A stochastic analysis of static complexity

A stochastic analysis of static complexity

This paper presents static complexity in manufacturing systems. Static complexity can be viewed as a function of the structure of the system, connective patterns, variety of components and the strengths of interactions. We seek the acceptable static complexity levels for different types of manufacturing systems by means of their system performances.

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H. Kemal Ilter

June 25, 2014
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  1. A STOCHASTIC ANALYSIS OF STATIC COMPLEXITY in Manufacturing Systems Akif

    A. Bulgak, Ph.D., P.Eng. Department of Mechanical and Industrial Engineering, ENCS, Concordia University H. Kemal İlter, Ph.D. Department of Management Information Systems, SB, Yildirim Beyazit University YAEM June 2014
  2. CONTENTS Introduction The Problem Theoretical Systems vs Real Systems Complexity

    in Operations Management Brief Literature Types of Complexity in OM Measuring Complexity Critical Factors Entropical Measurement Simulation and Results Simulation Results References Discussion Questions
  3. INTRODUCTION The Problem This paper presents static complexity in manufacturing

    systems. Static complexity can be viewed as a function of the structure of the system, connective patterns, variety of components and the strengths of interactions. We seek the acceptable static complexity levels for different types of manufacturing systems by means of their system performances. In general, deterministic processing times or expected values of processing times are considered to measure the complexity and related system performance in the production and operations management field. We consider stochastic processing requirements with a number of different processing distributions and levels of variability on the static complexity measurements. The objective of this paper is to reveal characteristics of the static complexity by making observations about the behavior of different systems in a hypothetical manufacturing environment. This would help in analyzing the properties in different manufacturing systems due to static complexity and system performance.
  4. INTRODUCTION The Problem Mass Customization Product Variation Complex Manufacturing Systems

    Less initial investment Relatively low demand irregularities
  5. INTRODUCTION Theoretical Systems Non-modular Assembly Modular Assembly

  6. INTRODUCTION Real Systems BMW 7 Series 1017 Complex

  7. INTRODUCTION Real Systems Airbus A380 1021 Over-Complex

  8. COMPLEXITY IN OPERATIONS MANAGEMENT Brief Literature Supply chain complexity (Frizelle

    and Woodcock, 1995) Product variation complexity (MacDuffie et al., 1996) Entropic complexity (Deshmukh et al., 1998) Complexity interactions of different processes (Fujimoto et al., 2003) Complexity measurement (ElMaraghya et al., 2005) Supply Chain Complexity Manufacturing System Complexity Product Variation Complexity
  9. COMPLEXITY IN OPERATIONS MANAGEMENT Types of Complexity in OM The

    complexity of a physical system can be characterized in terms of its static structure or time dependent behavior. Static complexity can be viewed as a function of the structure of the system, connective patterns, variety of components, and the strengths of interactions. Dynamic complexity is concerned with unpredictability in the behavior of the system over a time period. The manufacturing environment consists of physical systems in which a series of sequential decisions need to be made in order to produce finished parts. The sequence and nature of these decisions are not only dependent on the system capabilities but also on the products being manufactured in the system. Any measure of system complexity should be dependent on both the system and the product information. Static Complexity (Structural) Dynamic Complexity (Operational) Associated with the variety embedded in the static system Associated with the uncertainty of the dynamic system
  10. MEASURING COMPLEXITY Critical Factors •  Number of suppliers •  Variety

    of interchangable parts •  Number of work stations •  Structure of the assembly line •  Structure of the manufacturing system
  11. MEASURING COMPLEXITY Entropical Measurement Supplier A Supplier B Station 1

    Station 2 A1 , A2 , A3 B1 , B2 A1 B1 A1 B2 A2 B1 A2 B2 A3 B1 A3 B2 Assembly Line
  12. MEASURING COMPLEXITY Entropic Measurement Station 1 j-1 H0 j j+1

    Station n H1 Hj-1 Hj Hj+1 Hn C0,j C1,j Cj-1,j Cj,j+1 Cj,n
  13. SIMULATION AND RESULTS Simulation

  14. SIMULATION AND RESULTS Simulation Modular Non Modular

  15. SIMULATION AND RESULTS Results

  16. SIMULATION AND RESULTS Results Modular Non Modular

  17. REFERENCES Bernstein, F. and DeCroix, G. A., 2004, “Decentralized pricing

    and capacity decisions in a multi-tier system with modular assembly,” Management Science, 50(9), pp. 1293–1308. Calinescu, A et al. "Complexity in manufacturing: an information theoretic approach." Proceedings of the International Conference on Complex Systems and Complexity in Manufacturing 19 Sep. 2000. Deshmukh, A. V., Talavage, J. J., and Barash, M. M., 1998, “Complexity in manufacturing systems, part 1: Analysis of static complexity,” IIE Transactions, 30(7), pp. 645–655. ElMaraghy, HA, O Kuzgunkaya, and RJ Urbanic. "Manufacturing systems configuration complexity." CIRP Annals-Manufacturing Technology 54.1 (2005): 445-450. Fisher, M. L. and Ittner, C. D., 1999, “The impact of product variety on automobile assembly operations: Empirical evidence and simulation,” Management Science, 45(6), pp. 771–786. Fredriksson, P., 2006, “Mechanisms and rationales for the coordination of a modular assembly system: the case of Volvo cars,” International Journal of Operations & Production Management, 26(4), pp. 350–370. Frizelle, G., 2004, “Complexity in the supply chain,” IEEE International Engineering Management Conference, vol. 3, pp. 1181–1184. Kuzgunkaya, Onur, and Hoda A ElMaraghy. "Assessing the structural complexity of manufacturing systems configurations." International Journal of Flexible Manufacturing Systems 18.2 (2006): 145-171. MacDuffie, J. P., Sethuraman, K., and Fisher, M. L., 1996, “Product variety and manufacturing performance: Evidence from the international automotive assembly plant study,” Management Science, 42(3), pp. 350–369. Randall, T. and Ulrich, K., 2001, “Product variety, supply chain structure, and firm performance: analysis of the U. S. bicycle industry,” Management Science, 47(12), pp. 1588–1604. Rosling, K., 1989, “Optimal inventory policies for assembly systems under random demand,” Operations Research, 37(4), pp. 565–579. Salvador, F., Rungtusanatham, M., and Forza, C., 2004, “Supply-chain configurations for mass customization,” Production Planning and Control, 15(4), pp. 381–397. Schmidt, C. P. and Nahmias, S., 1985, “Optimal policy for a two-stage assembly system under random demand,” Operations Research, 33(5), pp. 1130–1145. Sivadasan, S., Efstathiou, J., Frizelle, G., and Shirazi, R., 2002, “An information-theoretic methodology for measuring the operational complexity of supplier-customer systems,” International Journal of Operations & Production Management, 22(1), pp. 80–102. Wang, H., Aydin, G., and Hu, S., 2009, “A complexity model for assembly supply chains in the presence of product variety and its relationship to cost,” Under Review. Wang, H., Zhu, X., Hu, S. J., and Koren, Y., 2008, “Complexity analysis of assembly supply chain configurations,” Proceedings of the 9th Biennial ASME Conference on Engineering Systems Design and Analysis, Haifa, Israel. Webbink, R. F. and Hu, S. J., 2005, “Automated generation of assembly system- design solutions,” IEEE Transactions on Automation Science and Engineering, 2(1), pp. 32–39. Whang, Y. and Gerchak., Y., 2003, “Capacity games in assembly systems with uncertain demand,” Manufacturing & Service Operations Management, 5(3), pp. 252–267. Wiendahl, H-P, and Peter Scholtissek. "Management and control of complexity in manufacturing." CIRP Annals-Manufacturing Technology 43.2 (1994): 533-540. Zhu, X., Hu, S. J., Koren, Y., and Marin, S. P., 2008, “Modeling of manufacturing complexity in mixed-model assembly lines,” Journal of Manufacturing Science and Engineering, 130(5).
  18. DISCUSSION QUESTIONS

  19. A STOCHASTIC ANALYSIS OF STATIC COMPLEXITY in Manufacturing Systems THANKS

    Akif A. Bulgak, Ph.D., P.Eng. bulgak@encs.concordia.ca Department of Mechanical and Industrial Engineering, ENCS, Concordia University H. Kemal İlter, Ph.D. kilter@ybu.edu.tr Department of Management Information Systems, SB, Yildirim Beyazit University YAEM June 2014