How can a data-driven risk-based inspection approach be designed and implemented at the ANVS to improve efficiency and accuracy under uncertainty?

Abstract (Platte tekst, max. 500 woorden)
- In nuclear safety and radiation protection, inspection capacity is limited while the consequences of misprioritisation are high. The ANVS applies a risk-based inspection (RBI) approach, but current risk profiles rely partly on unstructured information and a limited set of measurable indicators. This may lead to blind spots, particularly under changing external conditions.

This study examines how a data-driven RBI approach can be designed for the ANVS to improve efficiency and accuracy under uncertainty. Inspectors’ tacit knowledge is translated into a structured set of risk factors combining measurable characteristics, behavioural dynamics, and external developments. A simulation model is used to analyse how risk evolves over time and how uncertainty in risk prediction affects inspection prioritisation and outcomes.

Results show that uncertainty mainly leads to temporary shifts in prioritisation rather than structural failures. Its impact is limited under stable conditions but increases during unfavourable external developments, when overall risk levels rise and missed high-risk inspections become more likely. Improving data quality has the strongest long-term effect on inspection performance, while increasing capacity primarily yields short-term improvements.

The study concludes that a data-driven RBI approach can strengthen inspection planning when uncertainty is explicitly considered and professional judgement remains integrated in final decisions.