In mathematics, Queuing Theory is the study of waiting lines or queues as they are often called. With queuing theory, mathematicians are able to analyze several related processes such as joining the queue, waiting in the queue, and being served at the front of the queue.

Queuing theory takes into consideration a number of variables, such as: the position where the queue is joined, the time it took to stand in line before being served, the time it takes to get served, who serves, and a host of other variables. For this reason, it is safe to conclude that queuing theory is more of a statistical theory than an actual mathematical one.

In a service setting, we’re used to seeing few different methods for serving customers, all of which have been identified through queuing theory. In a small business with a low volume of customers, queuing usually consists of a single server, where there is one counter and customers line up to be helped by one server. For larger volumes, there are other options like parallel servers, where customers line up at a single counter and will be helped by one of several servers, or the tandem queue, where there are many counters and customer can decide which one they’d like to use (grocery stores often use this method).

Queuing Theory in an Organizational Setting:


Since queuing theory is based on joining queues, waiting, and getting served, how can this be related to an organizational setting? For simplification, we’ll look at a manufacturing process for a bottle. In a simple bottle manufacturing plant for a fruit juice packaging company, the process for getting the bottles ready for the fruit juice to be poured into them may go as follows:


  1. Bottle arrives at the start point of the process and joins the queue.
  2. The bottle waits for its turn to be cleaned, disinfected, dried, and labeled.
  3. The bottle moves slowly in line.
  4. The cleaning, disinfecting, drying, and labeling process begins.
  5. The process is completed.

Queuing theory can be used to analyze a number of issues that could affect the bottle preparation process. For instance, how long does it take the bottle to make it to the queue? Once it gets to the queue, how long does it have to wait and how long does the cleaning take? In addition, knowing how many bottles get cleaned at a time is important. These issues, once discovered, will help the management staff understand the process better and come up with ways to improve it.

The aim is to get the line moving as fast as possible because the faster a line can move and get served, the faster and more productive the manufacturing process is.

Based on this example, we can conclude that understanding the possible variables present in a manufacturing process will make it easier to improve or speed up the process, while maintaining good quality to meet customers’ expectations. To put it simply: queuing theory can help companies create a manufacturing process that works best for their business.

Looking for more ways to improve your manufacturing process? Please visit our article on Kaizen.


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