0%Support overhead reduced by 15%.
0%Reduction in tickets routed to humans by 22%.
0%Automation increased to 94%.
0%First response time for tickets improved by 20%.
Our customer’s data center management costs were increasing over time and not giving the customer the economies of scale they had forecasted. The customer was looking for a vendor that could reduce the ticket volume flowing to the support engineers by using RPA with rule engines to auto-resolve alarms and tickets.
- An average of 10M alarms per month was generated from the data centers.
- After the elimination of duplicate alarms, the monthly average ticket volume was still at 7M.
- The percentage of tickets being auto resolved was at 70%.
- Currently, business rules could only be updated by the development team.
- Increase the auto-resolution of tickets to 90%.
- Reduce monthly support costs by 10%.
- Improve first response time by 20%.
- Integrated a newer rules-engine (JEXL) that could be leveraged to perform more complex maintenance activities driven by business rules.
- Built alarm correlation algorithms to group related alarms into a single ticket with the corresponding log files.
- Auto-routing of tickets to skill groups was enhanced by automatically attaching log files to tickets using RPA. This improved the first response time.
- Enabled the import of business rules using CSV files to eliminate the dependency on the development team.
- The CSV files enabled faster updates to rules and maintenance commands resulting in increased auto resolution.
- Automation layer added to auto-resolve device on-boarding tickets. The automation workflow would be retried based on policies, before assigning the ticket to a human.
- Implemented Anomaly Detection, Alerting, and Auto Resolution workflows.
- Implemented Alarm Management to handle customer maintenance windows.
Support overhead reduced by 15%.
Reduction in tickets routed to humans by 22%.
Automation increased to 94%.
First response time for tickets improved by 20%.
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