---
title: 'Guidance for Retail Demand Forecasting and Planning on AWS'
canonical_url: https://docs.aws.amazon.com/solutions/retail-demand-forecasting-and-planning-on-aws/
source: aws-documentation
generated_on: 2026-10-01
---

# Guidance for Retail Demand Forecasting and Planning on AWS

## Overview

This Guidance demonstrates how retail organizations can develop accurate estimates of future customer demand through systematic forecasting processes. By analyzing internal and external variables, businesses can significantly improve demand forecast accuracy, leading to better inventory management, reduced costs, and enhanced customer satisfaction. The solution offers flexible implementation options—from low-code and no-code approaches for business users to advanced techniques for technical teams—enabling organizations of varying technical maturity to optimize their demand planning capabilities and make data-driven decisions that directly impact operational efficiency and profitability.

## Benefits

### Accelerate smarter inventory decisions

Deploy machine learning-powered demand forecasting that automatically trains models and runs batch inference on your retail data. Reduce overstock and stockout risks by surfacing actionable predictions through an interactive dashboard your team can use immediately.


### Scale forecasting without managing infrastructure

Run end-to-end forecasting pipelines using serverless compute and fully managed ML services that scale automatically with your data volume. Focus your team on refining forecasting strategies rather than provisioning or maintaining underlying infrastructure.


### Explore forecast data with confidence

Query and visualize cataloged retail datasets through a secure, interactive dashboard backed by automated data crawling and serverless querying. Protect sensitive data with encryption at rest, least-privilege access controls, and user authentication built into the architecture.


## How it works

This architecture diagram shows how to forecast retail product demand using machine learning, serverless APIs, and interactive analytics dashboards on AWS. [Download the architecture diagram](downloads/retail-demand-forecasting-and-planning-on-aws.pdf)

![Architecture diagram for Retail Demand Forecasting and Planning on AWS](/images/solutions/retail-demand-forecasting-and-planning-on-aws/images/retail-demand-forecasting-and-planning-on-aws-1.png)

1. **Step 1**: You access the web application served by Amazon CloudFront from a static site hosted in Amazon S3.
1. **Step 2**: You sign in through Amazon Cognito, which authenticates users and authorizes API access.
1. **Step 3**: Your requests reach the Amazon API Gateway REST API.
1. **Step 4**: API Gateway invokes AWS Lambda functions that handle products, forecasts, training, and inference.
1. **Step 5**: Administrators upload datasets through AWS Lambda into Amazon S3, the data is stored encrypted at rest with least privilege controlled by IAM, and the status is tracked in Amazon DynamoDB.
1. **Step 6**: New data in Amazon S3 triggers AWS Lambda that starts an AWS Step Functions pipeline that uses Amazon SageMaker — a fully managed service for AI — to train a forecast model and run batch inference, storing the prediction results back to Amazon S3.
1. **Step 7**: AWS Glue crawls and catalogs datasets, AWS Lambda uses Amazon Athena over Amazon S3 to query this catalog and returns the results to the web application for user interaction via a custom dashboard.
## Deploy with confidence

Everything you need to launch this Guidance in your account is right here.

- **Let's make it happen**: Ready to deploy? Review the sample code on GitHub for detailed deployment instructions to deploy as-is or customize to fit your needs.

[Go to sample code](https://github.com/aws-solutions-library-samples/guidance-for-demand-forecast-for-retail-on-aws)


[Read usage guidelines](/solutions/guidance-disclaimers/)