2025, Vol. 6, Issue 2, Part D
Modeling and evaluation of queue-based biometric attendance systems for organizational efficiency
Author(s): Patrick Nnaemeka Okafor and HN Kama
Abstract: Efficient workforce management requires reliable and time-conscious attendance systems, especially in organizations where delays can significantly impact productivity. Traditional attendance methods often lead to congestion and time wastage during peak hours. This study presents a queue-based modeling and evaluation of biometric attendance systems to measure their performance and contribution to organizational efficiency. Using M/M/1 and M/M/2 queue models, simulations were conducted to analyze employee arrival patterns, service times, waiting times, and throughput under single-server and multi-server configurations. Results show that while a single biometric device (M/M/1) provides an average waiting time of approximately 2.1 minutes with a maximum delay of over 6 minutes, the dual-device system (M/M/2) significantly reduces the average waiting time to 0.8 minutes and doubles the service throughput. These findings highlight the importance of scaling biometric systems according to workforce size to minimize delays, improve employee satisfaction, and enhance organizational efficiency. Finding: average waiting time from 1.25 minutes to 0.046 minutes, utilization of 0.79, high. With two servers, utilization per server drops to 0.33, Average service times (17-19 seconds) are short, but even short service times accumulate into queues during clustered arrivals. The study demonstrates that queueing theory offers a robust analytical framework for evaluating and optimizing biometric attendance systems in real-world organizational settings.
DOI: https://doi.org/10.22271/math.2025.v6.i2d.259
Pages: 672-678 | Views: 153 | Downloads: 69
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How to cite this article:
Patrick Nnaemeka Okafor and HN Kama. Modeling and evaluation of queue-based biometric attendance systems for organizational efficiency. Journal of Mathematical Problems, Equations and Statistics. 2025; 6(2): 672-678. DOI: 10.22271/math.2025.v6.i2d.259
