Waiting lines are created when the time a service takes to be completed is greater than the time it takes for its demand to increase. For instance, if in average a bank teller takes more time to complete an average task than the time between client arrivals to the facility, an increasing waiting line would be created. A conscious manager would have to ask himself if an additional cashier is needed and whether it would be profitable. To analyze these cases, TIS Consulting suggests a queue optimization study.
For any given company, learning how to optimize queues and/or customer waiting times will affect the quality of the service provided. The client’s experience during queues is a decisive factor between choosing a company over the competition. Many important service providers have adopted implementations that are focused in improving their clients’ experience:
- Six Flags provides a service at an additional price that allows the person to be in the line virtually via a portable device and it alerts the user when he is ready to entry the attraction. (Lo-Q 2013) (The Dolan Company 2002)
- The Indianapolis International Airport uses Bluetooth technology to monitor in real time waiting times in the security lines, which any person can look over it in their webpage. (Writers 2009)
- Several airlines reduce the time registration lasts by facilitating the usage of electronic devices to do some of these processes remotely. Likewise, they use the same system as a competitive advantage by notifying the clients about the status of their flight through it. (Lufthansa)
The first step to start a queue optimization study for any company is to understand what the client’s expectations are. With this the decision maker can understand the clients’ behavior while they wait and what can be done in order to keep this from causing a negative impression for the company. It is also recommended to consider the psychological aspect to derive alternative solutions that might be more profitable and require less effort. Some of these considerations are:
- Clients will accept longer waiting times the more important the service is
- If the client is in group or busy, he will perceive less waiting time.
- Anxiety and uncertainty increases the perceived waiting time. (Maister 2005)
Disney has applied operational and psychological solutions for their queues in a group of queue management integrating more than 75 engineers. Disney analyzes and adds capacity to attractions according to demand but also considers other psychological aspects by keeping their clients occupied with Disney’s characters or involved in visual and/or interactive activities. Other aspect that has been implemented is the design of queues using line patterns, making them to perceive less distance, and several signs, allowing them to know the expected waiting time from their position. (Barnes 2010) (Stone 2012)(Pawlowski 2008)
After considering the psychology of the queues, some elements are to be studied from a more technical point of view in order to reach alternatives that can be comparable and quantified. The queuing theory is useful for situations that follow certain common assumptions. Some of the elements that it takes into account are as follows (Winston 2008):
- Understanding the properties and frequency of the inputs (if its frequency is expected or random, if they come in group…)
- Queuing line configuration (if an individual or several queuing lines are being used…)
- Resource configuration (if the process is completed by one server or if it is divided in various sequential processes, like a carwash that uses multiple stations for a service)
- Outputs (if a client needs to go back to a certain point of the system, rework)
Going back to the cashier’s problem, this could be analyzed under several assumptions:
- The inputs are random with an average of 15 clients per hour and they never leave the waiting line, regardless the waiting time.
- There is an unlimited capacity for waiting clients in the system.
- Clients are served by one cashier at a rate of 20 clients per hour in the order that they got in line. (Winston 2008)
Following this assumptions and using the basic queuing theory, it can be concluded that the cashier will be 75% of his time occupied in serving the customers (which is calculated dividing the inputs rate with the serving rate) and it’s highly probable that at any time there will be 2 customers in line. This information isn’t enough to get to the conclusion of hiring a new cashier since the manager needs to know if it is profitable for the company by comparing the cost of having the clients waiting with the cost of having an additional cashier.
The complexity of the problem will depend on the requirements and nature of the system, which is not always easy or reliable to be analyzed with queuing theory. These situations can be analyzed with more elaborated techniques, like computer assisted simulation using specialized software. Some of its principal advantages are their capacity to analyze complex situations, compare different scenarios, and save in implementation costs. Additionally, it can be a useful tool of visual validation of the situation and its possible solutions.
This method is used with processes where experimental changes are not viable or if the modification could affect their productivity. With simulation, the benefits are expected to be validated and quantified before it is implemented. Some areas where the simulation is applied are in the design of transport systems, evaluation in customer service and in the analysis of economic systems.
These methods are essential to understand and solve problems of queuing lines; from the queuing theory for simpler cases to simulation for those that are complex. This studies assist in the validation of the company to carry out the most profitable solution without compromising the level of satisfaction required from the client and considering alternative approaches, like the psychology of lines, that can get to the same results but more profitable and with less needed effort.
TIS Consulting Group uses these studies to understand the system and simulate it in accordance with its inputs to generate accurate information of costs, benefits and even environmental impact that can be involved with the queues. With these studies it is possible to use information in order to validate proposed solutions and compare each of their expected quantitative and/or qualitative benefits.
If you have any questions or comments about this post contact us.
Barnes, Brooks. 2010. “Disney Command Center Aims to Keep Lines Moving.” The New York Times, Diciembre 27, sec. Business Day / Media & Advertising. http://www.nytimes.com/2010/12/28/business/media/28disney.html.
Lo-Q. 2013. “Welcome to Lo-Q.” Hi-tech for Low Queues. http://www.lo-q.com/.
Lufthansa. “Lufthansa eFlyServices on Your Mobile Phone.” Lufthansa. http://www.lufthansa.com/uk/en/Lufthansa-eFlyServices-on-your-mobile-phone.
Maister, David H. 2005. “The Psychology of Waiting Lines.” Columbia University. http://www.columbia.edu/~ww2040/4615S13/Psychology_of_Waiting_Lines.pdf.
Pawlowski, A. 2008. “Queuing Psychology: Can Waiting in Line be Fun?” CNN. Noviembre 20. http://www.cnn.com/2008/TECH/science/11/20/queuing.psychology/.
Stone, Alex. 2012. “Why Waiting in Line Is Torture.” The New York Times, Agosto 18, sec. Opinion / Sunday Review. http://www.nytimes.com/2012/08/19/opinion/sunday/why-waiting-in-line-is-torture.html.
The Dolan Company. 2002. “System Gives Alternative to Waiting in Line at Six Flags.”Journal Record, Febrero 4.
Winston, Wayne. 2008. Investigación de Operaciones. Aplicaciones y Algoritmos. Cuarta. Cengage Learning.
Writers, Staff. 2009. “Bluetooth Signals Show Airport Security-Line Waiting Times.” UPI Space Daily, Junio 9. http://search.proquest.com/docview/454547430/1410929C97860AFAB17/56?accountid=11643.