What challenges did Reliance Steel & Aluminum Co. face before AI implementation?

High downtime and maintenance costs due to legacy machinery failures. Need to improve operational efficiency and avoid production disruptions caused by machine breakdowns. Connectivity challenge for older industrial machinery not designed for internet. Data collection and analysis for actionable predictive maintenance insights.

Source

ProductionEvidence: Low40/100

Reliance Steel & Aluminum uses AWS IoT and Amazon SageMaker for predictive maintenance in industrial manufacturing

Use case typePredictive maintenanceUpdated Jun 13, 2026

Reliance Steel & Aluminum Co. partnered with AWS and TensorIoT to implement an Industrial IoT and machine learning solution for predictive maintenance. Legacy industrial machinery was connected to the internet using IoT sensors and AWS IoT services to collect telemetry data. Data collected from sensors is ingested and analyzed using AWS IoT Analytics and Amazon SageMaker, which trains models to predict machine failures. The predictive maintenance approach reduces unplanned downtime and maintenance costs, improving operational efficiency. Amazon Cognito enables secure role-based access for device management and data access control.

Organization: Reliance Steel & Aluminum Co.
Industry: Manufacturing
Location: United States
Published: November 2019

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Reliance Steel & Aluminum Co.
Provider: AWS
Maturity: Production
Linked source: AWS Partner Network Blog

The predictive maintenance approach reduces unplanned downtime and maintenance costs, improving operational efficiency

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

1Predictive maintenance
2Industrial IoT
3Machine learning

Integration of IoT sensors with legacy industrial machines.
Utilization of AWS IoT Core, AWS IoT Greengrass, AWS IoT Analytics to manage data ingestion, processing, and storage.
Use of Amazon SageMaker to train and deploy ML models for fault prediction and preventive maintenance.
Creation of a secure feedback loop from machines to cloud enabling real-time monitoring and control.
Implementation of role-based access management with Amazon Cognito for secure device operation.

Technologies

AWS IoT Core AWS IoT Greengrass AWS IoT Analytics Amazon SageMaker AWS Lambda Amazon SNS Amazon Cognito

Architecture

Architecture includes IoT sensors on legacy machines feeding data into AWS IoT Core and Greengrass, with AWS IoT Analytics for data processing and Amazon SageMaker for training ML models for predictive maintenance. Notifications are sent via Amazon SNS.

Sources & evidence1

Evidence: Low40/100Evidence strength