Uncertainty management in multidisciplinary design of critical safety systems

Managing the uncertainty in multidisciplinary design of safety-critical systems requires not only the availability of a single approach or methodology to deal with uncertainty but a set of different strategies and scalable computational tools (that is, by making use of the computational power of a cluster and grid computing). The availability of multiple tools and approaches for dealing with uncertainties allows cross validation of the results and increases the confidence in the performed analysis. This paper presents a unified theory and an integrated and open general-purpose computational framework to deal with scarce data, and aleatory and epistemic uncertainties. It allows solving of the different tasks necessary to manage the uncertainty, such as uncertainty characterization, sensitivity analysis, uncertainty quantification, and robust design. The proposed computational framework is generally applicable to solve different problems in different fields and be numerically efficient and scalable, allowing for a significant reduction of the computational time required for uncertainty management and robust design. The applicability of the proposed approach is demonstrated by solving a multidisciplinary design of a critical system proposed by NASA Langley Research Center in the multidisciplinary uncertainty quantification challenge problem

Automatic Generation of Questionnaires for Supporting Users during the Execution of Declarative Business Process Models

When designing an imperative business process (BP) model, analysts have to face many design requirements (e.g., managing uncertainty, optimizing conflicting objective functions). To facilitate such design, declarative BP models are increasingly used. However, how to execute a given declarative model can be quite challenging since there are typically several variants related to such model, each one presenting different degree of goodness. To support users working on declarative models while a high flexibility is maintained, we propose removing the worst variants from the source declarative model at design time while keeping the best variants. This way, the variants which are kept are narrowed down incrementally during run-time. For managing these variants during run-time we suggest to build upon configurable BP models. To configure such models, we additionally propose to automatically generate questionnaires. The results over a real case study are promising

Extended Model of Managing Risk in New Product Development Projects

The aim of this research was to study new product development (NDP) projects-related risks and the literature in this field, as well as to develop a specific extendedmodel of managing risks in npd projects, which will consider the nature of npd projects. Data were collected with the help of the developed questionnaire, and project managers with several years of experience in the field of NPD projects were included. The data and hypotheses were tested with the use of statistical methods. Results of the study show that for NPD projects, it seems to be crucial to plan risks in the early stages of the project, especially focused on the definition of the technical requirements for the product and the related clear project objectives. Poorly defined technical requirements for the product present an important risk related with the design uncertainty of the product. The more imprecise the technical requirements for the product before the project starts, the higher is the design uncertainty of the product after its development. Unclear project objectives have a significant effect on the time-delay of NPD projects. The more imprecisely the project objectives are defined before the project starts, the greater is the time-delay on the NPD project.project management risk, factors, product development, planning, model

Decision-making under uncertainty: A Brehmerian approach

This article discusses the contributions of the late Professor Berndt Brehmer with an emphasis on dynamic decision making under uncertainty. This concept has a long history as ambiguity implied in selective attention, later emphasised by prospect theory, which incorporates a time dimension. Time may be a solution to problems of uncertainty, not least the timing of decisions with each other and with environmental developments. This approach sees  decision making, from a process perspective, ultimately asking whether it makes sense to frame decisions as specific events or as an expression of an ongoing design process where the possibility spaces are expanded rather than limited to decision making among pre-existing alternatives. A dynamic view of the time dimension also encourages decision making as learning through probing actions and negotiation and collaboration, as well as with the environment. As much as this may sound like a recipe for managing second-track processes, it is also a recipe for managing through direct interaction, albeit a less-than-objective one understood through the biased perception of boundedly rational actors

Managing in Uncertainty : Complexity and the paradoxes of everyday organizational life

© 2015 Chris Mowles. All rights reserved.The reality of everyday organizational life is that it is filled with uncertainty, contradictions and paradoxes. Yet leaders and managers are expected to act as though they can predict the future and bring about the impossible: that they can transform themselves and their colleagues, design different cultures, choose the values for their organization, be innovative, control conflict and have inspiring visions. Whilst managers will have had lots of experiences of being in charge, they probably realise that they are not always in control. So how might we frame a much more realistic account of what’s possible for managers to achieve? Many managers are implicitly aware of their messy reality, but they rarely spend much time reflecting on what it is that they are actually doing. Drawing on insights from the complexity sciences, process sociology and pragmatic philosophy, Chris Mowles engages directly with some principal contradictions of organizational life concerning innovation, culture change, conflict and leadership. Mowles argues that if managers proceed from the expectation that organizational life as inherently uncertain, and interactions between people are complex and often paradoxical, they start noticing different things and create possibilities for acting in different ways. Managing in Uncertainty will be of interest to practitioners, advanced students and researchers looking at management and organizational studies from a critical perspective

Psychometric Evaluation and Design of Patient-Centered Communication Measures for Cancer Care Settings

Objective To evaluate the psychometric properties of questions that assess patient perceptions of patient-provider communication and design measures of patient-centered communication (PCC). Methods Participants (adults with colon or rectal cancer living in North Carolina) completed a survey at 2 to 3 months post-diagnosis. The survey included 87 questions in six PCC Functions: Exchanging Information, Fostering Health Relationships, Making Decisions, Responding to Emotions, Enabling Patient Self-Management, and Managing Uncertainty. For each Function we conducted factor analyses, item response theory modeling, and tests for differential item functioning, and assessed reliability and construct validity. Results Participants included 501 respondents; 46% had a high school education or less. Reliability within each Function ranged from 0.90 to 0.96. The PCC-Ca-36 (36-question survey; reliability=0.94) and PCC-Ca-6 (6-question survey; reliability=0.92) measures differentiated between individuals with poor and good health (i.e., known-groups validity) and were highly correlated with the HINTS communication scale (i.e., convergent validity). Conclusion This study provides theory-grounded PCC measures found to be reliable and valid in colorectal cancer patients in North Carolina. Future work should evaluate measure validity over time and in other cancer populations. Practice implications The PCC-Ca-36 and PCC-Ca-6 measures may be used for surveillance, intervention research, and quality improvement initiatives

Taming Uncertainty in the Assurance Process of Self-Adaptive Systems: a Goal-Oriented Approach

Goals are first-class entities in a self-adaptive system (SAS) as they guide the self-adaptation. A SAS often operates in dynamic and partially unknown environments, which cause uncertainty that the SAS has to address to achieve its goals. Moreover, besides the environment, other classes of uncertainty have been identified. However, these various classes and their sources are not systematically addressed by current approaches throughout the life cycle of the SAS. In general, uncertainty typically makes the assurance provision of SAS goals exclusively at design time not viable. This calls for an assurance process that spans the whole life cycle of the SAS. In this work, we propose a goal-oriented assurance process that supports taming different sources (within different classes) of uncertainty from defining the goals at design time to performing self-adaptation at runtime. Based on a goal model augmented with uncertainty annotations, we automatically generate parametric symbolic formulae with parameterized uncertainties at design time using symbolic model checking. These formulae and the goal model guide the synthesis of adaptation policies by engineers. At runtime, the generated formulae are evaluated to resolve the uncertainty and to steer the self-adaptation using the policies. In this paper, we focus on reliability and cost properties, for which we evaluate our approach on the Body Sensor Network (BSN) implemented in OpenDaVINCI. The results of the validation are promising and show that our approach is able to systematically tame multiple classes of uncertainty, and that it is effective and efficient in providing assurances for the goals of self-adaptive systems

Traveling of Requirements in the Development of Packaged Software: An Investigation of Work Design and Uncertainty

Software requirements, and how they are constructed, shared and translated across software organizations, express uncertainties that software developers need to address through appropriate structuring of the process and the organization at large. To gain new insights into this important phenomenon, we rely on theory of work design and the travelling metaphor to undertake an in-depth qualitative inquiry into recurrent development of packaged software for the utility industry. Using the particular context of software provider GridCo, we examine how requirements are constructed, shared, and translated as they travel across vertical and horizontal boundaries. In revealing insights into these practices, we contribute to theory by conceptualizing how requirements travel, not just locally, but across organizations and time, thereby uncovering new knowledge about the responses to requirement uncertainty in development of packaged software. We also contribute to theory by providing narrative accounts of in situ requirements processes and by revealing practical consequences of organization structure on managing uncertainty