Insight最新文献

In early design stages, business developers and systems engineers deal with uncertainties on the business problem, in line with the company's strategy. Before designing the system, the business developers need to set the boundaries of the business problem: What are the values to deliver to which stakeholders? What are their preferences? What are the future trends or the evolution of the markets and the external context? These questions regarding the uncertainties on the definition of the problem may not have answers and need to be investigated to assess the value robustness of the possible design alternatives. The aim of this work is to support decision-making in business and system design thanks to a broad and rapid analysis of a large amount of business design alternatives under uncertainty. We introduce a decision-making support method, called ValXplore, based on visual analysis and data analytics to explore the uncertainties on and in the business problem. The method was tested and validated on an industrial case study to assess the benefits and limits of the semi-reusability of a launch vehicle. Both business developers and systems engineers can rapidly explore a broad space of alternatives to increase the value to the stakeholders, by performing sensitivity and uncertainty analyses.

Although theoretically independent, requirements within a decomposition level of a system architecture are not isolated elements. For an existing design, a change of a requirement may endanger or facilitate fulfillment of other requirements within the same level of the decomposition. The present research suggests a requirement connectivity metric to evaluate the potential consequences that changing a requirement may have on a system with respect to fulfillment of other requirements. A particular aspect of the present research is the assumption that connectivity accounts only for requirements within the same decomposition level of an architecture, not for those flowing up or down the decomposition. The metric is used to evaluate different cases in which requirements are changed due to triggering of uncertain events during a project life cycle.

Although a number of recent studies on using Bayesian Networks (BN) for system reliability estimation have been proposed, these studies are based on the assumption that a pre-built BN was designed to represent the system. In these studies, the task of building the BN is typically left to a group of specialists who are BN and domain experts. However, the process of building a system-specific BN is generally very time consuming and may lead to incorrect deductions. As there are no existing studies to eliminate the need for a human expert in the process of system reliability estimation, this paper introduces a holistic method that uses historical data about the system to be modeled as a BN and provides efficient techniques for automated construction of the BN model and estimation of the system reliability. Moreover, very limited human intervention is sufficient for the process of BN construction and reliability estimation.

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.