Skip to main content
SpringerLink
Log in

A model for measuring complexity of automated and hybrid assembly systems

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

The demand for delivering product variety has been increasing. Increased product variety caused by product customization, personalization, evolution and changes in their manufacturing systems. Variety allows manufacturers to satisfy a wide range of customer requirements, but it can also be a major contributing factor to increased complexity of assembly. Complexity is generally believed to be one of the main causes of the present challenges in manufacturing systems such as lengthy and costly design processes, higher life cycle costs and the existence of numerous failure modes. Complex assembly systems are costly to implement, run, control and maintain. Assessing complexity of assembly helps guides designers in creating assembly-oriented product designs and following steps to reduce and manage sources of assembly complexity. On the other hand, reducing complexity of assembly helps lower assembly cost and time, improves productivity and quality and increases profitability and competitiveness. The complexity of assembly should be assessed by considering both products and their assembly systems. In this paper, a structural classification coding scheme has been used to measure assembly systems complexity. It considers the inherent structural complexity of typical assembly equipment. The derived assembly systems complexity accounts for the number, diversity and information content within each class of the assembly system modules. A domestic appliance drive assembly system is used to demonstrate the use of the classification code to calculate the assembly system complexity. The developed complexity metrics can be used by designers as decision support tools to compare and rationalize various automated assembly systems alternatives and select the design that meets the requirements while reducing potential assembly complexity and associated cost.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Wang H, Ko J, Zhu X, Hu SJ (2010) A complexity model for assembly supply chains and its application to configuration design. J Manuf Sci Eng 132(2):021005–021012. doi:10.1115/1.4001082

    Article  Google Scholar 

  2. Yunqing R, Kunpeng W, Mengchang W (2010) Modeling impact of product variety on performance in mixed-model assembly system: an artificial neural network meta-modeling approach. Paper presented at the IEEE international conference on industrial engineering & engineering management, Piscataway, NJ, USA, 7–10 Dec

  3. Su Q, Liu L, Whitney DE (2010) A systematic study of the prediction model for operator-induced assembly defects based on assembly complexity factors. IEEE Trans Syst Man Cybern Syst Hum 40:107–120

    Article  Google Scholar 

  4. Nof SY, Wilhelm WE, Warnecke HJ (1997) Industrial assembly. Chapman & Hall, London

    Book  Google Scholar 

  5. Whitney DE (2004) Mechanical assemblies: their design, manufacturing, and role in product development. Oxford University Press, New York

    Google Scholar 

  6. Lee T-S (2003) Complexity theory in axiomatic design. Ph.D., Massachusetts Institute of Technology

  7. Rodriguez-Toro C, Tate S, Jared G, Swift K (2002) Shaping the complexity of a design. In: IMECE DFM symposium on design for manufacturability, New Orleans, LA, United States, 17–22 November. ASME, pp 641–649

  8. Wang J, Chang Q, Xiao G, Wang N, Li S (2011) Data driven production modeling and simulation of complex automobile general assembly plant. Comput Ind 62(Compendex):765–775

    Article  Google Scholar 

  9. Kuzgunkaya O, ElMaraghy H (2006) Assessing the structural complexity of manufacturing systems configurations. Int J Flex Manuf Syst 18(2):145–171

    Article  MATH  Google Scholar 

  10. Samy SN, ElMaraghy H (2008) Effect of variety on assembly complexity. In: Proceedings of 2nd CIRP conference on assembly technologies & systems, Toronto, ON, Canada, 21–23 Sept. CATS2008, pp 184–197

  11. Orfi N, Terpenny J, Sahin-Sariisik A (2011) Harnessing product complexity: step 1 establishing product complexity dimensions and indicators. Eng Econ 56:59–79, Compendex

    Article  Google Scholar 

  12. Yu X, Kong F-S (2010) The gearbox factory assembly line quality accident complexity mensuration due to human errors. Paper presented at the international conference on computer, mechatronics, control and electronic engineering, Piscataway, NJ, USA, 24–26 Aug

  13. Calinescu A, Efstathiou J, Sivadasam S, Schirn J, Huaccho-Huatucco L (2000) Complexity in manufacturing: an information theoretic approach. In: International conference on complex systems and complexity in manufacturing, The University of Warwick, UK, 19–20 September, pp 30–44

  14. Cimatti B, Tani G (2009) New methodologies to measure product technological complexity for manufacturing optimization. In: IEEE International Conference on Industrial Engineering and Engineering Management (IEEM 2009), Piscataway, NJ, USA, 8–11 December, IEEE, pp 1229–1233

  15. Deshmukh AV, Talavage JJ, Barash MM (1998) Complexity in manufacturing systems—analysis of static complexity. IIE Trans 30(7):645–655

    Google Scholar 

  16. Suh NP (2005) Complexity: theory and applications. MIT-Pappalardo series in mechanical engineering, vol Book, Whole. Oxford University Press, New York

    Google Scholar 

  17. ElMaraghy WH, Urbanic RJ (2003) Modelling of manufacturing systems complexity. CIRP Ann Manuf Technol 52(1):363–366

    Article  Google Scholar 

  18. ElMaraghy WH, Urbanic RJ (2004) Assessment of manufacturing operational complexity. CIRP Ann Manuf Technol 53(1):401–406

    Article  Google Scholar 

  19. Martin MV, Ishii K (1996) Design for variety: a methodology for understanding the costs of product proliferation. In: Proceedings of the ASME design engineering and computers in engineering conference, Irvine, California, 18–22 August. Paper no. 96-DETC/DTM-1610

  20. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423

    MathSciNet  MATH  Google Scholar 

  21. ElMaraghy H (2006) A complexity code for manufacturing systems. Proceedings of the international conference on manufacturing science and engineering. American Society of Mechanical Engineers, Ypsilanti

  22. ElMaraghy H, Kuzgunkaya O, Urbanic J (2005) Comparison of manufacturing system configurations—a complexity approach. CIRP Ann Manuf Technol 54(1):445–450

    Article  Google Scholar 

  23. ElMaraghy H, Samy SN, Espinoza V (2010) A classification code for assembly systems. In: 3rd CIRP Conference on Assembly Technologies and Systems, CATS2010, Trondheim, Norway, 1–3 June, pp 145–150

  24. MacDuffie JP, Sethuraman K, Fisher ML (1996) Product variety and manufacturing performance: evidence from the international automotive assembly plant study. Manag Sci 42(3):350–369

    Article  MATH  Google Scholar 

  25. Fisher ML, Ittner CD (1999) The impact of product variety on automobile assembly operations: empirical evidence and simulation analysis. Manag Sci 45(6):771–786

    Article  Google Scholar 

  26. Fujimoto H, Ahmed A, Iida Y, Hanai M (2003) Assembly process design for managing manufacturing complexities because of product varieties. Int J Flex Manuf Syst 15(4):283–307

    Article  Google Scholar 

  27. Sarkis J (1997) An empirical analysis of productivity and complexity for flexible manufacturing systems. Int J Prod Econ 48(1):39–48

    Article  Google Scholar 

  28. Lotter B, Wiendahl H-P (2009) Changeable and reconfigurable assembly systems. In: ElMaraghy H (ed) Changeable and reconfigurable manufacturing systems. Springer, New York, pp 127–142

    Chapter  Google Scholar 

  29. Mital A, Desai A, Subramanian A, Mital A (2008) Product development: a structured approach to consumer product development, design, and manufacture, vol Book, Whole. Butterworth-Heinemann, Oxford

    Google Scholar 

  30. Houtzeel A, Schilperoort BA (1976) A chain-structured part classification system (MICLASS) and group technology. In: Proceedings of the 13th annual meeting and technical conference, Cincinnati, Ohio, March, pp 383–400

  31. Opitz H (1970) A classification system to describe work pieces. Pergamon, Oxford

    Google Scholar 

  32. Agarwali M, Kamrani AK, Parsaei HR (1994) An automated coding and classification system with supporting database for effective design of manufacturing systems. J Intell Manuf 5:235–249

    Article  Google Scholar 

  33. Barton J, Love D (2005) Retrieving designs from a sketch using an automated GT coding and classification system. Prod Plan Control 16:5–6

    Google Scholar 

  34. Lotter B (1989) Manufacturing assembly handbook. Butterworths, London

    Google Scholar 

  35. Rampersad HK (1994) Integrated and simultaneous design for robotic assembly. A Wiley Series in Product Development: Planning, Designing, Engineering, Chichester, UK

Download references

Author information

Authors and Affiliations

  1. Intelligent Manufacturing Systems (IMS) Centre, Industrial and Manufacturing Systems Engineering, University of Windsor, Windsor, ON, Canada

    S. N. Samy & H. ElMaraghy

Authors
  1. S. N. Samy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. H. ElMaraghy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to H. ElMaraghy.

Appendices

Appendix 1: Equipment characteristics and codes

This appendix presents the annotations of the various digits of the SCC as shown in Tables 17, 18, 19, 20, 21 and 22.

Table 17 Machine type CC annotations
Full size table
Table 18 Handling equipment CC annotations
Full size table
Table 19 Buffers equipment CC annotations
Full size table
Table 20 Controls CC annotations
Full size table
Table 21 Programming CC annotations
Full size table
Table 22 Operation CC annotations
Full size table

Appendix 2: Equipment characteristics and codes

This appendix presents the structural classification code analysis of the three-pin electric power plug assembly systems. Tables 23, 24, 25, 26, 27, 28, 29, 30 and 31 show the main characteristics, normalized digit value and complexity index of individual equipment of the assembly system.

Table 23 SCARA robot (machine analysis)
Full size table
Table 24 Index transfer (buffer analysis)
Full size table
Table 25 Magazine (buffer analysis)
Full size table
Table 26 Bowel feeder (MHS analysis)
Full size table
Table 27 Stacked bowl feeder (MHS analysis)
Full size table
Table 28 Vibratory bowl feeder with screw driver (MHS analysis)
Full size table
Table 29 Linear vibratory feeder (MHS analysis)
Full size table
Table 30 Stacked magazine (buffer analysis)
Full size table
Table 31 Power-and-free conveyor (MHS analysis)
Full size table

Rights and permissions

Reprints and permissions

About this article

Cite this article

Samy, S.N., ElMaraghy, H. A model for measuring complexity of automated and hybrid assembly systems. Int J Adv Manuf Technol 62, 813–833 (2012). https://doi.org/10.1007/s00170-011-3844-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-011-3844-y

Keywords

Search

Navigation