Probabilistic risk analysis applied to the Space-Shuttle main engine

Fragola, J.R.; Kurth, Robert E.; Maggio, Gaspare; Epstein, Steve; Frank, M.V.

doi:10.1109/rams.1994.291156

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…Most of these studies involved stakeholders in the SD process. SD also have a history in context of social-technical dimensions of human-machine interactions and complex systems development (Funabashi et al, 2005, Woods et al, 2006, Yomo et al, 2015, especially with application in the military (Prasolova-Førland et al, 2013, Urwin et al, 2011, Xinye et al, 2012, Yuan Kwei et al, 2009) and spaceflight (Bolton et al, 2013, Fragola et al, 1994, Kordon et al, 2005 domain.…”

Section: History and Applicationmentioning

confidence: 99%

Exploring scenario development – a case study of two collaborative research projects

Fossum

Aarseth

Andersen

2019

IJMPB

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Purpose -The paper explores scenario development (SD) as a method for engaging known challenges in collaborative research projects, i.e. SD is the construct under investigation.Design/methodology/approach -Criticism of the dominant, rational approach to project management (PM) and its underlying hypotheses highlights a considerable PM research gap for research projects (research problem). We undertake a six-step constructive research approach to investigate if SD (the construct) constitutes a fruitful method to support the management of collaborative research projects. A two-part literature review summarizes known challenges in collaborative research projects and introduces the history and application of SD methodology. The work includes participatory action research (PAR) in two case studies, constituting a qualitative research method.Findings -We found the SD method to be useful for structuring and analysing intuitive project processes. However, using SD in the management of single projects presents some fundamental challenges. SD, like PM, struggles with issues related to myopic decisions, a "predict and provide" attitude with clear aspects of path dependency in the project front-end as well as inconsistent and/or missing identification of success criteria among different stakeholders.Research limitations/implications -This paper does not provide any comprehensive, normative account of scenario techniques or compare SD with other foresight and future studies methods. Although PAR is in itself a research method that demands systematic description and execution, the focus of this paper is the overall constructive research approach.Practical implications -The paper offers a broadened repertoire of methods to describe and analyse project stakeholder situations (collaborative aspects) and to structure and balance the need for both rational and intuitive project processes (research aspects). The SD method also supports development of graphical storylines and facilitates the use of influence diagrams, event trees and cost/benefit analysis.Originality/value -Although PM literature contains several references to SD, the practical application of SD at single-project level has, to our knowledge, never been described in the PM literature.

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Section: History and Applicationmentioning

confidence: 99%

Exploring scenario development – a case study of two collaborative research projects

Fossum

Aarseth

Andersen

2019

IJMPB

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“…A reliability model was created of the whole APU system based upon the work of the STS PRA [9]. While the PRA examined the contribution to loss of vehicle by specific subsystems, the estimates will be used as a most conservative value for reliability.…”

Section: Problem Statementmentioning

confidence: 99%

Cost of Safety for Space Transportation

Krevor

Wilhite

2006

57th International Astronautical Congress

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This paper proposes a methodology that explores the tradeoff between increasing component reliability and utilizing component redundancy as the strategy to meet space transportation reliability requirements. This technique would be employed by design engineers to make decisions about a reliability approach. The tradeoff between component redundancy and making parts more reliable warrants more investigation. System level reliability decisions are being made without a thorough exploration of cost saving opportunities. The impact of using redundancy on a system, including how it affects metrics such as development and operations cost, is presented. Additionally, there is little understood about the resources required to improve component reliability to acceptable levels. The process of making parts more reliable is studied and quantified. To incorporate the uncertainty that exists from reliability applications, a stochastic approach is used. Case studies of historical space systems are presented to demonstrate how this methodology is applicable. The findings show how a different reliability approach may have resulted in significant cost reductions. Conclusions are drawn about how to best meet reliability requirements while remaining within strict budgetary guidelines.

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“…The APRAM model, in addition to decision analysis and optimization, also incorporates probabilistic risk analysis (PRA) as a key component of the decision model. (For background information on PRA see, for example, Henley and Kumamoto (1992), Garrick (1984), Kaplan and Garrick (1981), and Fragola et al (1994).) Quantifying the risk and optimizing the use of available resources given different development problem scenarios can thus provide decision support for both program and project managers.…”

Section: Introductionmentioning

confidence: 99%

Programmatic Risk Analysis for Critical Engineering Systems Under Tight Resource Constraints

Dillon

Patè‐Cornell

Guikema

2003

Operations Research

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Managers of complex engineering development projects face a challenge when deciding how to allocate scarce resources to minimize the risks of project failure. As resource constraints become tighter, balancing these failure risks is more critical, less intuitive, and can benefit from the power of quantitative analysis. This paper describes the Advanced Programmatic Risk Analysis and Management model (APRAM), a decision-support framework for the management of the risk of failures of dependent engineering projects within programs. Our goal is to guide the management of the design, the development, and the budget of dependent projects. Considering first a single project, our approach is to optimize the use of the budget reserves and of the funds dedicated to the system itself for each possible budget allocation. This phase involves separate optimizations of the system design and a strategy for resolving development problems based on a chosen objective function. The model also allows checking that specified thresholds of maximum acceptable risks are met, and if not, indicates how much is required to satisfy them. It is then extended to include project dependencies within a program. Finally, it allows checking that the level of resources available is appropriate by computing the shadow "risk cost" of the budget constraint. The NASA Jet Propulsion Laboratory has supported the development of this model, so each step of APRAM is illustrated by the schematic case of an unmanned space program involving two dependent projects. Also, where applicable, we discuss our experiences working with the APRAM concepts for the management of unmanned space missions.

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Probabilistic risk analysis applied to the Space-Shuttle main engine

Cited by 5 publications

References 3 publications

Exploring scenario development – a case study of two collaborative research projects

Exploring scenario development – a case study of two collaborative research projects

Cost of Safety for Space Transportation

Programmatic Risk Analysis for Critical Engineering Systems Under Tight Resource Constraints

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