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K. Agyapong-Kodua; R. H. Weston; S. Ratchev (2012)
Publisher: Hindawi Limited
Journal: Advances in Decision Sciences
Languages: English
Types: Article
Subjects: Mathematics, HB, HD, Article Subject, QA1-939
Enterprise modelling techniques support business process re-engineering by capturing existing processes and based on perceived outputs, support the design of future process models capable of meeting enterprise requirements. System dynamics modelling tools on the other hand are used extensively for policy analysis and modelling aspects of dynamics which impact on businesses. In this paper, the use of enterprise and system dynamics modelling techniques has been integrated to facilitate qualitative and quantitative reasoning about the structures and behaviours of processes and resource systems used by a Manufacturing Enterprise during the production of composite\ud bearings. The case study testing reported has led to the specification of a new modelling methodology for analysing and managing dynamics and complexities in production systems. This methodology is based on a systematic transformation process, which synergises the use\ud of a selection of public domain enterprise modelling, causal loop and continuous simulationmodelling techniques. The success of the modelling process defined relies on the creation of useful CIMOSA process models which are then converted to causal loops. The causal loop models are\ud then structured and translated to equivalent dynamic simulation models using the proprietary continuous simulation modelling tool iThink.
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    • 1 R. Askin, Modelling and Analysis of Manufacturing Systems, John Wiley & Sons, 1993.
    • 2 P. Bernus and L. Nemes, Enterprise Integration-Engineering Tools for Designing Enterprises, Chapman & Hall, Sydney, Australia, 1996.
    • 3 F. B. Vernadat, Enterprise Modelling and Integration; Principles and Applications, Chapman & Hall, London, UK, 1996.
    • 4 R. Weston, “Model-driven, component-based approach to reconfiguring manufacturing software systems,” International Journal of Operations and Production Management, vol. 19, no. 8, pp. 834-855, 1999.
    • 5 J. Sterman, Business Dynamics: Systems Thinking and Modeling for a Complex World, McGraw-Hill, 2000.
    • 6 K. Agyapong-Kodua, Multi-product cost and value stream modelling in support of business process analysis [Ph.D. thesis], Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, Loughborough, UK, 2009.
    • 7 K. Agyapong-Kodua and R. H. Weston, “Systems approach to modelling cost and value dynamics in manufacturing enterprises,” International Journal of Production Research, vol. 49, no. 8, pp. 2143-2167, 2011.
    • 8 J. D. Goldhar and M. Jelinek, Plan for Economies of Scope, Harvard Business Review, 1983.
    • 9 B. Scholz-Reiter, M. Freitag, and A. Schmieder, “Modelling and control of production systems based on nonlinear dynamics theory,” CIRP Annals, vol. 51, no. 1, pp. 375-378, 2002.
    • 10 A. Rahimifard and R. Weston, “The enhanced use of enterprise and simulation modellingtechniques to support factory changeability,” International Journal of Computer Integrated Manufacturing, vol. 20, no. 4, pp. 307-328, 2007.
    • 11 C. Batur, A. Srinivasan, and C. C. Chan, “Automated rule based model generation for uncertain complex dynamic systems,” in 1991 IEEE International Symposium on Intelligent Control, pp. 275-279, August 1991.
    • 12 L. Wang, “Analysis and design of fuzzy systems,” USC-SIPI Report 206, 1992.
    • 13 J. Yester and J. Sun, “Design and automatic tuning of fuzzy logic control for an active suspension system,” in Proceedings of the 12th IFAC World Conference, 1993.
    • 14 P. Srinoi, E. Shayan, and F. Ghotb, “A fuzzy logic modelling of dynamic scheduling in FMS,” International Journal of Production Research, vol. 44, no. 11, pp. 2183-2203, 2006.
    • 15 S. Vinodh and S. R. Balaji, “Fuzzy logic based leanness assessment and its decision support system,” International Journal of Production Research, vol. 49, no. 13, pp. 4027-4041, 2011.
    • 16 M. Minsky and S. Papert, An Introduction to Computational Geometry, MIT Press, 1969.
    • 17 E. Gardner and B. Derrida, “Optimal storage properties of neural network models,” Journal of Physics A, vol. 21, no. 1, pp. 271-284, 1988.
    • 18 J. T Spooner and M. Maggiore, Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximator Techniques, John Wiley & Sons, New York, NY, USA, 2002.
    • 19 A. K. Gupta, “Predictive modelling of turning operations using response surface methodology, artificial neural networks and support vector regression,” International Journal of Production Research, vol. 48, no. 3, pp. 763-778, 2010.
    • 20 H. Zhu, F. Liu, X. Shao, and G. Zhang, “Integration of rough set and neural network ensemble to predict the configuration performance of a modular product family,” International Journal of Production Research, vol. 48, no. 24, pp. 7371-7393, 2010.
    • 21 J. Pearl, “Bayesian networks: a model of self-activated memory for evidential reasoning,” in Proceedings of the 7th Conference of the Cognitive Science Society, University of California, Irvine, Calif, USA, 1985.
    • 22 R. P. Cherian, P. S. Midha, and A. G. Pipe, “Modelling the relationship between process parameters and mechanical properties using Bayesian neural networks for powder metal parts,” International Journal of Production Research, vol. 38, no. 10, pp. 2201-2214, 2000.
    • 23 J. L. Peterson, Petri Net Theory and the Modeling of Systems, Prentice-Hall, Englewood Cliffs, NJ, USA, 1981.
    • 24 M. C. Zhou and K. Venkatesh, Modeling, Simulation and Control of Flexible Manufacturing Systems- A Petri Net Approach, World Scientific, Singapore, 1999.
    • 25 A. Gunasekaran and Z. Irani, “Editorial: modelling and analysis of outsourcing decisions in global supply chains,” International Journal of Production Research, vol. 48, no. 2, pp. 301-304, 2010.
    • 26 J. W. Forrester, Industrial Dynamics, MIT Press, Cambridge, Mass, USA, 1961.
    • 27 J. R. Burns and O. Ulgen, “A component strategy for the formulation of system dynamics models,” in Proceedings of the 20th International Conference of the System Dynamics Society, Palermo, Italy, 2002.
    • 28 L. Rabelo, M. Helal, C. Lertpattarapong, R. Moraga, and A. Sarmiento, “Using system dynamics, neural nets, and eigenvalues to analyse supply chain behaviour. A case study,” International Journal of Production Research, vol. 46, no. 1, pp. 51-71, 2008.
    • 29 K. Agyapong-Kodua, J. O. Ajaefobi, and R. H. Weston, “Modelling dynamic value streams in support of process design and evaluation,” International Journal of Computer Integrated Manufacturing, vol. 22, no. 5, pp. 411-427, 2009.
    • 30 V. Shukla, M. M. Naim, and E. A. Yaseen, “'Bullwhip' and “backlash” in supply pipelines,” International Journal of Production Research, vol. 47, no. 23, pp. 6477-6497, 2009.
    • 32 T. Binder, A. Vox, S. Belyazid, H. Haraldsson, and M. Svensson, “Developing system dynamics models from causal loop diagrams,” in Proceedings of the 22nd International Conference of the System Dynamic Society, Oxford, UK, 2004.
    • 33 J. Homer and R. Oliva, “Maps and models in system dynamics: a response to Coyle,” System Dynamics Review, vol. 17, no. 4, pp. 347-355, 2001.
    • 34 E. F. Wolstenholme, “Qualitative vs quantitative modelling: the evolving balance,” Journal of the Operational Research Society, vol. 50, no. 4, pp. 422-428, 1999.
    • 35 J. R. Burns, “Simplified translation of CLDs into SFDs,” in Proceedings of the International Conference of the System Dynamics Society, Atlanta, Ga, USA, 2001.
    • 36 J. Pearl, Causality: Models, Reasoning, and Inference, Cambridge University Press, Cambridge, Nass, USA, 2000.
    • 37 G. P. Richardson, “Reflections for the future of system dynamics,” Journal of the Operational Research Society, vol. 50, no. 4, pp. 440-449, 1999.
    • 38 R. H. Weston, A. Rahimifard, J. O. Ajaefobi, and Z. Cui, “On modelling reusable components of change-capable manufacturing systems,” Proceedings of the Institution of Mechanical Engineers, Part B, vol. 223, no. 3, pp. 313-336, 2009.
    • 39 AMICE, CIMOSA: Open System Architecture for CIM, 2nd Extended and Revised Version, Springer, Berlin, Germany, 1993.
    • 40 K. Kosanke, “Process oriented presentation of modelling methodologies,” in Proceedings of the IFIP TC5 Working conference on models and methodologies for Enterprise Integration, pp. 45-55, 1996.
    • 41 R. P. Monfared, A component based approach to design and construction of change capable manufacturing cell control systems [Ph.D. thesis], Loughborough University, UK, Loughborough, UK, 2000.
    • 42 J. O. Ajaefobi, Human systems modelling in support of enhanced process realisation [Ph.D. thesis], Loughborough University, UK, Loughborough, UK, 2004.
    • 43 K. Agyapong-Kodua, B. Wahid, and R. Weston, “Process cost modelling in Manufacturing Enterprises,” in Proceedings of the 4th International Conference on Digital Enterprise Technology, Bath, UK, 2007.
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