4 RESULTS AND DISCUSSION
4.1 SMV for trouser
The SMV for each task on trouser assembly was calculated according to Bongomin, Mwasiagi, Nganyi,
and Nibikora (2020b). The SMV for the 65 tasks on
trouser assembly was determined by summation of
the individual SMV. The result obtained shows an
SMV of 41.763. The SMV achieved in this study is
high because of the bottleneck on the trouser assembly line (Gebrehiwet & Odhuno 2017). However, the
SMV can be reduced by the addition of manpower
or resources (Mohibullah et al. 2019) for the operations such as knee patch attach, back patch pressing,
side pocket topstitches, right flybox attach, fly attach,
back patch attaches, hip pocket overlock, etc. These
are the tasks whose SMV are relatively higher than
the average SMV of 0.642 for the 65 tasks. In addition, SMV can be reduced by proper balancing of the
trouser assembly line.The operator performance rating
and machine allowances assignment have a significant
effect on the SMV determination. Therefore, proper
observation on operators’ performance is requisite for
achieving practically realistic SMV.
4.2 Operation bulletin
The operation bulletin was developed based on
the following line specifications: the total SMV
(41.763), planned efficiency (75%), total machine
SMV (37.712), helper SMV (4.051), and minutes per
day (480). The trouser assembly line with 65 operations and 61 planned operators was considered. The
target and manpower calculations were done for each
operation and are presented in Table 2. From the calculated manpower, the required manpower numbers were
determined.
line target =
480 × 0.75 × 61
41.763
= 525 pieces per day
Target for operation 1 =
480 × 0.75 × 1
0.312
= 1142 pieces per day
Manpower requirement for operation 1 =
525
1142
= 0.46
The manpower requirement with (*) for an operation means that the exact number can further be determined after observing the level of work in progress
(WIP) and idle time at the workstation. Most likely
they represent the bottleneck workstations. The bottleneck workstations are the ones whose capacity is
less than the demand placed on it and less than the
capacities of all other resources. In order to determine
the correct manpower requirement, prior knowledge of
bottleneck workstations is of paramount importance.
Therefore, this might result in an increase of manpower in the case of high WIP and a reduction of the
manpower required in the case of high idle time in
the workstation. To this end, for the line to perform to
the expectations: two ironing operations require four
ironers, 51 machine operations need 63 operators, and
12 helper operations require 13 helpers.
5 CONCLUSION
The present paper has demonstrated the function of IE
for developing an operation bulletin that can be used
for production planning. The accuracy and precision
during the time study is very essential for obtaining
a realistic SMV that is practically feasible. Therefore,
digital technology for a time study method should be
explored. Further study can take into consideration the
non-value-added or non-productive operations such as
separation of bundles, cutting of threads, and transfer
of bundles by operator to the next operator. In addition,
further study on line balancing is needed to reduce
the number of workstations or cycle time which will
improve the productivity and minimize the resource
cost.
REFERENCES
Abtew, M. A., Kumari, A., Babu, A., & Hong, Y. (2019). Statistical Analysis of Standard Allowed Minute on Sewing
Efficiency in Apparel Industry. Autex Research Journal,
1–7. https://doi.org/10.2478/aut-2019-0045
Al-khatib, B. A. (2012). The Effect of Using Brainstorming Strategy in Developing Creative Problem Solving
Skills among Female Students in Princess Alia University
College Department of Psychology and Special Education. American International Journal of Contemporary
Research, 2(10), 29–38.
Babu, V. R. (2012). Industrial engineering in apparel production. New Delhi, India: Woodhead Publishing India Pvt.
Ltd. https://doi.org/10.1533/9780857095541
Bahadır, S. K. (2011). Assembly Line Balancing in Garment
Production by Simulation. In W. Grzechca (Ed.),Assembly
Line - Theory and Practice (pp. 67–82). Rijeka, Croatia:
InTech.
Barton, R. R. (2004). Designing Simulation Experiments
. In R. G. Ingalls, M. D. Rossetti, J. S. Smith, & B.
A. Peters (Eds.), Proceedings of the 2004 Winter Simulation Conference (pp. 73–79). University Park, USA.
https://doi.org/10.1109/WSC.2004.1371304
Baset, M. A., & Rahman, M. (2016). Application of Industrial
Engineering in Garments Industry for Increasing Productivity of Sewing Line. International Journal of Current
Engineering and Technology, 6(3), 1038–1041.
Bashar, A., & Hasin, A. A. (2019). Impact of JIT Production
on Organizational Performance in the Apparel Industry in
Bangladesh. In MSIE 2019, May 24–26 (pp. 184–189).
Phuket, Thailand: Association for Computing Machinery.
https://doi.org/10.1145/3335550.3335578
Behr, O. (2018). Fashion 4.0 – Digital Innovation in the
Fashion Industry. Journal of Technology and Innovation
Management, 2(1), 1–9.
Bertola, P., & Teunissen, J. (2018). Fashion 4.0. Innovating fashion industry through digital transformation.
81
4.1 SMV for trouser
The SMV for each task on trouser assembly was calculated according to Bongomin, Mwasiagi, Nganyi,
and Nibikora (2020b). The SMV for the 65 tasks on
trouser assembly was determined by summation of
the individual SMV. The result obtained shows an
SMV of 41.763. The SMV achieved in this study is
high because of the bottleneck on the trouser assembly line (Gebrehiwet & Odhuno 2017). However, the
SMV can be reduced by the addition of manpower
or resources (Mohibullah et al. 2019) for the operations such as knee patch attach, back patch pressing,
side pocket topstitches, right flybox attach, fly attach,
back patch attaches, hip pocket overlock, etc. These
are the tasks whose SMV are relatively higher than
the average SMV of 0.642 for the 65 tasks. In addition, SMV can be reduced by proper balancing of the
trouser assembly line.The operator performance rating
and machine allowances assignment have a significant
effect on the SMV determination. Therefore, proper
observation on operators’ performance is requisite for
achieving practically realistic SMV.
4.2 Operation bulletin
The operation bulletin was developed based on
the following line specifications: the total SMV
(41.763), planned efficiency (75%), total machine
SMV (37.712), helper SMV (4.051), and minutes per
day (480). The trouser assembly line with 65 operations and 61 planned operators was considered. The
target and manpower calculations were done for each
operation and are presented in Table 2. From the calculated manpower, the required manpower numbers were
determined.
line target =
480 × 0.75 × 61
41.763
= 525 pieces per day
Target for operation 1 =
480 × 0.75 × 1
0.312
= 1142 pieces per day
Manpower requirement for operation 1 =
525
1142
= 0.46
The manpower requirement with (*) for an operation means that the exact number can further be determined after observing the level of work in progress
(WIP) and idle time at the workstation. Most likely
they represent the bottleneck workstations. The bottleneck workstations are the ones whose capacity is
less than the demand placed on it and less than the
capacities of all other resources. In order to determine
the correct manpower requirement, prior knowledge of
bottleneck workstations is of paramount importance.
Therefore, this might result in an increase of manpower in the case of high WIP and a reduction of the
manpower required in the case of high idle time in
the workstation. To this end, for the line to perform to
the expectations: two ironing operations require four
ironers, 51 machine operations need 63 operators, and
12 helper operations require 13 helpers.
5 CONCLUSION
The present paper has demonstrated the function of IE
for developing an operation bulletin that can be used
for production planning. The accuracy and precision
during the time study is very essential for obtaining
a realistic SMV that is practically feasible. Therefore,
digital technology for a time study method should be
explored. Further study can take into consideration the
non-value-added or non-productive operations such as
separation of bundles, cutting of threads, and transfer
of bundles by operator to the next operator. In addition,
further study on line balancing is needed to reduce
the number of workstations or cycle time which will
improve the productivity and minimize the resource
cost.
REFERENCES
Abtew, M. A., Kumari, A., Babu, A., & Hong, Y. (2019). Statistical Analysis of Standard Allowed Minute on Sewing
Efficiency in Apparel Industry. Autex Research Journal,
1–7. https://doi.org/10.2478/aut-2019-0045
Al-khatib, B. A. (2012). The Effect of Using Brainstorming Strategy in Developing Creative Problem Solving
Skills among Female Students in Princess Alia University
College Department of Psychology and Special Education. American International Journal of Contemporary
Research, 2(10), 29–38.
Babu, V. R. (2012). Industrial engineering in apparel production. New Delhi, India: Woodhead Publishing India Pvt.
Ltd. https://doi.org/10.1533/9780857095541
Bahadır, S. K. (2011). Assembly Line Balancing in Garment
Production by Simulation. In W. Grzechca (Ed.),Assembly
Line - Theory and Practice (pp. 67–82). Rijeka, Croatia:
InTech.
Barton, R. R. (2004). Designing Simulation Experiments
. In R. G. Ingalls, M. D. Rossetti, J. S. Smith, & B.
A. Peters (Eds.), Proceedings of the 2004 Winter Simulation Conference (pp. 73–79). University Park, USA.
https://doi.org/10.1109/WSC.2004.1371304
Baset, M. A., & Rahman, M. (2016). Application of Industrial
Engineering in Garments Industry for Increasing Productivity of Sewing Line. International Journal of Current
Engineering and Technology, 6(3), 1038–1041.
Bashar, A., & Hasin, A. A. (2019). Impact of JIT Production
on Organizational Performance in the Apparel Industry in
Bangladesh. In MSIE 2019, May 24–26 (pp. 184–189).
Phuket, Thailand: Association for Computing Machinery.
https://doi.org/10.1145/3335550.3335578
Behr, O. (2018). Fashion 4.0 – Digital Innovation in the
Fashion Industry. Journal of Technology and Innovation
Management, 2(1), 1–9.
Bertola, P., & Teunissen, J. (2018). Fashion 4.0. Innovating fashion industry through digital transformation.
81