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Cost Estimation and Optimisation Framework for Rapid Product Development

  • Shane (S.Q.) Xie
  • Yiliu Tu
Chapter
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Abstract

The ultimate goal of mass customisation is to achieve economies of both scope and scale. This goal implies a conflict between customisation and economy of scale (or mass production) in the traditional manufacturing paradigm. However, recent developments in computer and Internet communication technologies, along with concurrent engineering, as well as modular design methodology provide concepts, methods and technology infrastructure for realising mass customisation. One of the findings from numerous research efforts on mass customisation is the use of e-commerce technologies to manage a product development chain that links customers, suppliers and manufacturers together to approach concurrently customised products in a short time and at the low cost level of mass production, which is the very definition of mass customisation. To ensure the success of mass customisation in a product development (PD) chain, a rapid, automatic yet accurate cost estimate and control system is needed. This chapter presents a novel cost index structure, together with two novel cost estimate methods, namely the generative cost estimate method and the variant cost estimate method, used for the development of a semiautomatic or fully automatic computer aided cost estimate and control system in mass customisation. Finally, an industrial case is reported to illustrate the principles and feasibility of the proposed data structure, methods and system framework.

Keywords

Sheet Metal Product Development Cost Estimate Mass Customisation Logistics Cost 
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.

References

  1. Becker, J. and Prischmann, M., 1993, Supporting the design process with neural network complex application of cooperating neural networks and its implementation, Journal of Information Science and Technology, 3, 79–95.Google Scholar
  2. Bernard, A. , Perry, N., Delplace, J. C., 2007, Concurrent cost engineering for decisional and operational process enhancement in a foundry, International Journal of Production Economics, 109(1–2), 2–11.CrossRefGoogle Scholar
  3. Bode, J., 1998, Neutral networks for cost estimation, Journal of Cost Engineering, 40, 25–30.Google Scholar
  4. Bode, J., 2000, Neutral networks for cost estimation: Simulations and pilot applications, International Journal of Product Research, 38(6), 1231–1254.zbMATHCrossRefGoogle Scholar
  5. Castagne, S., Curran, R., Rothwell, A., Price, M., Benard, E., Raghunathan, S., 2008, A generic tool for cost estimating in aircraft design, Res Eng. Design, 18(4), 149–162, DOI: 10.1007/s00163-007-0042-x.CrossRefGoogle Scholar
  6. Casey, J., 1987, Digitizing speeds cost estimating, American Machinist and Automated Manufacturing, 131(1), 71–73.MathSciNetGoogle Scholar
  7. Cawthorne-Nugent, M., Vieira J. D. L. and Watson, P. A., 1989, An intelligent knowledge-based system for cost estimating in the make-to-order environment, Computer-Aided Engineering, 6(4), 121–127.CrossRefGoogle Scholar
  8. Goldberg, J., 1987, Rating cost-estimating software, Manufacturing Engineering, 98(2), 37–41.Google Scholar
  9. Geiger, T. S. and Dilts, D. M., 1997, Automated design to cost: integrating costing into design decision, Journal of Computer Aided Design, 29(6/7), 423–438.Google Scholar
  10. Ju, B. and Xi, L.F., 2008, A product cost estimation for the early design of sedans using neural networks. International Journal of Automotive Technology and Management, 8(3), 331–349.CrossRefGoogle Scholar
  11. Horvath, P., Niemand, S. and Wolbold, M., 1994, Target costing: state of the art review. Manufacturing Competitive Frontiers, 18(3), 30–40.Google Scholar
  12. Hubert, K., Brinke, E. T., Lutters, E. and Streppel, T., 1999, Integrated Cost Estimation Based on Information Management Univ. Ternte, Lab. Design, Production, and Management, Twente, The Netherlands, Available at: http://opm.wb.utwente.nl/staff/erik/phd/wgp99.pdf.
  13. Kamarthi, S. V., Cohen, P. H. and Demetter, E. C., 1993, CMES: Cost minimisation and estimation system for sheet metal parts, International Journal of Flexible Automation Integrated Manufacturing, 1(1), 69–79.Google Scholar
  14. Kawada, M. and Johnson, D. F., 1993, Strategic management accounting – why and how, Manage. Accounting, 75(2), 32–38, 1993.Google Scholar
  15. Koltai, T., Lozano, S., Huerrero, F. and Onieva, L., 2000, A flexible costing system for flexible manufacturing systems using activity based costing, International Journal of Product Research, 38(7), 1615–1630.zbMATHCrossRefGoogle Scholar
  16. Marx, W. J., Mavris, D. N. and Schrage, D. P., 1998, Knowledge based system integrated with numerical analysis tool for aircraft life cycle design, Artificial Intelligence for Engineering, Design, Analysis and Manufacturing, 12(3), 211–229, 1998.CrossRefGoogle Scholar
  17. Monden, Y. and Lee J.Y., How Japanese auto makers reduce costs, Manag. Acc., 75(2), 22–26, 1993.Google Scholar
  18. Nadeau, M. C., Karb, A., Rotha, R., Kirchain, R., 2010, A dynamic process-based cost modeling approach to understand learning effects in manufacturing. International Journal of Production Economics, Volume 128, pp. 223–234.CrossRefGoogle Scholar
  19. Ostwald, P., 1984, Cost Estimating, 2nd edn. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
  20. Ou-Yang, C. and Lin, T. S., 1997, Developing an integrated framework for feature-based early manufacturing cost estimation, The International Journal of Advanced Manufacturing Technology, 13(9), 618–629.CrossRefGoogle Scholar
  21. Romero Rojo, F., Roy, J. R. and Shehab, E., 2009, A Methodology for Variability Reduction in Manufacturing Cost Estimating in the Automotive Industry based on Design Features, Proceedings of the 19th CIRP Design Conference – Competitive Design, Cranfield University, 30–31 March, p. 197.Google Scholar
  22. Smunt, T. L., 1999, Log-linear and non-log-linear learning curve models for production research and cost estimation, International Journal of Product Research, 37(17), 3901–3911.zbMATHCrossRefGoogle Scholar
  23. Tamas, K., Lozano, S., Guerrero, F. and Onieva, L., 2000, A flexible costing system for flexible manufacturing systems using activity based costing, International Journal of Product Research, 38(7), 1615–1630.zbMATHCrossRefGoogle Scholar
  24. Tu, Y. L., 1997, Production planning and control in a virtual OKP company, Computer in Industry, 34, 271–283.CrossRefGoogle Scholar
  25. Tu, Y. L. and Jiang, Z. B., 1997, A neural network based approach for the quotation of a product in Oneof-a-Kind Production, in Proc. 1st Int.Conf. Engineering Design and Automation, Bangkok, Thailand, March 18–21, 1997, pp. 307–310.Google Scholar
  26. Tu, Y. L., Kam, J. J. and Xie, S. Q., 2002, Rapid one-of-a-kind PD in a global and virtual manufacturing environment, presented at the International Conference of Advanced Production Management Systems (APMS 2002), Eindhoven, The Netherlands, Sept. 8–13.Google Scholar
  27. Tu, Y. L., Chu, X. L. and Yang, W. Y., 2000a, Computer aided process planning in virtual one-of-a-kind production, Computer in Industry, 41, 99–110.CrossRefGoogle Scholar
  28. Tu, Y.L., Chu, X.L. and Yang, W. Y., 2000b, Computer Aided Process Planning in Agile One-of-a-Kind Production, Computer in Industry, 41, 99–110.CrossRefGoogle Scholar
  29. Tu, Y. L., Kam, J. J., Fung, R. Y. K and Tang, J. F., 2002, Resource deployment in earlier PD stages, in Proc. 4th Int. Symp. Tools and Methods of Competitive Engineering (TMCE 2002),Wuhan, China, April 22–26, pp. 509–518.Google Scholar
  30. Tu, Y. L., Xie, S.Q. and Fung, R. Y. K., 2000, Rapid PD in a virtual production environment, in Proc. IFAC Symp. Manufacturing, Modeling, Management and Control (MIM 2000), Patras, Greece, July 12–14, pp. 63–68.Google Scholar
  31. Tu, Y. L. and Xie, S. Q., 2000, A WWW based Integrated Product Development Information Management System, IFAC, MIMO2000, Greek, July.Google Scholar
  32. Wang, C. H. and Bourne, D. A., 1995, Using features and their constraints to aid process planning of sheet metal parts, IEEE International Conference of Robotics and Automation, pp. 1020–1026, 21–27 May 1995, Nagoya, Japan.Google Scholar
  33. Winbourne, J. P. and Toolsie, G. M., 1991, Computer-aided tool cost estimating (CATE), in Proceeding 1991 ASME International Computers in Engineering Conference and Expo., Computer Engineering, 1, 617–621.Google Scholar
  34. Xie, S. Q., Tu, Y. L., Aitchison, D., Dunlop, R. and Zhou, Z. D., 2001, A WWW based Product Development Platform for Intelligent and Concurrent Sheet Metal Products Design and Manufacturing. International Journal of Product Research, 39(6), 3829–3852.zbMATHCrossRefGoogle Scholar
  35. Winston, W. L., 1994, Operations Research: Applications and Algorithms, 3rd edn. Belmont, CA: Duxbury, 772–818.zbMATHGoogle Scholar
  36. Zhang, Y. F. and Fuh, J. Y. H., 1998, Neural network approach for early cost estimation of packaging products, Computers & Industrial Engineering, 34(2), 433–450.CrossRefGoogle Scholar
  37. Zijm, H., 1984, The Optimality Equations in Multichain Denumerable State Markov Decision Processes with the Average Cost Criterion: The Unbounded Cost Case, C. Q. M. Note 22. Eindhoven, The Netherlands: Philips B.V., Center for Quantitative Methods.Google Scholar

Copyright information

© Springer 2011

Authors and Affiliations

  • Shane (S.Q.) Xie
    • 1
  • Yiliu Tu
    • 2
  1. 1.Department of Mechanical EngineeringUniversity of AucklandAucklandNew Zealand
  2. 2.Department of Mechanical and Manufacturing EngineeringUniversity of CalgaryCalgaryCanada

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