A Simple Approach to Calculating Turnaround Time (Part 2)

Last week, I wrote about calculating the potential queue size of an asynchronous channel using Little’s Law. One crucial variable in determining queue size is the average turnaround time for a ticket. In my opinion, this is the most challenging variable to calculate because it depends on accurately assessing labor hour requirements and availability, which are essentially the outputs of a capacity plan

There are several ways to calculate the turnaround time for a ticket, including the use of Erlang functions. However, for a quick, back-of-the-napkin calculation, as mentioned in my previous article, you can try the following method:

1. Calculate the labor hours required during an average hourly interval.

2. Calculate the labor hours available during an average hourly interval.

3. Divide the labor hours required by the labor hours available. Voilà, you have an average turnaround time for a ticket!

I typically use these calculations when I need to quickly forecast a contact center’s capacity. Rather than just predicting a single value, I create a range of turnaround times by assessing the labor hours required and available during peak and off-peak hourly intervals.

As with any quick calculation, I advise using this method only when you need to rapidly assess the queue size and turnaround time for a contact center. There are, of course, some obvious limitations to this approach:

- It assumes that the contact center operates with a low queue environment and doesn’t consistently run a queue.

- In an asynchronous environment, tickets are not always perfectly routed, meaning that, in reality, a significant portion of tickets may not be answered until manually routed to an associate.

- It doesn’t account for a multi-skill agent environment.

- It assumes a perfect distribution of demand and supply.

Despite these caveats, I find this quick calculation helpful because it allows me to perform a rapid diagnostic test on a contact center’s capacity without needing in-depth knowledge of the center. However, as any Workforce Management (WFM) professional would advise, if you are accountable for forecasting demand and capacity in a contact center, you should develop a comprehensive planning tool and use multiple methods, such as Erlang calculations, Monte Carlo simulations, and more.