sleutelfactor

Sleutelfactor is a term used in Dutch risk management and analytics to denote a factor that has outsized influence on an outcome. In practice, it refers to a small number of factors that account for a large share of variation or risk. The concept highlights that not all factors contribute equally; identifying sleutelfactor helps focus resources on the most impactful drivers.

Identification methods include statistical modeling (regression, factor analysis), sensitivity analysis (global or local), machine learning feature importance (permutation importance, SHAP values), and structured expert judgment. The aim is to rank factors by their contribution to outcomes such as defect rate, failure probability, cost, or safety risk.

Applications span risk assessment in manufacturing, safety engineering, finance, environmental science, and project management. For example, in manufacturing, temperature and material quality might emerge as sleutelfactors that explain the majority of defect variation, guiding mitigation efforts and process improvements.

Sleutelfactors are related to, but distinct from, concepts such as key drivers and critical success factors. They emphasize drivers of risk or poor performance rather than overall objectives, and are typically used as a prioritization tool rather than a formal causal model.

Limitations include sensitivity to data quality and model choice, the risk of equating correlation with causation, and the possibility that sleutelfactors change over time. Regular reevaluation and transparent methodology are advised to keep the list relevant.