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Automated Ticket Resolution

Reduced Support Costs by 15% While Improving Response Time by 20%

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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%.

CONTEXT

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.

KEY CHALLENGES:

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.

GOALS:

Increase the auto-resolution of tickets to 90%.
Reduce monthly support costs by 10%.
Improve first response time by 20%.

SOLUTION

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.