Reinforcement learning for replenishment

Model complexity should earn its place through better decisions, not novelty. This article explores reinforcement learning for replenishment and the baselines needed to establish its incremental value.

Practitioner lens

A responsible implementation of reinforcement learning for replenishment includes a fallback policy, approval thresholds, model-version records and a way to explain infeasible or conflicting recommendations.

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.