Lines are a fact of life. They result from a company trying to keep the costs of providing its services in check in the face of fluctuations in customer demand. With enough resources to handle spikes, companies never make customers wait. Of course profit would drop significantly, because employees would be idle much of the time. On the other hand, if the company staffs for the average demand, they won’t have enough resources to cover the busy periods. During those times, customers end up waiting in long lines.
The expected waiting time of an operation can be calculated with some math-intensive calculations. The answer, though only tells you how long a customer would have to wait in a given situation. It doesn’t necessarily tell you anything about whether or not a customer actually will wait.
What does that mean? Sometimes, the length of a line does little to discourage people from waiting. Imagine a group of people come up to a trendy nightclub with a line around the corner. They may queue up without batting an eye. The same situation occurs at theme parks at the premiere attractions. People wait in line for extended times for the best rides.
But what happens when the payoff for waiting is nothing extraordinary? Imagine you walk into a coffee shop or a fast food restaurant, and the line is out the door. There is a good chance that you will abandon the line, and go somewhere else.
Most businesses are in this position. Long lines mean lost business. But when margins are slim, there is not much wiggle room to add personnel to handle the spikes. Understanding queueing theory becomes exceptionally important in this situation.
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This term is not intended to present the math behind queueing theory. The calculations can be quite complicated, and take quite a bit of skill, especially for the more complicated equations. Instead, this term is presented to help you understand how queueing theory can help you improve your operation. Consult an expert in the field for assistance in crunching the numbers.
There are six major factors that define how a line develops.
Queueing theory is most relevant in service operations. Customers, in most cases, dislike waiting (also called queueing time). The length of time they will wait is directly related to the value they place in the service they are waiting for. The greater the perceived value, the more likely a person is to stand in line.
Managers who run operations that have customer queues (including queues of customer surrogates, like orders or service emails) should get a rudimentary understanding of queueing theory at a minimum. Developing a stronger understanding, though, tends to lead to more effective decisions about how to manage waiting time for customers. Managers who are not math wizard should find someone who is that can crunch the numbers for them. Having that working knowledge, though, will at least help them understand what data they need, and will help them know what levers that they can pull to improve their service levels.
As you may imagine with all that goes into determining how lines affect customers, you have a great many opportunities to make improvements. It all comes down to finding the balance between service levels and cost. Continuous improvement efforts reduce the costs to make lines flow more quickly.
CI efforts can affect operations with queues in a number of ways.
 Fundamentals of Operations Management. Davis, Aquilano, & Chase, 1999, p. 291
 Queueing theory can get complicated in a hurry. A single line might feed three service reps at a medical facility, which each feed one of several doctors with their own wait. The doctors share an X-ray machine, which then feeds back to the same doctor, and possible goes back to the X-ray, after waiting for a casting room. The patient then waits in a pharmacy line. Lots of math involved in figuring this one out!
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