AWS Supply Chain uses cloud infrastructure expertise to unify data and provide machine learning (ML)–powered actionable insights, built-in contextual collaboration, and demand planning. AWS Supply Chain connects to your existing enterprise resource planning (ERP) and supply chain management systems, without replatforming, upfront licensing fees, or long-term contracts.
Key product features
Supply chain data lake
AWS Supply Chain sets up a data lake using ML models for supply chains to understand, extract, and transform disparate, incompatible data into a unified data model. The data lake can ingest your data from various data sources, including your existing ERP systems, such as SAP S/4HANA, and supply chain management systems. To add data from variable sources such as EDI 856, AWS Supply Chain uses ML and natural language processing (NLP) to associate data from source systems to the unified data model. EDI 850 and 860 messages are transformed directly with predefined but customizable transformation recipes. You can also load data from other systems to an Amazon Simple Storage Service (S3) bucket, where it will be automatically ingested into the AWS Supply Chain data lake.
Real-time visual map
AWS Supply Chain contextualizes your data in a real-time visual map using a set of interactive, visual end-user interfaces built on a micro frontend (MFE) architecture. AWS Supply Chain then highlights current inventory selection and quantity, as well as the health of inventory at each location (for example, inventory that is at risk for stock out). Inventory managers can drill down into specific facilities and view the current inventory on hand, in transit, and potentially at risk in each location.
AWS Supply Chain automatically generates insights to potential supply chain risks (for example, overstock or stock outs) using the comprehensive supply chain data in the data lake and surfaces them in the real-time visual map.
AWS Supply Chain applies ML models, built on technology similar to what Amazon uses, to generate more accurate vendor lead time predictions. Supply planners can use these predicted vendor lead times to update static assumptions built into planning models to reduce stock-out or excess inventory risks.
Inventory managers, demand planners, and supply chain leaders can also create their own insight watchlists by selecting the location, type of risk (for example, stock-out or excess stock risk), and stock threshold, and then adding team members as watchers. If a risk is detected, AWS Supply Chain will generate an alert highlighting the potential risk and the locations impacted.
Recommended actions and collaboration
AWS Supply Chain automatically evaluates, ranks, and shares various rebalancing options to provide inventory managers and planners with recommended actions to take if a risk is detected. Recommendation options are scored by the percentage of risk resolved, the distance between facilities, and the sustainability impact. Supply chain managers can also drill down to review the impact that each option will have on other distribution centers across the network. AWS Supply Chain also continually learns from the decisions that you make to improve recommendations over time.
To help you come to a consensus with your colleagues and implement rebalancing actions, AWS Supply Chain provides built-in contextual collaboration capabilities. When teams chat and message each other, the information about the risk and recommended options is shared. This reduces errors and delays caused by poor communication so that you can resolve issues faster.
AWS Supply Chain Demand Planning generates more accurate demand forecasts, adjusts to market conditions, and empowers demand planners to collaborate across teams to help avoid excess inventory costs and waste. To help remove the manual effort and guesswork around demand planning, AWS Supply Chain uses ML to analyze historical sales data and real-time data (for example, open orders), create forecasts, and continually adjust models to improve accuracy. AWS Supply Chain Demand Planning also continually learns from changing demand patterns and user inputs to offer near real-time forecast updates, allowing companies to proactively adjust supply chain operations.