AWS Supply Chain
An ML-powered service that provides integrated inventory visibility, demand forecasting, and risk detection across the entire supply chain to optimize supply networks
Overview
AWS Supply Chain is a machine learning-powered service that provides unified visibility into inventory across the supply chain, demand forecasting, and supply risk detection. It connects to existing ERP, warehouse management, and logistics systems to create a unified data lake, then applies ML models to generate actionable insights for inventory optimization, demand planning, and risk mitigation.
Data Lake Construction and Heterogeneous System Integration
AWS Supply Chain ingests data from diverse source systems including SAP, Oracle, and custom ERP systems through pre-built connectors and APIs. The service normalizes data from different formats and schemas into a unified supply chain data model, creating a comprehensive data lake. This data lake serves as the foundation for all analytics and ML capabilities. Integration supports both batch and real-time data feeds, with change data capture for near-real-time inventory updates. The data model covers key supply chain entities: items, locations, inventory positions, purchase orders, sales orders, shipments, and bills of materials. Data quality rules automatically detect and flag anomalies such as negative inventory counts or impossible lead times, ensuring ML models train on clean data.
ML-Based Demand Forecasting and Inventory Optimization
The demand forecasting capability uses ML models trained on historical sales data, incorporating external signals such as weather, economic indicators, and promotional calendars. Forecasts are generated at configurable granularity (daily, weekly, monthly) and hierarchy levels (SKU, category, region). The system automatically selects the best-performing model for each item based on historical accuracy, handling items with different demand patterns (steady, seasonal, intermittent) appropriately. Inventory optimization recommendations suggest reorder points, safety stock levels, and order quantities that balance service level targets against holding costs. What-if analysis enables planners to simulate the impact of demand changes, lead time variations, or supplier disruptions on inventory positions before making decisions.
Supply Chain Risk Detection and Response
The risk detection module continuously monitors for supply chain disruptions using both internal signals (supplier delivery performance, quality metrics) and external signals (news feeds, weather events, geopolitical developments). When risks are detected, the system generates alerts with severity levels and recommended mitigation actions such as activating alternate suppliers, expediting shipments, or building safety stock. Risk scoring considers the probability of disruption, potential impact on revenue, and available mitigation options. Integration with AWS Supply Chain's visibility features enables tracing the impact of a disruption from raw material suppliers through to finished goods delivery. Historical risk data trains the ML models to improve future detection accuracy and reduce false positives over time.