Average Ticket Resolution Time KPI
Ticket Resolution Time is important KPI (Key Performance Indicator) for tech companies that support cloud computing and hosting services.
How to arrive at Ticket Resolution Time KPI by using Control Chart of Sig Sigma / SPC? Here, we can consider quarterly KPI for Ticket Resolution.
For every month, we can collect all-techs average ticket resolution in minutes on daily basis. In excel, we feed the data as given below
Column A – Month Day from 1 to 30 Column B – Jul All Techs Average Column C – Aug All Techs Average Column D – Sep All Techs Average
Column E – X-Bar (Average for Jul, Aug and Sep)
Column F – X-Doublebar (Grand Average – Average of Averages of a range $E$2:$E$31 – Centre Line) Cell – J1 – Standard Deviation (one Sigma) – 23.14
Column G – UCL (Upper Control Limit) – [X-Doublebar + 3 Sigma]=Fn + (3 * $J$1) – apply in F2 to F31 Column H – LCL (Lower Control Limit) – [X-Doublebar – 3 Sigma]=Fn – (3 * $J$1) – apply in F2 to F31 By analysing the below chart and data, we will have some valid points for our consideration
Centre line – 80 minutes
LCL – 10 minutes (at 3 Sigma – 99.7%)
UCL – 150 minutes – 2.30 hours (at 3 Sigma – 99.7%)
Techs resolved the Tickets mostly within 2 hours.
From the above data, we can conclude Techs Process Capability in resolving tickets is roughly 2 hours; so, we can commit to client our Process Capability is 2 to 3 hours in resolving tickets in our SLA.
For Continuous Improvement, We can do
- Weekly, biweekly, monthly analysis with more drilldown like Ticket Complexity, Waiting Period, Tech wise, shift wise, Escalation, etc that may give some trends for further analysis
- Root Cause Analysis like Pareto Charts, Fishbone Diagram, etc
- Kuppuram, CTO, We3cares