ENBIS-16 in Sheffield

11 – 15 September 2016; Sheffield Abstract submission: 20 March – 4 July 2016

Using Dynamic Bayesian Networks to Model Technical Risk Management Efficiency

13 September 2016, 14:30 – 14:50

Abstract

Submitted by
Anan Halabi
Authors
Anan Halabi (Department of Mathematics "G. Peano", University of Turin), Ron S. Kenett (Department of Mathematics "G. Peano", University of Turin), Laura Sacerdote (Department of Mathematics "G. Peano", University of Turin)
Abstract
The objective of this paper is to develop a mathematical model for achieving risk management efficiency in product development. The reduction of overall product risks before product release to production can be used as an indicator of risk management efficiency. Acceptable risks targets vary according to the factors such as: industry type, organization characteristics, regulations etc`. In general, risk management process contains the following phases: identification of risks, analysis, control and feedback. We propose a mathematical approach using dynamic Bayesian networks to evaluate how product risks progress over time during development. The properties of the derived model are evaluated using two validation methods: k-fold cross validation and leave one out. Mathematical imputation methods like multivariate normal imputation were used to deal with missing data. In addition, sensitivity analysis is performed to assess the uncertainty embedded by learned parameters of the dynamic Bayesian network.
A further application of the model is to support decision makers on their decision whether to release a product to customers for Beta testing or to conduct additional accelerated activities in order to reduce the overall risk level before customer shipment. In addition, the model is used for prediction purposes and provides an estimate of the expected risk at time t+1 based on the level of risk at time t.
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