Bayesian belief network-based risk likelihood assessment for sustainable product design decision making

Numerous methods can be used during the design stage to ensure that new products are developed to optimize performance with respect to economic, environmental, and societal objectives. When an optimally chosen design is launched, many uncertainties are likely to affect its performance over different lifecycle stages. Such uncertainties give rise to various risks that can potentially lead to deviations between the expected and actual economic, environmental, and societal implications of the product designed. Therefore, it is imperative that sustainable product design alternatives are also assessed to evaluate their robustness against such risks, should they occur, before design decisions are finalized. However, comprehensive quantitative methods for use during the product design stage to evaluate the influence of the likelihood of different risks on expected sustainability performance are lacking. The purpose of this paper is to address these gaps by developing a methodology for risk likelihood assessment. The sequence of risk identification, risk analysis, and risk evaluation for systematic risk assessment, recommended in the ISO 31000 Risk Management Guidelines, is followed to develop a methodology to quantify risks and their effects. For risk identification, a comprehensive taxonomy is used to identify risks that can influence total lifecycle product sustainability performance. For risk analysis, Bayesian Belief Network (BBN)-based approach is used to develop a method to assess the influence of risks on total lifecycle product performance through predictive and diagnostic inference. A new metric, the Operational Risk Index (ORI), is developed to better communicate influence of risk likelihoods. For risk evaluation, methods to evaluate risk mitigation strategies are presented. An industrial case study is used to demonstrate the application of the methods. The proposed BBN-based decision support tool enables assessing and visualizing the effect of risks on product design performance, identifying critical risks, and risk propagation paths. The tools can enable product designers to make risk-informed choices when identifying product designs that are more robust and ensure improved total lifecycle sustainability performance. The new ORI metric is helpful to compare multiple designs with respect to total lifecycle risk assessment. The methods proposed in this research present a comprehensive methodology for total lifecycle risk likelihood assessment in sustainable product design decision making.