ascknd

I recently encountered a scenario where two helpdesks were to merge. One large, with relatively poor response times, and a smaller one, with relatively good performance. The question was, what would be the impact of merging the call queues on the current customers of the smaller helpdesk? It might seem that the performance of the smaller helpdesk would necessarily be significantly harmed. I’m going to show that that isn’t necessarily the case.

Neil Gunther’s PDQ::Perl¹ can be used to do a Mean Value Analysis and predict the impact of combining the queues. That is to say, work out the average performance. I wrote a small Perl script to do this, documented here:

https://ascknd.com/2014/06/23/helpdesk-queue-analyser-pdqperl/

Paradoxically, what we find is that, under certain circumstances, merging the two queues delivers better performance than either helpdesk was capable of delivering on its own:

Example:
Site A has 4 staff working on their queue (e.g. NetApp support), with a call coming in on average every 12.5 minutes. It takes them 30 minutes to deal with each case (excluding time spent in the queue waiting for an operative to become free). At site B they have 6 staff dedicated to a similar queue, who also take 30 minutes to deal with each case, but have an incoming call rate of one call every 5.6 minutes.

Using the previously mentioned script, the behaviour of these helpdesks can be analysed with the following parameters:
perl ./helpdesk_response.pl 4 30 0.08 6 30 .18

What we find is that Site A is 60% utilized and typically only keeps calls waiting in the queue for just over 5 minutes. Site B is 90% utilized and keeps callers waiting in the queue for 37 minutes. When the two queues are combined both sites are 78% utilized and experience waiting times in the queue  of <5 minutes. So both sites experience better performance.

How did merging the work of a site with good performance and a site with relatively poor performance lead both sites to improve? Two things are going on:

1. The impact of utilization on performance is non-linear. Moving from 60% utilization to 78% utilization does relatively little harm to site A compared to the benefit experienced by site B.

2. More helpdesk operators are more efficient, from a queueing perspective, than fewer helpdesk operators given the same utilization. That is to say, 6 workers at 60% busy will be more responsive than 4 workers at 60% busy even though in both cases the individual helpdesk operators are performing identically. This is because the more operators there are, the greater the chance that one of them will be free when a call comes in.

Conclusion:
In the general case, one can’t assume that combining the queues of the two helpdesks will necessarily cause the performance of a smaller high performing helpdesk significant harm.

¹ http://www.perfdynamics.com/Tools/PDQcode.html