The Evaluation of Production Line Performance by Using ARENA – A Case Study
The purpose of this paper is to simulate the production process of a metal stamping industry and to evaluate the utilization of the production line by using ARENA simulation software. The process time and the standard time for each process of the production line is obtained from data given by the company management. Other data are collected through direct observation of the line. There are three work stations performing ten different types of processes in order to produce a single product type. Arena simulation model is then developed based on the collected data. Verification and validation are done to the Arena model, and finally the result of Arena simulation can be analyzed. It is found that utilization at each workstation will increase if batch size is increased although throughput rate remains/is kept constant. This study is very useful for the company because the company needs to improve the efficiency and utilization of its production lines.
[1] J. S. Carson, J. Bank, and B. L. Nelson, “Discrete-event system
simulation,” 3rd ed., Prentice Hall International, 1999.
[2] K. V. Nagarajan, and G. Awyzio, “Modeling and simulation of an alarm
based network management system for effective SLSA monitoring and
management,” in Proceeding of International Conference on
Informatics, 2003.
[3] J. Heizer, and B. Render, “Production and operations management,” 2nd
ed., Masachusetts: Allyn and Bacon, 1998, pp. 229-243.
[4] M. P. Groover, “Fundamentals of modern manufacturing,” 2nd ed., John
Wiley & Sons, 2002.
[5] S. Robinson, “Simulation: The practice of model development and use,”
John Wiley & Sons, 2003.
[6] J. Banks, “Hand Book of Simulation,” Wiley Inter Science Publications,
2007.
[7] W. D. Kelton, R. P, Sodowski, and D. T. Sturrock, “Simulation with
Arena,” 4th ed., New York: Mc Graw Hill, 2007
[8] S. Hewitt, “Comparing analytical and discrete-event simulation models
of manufacturing system,” Thesis Master, Institute for System Research,
University of Maryland, 2002.
[9] K. V. Nagarajan, P. Vial, and G. “The use of Arena simulation software
to illustrate network operations in an educational setting using case
studies,” in Proceeding SCI 2003, Orlando Florida, 2003.
[10] V. Boginski, S. Butenko, and P. M. Pardalos, “Modeling and
optimization in massive graphs,” Department of Industrial & System
Engineering, University of Florida, 2002.
[11] K. V. Nagarajan, P. Vial, and G. Awyzio, ““Simulation of SNMPV3
traffic flow meter mib using Arena simulation modeling software.,” in
Proceeding Modeling and Simulation, Marina Del Rey, USA, 2002.
[12] D. P. Connors, G. E. Feigin, and D. D. Yao, “A Queuing network model
for semiconductor manufacturing,” IEEE Transactions on
Semiconductor Manufacturing, 1996.
[13] J. G. Shantikumar, and J. A. Buzacott, “Open queuing network models
of dynamic job shops,” International Journal of Production Research,
pp. 255-266, 1981.
[14] C. W. Yang, K. S. Huang, G. Yu, and D. Y. Jan, “Using queuing theory
to estimate the storage space of stocker in automated handling system,”
in Proceeding Semiconductor Manufacturing Technology Workshop,
2002.
[15] K. V. Nagarajan, P. Vial, and G. Awyio. “Simulation of policy based
networks through differentiated service levels using Arena simulation
Software,” in Proceeding Parallel and Distributed Computing and
Systems Conference, 2002.
[16] G. E. Vieira, “Ideas for modeling and simulation of supply chains with
Arena,” in Proceeding of the 2004 Winter Simulation Conference, 2004.
[17] Seraj Yousef Abed, “A simulation study to increase the capacity of a
rusk production line,” International Journal of Mathematics and
Computers in Simulation, Issue 3, Vol. 2, pp. 228-237, 2008.
[18] W. J. Hopp, and M. L. Spearman,“To pull or not to pull: What is the
question,?” in Proceeding MSOM Spring, 2004.
[19] Walid Abdul-Kader, “Capacity improvement of an unreliable
production line – An analytical approach,” Computers & Operations
Research Vol. 33, pp. 1695-1712, 2006.
[20] M. Gamberi, R. Gamberi, R. Gamberini, Manzini and A. I. Regattieri,
“An analytical model to evaluating the implementation of a batchproduction-
oriented line,” International Journal of Production
Economics Vol. 111, pp. 729–740, 2008.
[1] J. S. Carson, J. Bank, and B. L. Nelson, “Discrete-event system
simulation,” 3rd ed., Prentice Hall International, 1999.
[2] K. V. Nagarajan, and G. Awyzio, “Modeling and simulation of an alarm
based network management system for effective SLSA monitoring and
management,” in Proceeding of International Conference on
Informatics, 2003.
[3] J. Heizer, and B. Render, “Production and operations management,” 2nd
ed., Masachusetts: Allyn and Bacon, 1998, pp. 229-243.
[4] M. P. Groover, “Fundamentals of modern manufacturing,” 2nd ed., John
Wiley & Sons, 2002.
[5] S. Robinson, “Simulation: The practice of model development and use,”
John Wiley & Sons, 2003.
[6] J. Banks, “Hand Book of Simulation,” Wiley Inter Science Publications,
2007.
[7] W. D. Kelton, R. P, Sodowski, and D. T. Sturrock, “Simulation with
Arena,” 4th ed., New York: Mc Graw Hill, 2007
[8] S. Hewitt, “Comparing analytical and discrete-event simulation models
of manufacturing system,” Thesis Master, Institute for System Research,
University of Maryland, 2002.
[9] K. V. Nagarajan, P. Vial, and G. “The use of Arena simulation software
to illustrate network operations in an educational setting using case
studies,” in Proceeding SCI 2003, Orlando Florida, 2003.
[10] V. Boginski, S. Butenko, and P. M. Pardalos, “Modeling and
optimization in massive graphs,” Department of Industrial & System
Engineering, University of Florida, 2002.
[11] K. V. Nagarajan, P. Vial, and G. Awyzio, ““Simulation of SNMPV3
traffic flow meter mib using Arena simulation modeling software.,” in
Proceeding Modeling and Simulation, Marina Del Rey, USA, 2002.
[12] D. P. Connors, G. E. Feigin, and D. D. Yao, “A Queuing network model
for semiconductor manufacturing,” IEEE Transactions on
Semiconductor Manufacturing, 1996.
[13] J. G. Shantikumar, and J. A. Buzacott, “Open queuing network models
of dynamic job shops,” International Journal of Production Research,
pp. 255-266, 1981.
[14] C. W. Yang, K. S. Huang, G. Yu, and D. Y. Jan, “Using queuing theory
to estimate the storage space of stocker in automated handling system,”
in Proceeding Semiconductor Manufacturing Technology Workshop,
2002.
[15] K. V. Nagarajan, P. Vial, and G. Awyio. “Simulation of policy based
networks through differentiated service levels using Arena simulation
Software,” in Proceeding Parallel and Distributed Computing and
Systems Conference, 2002.
[16] G. E. Vieira, “Ideas for modeling and simulation of supply chains with
Arena,” in Proceeding of the 2004 Winter Simulation Conference, 2004.
[17] Seraj Yousef Abed, “A simulation study to increase the capacity of a
rusk production line,” International Journal of Mathematics and
Computers in Simulation, Issue 3, Vol. 2, pp. 228-237, 2008.
[18] W. J. Hopp, and M. L. Spearman,“To pull or not to pull: What is the
question,?” in Proceeding MSOM Spring, 2004.
[19] Walid Abdul-Kader, “Capacity improvement of an unreliable
production line – An analytical approach,” Computers & Operations
Research Vol. 33, pp. 1695-1712, 2006.
[20] M. Gamberi, R. Gamberi, R. Gamberini, Manzini and A. I. Regattieri,
“An analytical model to evaluating the implementation of a batchproduction-
oriented line,” International Journal of Production
Economics Vol. 111, pp. 729–740, 2008.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:65108", author = "Muhammad Marsudi and Hani Shafeek", title = "The Evaluation of Production Line Performance by Using ARENA – A Case Study", abstract = "The purpose of this paper is to simulate the production process of a metal stamping industry and to evaluate the utilization of the production line by using ARENA simulation software. The process time and the standard time for each process of the production line is obtained from data given by the company management. Other data are collected through direct observation of the line. There are three work stations performing ten different types of processes in order to produce a single product type. Arena simulation model is then developed based on the collected data. Verification and validation are done to the Arena model, and finally the result of Arena simulation can be analyzed. It is found that utilization at each workstation will increase if batch size is increased although throughput rate remains/is kept constant. This study is very useful for the company because the company needs to improve the efficiency and utilization of its production lines.
", keywords = "Arena software, case study, production line, utilization. ", volume = "7", number = "9", pages = "1831-5", }
