Case studies · Payments · POS terminals
A leading POS network operator validates predictive maintenance at scale
Failure probability per terminal, automated diagnostics and service orders triggered before downtime.
Predictive maintenancePayments
About the client
A leading financial company in point-of-sale terminals, responsible for high availability across thousands of distributed devices.
The challenge
- Operational data spread across disconnected systems
- Manual external analysis to find failing machines
- No failure-probability indicators
- Reactive maintenance and avoidable downtime
What Grand Thera deployed
TheraOS
- Telemetry, transaction logs and maintenance records in one environment
- Health indicators per terminal
- Real-time monitoring
AIT Agents
- Failure probability across time horizons
- Automated diagnostics from anomaly patterns
- Service orders triggered on threshold breach
- Risk-based prioritization
- Operational data spread across disconnected systems
- Manual external analysis to find failing machines
- No failure-probability indicators
- Reactive maintenance and avoidable downtime
What was at stake.
Results
Results
- 01
Significant reduction in unplanned terminal downtime
- 02
Service orders generated from predictive signals
- 03
More efficient maintenance allocation
- 04
Higher network availability
Source: original case publication
