Insight最新文献

With the increasing complexity of commercial aircraft and the rapidly changing market demands, the system engineering development pattern extensively adopted by aircraft OEM has evolved from the traditional document-based systems engineering (DBSE) to model-based systems engineering (MBSE) and pattern-based systems engineering (PBSE). MBSE employs models to describe products, while PBSE builds upon MBSE by utilizing engineering patterns, which are validated in advance, to enhance the efficiency and quality of data production in both MBSE and DBSE. However, during PBSE engineering practices, we have observed certain challenges, such as the barriers to initializing product S* models and the low efficiency in generating instances. Artificial intelligence for systems engineering (AI4SE) is an emerging concept aimed at creating a more efficient and user-friendly systems engineering implementation environment through the integration of artificial intelligence (AI), machine learning (ML), and related technologies. This paper explores the application of AI4SE in real-world engineering projects by leveraging large language models (LLMs) to develop a methodology that reduces the deployment threshold of PBSE for enterprises and enhances the efficiency of instance generation.

One of the biggest challenges in designing complex systems is to design and specify the components in such a way that they contribute to the overall functionality of the system. Specialization, cost efficiency, or other reasons require that these components be co-developed, verified, and delivered by various suppliers, and then integrated, verified, and validated by the integrator. This makes the approaches of systems engineering crucial for integrators (OEMs). As the complexity of the systems to be managed increases, so does the complexity of designing, maintaining, and further developing these approaches. The methods of model-based systems engineering (MBSE) have already proven their relevance at the system level today. Companies cannot ensure the required quality of their systems without a model-based approach. The transition from document-based to model-based operational architectures presents a comparable challenge. SEREA is listed to be designated as an INCOSE technical product, offering a model-based reference enterprise architecture based on the uified architecture framework (UAF). With SEREA as a basis, a customized enterprise architecture can be created. Since SEREA is based on ISO/IEC/IEEE 15288, it is particularly suitable for companies dealing with the life cycle management of complex systems. This paper describes how SEREA has been developed and how shall be tailored.

The future of systems engineering depends on solving a pressing challenge: the fragmentation of data and models across engineering tools, enterprise systems, and lifecycle processes. While existing standards from the International Organization for Standardization (ISO), the Object Management Group (OMG), and other standard development organizations (SDOs) address specific domains and data exchange formats, no comprehensive framework exists to guide digital engineering (DE) across the full ISO/IEC/IEEE 15288 system lifecycle process—from technical and management processes to enabling and agreement activities. This disconnect limits organizations' ability to integrate, scale, and deliver consistent value across increasingly complex ecosystems.

This article examines the current gaps in interoperability standardization efforts and outlines opportunities for the International Council on Systems Engineering (INCOSE) to shape the future of digital transformation. It introduces the INCOSE agile systems engineering life cycle management (ASELCM) pattern as a foundation for aligning lifecycle processes with model-based technical exchanges through a federated digital thread. The authors call for INCOSE to adopt a strategic standardization role—working with other SDOs to define interoperability use cases, align lifecycle models, and model-based systems engineering (MBSE) standards, and develop a shared reference framework for interoperability. This collaboration is essential for enabling seamless, cross-enterprise interoperability—and for empowering organizations to realize the speed, scale, and economic advantage promised by digital transformation.

Organizations are embedding digital engineering and model-based engineering into their systems engineering lifecycles to achieve cost savings, higher product quality, and earlier delivery. From a business perspective, understanding the success of their digital transformation investment, has been lacking. To address this requested need, government, university, and industry experts have collaborated with the INCOSE Measurement Working Group to enhance the issuance of the initial Digital Engineering Measurement Framework (v1.1) to include measures that track, and report results related to digital engineering business operations and environmental benefits. This article describes the approach used to mature the guidance document, the benefits of the new measures, and recommendations for the sequential release.

In recent years, many organizations have embraced digital engineering (DE) as a strategy for enhancing integration and interoperability across the system lifecycle. However, research into the development of DE practices has shown that a lack of consensus on terminology can hinder progress. Common terms such as ‘digital thread’, ‘digital twin’, and ‘authoritative source of truth’ are defined inconsistently across domains and organizations, creating friction in digital information exchange. Automated, efficient information exchange requires a precise lexicon to facilitate understanding for humans and machines. The INCOSE Digital Engineering Information Exchange (DEIX) Working Group is working to address this challenge by developing a formal ontology of DE concepts. This article addresses some of the key terminology challenges facing DE practitioners and describes how a machine-readable ontology can help to create a shared understanding of DE and enable more effective implementation of DE practices.

In systems engineering, elegance is often associated with simplicity and control, but this risks ignoring the deeper systemic nature of elegance as coherence between complexity and purpose. This article reframes elegance not as minimalism, but as the systemic sufficiency of structure, behavior, and context. Drawing from systems science foundations and the triad of fit–form–function, we argue that true systemic elegance arises when relational complexity is harnessed, not suppressed, to yield emergent coherence across levels. Elegant systems are those that integrate complexity without introducing unnecessary complication; they achieve just enough richness to engage with the variety of their environment, while avoiding overdesign. This balance is not accidental; it results from rigorous architecting and purposeful design. Using examples and distinctions, this article offers a framework for systems engineers to recognize, cultivate, and evaluate elegance as a dynamic outcome of systemic coherence.

We have entered an era of rapid change and increasing uncertainty. The biggest mistake we can make as systems engineers is ignoring this change. The term “uncertainty” is deeply connected to complexity for many communities, including INCOSE's Complex Systems Working Group (CSWG). As uncertainty or complexity increases, our experience, and indeed logic, suggests that the practices and techniques developed for a world of sufficient certainty are no longer enough, no matter how gifted the engineer. At this point, it is essential to change our mindset from “I know enough” to “I know I am wrong about something”. This mindset shift triggers a desire and need for continuous learning, new practices and techniques which embrace different viewpoints, learning through failure, and results in flexible and adaptable systems. The work of the CSWG, described in the article, is to identify and create suitable practices for the practitioner systems engineer in this new, uncertain, and complex age. We are aiming to “burst the bubble of complexity” and enable engineers to deal effectively with uncertainty and complexity. But the pace of change is fast, the complexity landscape is vast, and the tradecraft still emerging. Hence, the only way to address this complexity challenge sufficiently is to recruit communities of experts with diverse views who can work collaboratively towards these common aims.