Queue theory is almost a distraction because it overcomplicates the situation. The underlying insight is that rates matter. If a system can handle 100 events a second, then 99 events per second everything is fine and 101 events per second the system is down. There is a threshold where everything falls apart.
Queue theory encourages people to think in terms of flow rates and has the technically correct de-rates to nominal capacity to account for variance. But normally that is overkill except in highly engineered systems.
The battle in an organisation is convincing people to look at flows at all. In my experience people love metrics that track stock (we have X widgets or can handle Y orders) and not flows (we built A widgets and sold B widgets). Anyone who has training in queue theory knows when it is appropriate to look at flows rather than stocks and that is where the value is. The formulas and variances tend to just scare people away from monitoring flows because they don't understand what is happening.
Queue theory encourages people to think in terms of flow rates and has the technically correct de-rates to nominal capacity to account for variance. But normally that is overkill except in highly engineered systems.
The battle in an organisation is convincing people to look at flows at all. In my experience people love metrics that track stock (we have X widgets or can handle Y orders) and not flows (we built A widgets and sold B widgets). Anyone who has training in queue theory knows when it is appropriate to look at flows rather than stocks and that is where the value is. The formulas and variances tend to just scare people away from monitoring flows because they don't understand what is happening.