Authors :
S. L. Bani; H. U. Nwosu
Volume/Issue :
Volume 9 - 2024, Issue 2 - February
Google Scholar :
http://tinyurl.com/yc7y2xv5
Scribd :
http://tinyurl.com/3j8caap4
DOI :
https://doi.org/10.5281/zenodo.10691984
Abstract :
This study focuses on the impact of waiting
time on operational machine performance in a
manufacturing company. The data was collected from
the case study company by observation of production
processes. The multiple service centres queue models
were used to analyzed the data. The results revealed that,
at the painting section, with 2-service centres and 4-
service centres compared, the performance outputs were
as follows: traffic intensity was 0.8000 (80%)and 0.4000
(40%), respectively; the probability that the 2-service
centre and 4-service centre were idle was 0.1111 and
0.1993, respectively; the average length of queue at the
two points of painting section were respectively, 1.4221
and 0.7255; the average jobs in the system was 3.0221
and 2.3255, respectively; the average time job spent in
queue was 0.1778 and 0.0907, respectively; the average
time job spent in the system was 0.3778 and 0.2907, the
waiting costs was 479.65 Naira and 369.14 Naira. Similarly,
at the drilling section, with 2-service centres and 4-service
centres compared, the performance outputs were as
follows: traffic intensity 0.8571(85%) and 0.4286 (43%),
respectively; the probability that the system was idle was
0.0769 and 0.1769, respectively; average length of queue
was 1.5819 and0.8911; average jobs in system 3.2962 and
2.6054; average time job spent in queue 0.0549 and
0.0149; average time job spent in system 0.0286 and
0.0435; and waiting costs 272.38 Naira and 414.29 Naira.
Four service channels are proposed at the painting
section and maintain the original 2-service centres at the
drilling section to improve on productivity.
Keywords :
Arrival Time, Operational Machine, Traffic Intensity, Machine Performance,Utilization Factor.
This study focuses on the impact of waiting
time on operational machine performance in a
manufacturing company. The data was collected from
the case study company by observation of production
processes. The multiple service centres queue models
were used to analyzed the data. The results revealed that,
at the painting section, with 2-service centres and 4-
service centres compared, the performance outputs were
as follows: traffic intensity was 0.8000 (80%)and 0.4000
(40%), respectively; the probability that the 2-service
centre and 4-service centre were idle was 0.1111 and
0.1993, respectively; the average length of queue at the
two points of painting section were respectively, 1.4221
and 0.7255; the average jobs in the system was 3.0221
and 2.3255, respectively; the average time job spent in
queue was 0.1778 and 0.0907, respectively; the average
time job spent in the system was 0.3778 and 0.2907, the
waiting costs was 479.65 Naira and 369.14 Naira. Similarly,
at the drilling section, with 2-service centres and 4-service
centres compared, the performance outputs were as
follows: traffic intensity 0.8571(85%) and 0.4286 (43%),
respectively; the probability that the system was idle was
0.0769 and 0.1769, respectively; average length of queue
was 1.5819 and0.8911; average jobs in system 3.2962 and
2.6054; average time job spent in queue 0.0549 and
0.0149; average time job spent in system 0.0286 and
0.0435; and waiting costs 272.38 Naira and 414.29 Naira.
Four service channels are proposed at the painting
section and maintain the original 2-service centres at the
drilling section to improve on productivity.
Keywords :
Arrival Time, Operational Machine, Traffic Intensity, Machine Performance,Utilization Factor.
