Testing a Flexible Manufacturing System Facility Production Capacity through Discrete Event Simulation: Automotive Case Study

Authors: Justyna Rybicka, Ashutosh Tiwari, Shane Enticott

Abstract:

In the age of automation and computation aiding manufacturing, it is clear that manufacturing systems have become more complex than ever before. Although technological advances provide the capability to gain more value with fewer resources, sometimes utilisation of the manufacturing capabilities available to organisations is difficult to achieve. Flexible manufacturing systems (FMS) provide a unique capability to manufacturing organisations where there is a need for product range diversification by providing line efficiency through production flexibility. This is very valuable in trend driven production set-ups or niche volume production requirements. Although FMS provides flexible and efficient facilities, its optimal set-up is key in achieving production performance. As many variables are interlinked due to the flexibility provided by the FMS, analytical calculations are not always sufficient to predict the FMS’ performance. Simulation modelling is capable of capturing the complexity and constraints associated with FMS. This paper demonstrates how discrete event simulation (DES) can address complexity in an FMS to optimise the production line performance. A case study of an automotive FMS is presented. The DES model demonstrates different configuration options depending on prioritising objectives: utilisation and throughput. Additionally, this paper provides insight into understanding the impact of system set-up constraints on the FMS performance and demonstrates the exploration into the optimal production set-up.

Keywords: Automotive, capacity performance, discrete event simulation, flexible manufacturing system.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1123707

References:

[1] Banks, J., Carson, J.S., Nelson, L.B., Nicol, D.M., (2005) Discrete-Event System Simulation, 4th Edition, Prentice Hall, New Jersey.
[2] Nagahban, A., Smith, J.S., (2014) Simulation for manufacturing system design and operation: Literature review and analysis, Journal of Manufacturing Systems, Vol. 33(2), pp. 241-261.
[3] Jahangirian, M., Eldabi, T., Naseer, A., Stergioulas, LK., Young, T., (2010) Simulation in manufacturing and business: A review, European Journal of Operational Research, Vol. 203 (2010), pp.1-13.
[4] Baykasoglu, A., Göçken, M., (2010) Capability-based distributed layout and its simulation based analyses, Journal of Intelligent Manufacturing, Vol. 21(4), pp. 471-485.
[5] Abd, K., Abhary, K., Marian, R., (2014) Simulation modelling and analysis of scheduling in robotic flexible assembly cells using Taguchi method, International Journal of Production Research, Vol. 52(9), pp. 2654-2666.
[6] Ali, M., Saifi, M., (2011) A Decision Support System for Flexibility Enabled Discrete Part Manufacturing System, Global Journal of Flexible Systems Management, Vol. 12 (3), pp. 1-8.
[7] Caprihan, R., Wadhwa, S., Kumar, S (2005) On the consequences of information delays in the scheduling of semi-automated flexible machines, International Journal of Flexible Manufacturing Systems, Vol. 16(3), pp. 251-275.
[8] Joseph, O., Sridharan, R., (2011b) Analysis of dynamic due-date assignment models in a flexible manufacturing system, Journal of Manufacturing Systems, Vol. 30 (1), pp.
[9] Browne, et.al (1984) Browne, J., Dubois, D., Rathmill, K., Sethi, A., P., Stecke, K., E., (1984) Classification of flexible manufacturing systems, The FMS Magazine, April 1984, pp. 114-117.
[10] Basnet, C., (2009) Selection of dispatching rules in simulation-based scheduling of flexible manufacturing, International Journal of Simulation: Systems, Science and Technology, Vol. 10 (7), pp.40-48.
[11] Chan, FTS., Chan, HK., (2005) Simulation modeling for comparative evaluation of supply chain management strategies, International Journal of Advanced Manufacturing Technology, Vol. 25 (9-10), pp.998-1006.
[12] Joseph, O., Sridharan, R., (2011a) Evaluation of routing flexibility of a flexible manufacturing system using simulation modelling and analysis, International Journal of Advanced Manufacturing Technology, Vol. 56(1-4), pp.274-289.
[13] Djassemi, M., (2007) A simulation analysis of factors influencing the flexibility of cellular manufacturing, International Journal of Production Research, Vol. 43(10), pp.2101-2111.
[14] Ali, M., Wadhwa, S., (2010) The effect of routing flexibility on a flexible system of integrated manufacturing, International Journal of Production Research, Vol.48 (19), pp. 5691-5709.
[15] Suresh Kumar, N., Sridharan, R., Kumar, N., D., (2009) Simulation modelling and analysis of part and tool flow control decisions in a flexible manufacturing system, Robotics & Computer-Integrated Manufacturing, Vol.25(4), pp.829-838.
[16] Renna, P., (2010) Capacity reconfiguration management in reconfigurable manufacturing systems, International Journal of Advanced Manufacturing Technology, Vol. 46(1-4), pp. 395-404.
[17] Sharma, D., Garg, S., K., Sharma, C., (2011) Impact of Delays on Performance of Semi-Automated Flexible Manufacturing Systems, Global Journal of Flexible Systems Management, Vol. 12(3), pp. 41-52.
[18] Robinson, S., Brooks, R., Kotiadis, K., van der Zee, D., (2011) Conceptual Modelling for DES, CRC Press Tylor& Francis Group, Boca Raton.