The maintenance objective
Bring equipment condition into the daily decision process
Maintenance teams at a working brewery balance scheduled inspections with the day-to-day demands of keeping equipment available. They need useful condition signals, a repeatable way to investigate them, and enough context to choose the next maintenance action.
Tactical Edge deployed Amazon Monitron at AB InBev's Houston Brewery to add vibration and temperature readings to that process. The goal was practical: help the maintenance team notice abnormal equipment conditions earlier, investigate them consistently, and use what they learned to guide maintenance priorities.
The working loop
From an equipment signal to a maintenance decision
The deployment connected sensor data with a simple operating workflow. Amazon Monitron surfaced changes for review, while technicians remained responsible for inspection and maintenance decisions.
- 01
Sense
Vibration and temperature sensors capture equipment-condition readings.
- 02
Analyze
Amazon Monitron analyzes those readings for changes in equipment condition.
- 03
Alert
Abnormal-condition alerts give the maintenance team a clear item to review.
- 04
Investigate
A technician inspects the equipment and decides the appropriate response.
- 05
Learn
Technician feedback adds operating context to future maintenance decisions.
AWS documents this same operating model as sensing, analysis, notification, technician action, and feedback into the service. See how Amazon Monitron works.
What the workflow supported
Better inputs for day-to-day maintenance
Condition monitoring is most useful when it fits the way technicians already evaluate equipment and plan work.
Signals between inspections
Use vibration and temperature readings to spot changing equipment conditions between routine inspections.
A repeatable inspection path
Route each alert through review, technician investigation, and recorded feedback.
Maintenance prioritization
Use condition signals to decide which equipment to inspect first and where to focus the investigation.
Reliability context
Bring equipment data and technician findings into the maintenance decisions that support reliability and uptime.
“It has been a pleasure working with Tactical Edge AI on improving our day-to-day operations and maintenance strategy at Anheuser-Busch InBev Houston Brewery.”
William Boettcher
Senior Maintenance Manager, AB InBev
Current planning
Planning beyond Amazon Monitron
AWS lists Amazon Monitron as a service in maintenance phase. The service closed to new customers on October 31, 2024. Existing customers can continue to use it, but AWS is no longer releasing new functionality.
For an existing deployment, the next step is to assess the installed sensors and gateways, monitored assets, available history, alert workflow, and the maintenance decisions built around them. That inventory shows what can remain in place and what should be evaluated for a future architecture.
AWS IoT SiteWise anomaly detection is one AWS-native path to consider. It can use asset-property data to look for abnormal equipment behavior. The fit depends on your available data, asset models, connectivity, operating process, and the experience you want technicians to have.
A useful assessment should answer:
- Which monitored assets and maintenance decisions matter most?
- What sensor history and equipment context are available for the next design?
- How should alerts reach technicians, and how should their findings be recorded?
- Which AWS services and field components fit the site's operating requirements?
AWS references
Service details and planning guidance
These AWS resources explain the services and current status described in this customer story.
AWS services in maintenance phase
Current Amazon Monitron availability and maintenance-phase status.
Amazon Monitron access and alternatives
AWS guidance for existing customers evaluating their next steps.
How Amazon Monitron works
AWS documentation for sensors, analysis, alerts, and technician feedback.
AWS IoT SiteWise anomaly detection
A current AWS option for detecting abnormal equipment behavior from asset data.