AWS Supply Chain Features

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 (Amazon S3) bucket, where it will be automatically mapped using generative AI and ingested into the AWS Supply Chain Data Lake.

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 to an inventory visualizaton map. The inventory visualization map 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).  

AWS Supply Chain also offers work order insights to provide visibility of maintenance-related materials from sourcing to delivery, providing order status, identifying delivery risks, and providing delivery risk-mitigation options.

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. Maintenance, procurement, and logistics supply chain leaders can use work order insights to reduce material expedites, material inventory buffers, and equipment downtime.

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.

AWS Supply Chain Supply Planning forecasts and plans purchases of raw materials, components, and finished goods. This capability draws on nearly 30 years of Amazon experience in developing and honing AI/ML supply planning models, and considers economic factors, such as holding and liquidation costs. AWS Supply Chain Supply Planning uses the comprehensive, standardized data from the AWS Supply Chain Data Lake, including demand forecasts generated by AWS Supply Chain Demand Planning (or any other demand planning system). Your organization benefits from improved service levels and reduced inventory costs by being able to better respond to variations in demand and disruptions in supply. Manufacturing customers can create supply plans for components and finished products across multiple levels in their bill of materials and improve in-stock and order fill rates by dynamically calculating inventory targets, while accounting for demand variability, actual vendor lead times, and ordering frequency.

AWS Supply Chain N-Tier Visibility works with Supply Planning or Work Order Insights to extend visibility beyond your organization to your external trading partners. This visibility lets you align and confirm orders with suppliers, improving the accuracy of planning and execution processes. Invite, onboard, and collaborate with your trading partners in just a few steps to confirm supply plans and obtain order commitments. Commitments and confirmations are received from partners and written to the supply chain data lake. This data can then be used to identify material or component shortages and update supply plans with new information and provide better-informed insights.

AWS Supply Chain Sustainability uses the same underlying technology as N-Tier Visibility to provide a more secure and efficient way for sustainability professionals to obtain the documents and datasets they need from their supplier network. These capabilities help you provide environmental and social governance (ESG) information based on a single, auditable record of the data.

AWS Supply Chain Analytics is a reporting and analytics feature powered by Amazon Quicksight that provides both out-of-the-box supply chain dashboards and enables custom reporting and
analytics. With this feature, you can access your data in the Data Lake simply from the AWS Supply Chain user interface. There are pre-built dashboards you can use as-is or easily modify to meet your needs, and you can build custom reports and dashboards using the embedded authoring tools. This feature offers you a unified, configurable, and scalable console for operational analytics.

Amazon Q in AWS Supply Chain is an interactive generative artificial intelligence assistant that helps you operate your supply chain more efficiently by analyzing the data in your AWS Supply Chain Data Lake, providing important operational and financial insights, and answering urgent supply chain questions. It reduces the time users spend searching for relevant information, simplifies the process of finding answers, and minimizes the time needed to learn, deploy, configure, or troubleshoot AWS Supply Chain.