Agent-based bullwhip experiments

The advanced question in agent-based bullwhip experiments is how the model behaves when assumptions fail. A sound design makes uncertainty, constraints and fallback actions explicit.

Practitioner lens

Separate prediction from action in agent-based bullwhip experiments: identify what is estimated, what is optimized and which business rules must remain binding regardless of model confidence.

First experiment

Test the method at the level where the decision is executed, then inspect how aggregation changes results.

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