LAI Initiatives and Research on Systems Engineering

AF/LAI Workshop progress report for 3rd quarter 2004 in the systems engineering knowledge are

Lean Enablers for Systems Engineering Handout

LAI Conference handout, Boston Hyatt Harborside Hote

Phase 2: Investigation of Leading Indicators for Systems Engineering Effectiveness in Model-Centric Programs

Acquisition Research Program Sponsored Report SeriesSponsored Acquisition Research & Technical ReportsThis 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.Approved for public release; distribution is unlimited.Approved for public release; distribution is unlimited

Investigation of Leading Indicators for Systems Engineering Effectiveness in Model-Centric Programs

Acquisition Research Program Sponsored Report SeriesSponsored Acquisition Research & Technical ReportsThis technical report summarizes the research conducted by Massachusetts Institute of Technology under contract award HQ0034-19-1-0002 during July 22, 2019 – August 31, 2021. Involved research team members include: Dr. Donna H. Rhodes, Principal Investigator; Dr. Eric Rebentisch, Research Associate; and Mr. Allen Moulton, Research Scientist. Systems engineering practice is evolving under the digital engineering paradigm, including use of model-based systems engineering and newer approaches such as agile. This drives a need to re-examine the existing use of metrics and leading indicators. Early engineering metrics were primarily lagging measures, whereas more recent leading indicators draw on trend information to provide more predictive analysis of technical and programmatic performance of the engineering effort. The existing systems engineering leading indicators were developed under the assumption of paper-based (traditional) systems engineering practice. This research investigates the model-based implications relevant to the existing leading indicators. It aims to support program leaders, transitioning to model-based engineering on their programs, in continued use of leading indicators. It provides guiding insights for how current leading indicators can be adapted for model-based engineering. The study elicited knowledge from subject matter experts and performed literature review in identifying these implications. An illustrative case was used to investigate how four leading indicators could be generated directly from a model-based toolset. Several recommendations for future research are proposed extending from the study. A companion research study (“phase 2”) under contract HQ0034-20-1-0008 provides insights for the art of the possible for future systems engineering leading indicators and their use in decision-making on model-centric programs. For completeness, selected background information and illustrative case are included in the technical reports in both studies. This research aims to provide insights for current practice within programs transforming to digital engineering, for continued use of systems engineering leading indicators. Several recommendations for future research are proposed extending from results of the study.Approved for public release; distribution is unlimited.Approved for public release; distribution is unlimited

LAI Initiative on Systems Engineering Leading Indicators

Follow-on initiative from the Air Force/LAI Workshop on Systems Engineering for Robustnes

Systems Engineering Leading Indicators Guide - Beta Release

This document is the beta release of the Systems Engineering Leading Indicators Guide. This project was initiated by the Lean Aerospace Initiative (LAI) Consortium in cooperation with the International Council on Systems Engineering (INCOSE). Leading measurement and systems engineering experts from government, industry, and academia volunteered their time to work on this initiative. Government and industry organizations are encouraged to tailor the information in this document for their purposes, and may incorporate this material into internal guidance documents. Please cite the original source and release level (currently beta) for traceability and baseline control purposes

Workshop Report Air Force/LAI Workshop on Systems Engineering for Robustness

Workshop repor

Needs Elicitation for Complex Systems Engineering

This practice builds on the sound systems engineering practice for needs elicitation. It extends the practice to emphasize important considerations, strategies, and key questions to consider. The practice ensures needs elicitation is conducted throughout the evolving system’s lifecycle, involving a large set of stakeholders, who may have changing needs as the system evolves

Lean Aerospace Initiative (LAI) MIT Research Studies Applicable to Systems Engineering

This publication contains abstracts for past research thesis projects related to systems engineering completed within the LAI research group at Massachusetts Institute of Technology