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dc.contributor.authorDonna H. Rhodes-
dc.contributor.authorEric Rebentisch-
dc.contributor.authorAllen Moulton-
dc.identifier.citationPublished--Unlimited Distributionen_US
dc.descriptionAcquisition Management / Defense Acquisition Community Contributoren_US
dc.description.abstractDigital engineering transformation changes the practice of systems engineering, and drives the need to re-examine how engineering effectiveness is measured and assessed. Early engineering metrics were primarily lagging measures. More recently leading indicators have emerged that draw on trend information to allow for more predictive analysis of technical and programmatic performance of the engineering effort. By analyzing trends (e.g., requirements volatility) in context of the program’s environment and known factors, predictions can be forecast on the outcomes of certain activities (e.g., probability of successfully passing a milestone review), thereby enabling preventative or corrective action during the program. This paper discusses continuing research on the adaptation of existing systems engineering leading indicators (developed under the assumptions of document-based engineering) for digital (model-based) engineering. Model-based implications identified in the research are discussed in support of the use of existing leading indicators in digital engineering programs. An illustrative example describes how measurement data can be extracted from a digital system model and composed into indicators. The importance of visualization and interactivity is discussed, especially the potential role of visual analytics and interactive dashboards. Several recommendations for future research are proposed based on interim research findings.en_US
dc.description.sponsorshipAcquisition Research Programen_US
dc.publisherAcquisition Research Programen_US
dc.relation.ispartofseriesAcquisition Management;SYM-AM-21-136-
dc.subjectSystems Engineeringen_US
dc.subjectDigital Engineering-
dc.subjectManagement Paradigm-
dc.titleInvestigation of Leading Indicators for Systems Engineering Effectiveness in Model-Centric Programsen_US
Appears in Collections:Annual Acquisition Research Symposium Proceedings & Presentations

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