Surrogate models for optimization speed

Advanced planning methods are only useful when they improve a real decision under real constraints. Here, surrogate models for optimization speed is framed as a testable system-design choice.

Practitioner lens

Complexity in surrogate models for optimization speed should be justified with sensitivity analysis. Vary key inputs and constraints to learn whether the recommended action is stable or fragile.

First experiment

Document assumptions and version the data, code, parameters and scenario set for reproducibility.

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