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Amazon uses a large AI forecasting model to predict what customers will buy, where and when, for hundreds of millions of products a day.
The model blends sales history with weather, holidays, regional tastes and promotions, then moves stock closer to likely buyers.
AWS now sells part of this system as Amazon Connect Decisions, though firms still need clean data and skilled staff to get results.
Amazon AI demand forecasting sits at the center of the company’s fast delivery promise. The retailer manages more than 400 million products, so a small forecast error can mean empty shelves or a warehouse full of unsold goods.
Amazon now uses a large AI model to predict what customers will buy, where, and when. This article explains how that system works, how it handles inventory and robots, and what other companies can learn from it.
Older forecasting tools looked mostly at past sales. This works for steady products, but it fails when habits change fast. Logistics Viewpoints notes that traditional methods rely on past performance and struggle to adapt to sudden shifts in buyer behavior or supply conditions. Amazon’s earlier systems followed the same pattern.
A single model must cover hundreds of millions of items, each with its own local pattern. The company later built a new demand forecasting model that adds more signals, and it announced the upgrade on June 11, 2025. At launch, it covered the US, Canada, Mexico and Brazil.
The new model predicts what customers want, where they want it and when, for hundreds of millions of products a day. It mixes sales data with weather, regional tastes, holidays and planned promotions. A clear example is ski goggles in Boulder, Colorado, where demand rises in peak ski season.
This shift matters because local demand is rarely smooth. A cold snap, a holiday or a sale can change what sells in one city but not in another. Abhishek Gupta, director of forecasting and labs at Amazon’s Supply Chain Optimization Technology (SCOT) team, told Supply Chain Dive that its models study past data, including holidays and promotions. TraxTech reported that regional forecasts improved by about 20% after the upgrade.
A good forecast only helps if goods sit in the right place. Keeping extra stock costs money for storage, handling and unsold goods, while too little stock costs sales. Amazon uses its predictions to move stock closer to likely buyers before orders arrive. This cuts shipping time and cost, and it lowers the risk of overstock.
Logistics Viewpoints adds that AI forecasting helps companies keep inventory costs low while improving order fulfillment rates. For shoppers, the result is simple: items arrive faster and fewer popular products sell out. For Amazon, the result is lower holding cost and less waste. At Amazon’s scale, even a small gain matters. The company hired 250,000 seasonal workers in its last peak season, so planning mistakes get costly fast.
Forecasting is only one layer. Amazon has also formed a team to build AI agents that let warehouse robots follow plain commands. Another tool, called Wellspring, uses generative AI to improve delivery maps. TraxTech reported that it identified 2.8 million apartment addresses in a few months.
Better addresses mean fewer missed drops and faster routes. Faster robots and better maps also cut errors in the last steps, where a wrong address can waste a whole delivery run. Amazon says these tools create value for customers, employees and delivery partners. Together, Amazon supply chain AI tools link forecasting, warehouse work and last-mile delivery in one chain.
In April 2026, AWS launched Amazon Connect Decisions, which uses Amazon’s own supply chain models and more than 25 specialized tools. It spots demand spikes and supplier delays, ranks which problems need human attention, and suggests next steps with costs and trade-offs. The tool can also review thousands of system alerts and help spot stock that a company does not need to carry.
Users can chat with the model to ask direct questions about the data, and charts help planners with forecast decisions. Colleen Aubrey, an AWS senior vice president, told GeekWire that early users run what-if scenarios in meetings.
At least one procurement team asked to use it beyond supply chain planning. Free trials of the tool are already available. Logistics Viewpoints warns that success needs clean, standard data and skilled staff.
Amazon AI demand forecasting shows how a retailer can turn data into faster delivery and lower waste. The company mixes sales history with weather, holidays and local tastes to predict demand, then places stock close to buyers before orders arrive. Robots, maps and AI agents support the same goal.
Its approach also shows that AI works best as a full system, not a single tool. Other firms can follow this path, but they need clean data and skilled teams first. With Connect Decisions, Amazon is now selling part of that playbook to the market.
1. How does Amazon use AI for demand forecasting?
Amazon uses a large AI model that predicts what customers will buy, where and when. It combines sales history with weather, holidays, regional tastes and promotions.
2. How many products does Amazon forecast?
Reports say Amazon manages more than 400 million products. Its forecasting model covers hundreds of millions of products a day.
3. What is SCOT at Amazon?
SCOT stands for Supply Chain Optimization Technologies. It is the Amazon team that builds the models used to forecast demand and decide what to buy and where to store it.
4. What are Amazon Connect Decisions?
It is an AWS product launched in April 2026. It uses Amazon’s supply chain models to spot demand spikes and supplier problems, rank issues for human review and suggest next steps.
5. Can smaller companies use AI forecasting like Amazon?
Yes, but they need clean, standard data and skilled staff. Tools like Connect Decisions are now available to outside firms, and AWS offers free trials.
