Product risk assessment: a Bayesian network approach
This work addresses product safety evaluation for regulators and manufacturers, but it is incremental as it adapts an existing method (Bayesian networks) to a new domain.
The paper tackles product risk assessment by proposing a Bayesian network model to address limitations of the existing RAPEX method, such as handling uncertainty and causality, and demonstrates its application to cases like a teddy bear and kettle, showing it replicates RAPEX results while being more powerful and flexible.
Product risk assessment is the overall process of determining whether a product, which could be anything from a type of washing machine to a type of teddy bear, is judged safe for consumers to use. There are several methods used for product risk assessment, including RAPEX, which is the primary method used by regulators in the UK and EU. However, despite its widespread use, we identify several limitations of RAPEX including a limited approach to handling uncertainty and the inability to incorporate causal explanations for using and interpreting test data. In contrast, Bayesian Networks (BNs) are a rigorous, normative method for modelling uncertainty and causality which are already used for risk assessment in domains such as medicine and finance, as well as critical systems generally. This article proposes a BN model that provides an improved systematic method for product risk assessment that resolves the identified limitations with RAPEX. We use our proposed method to demonstrate risk assessments for a teddy bear and a new uncertified kettle for which there is no testing data and the number of product instances is unknown. We show that, while we can replicate the results of the RAPEX method, the BN approach is more powerful and flexible.