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The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by researchers from the Department of Computer & Information Sciences involved in mathematically structured programming, similarity and metric search, computer security, software systems, combinatronics and digital health.

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Supporting reliability decisions during defense procurement using a Bayes linear methodology

Revie, Matthew and Bedford, Tim and Walls, Lesley (2011) Supporting reliability decisions during defense procurement using a Bayes linear methodology. IEEE Transactions on Engineering Management, 58 (4). pp. 662-673.

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Abstract

Defense procuring authorities assess the growth in reliability performance of new systems against requirements using evidence provided by contractors during product design and development. Since projects can be lengthy and complex, a structured methodology is required to support consistent assessment and inform decisions. A methodology grounded in the principles of Bayes linear is proposed because it provides a theoretical framework for modeling the epistemic uncertainty in the reliability management process during which the decision maker synthesizes information from supplier analyses to form an assessment that will be updated as new information becomes available. Methods for structuring and populating a Bayes linear model are developed and a strategy for sensitivity analysis to explore the robustness of results to variations in subjective engineering beliefs is proposed. The feasibility of the methodology is examined through an industrial application to support reliability assessment during a defense procurement project. Based on feedback, our findings show that it is feasible to develop a requisite Bayes linear model to support reliability assessment. We find that many strengths and weaknesses of a Bayes linear approach are shared by other Bayesian models, but that a Bayes linear method has potential benefits through reduced elicitation burden and straightforward sensitivity analysis.