Insight 

Abstract: The OMG Systems Modeling Language™ (SysML®) was adopted in 2006 and has been used by many organizations to support their efforts to transition to a model-based systems engineering (MBSE) approach. SysML v2 is the next generation Systems Modeling Language and is intended to address many of the limitations of SysML v1. This paper highlights how the SysML v2 language and the new standard Application Programming Interface (API) can enable MBSE and digital engineering. SysML v2 is being developed by the SysML v2 Submission Team (SST) in response to requirements issued by the Object Management Group (OMG). The final submission to the OMG is planned for 2022. The draft SysML v2 specifications and the open-source SysML v2 pilot implementation can be found at https://github.com/Systems-Modeling.

Abstract: Organizations continuously develop Digital Twins across a wide number of applications and industries. While this represents a testimony to the benefits and opportunities Digital Twins provide (AIAA 2022), their development, maintenance and evolution still face major challenges (Bordeleau et al. 2020). Most past and current efforts focusing on the development of Digital Twins have relied on ad-hoc approaches, where most of the efforts start with building models without properly framing the problem (Martin 2019, Lu et al. 2020). More importantly it has led to the development of models that provide a solution to the wrong problem and consequently fail to address the core questions and needs of the stakeholders (Martin 2019)]. The development and implementation of Digital Twins also lack standardization (Shao 2021), instead relying on bespoke methods and technologies (Niederer et al. 2021), which in turns leads to a lack of consistency in their description and implementation, as well as limited interoperability across applications, tools and disciplines (Niederer et al. 2021, Piroumian 2021). The development of Digital Twins has been plagued by a lack of scalable approaches, leading to implementations that are highly specialized and require considerable resources in terms of subject matter expertise (Niederer et al. 2021). The development of standardized methodologies has been identified as a means to unleash the full potential of Digital Twins (Shao 2021, Piroumian 2021) and increase their adoption across a wider range of disciplines and applications (Niederer et al. 2021). To that end, this paper presents a domain agnostic and descriptive reference model, which builds on recognized industry practices and guidelines, to support the planning, description, and analysis of Digital Twins. It also introduces real-world examples of aerospace Digital Twin use cases and discusses how the generic reference model supports the various use case applications. Finally, this paper briefly discusses recommendations and next steps on how to realize value from Digital Twins more broadly.

Abstract: Gaining the benefits of Digital Engineering is not only about implementing digital technologies. The Innovation Ecosystem is a system of systems in its own right, at least partly engineered, subject to the risks and challenges of evolving socio-technical systems. This article summarizes an aid to analyzing and understanding, planning, implementation, and ongoing improvement of the Innovation Ecosystem or its components. It is based on a generic ecosystem analysis reference model with particular focal viewpoints. It is represented as a configurable model-based formal pattern and the INCOSE MBSE Patterns Working Group initially applied it in a related INCOSE collaboration project led by the Agile Systems Engineering Working Group. Users of the resulting framework subsequently elaborated and applied aspects in the context of a wide variety of commercial and defense ecosystems across different domains. While connecting to several current and historical contexts, it is particularly revealing of Digital Engineering's special promise. By explicating the recurrent theme of Consistency Management that underlies all historical innovation, it enhances our understanding of historical as well as future engineering and life cycle management. This includes the ecosystem preparation of internal and supply chain human and technical resources to effectively consume and exploit digital information assets, not just create them. The ecosystem model carries its own representation of enhanced capability implementation by generation of agile release train increments, along with evolutionary steering based on feedback and group learning.

Abstract: INSIGHTVolume 1997, Issue 15 p. 28-28 Columnists Membership Lew Lee, Lew Lee INCOSE Membership Chair lew@svl.trw.com 408-743-6474 Search for more papers by this author Lew Lee, Lew Lee INCOSE Membership Chair lew@svl.trw.com 408-743-6474 Search for more papers by this author First published: 23 June 2015 https://doi.org/10.1002/inst.19971528Citations: 9AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article.Citing Literature Volume1997, Issue15Spring 1997Pages 28-28 RelatedInformation