Incorporation of risk preferences in a value‐based systems engineering framework

Abstract

Large‐scale complex engineered systems are designed in large organizations consisting of hundreds or thousands of individuals making design decisions at different stages in the process. These complex systems are inherently multidisciplinary and uncertain, involving diverse disciplines spanning geographical locations. Without a means to guide their decisions, these many decision‐makers fall back on selecting a design according to their own value and risk preferences. The primary focus of this paper is to enable consistency in design decision‐making with respect to both value and risk preferences of the stakeholder. A new value‐based systems engineering (VBSE) framework is presented that enables physics‐based system consistency as well as consistency in communication of value and risk preferences for design decision‐making in a hierarchically decomposed system. This framework embraces principles of value‐driven design and multidisciplinary design optimization, while also ensuring consistency in risk preferences throughout the system to address uncertainty. Improper communication of risk preferences could result in nonoptimal designs, which are risk biased by the design decision‐makers at different levels of the hierarchy, with respect to the stakeholder's preferences. Consistency in risk preferences in the context of the VBSE framework is addressed in this research by incorporating decision analysis techniques.

The role of risk mitigation actions in engineering projects: An empirical investigation

“…In broader terms, this paper contributes to the body of studies on work risk management in engineering projects 29,30 and, more specifically, on risk mitigation in the product development process of engineering systems. [30][31][32][33][34][35][36][37] The remainder of the paper is organized as follows: Section 2 lays out the literature-based results, describing previous empirical studies on risk mitigation and their utilization in the context of NPD methods. Section 3 describes the research methodology used to collect and analyze survey data from industrial practices.…”

“…In broader terms, this paper contributes to the body of studies on work risk management in engineering projects 29,30 and, more specifically, on risk mitigation in the product development process of engineering systems 30–37 …”

Identifying key parameters impacting cost in large‐scale complex space programs using simulation‐based global sensitivity analysis

“…Numerous other factors, such as complexity, immature technology, and poor leadership abilities, may influence the cost overruns 17,18 . These factors may range from the size of the project, stakeholders, organizations, political disruptions, changes in the requirements and scope in large complex programs 19 . Research on SE frameworks, such as value‐based modeling, has been investigated to reduce cost overrun 2 .…”

“…These systems are abundant in size and due to high risk and complexity, they involve large organizations for their design and development. The complex nature of these systems may arise due to technological complexity, managerial or organizational complexity, business system complexity, coupling of the systems, cognitive complexity, number of parts, lines of code, and many more 19 . Just like any other system, acquisition of LSCES involves the design, engineering, construction, testing, deployment, sustaining, and disposal of the system.…”

The derivation and visualization of supply network risk profiles from product architectures

“…The key contribution of this paper is a general purpose approach that can be used in preliminary design to explore the consequences of early design decisions (related to the design of the product architecture) on subsequent design and manufacturing organizations and processes. A number of authors (including Daub et al 18 ; Kannan et al 19 ; Lender et al 20 ) introduce approaches for the design of system architectures.…”