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Queue system - what do those stats really mean?

While comfortable waits would feel queue system shorter than uncomfortable waits. Providing information of waiting status to inform customers of their expected wait time, so as to signal the progress of the queue. Engaging the customers with activities such as entertainment and membership registration, so as to regulate customers’ perception of wait time. All these features would serve not only to queue system software managing customers’ image of a waiting line, but also to improve the customers’ perception of the service quality and the organization. Furthermore, according to previous studies, customers can be categorized into3 types based on their level of patience. These 3 types are attributed to various factors and demographic characteristics, such as age and past experiences. Within each type, customers’ level of patience varies differently as waiting time increases, which is as shown in Fig 6. The linear function represents how the group of standard customers would behave with waiting time theoretically.

While the logarithm function represents the group of less patient customers, who would tend to be more impatience very soon after the wait prolonged, and that the more patient group behave queue system software exponentially with waiting time. Undoubtedly that the latter group tends to have the highest level of tolerance towards waiting, so the ultimate goal in queue management is to simply optimize the queuing system and customers satisfaction, such that all customers would behave as the latter group, so that more customers more info would be willing to stay in line for longer period of time and thus in turns, maintain higher profitability of the organization. This framework and all of the other suggested design features are certainly not the most optimal solution to achieving this ultimate goal in queue management, there are certainly more aspect to be considered along with it, but it can definitely serve as the rules of thumb that provide guidance to successful management of queue, and ultimately, of service. 

And with its general nature, this framework is made applicable to all real-life scenarios in a general sense. There are several limitations in this research study, which can serve as opportunities for future improvement and research: One limitation is the lack of controllability to both the subjects’ sampling and responses. The sampled subjects majorly falls within the age of 18-25 who come from Canada or China. This relatively narrow scope of the sampling did not allow accurate comparison of the queue preferences across various demographics. As well, due to the fact that the queue system singapore survey was conducted online, it was impossible to provide any clarifications when needed or control to incomplete entries, which would lead to unfinished surveys, and thus affecting overall performance of the survey results.

Due to fact that more than half of the interviewees are currently engineering students, who have slight knowledge of queues, they might be biased towards responding to the question being asked. A wider scope of interviewees should be sampled in future studies. Next step to the research could be enhancing the completeness of the framework developed, it should incorporate key design features of queuing system, which may include (a) number of servers, (b) number of lines, and (c) whether customers are processed comprehensively or sequentially by multiple servers. It should also incorporate additional factors click here that are related to the queue, such as (a) customers’ demand, and (b) types of products or services sold. Besides, another further development of the research would be testifying the framework using the operations research approach through incorporating the framework and key design features into the formulation of simulation models, which would essentially determine the most optimal queue system singapore set of queue design combinations. As well, these simulated models may also preserve the general nature, such that it can be customized to fit into different on real life scenarios by incorporating other relevant factors that are conditioned to the specific scenarios, such as capacity of waiting area, nature of product/services and customer demand. Finally, the customers’ degree to which they value waiting time as well as their temporal orientation should be considered in order to better understand the characteristics of the customers.