Average minutes to airborne? Median? Mode? 90th percentile? 99th percentile?
Optimizing for each of those would result in a different process, different outcomes, and quite possibly worse outcomes for the stakeholders than "proportion under 30 minutes" which is just a sixth way to slice the same data.
> Average minutes to airborne? Median? Mode? 90th percentile? 99th percentile?
Facetious answer: yes.
More useful answer: you want to record the full timeseries of individual data points. From this you can derive mean, median, mode, 90th percentile, 99th percentile and any other statistic that is shaped like hole in your cost–benefit calculations. Including trends and changing variance.
For reporting purposes, the upper process behaviour limit is probably a good one as "the" numeric value of the KPI, but the true value is in the timeseries.
Average minutes to airborne? Median? Mode? 90th percentile? 99th percentile?
Optimizing for each of those would result in a different process, different outcomes, and quite possibly worse outcomes for the stakeholders than "proportion under 30 minutes" which is just a sixth way to slice the same data.