An influence diagram is a network representation of probabilistic inference and decision analysis models. The nodes correspond to variables that can be either constants, uncertain quantities, decisions, or objectives. The arcs reveal probabilistic dependence of the uncertain quantities and information available at the time of the decisions. The influence diagram focuses attention on relationships among the variables. As a result, it is increasingly popular for eliciting and communicating the structure of a decision or probabilistic model. This paper develops the framework for assessment and analysis of linear-quadratic-Gaussian models within the influence diagram representation. The "Gaussian influence diagram" exploits conditional independence in a model to simplify elicitation of parameters for the multivariate normal distribution. It is straightforward to assess and maintain a positive (semi-)definite covariance matrix. Problems of inference and decision making can be analyzed using simple transformations to the assessed model, and these procedures have attractive numerical properties. Algorithms are also provided to translate between the Gaussian influence diagram and covariance matrix representations for the normal distribution.influence diagram, Gaussian decision model, multivariate normal assessment
Link back to DTU Orbit Citation (APA):Oehmen, J., Olechowski, A., Kenley, C. R., & Ben-Daya, M. (2014). Analysis of the effect of risk management practices on the performance of new product development programs. Technovation, 34(8), 441-453. DOI: 10.1016441-453. DOI: 10. /j.technovation.2013 Analysis of the effect of risk management practices on the performance of new product development programs Highlights Investigates the association between risk management practices and new product development program performance Based on extensive empirical data collected through survey Presents new framework to define risk management success in NPD programs Identifies six categories of risk management practices associated with success Out of 95 analysed risk management "best practices", only 30 show significant associations with success. AbstractRisk management is receiving much attention, as it is seen as a method to improve cost, schedule, and technical performance of new product development programs. However, there is a lack of empirical research that investigates the effective integration of specific risk management practices proposed by various standards with new product development programs and their association with various dimensions of risk management success. Based on a survey of 291 new product development programs, this paper investigates the association of risk management practices with five categories of product development program performance: A. Quality Decision Making, B. High program stability; C. Open, problem solving organization; D. Overall NPD project success and E. Overall product success. The results show that six categories of risk management practices are most effective: 1. Develop risk management skills and resources; 2. Tailor risk management to and integrate it with new product development; 3. Quantify impacts of risks on your main objectives; 4. Support all critical decisions with risk management results; 5. Monitor and review your risks, risk mitigation actions, and risk management process; and 6. Create transparency regarding new product development risks. The data shows that the risk management practices are directly associated with outcome measures in the first three categories (improved decision making, program stability and problem solving). There is also evidence that the risk management practices indirectly associate with the remaining two categories of outcome measures (project and product success). Additional research is needed to describe the exact mechanisms through which risk management practices influence NPD program success. KeywordsRisk management, new product development, program management Manuscript (Final Revision): Effect of Risk Management Practices
This article provides a process for system architecting that incorporates a holistic approach for architecture design space characterization by integrating decision alternatives in functional, physical, and allocational design spaces and accounting for interactions. System architects are faced with numerous decisions for system form, functions, and operations when defining a system architecture. Systems designers are tasked with selecting design options which provide the necessary functionality in support of the architecture. Since modern systems, especially system-of-systems, are composed of interacting and interwoven functions and elements, it is imperative to holistically evaluate variations in the system architecture and system design, and discover interactions among and between architecture decisions and design decisions. In this article, this design space characterization is made an integral part of the system architecting process and a set-theoretic framework is developed for managing an extensive design space. The design space characterization problem is formulated as identification of the significant decisions variables and quantification of their impact on the system objectives. A Design of Experiments framework-utilizing Analysis of Variation (ANOVA) and Range Tests-is presented to holistically characterize system architecture design space including the interactions between system form, function, operations, and design decisions. K E Y W O R D Sdesign of experiments, design space exploration, system architecting, system-of-systems architecting, trade-off analysis Systems Engineering.
The paper explores the role of design in systems engineering. It reviews the treatment of design in systems engineering in general and in the Systems Engineering Handbook in particular, and concludes that design is the missing dimension in systems engineering. It provides a review of salient viewpoints from research in design that can enhance the understanding of the design dimension in system engineering.complex problems and discusses how these theories might be incorporated into the handbook and other systems engineering guidance. Design-Related Concepts in Systems EngineeringSystems engineering practitioners and scholars chose different terms that cover overlapping concepts based on their background and perception. This is especially true for design-related vocabulary. This section discusses the more common terms related to design: systems engineering, systems analysis, systems architecting, and systems design.Systems Engineering (SE): Brill (1994) described the long debate on the definition of systems engineering as a "semantics jungle." One problem is that systems engineering has both a 'systematic ' and 'systemic' nature (Chestnut 1967). The systematic nature of systems engineering focuses on management processes, and the systemic nature focuses on design methods.Stupples (2006) believes that the systematic nature of systems engineering is the current face of the discipline. He states, "SE has sadly lost its science foundation and is being practiced widely as the application of management processes to systems design and hence information required for decision making under uncertainty is not being generated." Comparing earlier definitions of systems engineering with recent ones supports Stupples' assertion. One of the earlier definitions (Chase 1974) states that SE is "the process of selecting and synthesizing the application of the appropriate scientific and technical knowledge to translate system requirements into system design and subsequently to produce the composite of equipment, skills, and techniques that can be effectively employed as a coherent whole to achieve some stated goal or purpose." Here, as Rhodes and Hastings (2004) pointed out, the emphasis was on the translation of requirements for the design process.The coordinative and managerial view to SE has emerged in the past two decades as can be seen in two definitions of SE. First, INCOSE (Haskins 2011) defined SE as "an interdisciplinary approach and means to enable the realization of successful systems. It focuses on defining customer needs and required functionality early in the development cycle, documenting requirements, and then proceeding with design synthesis and system validation while considering the complete problem: operations, cost and schedule, performance, training and support, test, manufacturing, and disposal. SE considers both the business and the technical needs of all customers with the goal of providing a quality product that meets the user needs." The intent of INCOSE definition may have been to capture the full context wi...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.