Product risk assessment: a Bayesian network approach

TL;DR

This paper introduces a Bayesian network approach for product risk assessment, offering a more flexible and comprehensive alternative to traditional methods like RAPEX, especially in handling uncertainty and causal reasoning.

Contribution

The paper develops a Bayesian network model that improves product risk assessment by addressing RAPEX limitations, demonstrating its effectiveness on real product examples.

Findings

BN approach replicates RAPEX results

BN provides enhanced handling of uncertainty

BN offers causal explanations for risk factors

Abstract

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.