This article explains how AWS Supply Chain uses machine learning to improve supply planning accuracy through dynamic lead time insights instead of traditional static methods.

• Traditional supply planning uses static average lead times, causing overstocking and understocking errors
• ML-based approach analyzes historical and current transactions with probabilistic lead time projections
• ML considers product features, delivery timelines, volumes, and transportation lanes for accurate forecasting
• Demand-supply dispersion is significantly lower with ML versus traditional methods
• AWS Supply Chain monitors lead time deviations and alerts planners when variance exceeds tolerance thresholds
• Watchlists enable tracking of specific products, locations, and lead time standard deviations
• Built-in collaboration tools allow teams to discuss and resolve supply plan adjustments within the application
• ML models continuously learn and adapt based on incoming transactional data

ML-based supply planning reduces inventory costs, prevents stockouts, and improves supply chain resilience by providing real-time visibility and faster decision-making.