Analysis of Post Covid-19 Lockdown Offline-Customer Service Delivery in Access Bank Akwanga Branch: A Queuing Theory Approach
Livinus L. Iwa, Monday A. Audu
Asian Journal of Probability and Statistics · pp. 23–32 · Published 3 Mar 2023
10.9734/ajpas/2023/v21i3465Abstract
Queues are formed when different people require similar services in the same place and at the same time interval, especially when current demand exceeds the current capacity to serve. In this paper, we present a post COVID-19 lockdown analysis of the offline-customer delivery unit of Access Bank Akwanga Branch using Queuing theory. We develop a suitable model for the system and used the quantitative method for the analysis, with the primary data obtained from observation. Results from the analysis show a reduction in customers’ waiting time, thereby encouraging COVID-19 preventive measure of social distancing; increasing the number of servers in the customer care unit causes a decrease in the average waiting time of customers in queue as well as in the system, implying an automatic adherence to COVID-19 safety measures. This means that Queuing theory improves customers waiting time as well as encourages social distancing.
Cited by 0
No indexed citations yet.
Related research
- COVID 19 Disease Caused by Coronavirus 2 (SARS-CoV-2) (Severe Acute Respiratory Syndrome) — shares topic coverage
- Assessment of Anxiety in Healthcare Providers Working in ICU during COVID-19 Pandemics — shares topic coverage
- Azithromycin and Hydroxychloroquine Accelerate Recovery of Outpatients with Mild/Moderate COVID-19 — shares topic coverage
- Elevated Levels of Lactate Dehydrogenase Predicts Poor Outcomes for Patients with COVID-19: A Review — shares topic coverage
- Addressing the Challenges of Containing Covid-19 Spread in a Rural, Poor Area in India: A Case Study — shares topic coverage
Article metrics
Real usage data collected on this platform.
0
Page views
0
PDF downloads
0
Outbound clicks
0
Citations
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
No views recorded yet.
Traffic sources
Referring site, by host.
No traffic recorded yet.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.