The Effects of Organizational Structure on MBSE Adoption in Industry: Insights from Practitioners

Abstract

Despite a general increase of use across the industry, Model-Based Systems Engineering (MBSE) continues to be reported as difficult to adopt in organizations. Data related to adoption is largely taken from surveys asking respondents to share what they think are barriers or challenges to adopting MBSE. Consistently, organizational-related factors such as culture and structure are often one of the top reported barriers to adopting MBSE. Despite this, there has been little research exploring how these organizational factors actually affect MBSE adoption. This study attempts to address that void by comparing data on organizational structure characteristics with adoption and implementation metrics. A survey was created and distributed amongst the Systems Engineering community to collect this data. Characteristics of organizational structure that were measured as variables include: Size, Formalization, Centralization, Specialization, Vertical Differentiation, Flexibility, and Interconnectedness. All of these variables except for Specialization were found to have some level of significant correlation with adoption and/or implementation variables as perceived by the respondents. Flexibility and Interconnectedness especially had strong correlations with both adoption and implementation variables. This study serves as an exploratory report of this type of organizational-related data as it relates to MBSE, seeding future work that enables Engineering Managers to derive strategies to adopt and implement MBSE customized to their organization.

Keywords:

• MBSE
• model-based systems engineering
• technology adoption
• organizational design

EMJ Focus Areas:

• Systems Engineering
• Technology Manage-ment

Acknowledgments

This study was approved by the Internal Review Board (IRB) at Virginia Tech with protocol number # 21-1003.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ There is also the issue that user activity in one tool is likely not a good representation of an organization’s use of MBSE as a whole, especially since multiple tools are often involved.

² Due to the inconsistencies in past studies, the authors present these hypotheses without specifying a direction (i.e., positive or negative impact). Any designation of direction in the hypotheses would have been guesswork from the authors.

³ See footnote regarding the hypotheses for OS1.

⁴ One study that examined the scales of centralization and formalization by Aiken and Hage used here found an ambiguity with the concept of autonomy (Dewar et al., 1980). There are aspects of autonomy in questions relating to job codification and centralization. Other studies did not find there to be an issue with the validity and reliability of the formalization measures (Ramayah & Abdullah, 2007). In the created MBSE questions, autonomy of the group will be classified as centralization, and autonomy of the individual will be classified as formalization.

⁵ When interviewing a top level executive as the source of organizational structure characteristics, researchers often interview more than one executive and aggregate the responses.

⁶ These classifications are taken from Motamedian’s MBSE adoption survey (Motamedian, 2013).

⁷ Measuring at the enterprise level of an organization may be easier to understand and convey to others the meaning of. However, organizations are often so large and complex that there could not be one response to a question that applies to an entire organization. In other words, the response of a single person is significantly less likely to represent a whole organization with any degree of accuracy.

Additional information

Notes on contributors

Kaitlin Henderson

Dr. Kaitlin Henderson has extensive research experience with the areas of Model-Based Systems Engineering/Digital Engineering adoption and measurement. Kaitlin earned her Doctorate, Masters, and Bachelors degrees in Industrial and Systems Engineering from Virginia Tech. She was a Core Team Contributor to the DE Measurement Framework published in 2022, which was developed in a joint collaboration between PSM, SERC, AIA, NDIA, INCOSE, DoD, and the Aerospace Corporation. Currently, Kaitlin works at Radiance Technologies as a Systems Architect.

Alejandro Salado

Dr. Alejandro Salado is a systems engineer, consultant, researcher, and instructor. He is currently an associate professor of systems engineering with the Department of Systems and Industrial Engineering at the University of Arizona. Previously, he spent over 10 years in industry developing space systems. He holds BSc/MSc degrees in electrical engineering, project management, electronics engineering, and space systems engineering, and a PhD in systems engineering from the Stevens Institute of Technology.