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.

Results: