Abstract
The evolution of manufacturing systems, according to changing internal and external conditions, requires design and assessment techniques that consider both strategic and financial criteria to evaluate the suitability of the Flexible and Reconfigurable system solutions in addressing these variations. In this paper, a fuzzy multi-objective mixed integer optimization model to evaluate RMS investments used in a multiple product demand environment is presented. The model incorporates in-house production and outsourcing options, machine acquisition and disposal costs, operational costs, and re-configuration cost and duration for the utilized modular machines. The resulting system configurations are optimized for lifecycle costs, responsiveness performance, and system structural complexity simultaneously. A complexity metric that incorporates the quantity of information using an entropy approach is used to represent the inherent structural complexity of the considered system configurations. It accounts for the complexity of the machine modules in a manufacturing system through the use of an index derived from a newly developed manufacturing systems classification code, which captures the effect machine types and technologies on the system’s structural complexity. A metric is proposed to measure the responsiveness ability and efficiency as well as the overall capability of each machine and effectiveness of machines changeover. The application of the developed planning and assessment model that incorporates these three criteria is illustrated with a case study where FMS and RMS alternatives were compared. The suitable conditions for investing in RMS are also discussed.
Similar content being viewed by others
References
Abdi MR, Labib AW (2004) Feasibility study of the tactical design justification for reconfigurable manufacturing systems using the fuzzy analytical hierarchical process. Int J Prod Res 42:3055–3076
Abdel-Malek L, Wolf C (1994) Measuring the impact of lifecycle costs, technological obsolescence, and flexibility in the selection of FMS design. J Manuf Syst 13(1):37–47
Amico M, Asl F, Pasek Z, Perrone G (2003) Real Options: an application to RMS Investment Evaluation. CIRP 2nd Conference on RMS, Ann Arbor, MI, USA
Bellman RE, Zadeh LA (1970) Decision-making in a fuzzy environment. Manage Sci 17:141–164
Bokhorst J, Slomp J, Suresh N (2002) An integrated model for part-operation allocation and investments in CNC technology. Int J Prod Econ 75(3):267–285
ElMaraghy HA (2006) A complexity code for manufacturing systems. In: Proceedings of 2006 ASME International Conference on Manufacturing Science and Engineering (MSEC), pp. MSEC2006-21075
ElMaraghy HA (2005) Flexible and reconfigurable manufacturing systems paradigms. Int J Flex Manuf Syst 17(4):261–276
ElMaraghy HA, Kuzgunkaya O, Urbanic RJ (2005) Manufacturing systems configuration complexity. CIRP Ann-Manuf Technol 54(1):445–450
Gindy NN, Saad SM (1998) Flexibility and responsiveness of machining environments. Integr Manuf Syst 9(4):218–227
Koren Y, Heisel U, Jovane F, Moriwaki T, Pritschow G, Ulsoy G, Van Brussel H (1999) Reconfigurable manufacturing systems. Ann CIRP 48(2):527–540
Kuzgunkaya O, ElMaraghy HA (2006) Assessing the structural complexity of manufacturing systems configurations. Int J Flex Manuf Syst 18(2):145–171
Lotfi V (1995) Implementing flexible automation: a multiple criteria decision making approach. Int J Prod Econ 38:255–268
Matson JB, McFarlane D (1999) Assessing the responsiveness of existing production operations. Int J Oper Prod Manage 19(8):765–784
Rajagopalan S, Singh MR, Morton TE (1998) Capacity expansion and replacement in growing markets with uncertain technological breakthroughs. Manage Sci 44(1):12–30
Spicer JP (2002) A design methodology for scalable machining systems. PhD dissertation, University of Michigan
Suresh NC (1992) A Generalized multimachine replacement model for flexible automation investments. IIE Trans 24(2):131–143
Van Hop N (2004) Approach to measure the mix response flexibility of manufacturing systems. Int J Prod Res 42(7):1407–1418
Visionary Manufacturing Challenges For 2020 (1998) National Academy Press Washington, DC
Wiendahl HP, ElMaraghy HA, Nyhuis P, Zäh MF, Wiendahl H-H, Duffie N, Kolakowski M (2007) Changeable manufacturing—classification, design and operation. CIRP Ann-Manuf Technol 56(2):783–809
Wiendahl HP, Heger CL (2003)Justifying Changeability. A Methodical approach to Achieving Cost Effectiveness. CIRP 2nd Conf on RMS, Ann Arbor, MI, USA
Yan P, Zhou M, Caudill R (2000) A lifecycle engineering approach to FMS development. In: Proceedings of the 2000 IEEE Int. Conf. on Robotics and Automation. San Francisco, CA, pp 395–400
Zhang G, Glardon R (2001) An Analytical Comparison on Cost and Performance among DMS, AMS, FMS, and RMS. 1st Conference on Agile Reconfigurable Manufacturing 21–22 May 2001 Ann Arbor, Michigan, USA
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kuzgunkaya, O., ElMaraghy, H.A. Economic and strategic perspectives on investing in RMS and FMS. Int J Flex Manuf Syst 19, 217–246 (2007). https://doi.org/10.1007/s10696-008-9038-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10696-008-9038-8