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Guidance for Demand Forecasting for Retail on AWS

Prepare for customer demand with accurate forecasts powered by machine learning

Overview

This Guidance helps you improve the customer experience by using demand forecasting to estimate future customer demand. By predicting future demand and purchasing patterns, you can plan ahead for supply chain optimization and inventory management. This architecture shows the process of developing an estimate of future customer demand. Retail organizations can apply this Guidance to increase demand forecast accuracy, analyze various internal and external variables, and highlight low-code, no-code, or other advanced approaches.

How it works

These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.

Download the architecture diagram

Well-Architected Pillars

The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.

This Guidance highlights low-code and advanced options for customers to achieve more accurate forecasting. Each option (SageMaker and SageMaker Canvas) has service observability built in to ensure models train efficiently according to objective metrics. The entire flow is an iterative process that ensures the business objective is achieved and the process is efficient. 

Read the Operational Excellence whitepaper 

Direct Connect or Site-to-Site VPN is used to provide a secure connection between retail store or external systems and the AWS Cloud. Also, we leverage IAM and AWS KMS for securing and encrypting data.

Read the Security whitepaper 

Highly available and reliable managed services like Amazon S3, DynamoDB, AWS Glue, and SageMaker help with platform scalability and high availability. Options exist for regional failover for all services with proper architecting, but they are not called out in this design specifically.

Read the Reliability whitepaper 

Scalable and highly available services like DynamoDB and Amazon S3 are used as core components to improve performance. During model training, customers can leverage automatic tuning features, which can help achieve the most performant models.

Read the Performance Efficiency whitepaper 

Customers are only charged for the time the models are trained. Customers can additionally leverage spot instances and elastic inference. Customers can also use cost effective methods like batch inference and serverless inference, although these are not specifically mentioned. Serverless services like Lambda, Step Functions, Athena, and AWS Glue are used to reduce the cost of the solution.

Read the Cost Optimization whitepaper 

AWS managed services help with scale up and down according to business requirement and traffic and are inherently more sustainable than on-premises solutions. Additionally, we have leveraged serverless components to automate the process of infrastructure management and make it more sustainable.

Read the Sustainability whitepaper 

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Disclaimer

The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.

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