Abstract
This paper presents a model for assessing different capacity scalability policies in Reconfigurable Manufacturing System (RMS) for different changing demand scenarios. The novelty of this approach is two fold: (1) it is the first attempt to explore different capacity scalability policies in RMS based on multiple performance measures, mainly scaling rate, Work In Process level, inventory level and backlog level; and (2) the dynamic scalability process in RMS is modeled for the first time using System Dynamics. Different policies for capacity scalability for various demand scenarios were assessed. Numerical simulation results obtained using the developed capacity scalability model showed that the best capacity scalability policy to be adopted for RMS is dependent on the anticipated demand pattern as well as the various manufacturing objectives. The presented assessment results will help the capacity scalability planners better decide the different tradeoffs between the competing strategic and operational objectives of the manufacturing enterprise, before setting the suitable capacity scalability plan parameters.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Anderson E, Morrice D, Lundeen G (2005) The “physics” of capacity and backlog management in service and custom manufacturing supply chains. Syst Dyn Rev 22/3:217–247
Asl R, Ulsoy A (2002) Capacity management via feedback control in reconfigurable manufacturing systems, Proceeding of Japan-USA symposium on flexible manufacturing automation, Hiroshima, Japan
Baines T, Harrison D (1999) An opportunity for system dynamics in manufacturing system modeling. J Prod Plan Control 10/6:542–552
Deif A, ElMaraghy W (2006) A control approach to explore capacity scalability scheduling in reconfigurable manufacturing systems. J Manuf Systems 25/1:12–24
Deif A, ElMaraghy W (2007) Integrating static and dynamic analysis in studying capacity scalability in RMS. Int J Manuf Res 2/4:414–427
Duffie N, Falu I (2002) Control-theoretic analysis of a closed loop PPC system. Ann CIRP 52/1:379–382
ElMaraghy H (2005) Flexible and reconfigurable manufacturing systems paradigms. Int J Flexible Manuf Syst. Special Issue Reconfig Manuf Syst 17/4:261–276
Evans G, Naim M (1994) The dynamics of capacity constrained supply chains. Proceedings of international system dynamics conference, Stirling, pp 28–35
Forrester JW (1961) Industrial dynamics. MIT Press, Cambridge, MA
Fowler A (1999) Feedback and feedforward as systematic framework for operation control. Int J Oper Prod Manage 19/2:182–204
Goncalves P, Hines J, Sterman J (2005) The impact of endogenous demand on push-pull production systems. Syst Dyn Rev 22/3:217–247
Helo P (2000) Dynamic modeling of surge effect and capacity limitation in supply chains. Int J Prod Res 38/17:4521–4533
Holt CC, Modigliani F, Muth JF, Simon HA (1960) Planning production, inventories, and work force. Prentice-Hall, Englewood Cliffs, NJ
John S, Towill DR, Naim M (1994) Dynamic analysis of a WIP compensated support system. Int J Manuf Syst Des 1:283–297
Kim J-H, Duffie N (2004) Backlog control design for a closed loop PPC system. Ann CIRP 54/1:456–459
Kim J-H, Duffie N (2005) Design and analysis for a closed loop capacity control of a multi workstation production system. Ann CIRP 55/1:470–474
Koren Y, Heisel U, Jovane F, Moriwaki T (1999) Reconfig Manuf Syst, Ann CIRP Vol. 48/2/1999
Luss H (1982) Operation research and capacity expansion problems: a survey. Oper Res 3/5:907–947
Manne Alan S (1967) Investments for capacity expansion, size, location, and time-phasing. The MIT Press, Cambridge, MA
Mehrabi M, Ulsoy G, Koren Y (2000) Reconfigurable manufacturing systems: Key to future manufacturing. J Int Manuf 11:403–419
Nyhuis P (1994) Logistic operating curves—A comprehensive method for rating logistic potentials, EURO XIII/OR36, University of Strathclyde, Glasgow
Sethi AK, Sethi PS (1990) Flexibility in manufacturing: a survey. Int J Flexible Manuf Syst 2:289–328
Sethi SP, Thompson GL (2000) Optimal control theory: applications to management science and economics. Kluwer, Boston, MA
Sterman JD (2000) Business dynamic—systems thinking and modeling for a complex world. McGraw-Hill, New York
Towill DR (1993) System dynamics—background, methodology, and applications—part 1: background and methodology. Comput Control Eng J 4/5:201–208
Towill DR, Del Vecchio A (1994) The application of filter theory to the study of supply chain dynamics. Prod Plan Control 5:82–96
Wiendahl HP, Breithaupt J (1999) Modeling and controlling of dynamics of production system. J Prod Plan Control 10/4:389–401
Wiendahl HP, Breithaupt J (2000) Automatic production control applying control theory. Int J Prod Econ 63:33–46
Wiendahl H-P, ElMaraghy HA, Nyhuis P, Zaeh M, Wiendahl H-H, Duffie N, Kolakowski M (2007) Changeable manufacturing: classification, design, operation, Keynote Paper, CIRP Annals, 56/2
Wikner J, Towill DR, Naim M (1991) Smoothing supply chain dynamics. Int J Prod Econ 22:231–248
Acknowledgments
The contributions from the Canada Research Chairs (CRC) program and the Natural Sciences and Engineering Research Council (NSERC) of Canada, in support of this research, are greatly acknowledged.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Deif, A.M., ElMaraghy, H.A. Assessing capacity scalability policies in RMS using system dynamics. Int J Flex Manuf Syst 19, 128–150 (2007). https://doi.org/10.1007/s10696-008-9031-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10696-008-9031-2