Insight最新文献

Exploratory modelling is an emerging approach which can address the challenge of model-based decision making in dealing with input model uncertainties. Exploratory modelling samples from an input uncertainty space and generates extensive computational experiments to analyse possible model behaviours in an output solution space. The way that the input uncertainty space is delineated influences the results of exploratory modelling and its computational cost. In this article, we show the statistical significance of the implication of the size of an input uncertainty space on the resulted output solution space. We also propose a heuristic approach which informs the delineation of input uncertainties by screening the relevant model behaviour in the solution space. An illustrative example of an aircraft fleet management system is used to demonstrate the implementation of our approach in practice. We conclude that the delineation of input uncertainty space can be a way to control simulations in exploratory modelling and to enhance the efficiency of the exploration process and the confidence of the final results.

Currently, technology readiness assessments (TRAs) are used in determining the maturity of the critical technology elements (CTEs) of a system as it moves forward in the system development life cycle. The TRA method uses technology readiness levels (TRLs) as the decision metric. TRL values are assessed and determined by subject matter experts (SMEs). Since expert evaluators often differ in their judgment when scoring a system element against the TRL scale criteria, this paper argues for the use of a Bayesian network model to provide a mathematical method to consistently combine and validate the judgment of these SMEs and increase the confidence in the determination of the readiness of system components and their technologies.

Traditional systems engineering pays attention to careful composition of prose requirements statements. Even so, prose appears less than what is needed to advance the art of systems engineering into a theoretically based engineering discipline comparable to electrical, mechanical, or chemical engineering. Ask three people to read a set of prose requirements statements, and a universal experience is that there will be three different impressions of their meaning. The rise of model-based systems engineering might suggest the demise of prose requirements, but we argue otherwise. This paper shows how prose requirements can be productively embedded in and a valued formal part of requirements models. This leads to the practice-impacting insight that requirements statements can be non-linear extensions of linear transfer functions, shows how their ambiguity can be further reduced using ordinary language, how their completeness or overlap more easily audited, and how they can be “understood” more completely by engineering tools.

Traditionally, engineering encourages requirements statements that are objective, testable, quantitative, atomic descriptions of system technical behavior. But what about “soft” requirements? When products deliver psychologically or emotionally based human experiences, subjective descriptions may frustrate engineers. This challenge is important for products appealing to senses of style, enjoyment, fulfillment, stimulation, power, safety, awareness, comfort, or similar emotional or psychological factors. Automobiles, buildings, consumer products, packaging, graphic user interfaces, airline passenger compartments and flight decks, and hospital equipment provide typical examples. This paper shows how model-based systems engineering helps solve three related problems: (1) integrating models of “soft” human experience with hard technical product requirements, (2) describing how to score traditional “hard” technology products in terms of “fuzzier” business and competitive marketplace issues, and (3) coordinating marketing communication and promotion with the design process. The resulting framework integrates the diverse perspectives of engineers, stylists, industrial designers, human factors experts, and marketing professionals.

Gaining benefits of digital engineering is not only about implementing digital technologies. An ecosystem for innovation is a system of systems in its own right, only partly engineered, subject to risks and challenges of evolving socio-technical systems. This paper summarizes an aid to planning, analyzing, implementing, and improving innovation ecosystems. Represented as a configurable model-based reference pattern used by collaborating INCOSE working groups, it was initially applied in targeted INCOSE case studies, and subsequently elaborated and applied to diverse commercial and defense ecosystems. Explicating the recurrent theme of consistency management underlying all historical engineering, it is revealing of digital engineering's special promise, and enhances understanding of historical as well as future engineering and life cycle management. It includes preparation of human and technical resources to effectively consume and exploit digital information assets, not just create them, capability enhancements over incremental release trains, and evolutionary steering using feedback and group learning.

Held in Dublin, Ireland, IS2024 invites us to refresh understanding of contributions to systems engineering by Ireland's greatest mathematician— Sir William Rowan Hamilton (1805–1865), professor of astronomy at Trinity College Dublin and royal astronomer of Ireland. His profound contributions to science, technology, engineering, and math (STEM) deserve greater systems community attention. Supporting theory and practice, they intersect foundations and applications streams of INCOSE's future of systems engineering (FuSE) program. Strikingly, key aspects apply to systems of all types, including socio-technical and information systems. Hamilton abstracted the energy-like generator of dynamics for all systems, while also generalizing momentum. Applied to the INCOSE innovation ecosystem pattern as dynamics of learning, development, and life cycle management, this suggests an architecture for integration of the digital thread and machine learning in innovation enterprises, along with foundations of systems engineering as a dynamical system.

Processes for system failure analysis (for example, FMEA) are structured, well-documented, and supported by tools. Nevertheless, we hear complaints that FMEA work feels (1) too labor intensive to encourage engagement, (2) somewhat arbitrary in identifying issues, (3) overly sensitive to the skills and background of the performing team, and (4) not building enough confidence of fully identifying the risks of system failure. In fairness to experts in the process, perhaps such complaints come from those less experienced — but even so, we should care how to describe this process to encourage better technical and experience outcomes. This paper shows how model-based systems engineering (MBSE) answers these challenges by deeper and novel integration with requirements and design. Just as MBSE powered the requirements discovery process past its earlier, more subjective performance, so also can MBSE accelerate understanding and performance of failure risk analysis — as a discipline deeply connected within the systems engineering process.

This paper summarizes how a well-understood problem—optimal control and estimation in “noisy” environments—provides a framework to advance understanding of a well-known but less well-mastered problem—system innovation life cycles and management of decision risks and learning. The ISO15288 process framework and its exposition in the INCOSE Systems Engineering Handbook (2015) describe system development and other life cycle processes. Concerns about improving the performance of processes in dynamic, uncertain, and changing environments are partly addressed by “agile” systems engineering approaches. Both are typically described in the procedural language of business processes, so it is not always clear whether the different approaches are fundamentally at odds, or just different sides of the same coin. Describing the target system, its environment, and the life cycle management processes using models of dynamical systems allows us to apply earlier technical tools, such as the theory of optimal control in noisy environments, to emerging innovation methods.