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Title: Phase 2: Investigation of Leading Indicators for Systems Engineering Effectiveness in Model-Centric Programs
Authors: Donna H. Rhodes
Keywords: Systems Engineering
augmented intelligence
digital engineering
Issue Date: 24-Sep-2021
Publisher: Acquisition Research Program
Citation: Published--Unlimited Distribution
Series/Report no.: Systems Engineering;MIT-SE-21-242
Abstract: This technical report summarizes the work conducted by Massachusetts Institute of Technology under contract award HQ0034-20-1-0008 during the performance period May 22, 2020 – July 31, 2021. Digital 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. Augmenting a companion research study under contract HQ0034-19-1-0002 on adapting and extending existing systems engineering leading indicators, this study takes a future orientation. This report discusses how base measures can be extracted from a digital system model and composed as leading indicators. An illustrative case is used to identify how the desired base measures could be obtained directly from a model-based toolset. The importance of visualization and interactivity for future leading indicators is discussed, especially the potential role of visual analytics and interactive dashboards. Applicability of leading edge technologies (automated collection, visual analytics, augmented intelligence, etc.) are considered as advanced mechanisms for collecting and synthesizing measurement data from digital artifacts. This research aims to provide insights for the art of the possible for future systems engineering leading indicators and their use in decision-making on model-centric programs. Several recommendations for future research are proposed extending from the study.
Description: Systems Engineering
Appears in Collections:Sponsored Acquisition Research & Technical Reports

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