Advertisement

Dynamic Capacity Planning and Modeling Its Complexity

  • A. Deif
  • Hoda A. ElMaraghy
Chapter
Part of the Springer Series in Advanced Manufacturing book series (SSAM)

Abstract

Uncertainty associated with managing the dynamic capacity in changeable manufacturing is the main source of its complexity. A system dynamics approach to model and analyze the operational complexity of dynamic capacity in multi-stage production is presented. The unique feature of this approach is that it captures the stochastic nature of three main sources of complexity associated with dynamic capacity. The model was demonstrated using an industrial case study of a multi-stage engine block production line. The analysis of simulation experiments results showed that ignoring complexity sources can lead to wrong decisions concerning both capacity scaling levels and backlogmanagement scenarios. In addition, a general trade-off between controllability and complexity of the dynamic capacity was illustrated. A comparative analysis of the impact of each of these sources on the complexity level revealed that internal delays have the highest impact. Guidelines and recommendations for better capacity management and reduction of its complexity, in changeable manufacturing environment, are presented.

Keywords

Supply Chain Operational Complexity Capacity Planning Bullwhip Effect Dynamic Capacity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anderson, E., Morrice, D. and Lundeen, G. (2005), The “physics” of capacity and backlog management in service and custom manufacturing supply chains, System Dynamics Review, 22/3: 217-247CrossRefGoogle Scholar
  2. Asl R. and Ulsoy A., 2002, Capacity management via feedback control in reconfigurable manufacturing systems, Proceeding of Japan-USA symposium on Flexible Manufacturing Automation, Hiroshima, JapanGoogle Scholar
  3. Deif A., ElMaraghy H.A., 2007, Assessing capacity scalability policies in RMS using system dynamics, International Journal of Flexible Manufacturing Systems (IJFMS) Special Issue on Capacity Planning in Flexible and Dynamic Manufacturing 19/3:128--150 doi: 10.1007/s10696-008-9031-2Google Scholar
  4. Deif, A. and ElMaraghy, W. (2006), A control approach to explore capacity scalability scheduling in reconfigurable manufacturing systems, Journal of Manufacturing Systems. 25/1: 12-24CrossRefGoogle Scholar
  5. Deif A.M., ElMaraghy W.H., 2007, Integrating static and dynamic analysis in studying capacity scalability in RMS, International Journal of Manufacturing Research (IJMR) 2/4:414--427Google Scholar
  6. Deshmukh A., Talavage J. and Barash M., 1998, Complexity in manufacturing systems, part 1: Analysis of static complexity, IIE Transactions, 30:645--655Google Scholar
  7. Duffie, N. and Falu, I., (2002), Control-theoretic analysis of a closed loop PPC system. Annals of CIRP, 52/1: 379-382CrossRefGoogle Scholar
  8. ElMaraghy H.A., 2006, A complexity code for manufacturing systems, 2006 ASME Int. Conference on Manufacturing Science & Engineering (MSEC), Symposium on Advances in Process & System Planning, Ypsilani, MI, USAGoogle Scholar
  9. ElMaraghy H.A., Kuzgunkaya O. and Urbanic J., 2005, Comparison of manufacturing system configurations~--~A complexity approach, 55th CIRP Annals, 54/1:445--450Google Scholar
  10. Evans G. and Naim M., 1994, The dynamics of capacity constrained supply chains. Proceedings of International System Dynamics Conference, Stirling: 28--35Google Scholar
  11. Frank, C., Drezner, Z., Ryan, JK., and Simchi-Levi, D., (2000), Quantifying the bullwhip effect: the impact of forecasting, lead-time and information, Management Science, Vol. 46(3): 436-443CrossRefGoogle Scholar
  12. Frizelle, G. (1998) The Management of Complexity in Manufacturing, Business Intelligence, LondonGoogle Scholar
  13. Goncalves, P., Hines, J. and Sterman, J. (2005), The impact of endogenous demand on push-pull production systems, System Dynamics Review, 22/3: 217-247Google Scholar
  14. Helo P., 2000, Dynamic modeling of surge effect and capacity limitation in supply chains. International Journal of Production Research, 38/17:4521--4533CrossRefGoogle Scholar
  15. Holt CC, Modigliani F, Muth JF, Simon H. A., (1960), Planning production, inventories, and work force, Prentice-Hall: Englewood Cliffs, NJGoogle Scholar
  16. Hopp and Spearman, 2002, Factory Physics, McGraw HillGoogle Scholar
  17. Hoyt J. 1980, Determining Lead Time for Manufactured Parts in a Job Shop, Computers in Manufacturing, (ed) J.J. Pennsanken. pp 1--12Google Scholar
  18. Huh WT, Roundy RO, and Cakanyildirim M. (2006), A general strategic capacity planning model under demand uncertainty, Naval Research Logistics, Vol. 2: 137-150CrossRefMathSciNetGoogle Scholar
  19. John, S., Naim, M., and Towill, DR., (1994), Dynamic analysis of a WIP compensated support system, Intl. Journal of Manufacturing System Design, Vol.1/4: 283-297Google Scholar
  20. Kim, J-H., and Duffie, N., (2004), Backlog control design for a closed loop PPC system. Annals of CIRP, 54/1: 456-459.Google Scholar
  21. Kim, J-H., and Duffie, N., (2005), Design and analysis for a closed loop capacity control of a multi workstation production system. Annals of CIRP, 55/1: 470-474Google Scholar
  22. Luss, H. (1982), Operation research and capacity expansion problems: A survey. Operation Research, 3/5: 907-947CrossRefMathSciNetGoogle Scholar
  23. Manne Alan S., (1967), Investments for capacity expansion, size, location, and time-phasing. The MIT Press, Cambridge, MAGoogle Scholar
  24. van Mieghem, J., (2003), Capacity management, investment and hedging: Review and recent developments. Manufacturing and Service Operation Management, Vol.5(4): 269-302CrossRefGoogle Scholar
  25. Nyhuis, P., (1994), Logistic operating curves – A comprehensive method for rating logistic potentials, EURO XIII/OR36, University of Strathclyde GlasgowGoogle Scholar
  26. Schmitz, J.P.M, van Beek, D. And Rooda, J., (2002), “Chaos in Discrete Production Systems”, Journal of Manufacturing Systems, Vo. 21, pp. 23-35CrossRefGoogle Scholar
  27. Sethi SP, Thompson GL. (2000), Optimal control theory: Applications to management science and economics. Kluwer: Boston, MAzbMATHGoogle Scholar
  28. Sterman, J.D. (2000), Business Dynamic - Systems Thinking and Modeling for a Complex World. McGraw-HillGoogle Scholar
  29. Suh, N. P. (2005) Complexity in Engineering. CIRP Annals, 54, 581-598MathSciNetCrossRefGoogle Scholar
  30. Wiendahl, H.P., and Breithaupt, J., (1999); Modeling and controlling of dynamics of production system. Journal of Production Planning and Control, 10/4: 389-401CrossRefGoogle Scholar
  31. Wiendahl, H.P., and Breithaupt, J., (2000); Automatic production control applying control theory. International Journal of Production Economics, 63: 33-46CrossRefGoogle Scholar
  32. Wiendahl H.-P., ElMaraghy H.A., Nyhuis P., Zaeh M., Wiendahl H.-H., Duffie N. and Kolakowski M., 2007, Changeable manufacturing: classification, design, operation, Keynote Paper, CIRP Annals, 56/2Google Scholar
  33. Wikner, J., Naim, M., and Rudberg, M., (2007), Exploiting the order book for mass customized manufacturing control systems with capacity constrains, IEEE Transaction on Engineering Management, Vol. 54(1): 145-155CrossRefGoogle Scholar
  34. Vlachos, D., Georgiadis, P., and Iakovou, E., (2007), A system dynamics model for dynamic capacity planning of remanufacturing in closed-loop supply chains, Journal of Computers and Operation Research, Vol. 34: 367-394zbMATHCrossRefGoogle Scholar

Copyright information

© Springer London 2009

Authors and Affiliations

  • A. Deif
    • 1
  • Hoda A. ElMaraghy
    • 2
  1. 1.Industrial Systems EngineeringUniversity of ReginaReginaCanada
  2. 2.Department of Industrial & Manufacturing Systems EngineeringUniversity of WindsorWindsorCanada

Personalised recommendations