Acquisition Research Program (ARP) - Investigation of Leading Indicators for Systems Engineering Effectiveness in Model-Centric Programs
Abstract
This research will be led by Dr. Donna H. Rhodes, Massachusetts Institute of Technology. She is a principal research scientist and director of the Systems Engineering Advancement Research Initiative (SEAri) within the MIT Sociotechnical Systems Research Center. Digital transformation is changing the practice of systems engineering, and accordingly drives the need to re-examine how effectiveness of engineering is assessed. This research builds on a phase 1 project that investigated the adaptation of existing systems engineering leading indicators (developed for traditional engineering practice) for digital (model-based) engineering, including guidance for how program leaders could use these indicators for assessing systems engineering effectiveness on a model-centric program. This proposed second of phase research focuses on leading-edge technology in measurement, and exploring how measurement data can be composed into new indicators and displayed in a comprehendible and actionable manner. The first objective of this proposed phase 2 research is to investigate how digital engineering measurement data can be composed as leading indicators specific to engineering effectiveness under model-based acquisition. This includes how information can most effectively be displayed as leading indicator information to facilitate decision maker judgement of engineering effectiveness. The second objective is to investigate leading edge techniques and approaches for collection and synthesis of measurement data, as enabled by digital engineering practices and environments. The research tasks are: (1) literature survey and knowledge gathering; (2) investigation of measurement data composability and visualization; (3) investigation of applicable leading-edge technologies and techniques; (4) final technical report and paper to share results. Knowledge gathering is used to identify measurement composability approaches, measurement visualization, and leading edge techniques that may be useful for collection and synthesis of measurement data from digital artifacts and environments. This includes investigation of publications, studies, workshop reports and interim research findings from academic research groups, professional and industry societies and cross-industry initiatives. Using this knowledge, scenarios for experimenting with the composability and display of measurement data as leading indicators are identified. Experiments with how measurable data can be extracted and composed are performed using public-access models and small models. Applicability of leading edge technologies and techniques (automated collection, visual analytics, augmented intelligence, etc.) are studied as advanced mechanisms for collecting and synthesizing measurement data from digital artifacts. Benefits, impacts and risks of these technologies in a digital engineering context are explored. Potential impact of adapted and extended leading indicators is twofold: (1) to continue to provide visibility into the future state through use of leading indicators in model-centric programs; and (2) to enhance insights provided by the leading indicators related to new digital artifacts that enrich the systems development practice. Advanced approaches for composability and leading-edge technologies are enablers for achieving the desired impact on systems programs.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- May 26, 2020
- Source ID
- HQ00342010008
Entities
People
- Donna H. Rhodes
Organizations
- Massachusetts Institute of Technology
- Office of the Secretary of Defense
- Washington Headquarters Services