* Is High or Low Best: Lower is Better
Mean Time to Repair measures the IT function's ability to respond to and resolve a system or application failure, or service interruption, and ensure that the resolution is rolled out to all required workstations, devices, servers, etc. A large value for this metric may indicate that the IT function's response procedures are lacking and/or that systems are not built in such a way that facilitates speedy debugging and recovery. A high value for Mean Time to Repair can also have a substantial effect on everyday business operations (e.g., accessing important company data or customer records), which can harm the company's relationship with its customers.
The average amount of time (measured in hours) required to repair a system or application to full functionality following a failure (i.e., a service interruption), measured from the time that the failure occurs until when the repair is completed and rolled out to all required locations (servers, devices, workstations, etc.).
The times of two events are used to derive time to repair: (1) the time when the system failure occurred, and (2) the time when the system was restored to full functionality following the failure (i.e., repaired). Time to repair is the difference of these two times. In order to derive the mean (i.e., average) value for time to repair, take the sum of time to repair across all systems being examined and divide it by the number of repairs performed on those systems. Only repairs following a system failure should be included in this calculation. A failure should be counted as any instance where a system must cease operation due to a critical issue. This KPI should be segmented by system type (critical, support, or customer-facing) to improve context and operational relevance of any related analysis.
Sum Of Time To Repair For All Systems / Number Of Repairs Completed During Examination Period Across All Systems
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