engineers select the set of technical measures for each system, informed by previous systems, guidebooks, and systems engineering research. If guidance for selecting technical measures is contradictory or does not ensure that a satisfactory set is selected, then the selected set of technical measures may not facilitate directing a system development as desired. Within large‐scale complex engineered systems development, a technical measure set provides clear targets for a system and can be used to coordinate and integrate efforts by many individuals and teams. This systematic literature review of 89 academic and industry developed engineering literature sources examines over 2500 guidance statements for technical measure selection. Most guidance provides examples or abstract qualities that technical measures should exhibit. Such guidance can result in multiple sets of technical measures that each drive toward different designs. The guidance identifies origins for technical measure selection, such as from stakeholders, but lacks detail on how to elicit a set of technical measures from stakeholders. This research's systematic literature review audits the consistency, contradictions, and omissions in current guidance for selecting technical measures. From these findings, we seek to establish evidence‐based research needs in technical measure selection. [ABSTRACT FROM AUTHOR]
Copyright of Systems Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: The decisions that direct system development are based on assessments of a system, referred to as technical measures. Systems engineers select the set of technical measures for each system, informed by previous systems, guidebooks, and systems engineering research. If guidance for selecting technical measures is contradictory or does not ensure that a satisfactory set is selected, then the selected set of technical measures may not facilitate directing a system development as desired. Within large‐scale complex engineered systems development, a technical measure set provides clear targets for a system and can be used to coordinate and integrate efforts by many individuals and teams. This systematic literature review of 89 academic and industry developed engineering literature sources examines over 2500 guidance statements for technical measure selection. Most guidance provides examples or abstract qualities that technical measures should exhibit. Such guidance can result in multiple sets of technical measures that each drive toward different designs. The guidance identifies origins for technical measure selection, such as from stakeholders, but lacks detail on how to elicit a set of technical measures from stakeholders. This research's systematic literature review audits the consistency, contradictions, and omissions in current guidance for selecting technical measures. From these findings, we seek to establish evidence‐based research needs in technical measure selection. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Systems Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: May2025
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