Service Quality Control using Queuing Theory
DOI:
https://doi.org/10.69996/ijari.2023006Keywords:
Service quality control, MG1 queue model, GM1 queue model, GG1 queue modelAbstract
When a service system is extremely busy, customers are forced to wait in line. In addition to decreasing consumer satisfaction, this issue results in financial losses for the organization. This research suggests a queue model of consumers' queuing behavior in order to account for consumer losses. Researchers are examining the best practices for queue formation and optimization in random service systems with the aim of reducing customer losses. The MG1, GM1, and GG1 queuing models were created to estimate the quality control of the service. We look into queueing systems that use customer behavior models to predict responses for service quality control. Although GM1 and GG1 findings are quite close to MG1 results, the investigation concluded that MG1 produces the highest quality service.
References
[1] S. A. Afolalu, K. O. Babaremu, S. O. Ongbali, A. A. Abioye, A. Abdulkareem and S. B. Adejuyigbe, “Overview Impact of Application Of Queuing Theory Model On Productivity Performance In A Banking Sector”, Journal of Physics: Conference Series, 1378(3), 2019, 032033.
[2] J. F. Avilés-González and S. V. Avilés-Sacoto, “Application of Lean Techniques and Queuing Theory in Food Services”, Springer International Publishing, 2021.
[3] J. Bae and S. Kim, “The stationary workload of theG/M/1 queue with impatient customers”, Queueing Systems: Theory and Applications, 64(3), 2010, 253–265.
[4] CommBox. (2020). $1.6 Trillion Is Lost Each Year Due to Poor Customer Service—What You Can Do to Stay out of This Statistic. CommBox.
[5] L. F. Hsu, and C. S. Tapiero, “Quality control of the M/G/1 queue”, European Journal of Operational Research, 42(1), 1989, 88–100.
[6] K. Kuzu, “Comparisons of Perceptions and Behavior in Ticket Queues and Physical Queues”, Service Science, 7(4), 2015, 294–314.
[7] R. C. Larson, “OR Forum—Perspectives on Queues: Social Justice and the Psychology of Queueing”, Operations Research, 35(6),1987, 895–905.
[8] V. V. Satanaryana, S. Dawood, R. Karthikeyan, and N. Khan, “Application of queuing theory models foroptimized service to airline passengers”, International Journal of Current Research, 7(9), 2015, 20544–20547.
[9] Statista. (2022). Customers: Share lost due to poorservice experience 2018.
[10] S. Williams, “Poor service quality costing companies’ significant revenue”, E-Commerce News New Zealand,2021.
[11] S. H. Xu, L. Gao, and J. Ou, “Service Performance Analysis and Improvement for a Ticket Queue with Balking Customers”, Management Science, 53(6), 2007, 971–990.
[12] A. Yadav, and D. N. Sohani, “Application of Queuing Theory On a Food Chain”, International Journal of Scientific and Technology Research, 8(08), 2009, 6.
[13] M. Damle, P. Thenuan & J.Raval, “Genesis of Payments Banks: It’s Stimulus on the financial inclusion in India,” International Journal of Scientific and Engineering Research, 7(3), 2016, 783-791.
[14] K. P. Gupta, R. Manrai & U. Goel, “Factors influencing adoption of paymentss banks by Indian customers: extending UTAUT with perceived credibility,” Journal of Asia Business Studies, 2019.
[15] T.Huggins, & IzushI, “The competitive advantage of nations: Origins and Journey,” Emerald, 25(5), 2015, 458-470.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Advance Research and Innovation(IJARI, 2347-3258)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Fringe Global Scientific Press publishes all the papers under a Creative Commons Attribution-Non-Commercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/) license. Authors have the liberty to replicate and distribute their work. Authors have the ability to use either the whole or a portion of their piece in compilations or other publications that include their own work. Please see the licensing terms for more information on reusing the work.
