Importance sampling for disruption stress tests

Model complexity should earn its place through better decisions, not novelty. This article explores importance sampling for disruption stress tests and the baselines needed to establish its incremental value.

Practitioner lens

A model for importance sampling for disruption stress tests should state its objective and feasible set explicitly. Hidden penalty weights or relaxed constraints can make an impressive result operationally impossible.

First experiment

Report feasibility, tail outcomes, runtime and sensitivity—not only the best-case objective value.

This is an original educational brief. It is a starting point for discussion, not a claim that one technique fits every supply chain.