TY - GEN
T1 - MEASUREMENT FRAMEWORK FOR MODEL-BASED SYSTEMS ENGINEERING (MBSE)
AU - Henderson, Kaitlin
AU - McDermott, Thomas
AU - Salado, Alejandro
AU - van Aken, Eileen
N1 - Funding Information:
This material is based upon work supported by the U.S. Department of Defense through the Systems Engineering Research Center (SERC) under Contract HQ0034-19-D-0003. SERC is a University Affiliated Research Center managed by Stevens Institute of Technology. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the U.S. Department of Defense.
Publisher Copyright:
© American Society for Engineering Management, 2021
PY - 2021
Y1 - 2021
N2 - Model-Based Systems Engineering (MBSE) is an increasingly accepted practice in the Systems Engineering community, however, little has been done to empirically show that MBSE provides value. Furthermore, as the industry continues in the direction of digital transformation, MBSE will become a critical component of the larger Digital Engineering (DE) approach. This paper presents a measurement framework for selecting and developing appropriate metrics to assess the value/benefits of MBSE and subsequently DE. Utilizing expected benefits identified in a review of MBSE literature, a causal map was hypothesized to show how expected benefits (potential metrics) influence and relate to each other. This was done in order to systematically determine which benefits would be the most impactful to measure. The hypothesized causal model was presented to subject-matter experts from the Digital Engineering Working Group who provided feedback and validation for the model. Based on the causal map and subsequent analysis, we can recommend the first metrics to be employed for DE/MBSE based on the most influential nodes of the causal model.
AB - Model-Based Systems Engineering (MBSE) is an increasingly accepted practice in the Systems Engineering community, however, little has been done to empirically show that MBSE provides value. Furthermore, as the industry continues in the direction of digital transformation, MBSE will become a critical component of the larger Digital Engineering (DE) approach. This paper presents a measurement framework for selecting and developing appropriate metrics to assess the value/benefits of MBSE and subsequently DE. Utilizing expected benefits identified in a review of MBSE literature, a causal map was hypothesized to show how expected benefits (potential metrics) influence and relate to each other. This was done in order to systematically determine which benefits would be the most impactful to measure. The hypothesized causal model was presented to subject-matter experts from the Digital Engineering Working Group who provided feedback and validation for the model. Based on the causal map and subsequent analysis, we can recommend the first metrics to be employed for DE/MBSE based on the most influential nodes of the causal model.
KW - Digital engineering
KW - Model-based systems engineering
KW - Performance measurement
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M3 - Conference contribution
AN - SCOPUS:85124429880
T3 - 2021 ASEM Virtual International Annual Conference "Engineering Management and The New Normal"
SP - 312
EP - 321
BT - 2021 ASEM Virtual International Annual Conference "Engineering Management and The New Normal"
PB - American Society for Engineering Management
T2 - 42nd International Annual Conference of the American Society for Engineering Management: Engineering Management and The New Normal
Y2 - 27 October 2021 through 30 October 2021
ER -