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AWS Solutions Library

Guidance for Automotive Warranty Analytics on AWS

Overview

This Guidance provides automakers and suppliers with a suite of modules to ingest, store, and analyze warranty data collected from dealerships across the world. Using machine learning (ML) and analytics, this Guidance predicts components that are likely to become major defects or recalls and enables customers to take corrective actions to reduce faulty inventory. You can expose these predictions with an easy-to-use dashboard, which allows you to review predicted issues, and then use insights prioritize tasks across your organization. With visibility into potential defects, you can reduce faulty inventory and warranty costs and prevent brand damage.

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 includes CloudWatch logs to help you understand system performance. You can see you are achieving your business outcomes through successful end-user content consumption.

Read the Operational Excellence whitepaper 

This Guidance uses AWS CloudFormation to deploy resources to the AWS Cloud, reducing the risk of human error during manual configuration or management. Additionally, AWS Secrets Manager stores all credentials required for connection to outside portals. AWS Identity and Access Management (IAM) roles associated with each resource in this architecture were designed according to the principle of least privilege with minimal permissions. Data in Amazon S3 and Amazon Redshift is encrypted at rest.

Read the Security whitepaper 

This Guidance includes dependencies on external systems, which may impact the reliability of the batch ingestion process. To address this potential issue, the Guidance includes rules in CloudWatch that will prompt Amazon Simple Notification Service (Amazon SNS)to send a notification to proactively alert operations staff in the event of failures. Additionally, the architecture incorporates managed services and serverless technologies where possible for processing and exposing data.

Read the Reliability whitepaper 

Scalable and highly available services like Amazon S3, AWS Glue, and Amazon Redshift are purpose-built for data analytics workloads.

Read the Performance Efficiency whitepaper 

This Guidance uses managed services, such as Amazon Aurora and SageMaker, that can scale to match demand. Most of the services are also serverless, such as Amazon S3, QuickSight, and Lambda, which reduces infrastructure management and idle resources.

Read the Cost Optimization whitepaper 

Compute and memory sizes can be right-sized at all levels of the design to minimize resource utilization. Managed services like AWS Glue and SageMaker distribute sustainability impact across all tenants of the service.

Read the Sustainability whitepaper 

Related content

How Machine Learning on AWS can help customers predict the risk of Automotive Part Recalls

This post demonstrates how to use Long Short Term Memory (LSTM) machine learning models to help customers predict parts that are likely to become defects or recalls.
Learn more

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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