Waiting is really boring. You just ask K’naan: It’s a drug. That awkward moment when you want to get something urgently but realize you’ll join a queue that seems like it would take forever before it gets to you. Thinking of it even sounds boring enough. There’s a mathematical science based theory behind why the line is so long. We said math and science, oh, don’t worry, its actually going to solve your woes in waiting if you thought otherwise.
Related media: The Psychology Of Waiting In Line
Waiting Is A Drug
Queuing theory is the mathematical science behind why the line is so long. A queue on the other hand is just a word that describes a line of items waiting their turn — whether their people waiting to be served at a bank, or finished goods rolling off the assembly line, that’s a queue. Of course, there can be many reasons for a long line — its often thought of as a business strategy to halt more customers. Well, if that’s a business tactic, then that’s one bad one.
One possibility is that, managers place more value on their cost to provide services: if that’s the case, then the manager think the more you wait for a service is more valuable than your time waiting, this scenario is more likely not the reason. Another reason is, you’re waiting for a service that is highly sought after by many people: if that’s the case, it seems you value whatever’s at the other end more than your time waiting for your turn. This scenario sounds plausible, though it seems more likely not.
The most promising scenario is probably your misunderstanding of the line’s formation in the first place. Seeing a line of people mazed in front of a store can be deceptive as to how long you might wait your turn. In such a scenario, though the line appears to be very long, yet the service rate might be very efficient that the line might not take that much of your time.
The Math Of The Matter
This is a mathematical concept known as Little’s Law; named after John Dutton Conant Little, who proposed the idea, then a professor specialized in operations research at the Massachusetts Institute of Technology (MIT). The law basically states that over time, the number of customers in a system is equal to their rate of arrival multiplied by the average time they spend in that system. It provides the math that researchers use to check out different system designs employed in various instances of waiting lines.
In reality, queues vary depending on what’s at hand at the moment. For instance, some lines vary in terms of services being rendered — like the post office or passport office; whereas some services are fixed and the lines moves at a constant pace — like a mechanized car wash or an assembly line. That being the real case, then different formulas need to be applied to each scenario to assist operation managers devise queuing systems to suit their business.
Over the years, its been proven — with the aid of Little’s Law equation — that the longer the line, the better your chance might be in waiting. Let’s explain. Now imagine a scenario where you have many queues, say the bank, where each queue has its own teller. Let’s call it the bank model, or the single-teller model, (that’s official, though). Your safest bet is to predict the shortest or fastest line to join in order to be quick; and if you’re bad at guessing, spoiler, that might even waste your time more.
Now The Wait Is Over
However, just a single longer queue being served by a teller — usually at a bank or airport security — is actually quite faster for everyone. The main rationale is that, if there’s a delay like a price check, or a very slow customer, that delay in turn affects only the teller who’s handling that situation — the rest of the queue moves on. The delay that one teller gets is evenly distributed across the system in a multiple-teller model, instead of completely stalling out just that one queue, as in the single-teller model.
Next time you see a very long line, and seeming that’s your only option, you should join it anyway. Little’s Law has proven that’s the efficient means of getting things moving faster than you thought otherwise. It’ll be over soon.
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Written by: Nana Kwadwo, Fri, Jul 05, 2019.