AWS Predictive Maintenance in Manufacturing Use Case

Commonwealth Bank of Australia faces challenges with equipment failure and degradation causing unplanned downtime and high maintenance costs in manufacturing operations. They implemented a predictive maintenance solution using AWS technologies to optimize equipment performance and extend asset lifespan by predicting failures before they occur.

Published
April 2026

Reported outcomes

Strategic outcomes

Scale & capacityIncreased asset uptime and availabilityCost efficiencyReduced maintenance expensesBetter decisions & insightImproved equipment health understandingNew product / capabilityImplemented predictive maintenance capability

Primary read

Use case focus

Showing 2 of 2

  • 1Predictive Maintenance
  • 2IoT Monitoring
Equipment failure leads to costly unplanned downtime and reduced operational efficiency in manufacturing.
  • Deployment of machine learning-based predictive maintenance system using Amazon Monitron, Amazon Rekognition for anomaly detection in images/videos, and Amazon SageMaker for custom ML models.
  • Integration of IoT sensors via AWS IoT Core, Device Management, and IoT Events to monitor equipment conditions like temperature, vibration, and humidity in real-time.
  • Alerts and maintenance scheduling are optimized based on analyzed sensor data to prevent failures and reduce costs.
  • Increased asset uptime and availability in manufacturing operations.
  • Reduced maintenance expenses through condition-based servicing rather than scheduled maintenance.
  • Improved understanding of equipment health and operational performance through advanced AI and IoT integration.
Sources & evidence1
Groundedness: 4/5

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