The key factors are environment complexity, data availability, and disruption frequency. Rule-based scheduling or APS typically works well in stable, low-mix environments with predictable demand and few disruptions. Machine learning fits environments that are complex but well-documented historically, with clean production records going back several years. Reinforcement learning trained in simulation adds the most value in high-mix, high-disruption environments, or where clean historical data isn't available. If you already use APS but struggle with dynamic replanning and frequent disruptions, an RL-based approach is worth evaluating.
