Cross-training simulator
Pooling queues saves agents, but nobody can do every job. The real question is how many people need a second skill and which second skill, and no formula answers it because the answer depends on how the skills join the queues together. This simulates your centre under each arrangement, running every one against the same contacts so the comparison is fair.
Simulating…
Running the first batch…
How the cross-training simulation works
Erlang C and its relatives assume one pool of identical agents. The moment some agents can take two queues and others cannot, no formula applies, so the only honest answer is to simulate: generate a half hour of contacts, with arrival times, handle times and how long each caller will wait, then run the centre minute by minute.
Every skill arrangement is run against exactly the same contacts. That matters more than it sounds. Comparing arrangements on different random customers buries the difference in noise; replaying identical customers means the gap between two arrangements is the arrangement, and the simulation needs a quarter as many runs to see it.
A cross-trained agent here covers their own queue and the next one along, so the queues form a single chain. That is deliberate. Arranging exactly the same second skills as closed pairs, where two queues only ever cover each other, captures almost none of the benefit, because the centre still behaves as several small centres. The table shows both so the difference is visible.
Because handle times, patience and daily volume are all random, every figure comes with a margin. The simulation keeps running until the margin is small enough to answer the question, and says so when it cannot. Differences smaller than the margin are not real, however tempting they look.
Frequently asked questions
- How many agents should be cross-trained?
- Fewer than most people expect for a partial benefit, and more than most expect for the full one. Cross-training a handful of people per queue captures a modest share of what full flexibility offers. Giving every agent one second skill, arranged as a chain, captures around 90 per cent of it. The step from a second skill to a third buys comparatively little.
- Why does it matter which queues an agent is trained on?
- Because flexibility only helps if it can move work to where the spare capacity is. If queues one and two cover each other and queues three and four cover each other, work can never cross the middle, and the centre performs as two smaller centres. Joining every queue into one chain uses the same number of second skills and captures far more.
- Is more cross-training always better?
- No, and this is the most common mistake. Agents are usually slower on a skill they use less, and once everyone handles everything, every contact risks going to someone slower at it. Past a point, that costs more than the flexibility earns, and a partly cross-trained team beats a fully cross-trained one. Raise the handle-time penalty in the tool and watch full pooling lose.
- Why does the answer move when I run it again?
- It should not: the same inputs give the same figures. But the figures carry a margin, because a real half hour is random. Ask for a tighter margin in the advanced section if you need to separate two close designs, and treat any gap smaller than the margin as no gap at all.
- How accurate is this?
- It gives the same answers as Erlang C, Erlang A and Erlang B wherever those formulas apply. Beyond them it is only as good as its assumptions, and the one that rests on your judgement is how much longer a second skill takes: measure it on the agents who already hold both skills if you can. Real centres also have redials, coaching and agents of differing speed, none of which are modelled here.