Amazon Supply Chain and Logistics

Unlock supply chain value with data and AI

Last month I shared my key predictions for 2024. This blog expands on the necessity of gathering scattered data across multiple systems into a unified data model. Supply chain leaders face increasing complexity from sprawling global networks and rising customer expectations. As a result, supply chain management has transformed into a strategic differentiator as customers demand more selection, improved sustainability, and on-demand delivery of goods and services.

The digital supply chain promises operational benefits, from visibility and resilience to speed and agility. However, unlocking its full potential requires extracting value from fragmented data spread across disconnected systems. Data fuels new experiences and insights that spur innovation, but building a data strategy that unlocks value across the organization is challenging. Supply chain teams also want self-service access to data without the burden of manual reports, spreadsheets, and complex data manipulation. Data systems sprawl, silo, and complicate, with diverse data sets spread out across the enterprise and even extending to trading partner systems. This forces supply chain teams to spend hours stitching data together and then having to restart this process if a data model changes, creating a near-perpetual cycle of manual tasks. Gaining end-to-end visibility and meaningful insights requires integrating and harmonizing data across different sources. Organizations must overhaul how data is used across the supply chain to innovate faster, prepare for future disruptions, and meet customer needs.

Chief supply chain officers (CSCOs), heads of supply chains, and other supply chain leaders recognize the need for a unified data model to enable a strong digital core, AI, and advanced analytics. Manual processes to aggregate and normalize this data are complex, time-consuming, and prone to errors. Many organizations also lack the internal skills and resources needed for custom data integration work. This blog outlines how a unified view of supply chain data across the organization provides a single source of truth that improves end-to-end process visibility, increases agility, boosts resilience, reduces operational costs, and reduces overall sustainability risks. It also describes how AWS Supply Chain provides a unified data foundation that empowers organizations to optimize planning, boost efficiency, and exceed customer demands.

The AWS Supply Chain Data Lake

AWS Supply Chain provides integrated data management through a supply chain data lake (SCDL), which enables end-to-end visibility, more accurate demand forecasting, and supply chain resilience. The SCDL is a flexible, scalable data infrastructure that provides pre-built capabilities to ingest, standardize, and integrate data across fragmented systems. It aggregates and associates supply chain data into a high quality, unified data asset, which enables organizations to tap into the collective intelligence of their enterprise. It empowers organizations to optimize existing processes while exploring emerging technologies. The SCDL also enables increased forecast accuracy, reduces excess inventory, and accelerates cycle times. Data-driven insights enhance legacy system capabilities and simplifies longer term modernization.

Purpose-built connectors allow rapid ingestion from common supply chain data sources and formats, including enterprise resource planning (ERP), warehouse management systems (WMS) order management systems (OMS), transportation management systems (TMS), procurement systems, flat files, and databases. The SCDL also leverages machine learning (ML) and natural language processing to parse unstructured data and understand supply chain context. The ML algorithms come pre-trained on common supply chain data structures and relationships. This enables more accurate entity recognition and mapping compared to generic data lake tools.

The SCDL integrates with analytics and data science tools like Amazon QuickSight, Amazon SageMaker, AWS Glue, and AWS Glue DataBrew. Read our previous blog about using AWS services to transform non-standardized data. This acceleration lets data teams deliver high-impact models faster and simplify ETL (extract, transform, and load) operations. As IoT, machine learning, and other innovations expand, the SCDL serves as the analytical foundation that future-proofs the supply chain data infrastructure. AWS is also investing in a zero-ETL future to help you discover new insights, innovate faster, and make better data-driven decisions.

By eliminating time spent on complex ETL and data modeling, you can quickly adopt high-value use cases where unified data removes vulnerabilities and bottlenecks. The SCDL provides a single source of truth that drives end-to-end visibility and unlocks data-driven insights.

The SCDL is the foundation for AWS Supply Chain modules like Insights, providing a comprehensive supply chain view to enhance inventory visibility, and ML recommendations to mitigate inventory and lead time risks. Demand Planning combines Amazon’s supply chain expertise with ML to analyze historical and current sales data, create forecasts, and continuously refine models for accuracy. Organizations enhance resilience by using ML to analyze trends and anomalies, predict disruptions, and prioritize actions when issues occur. Analysts spend less time wrangling data and more time applying insights to business decisions. AWS Supply Chain allows you to increase awareness, optimize plans, align supply with demand, and improve the alignment of the data with evolving business needs.

Later this year we will also release Amazon Q in AWS Supply Chain, which is a generative AI assistant powered by Amazon Bedrock that will improve supply chain management productivity. Amazon Q in AWS Supply Chain will provide a natural language interface to answer key questions around demand variability, inventory positions, and vendor lead time uncertainty. You will be able to query data within the SCDL, and receive data-driven answers to “what” and “why” questions. Amazon Q in AWS Supply Chain is specifically designed for work and will be tailored to your organization using your SCDL.


The pace of innovation and customer demands is accelerating globally. Organizations must tap into their collective data intelligence to achieve the visibility, speed, and resilience needed to compete. Data is the foundation and driving force for digital transformation that enables the full potential of AI/ML.

The SCDL provides purpose-built capabilities to unify fragmented supply chain data rapidly into a scalable, high quality asset. This powers data-driven innovations from forecasting to supply chain digitization. AWS Supply Chain delivers a fast, simple way to unlock value from existing supply chain data investments and key benefits include:

  1. Accelerated time-to-value with prebuilt data connectors.
  2. Reduced costs by minimizing complex manual data integration.
  3. Improved visibility into inventory health with aggregated analysis.
  4. Increased supply chain resilience from data-driven insights.
  5. Higher forecast accuracy and lower inventory costs.
  6. Faster innovation cycles powered by supply chain-specific capabilities.

Please visit AWS Supply Chain to learn more about unlocking your supply chain data at speed and scale. For a self-paced technical overview, visit AWS Workshops.

Diego Pantoja-Navajas

Diego Pantoja-Navajas

Diego Pantoja-Navajas is the Vice President of AWS Supply Chain and is responsible for the vision and execution of business applications. He and his team have reimagined how supply chains can operate and are focused on bringing the world’s first continuously improving supply chain system of record to the market. He is passionate about his customers’ success and using SaaS, cloud, and AI/ML technologies to build highly usable and intelligent B2B enterprise software solutions to solve business problems related to supply chains, e-commerce, and fulfillment. Diego is an honor graduate of the Georgia Institute of Technology and has continued his training, completing executive education courses in Artificial Intelligence & Machine Learning at MIT. He has also participated in multiple leadership courses in partnership with IESE Business School and the University of Michigan, Ross Business School. He lives with his family in South Florida and is always happy to learn more about innovative products or solutions that will continue driving business success for his customers.