Validation of production system throughput potential and simulation experiment design
Keywords:Throughput potential validation, Kingman’s equation, discrete event simulation
The throughput potential of a production system must be designed and validated before implementation. Design includes creating product flow by setting the takt time consistent with meeting customer demand per time period and the average cycle time at each workstation being less than the takt time. Creating product flow implies that the average waiting time preceding each workstation is no greater than the takt time. Kingman’s equation for the average waiting time can be solved for the variation component given the utilization, and the cycle time. The variation component consists of the variation in the demand and the variation in cycle time. Given the variation in demand, the maximum allowable variation in cycle time to create flow can be determined. Throughput potential validation is often performed using discrete event simulation modeling and experimentation. If the variation in cycle time at every workstation is small enough to create flow, then a deterministic simulation experiment can be used. An industrial example concerning a tier-1 automotive supplier with two possible production systems designs and various levels of variation in demand assumed is used to demonstrate the effectiveness of throughput validation using deterministic discrete event simulation modeling and experimentation.
Atalan, A., Dönmez, C.C. (2020). Optimizing experimental simulation design for the emergency departments. Brazilian Journal of Operations & Production Management, 17(4), e2020854. https://doi.org/10.14488/BJOPM.2020.026
Askin, R.G., Standridge, C.R. (1993). Modeling and analysis of manufacturing systems. New York: John Wiley and Sons.
Dagkakis, G., Rotondo, A., Heavey, C. (2019). Embedding optimization with deterministic discrete event simulation for assignment of cross-trained operators: an assembly line case study. Computers and Operations Research, 111, 99-115. https://doi.org/10.1016/j.cor.2019.06.008
Ferrin, D.M., Miller M.J., Muthler D. (2005). Lean sigma and simulation, so what's the correlation?, in Proceedings of the 2005 Winter Simulation Conference, IEEE, USA. Retrieved July 22, 2020 from: https://informs-sim.org/wsc05papers/249.pdf
Hopp, W.J., Spearman, M.L. (2011). Factory Physics: Foundations of manufacturing management, 3rd ed. Long Grove, IL: Waveland Press.
Jayaraman, A., Gunal, A.K. (1997). Applications of discrete event simulation in the design of automotive powertrain manufacturing systems". In Proceedings of the 1997 Winter Simulation Conference, IEEE, USA. https://doi.org/10.1145/268437.268620
Khan, S., Standridge, C.R. (2019). Aggregate simulation modeling with application to setting the CONWIP limit in an HMLV cell. International Journal of Industrial Engineering Computation, 10(2), 149-160. https://doi.org/10.5267/j.ijiec.2018.10.002
Kingman, J.F.C. (1961). The single server queue in heavy traffic. Mathematical Proceedings of the Cambridge Philosophical Society, 57(4), 902. https://doi.org/10.1017/S0305004100036094
Kleijnen, J.P.C. (2015). Design and analysis of simulation experiments. New York: Springer. https://doi.org/10.1007/978-3-319-18087-8
Kleijnen, J.P.C., Standridge, C.R. (1988). Experimental design and regression analysis in simulation: an FMS case study. European Journal of Operations Research, 33, 257-261. https://doi.org/10.1016/0377-2217(88)90168-3
Law, A.M. (2014). Simulation modeling and analysis, 5th ed. New York: McGraw-Hill.
Little, J.D.C. (1961). A proof for the queuing formula: L = λW. Operations Research, 9(3), 383-387. https://doi.org/10.1287/opre.9.3.383
Marvel, J.H., Standridge, C.R. (2009). A simulation enhanced lean design process. Journal of Industrial Engineering and Management, 2(1), 90-113. https://doi.org/10.3926/jiem.2009.v2n1.p90-113
Mourtzis, D. (2019) Simulation in the design and operation of manufacturing systems: state of the art and new trends. International Journal of Production Research, 58(7), 1927-1949. https://doi.org/10.1080/00207543.2019.1636321
Pinheiro, N.M.G, Cleto, M.G., Zattar, I.C., Muller, S.I.M.G. (2019). Performance evaluation of pulled, pushed and hybrid production through simulation: a case study. Brazilian Journal of Operations & Production Management, 16, 685-697. https://doi.org/10.14488/BJOPM.2019.v16.n4.a13
Pritsker, A.A.B. (1989). Why simulation works. In Proceedings of the 1989 Winter Simulation Conference, IEEE, USA. https://doi.org/10.1145/76738.76739
Puvanasvaran, P., Teoh, Y.S., Ito, K. (2020). Novel availability and performance ratio for internal transportation and manufacturing processes in job shop company. Journal of Industrial Engineering and Management, 13(1), 1-17. https://doi.org/10.3926/jiem.2755
Sanchez, S.M., Sanchez, P.J., Wan, H. (2020). Work smarter, not harder: a tutorial on designing and conducting simulation experiments. In Proceedings of the 2020 Winter Simulation Conference, IEEE, USA. Retrieved December 23, 2020 from https://informs-sim.org/ wsc20papers/135.pdf
Schruben, L. (1983). Simulation modeling with event graphs. Communications of the A.C.M., 26(11). https://doi.org/10.1145/182.358460
Spearman, M.L., Woodruff, D.L., Hopp, W.J. (1990). CONWIP: A pull alternative to Kanban, International Journal of Production Research, 28(5), 879-894. https://doi.org/10.1080/00207549008942761
Standridge, C.R. (2019). Introduction to production: philosophies, flow, and analysis. Allendale Michigan: Grand Valley State University Libraries. Retrieved July 22, 2020 from: https://scholarworks.gvsu.edu/books/22/
Tapping, D., Luyster, T., Shuker, T. (2002). Value stream management. Boca Raton: CRC Press. https://doi.org/10.4324/9781482278163
Tribastone, M., Vandin, A. (2018). Speeding up stochastic and deterministic simulation by aggregation: an advanced tutorial. . In Proceedings of the 2018 Winter Simulation Conference, IEEE, USA. https://doi.org/10.1109/WSC.2018.8632364
Uriarte, A.G., Ng, A.H.C., Moris, M.U. (2020). Bringing together Lean and simulation: a comprehensive review, International Journal of Production Research, 58(1), 87-117. https://doi.org/10.1080/00207543.2019.1643512
Zupan, H., Herakovic. N. (2015). Production line balancing with discrete event simulation: a case study", IFAC-PapersOnLine, 48(3), 2305- 2311. https://doi.org/10.1016/j.ifacol.2015.06.431
How to Cite
This work as of Vol. 11 Iss. 2 (2023) is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike- 4.0 International License