Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-25T20:40:01.764Z Has data issue: false hasContentIssue false
  • English
  • Français

Evaluation and selection in product design for mass customization: A knowledge decision support approach

Published online by Cambridge University Press:  28 January 2005

XUAN F. ZHA ,
RAM D. SRIRAM  and
WEN F. LU
XUAN F. ZHA
Affiliation:
Manufacturing System Integration Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA
RAM D. SRIRAM
Affiliation:
Manufacturing System Integration Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA
WEN F. LU
Affiliation:
Product Design and Development Group, Singapore Institute of Manufacturing Technology, 71 Nanyang Drive, Singapore 638075
Get access Rights & Permissions [Opens in a new window]

Abstract

Mass customization has been identified as a competitive strategy by an increasing number of companies. Family-based product design is an efficient and effective means to realize sufficient product variety, while satisfying a range of customer demands in support for mass customization. This paper presents a knowledge decision support approach to product family design evaluation and selection for mass customization process. Here, product family design is viewed as a selection problem with the following stages: product family (design alternatives) generation, product family design evaluation, and selection for customization. The fundamental issues underlying product family design for mass customization are discussed. Then, a knowledge support framework and its relevant technologies are developed for module-based product family design for mass customization. A systematic fuzzy clustering and ranking model is proposed and discussed in detail. This model supports the imprecision inherent in decision making with fuzzy customers' preference relations and uses fuzzy analysis techniques for evaluation and selection. A neural network technique is also adopted to adjust the membership function to enhance the model. The focus of this paper is on the development of a knowledge-intensive support scheme and a comprehensive systematic fuzzy clustering and ranking methodology for product family design evaluation and selection. A case study and the scenario of knowledge support for power supply family evaluation, selection, and customization are provided for illustration.

Keywords

Customer-Driven DesignDesign EvaluationFuzzy ClusteringFuzzy RankingKnowledge SupportMass CustomizationMulticriteria Decision MakingProduct Family DesignProduct Platform
Type
Research Article
Information
AI EDAM , Volume 18 , Issue 1 , February 2004 , pp. 87 - 109
Copyright
© 2004 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Barkmeyer, E., Christopher, N., Feng, S., Fowler, J.E., Frechette, S., Jones, A., Jurrens, K.K., McLean, C., Pratt, M., Scott, H.A., Senehi, M.K., Sriram, R.D., & Wallace, E. (1997). SIMA Reference Architecture Part I: Activity Models, NISTIR 5939. Gaithersburg, MD: National Institute of Standards and Technology.
Baudin, M. (2001). Eight strategies for mass customization. Manufacturing Management & Technology Institute. http://www.mmt-inst.com.
Boender, C.G., de Graan, J.G., & Lootsma, F.A. (1989). Multi-criteria decision analysis with fuzzy pairwise comparisons. Fuzzy Sets and Systems 29, 133–143.Google Scholar
Carnahan, J.V., Thurston, D.L., & Liu, T. (1994). Fuzzy rating for multi-attribute design decision-making. Journal of Mechanical Design, Transactions of the ASME 116(2), 511–521.Google Scholar
Chen, W., Allen, J.K., Mavris, D., & Mistree, F. (1996). A concept exploration method for determining robust top-level specifications. Engineering Optimization 26, 137–158.Google Scholar
Clausing, D. (1994). Total Quality Development: A Step-by-Step Guide to World Class Concurrent Engineering. New York: ASME.
Dixon, J.R., Howe, A., Cohen, P.R., & Simmons, M.K. (1986). Dominic I: Progress towards domain independence in design by iterative redesign. Proc. ASME 1986 Computers in Engineering Conf., Vol. 1, pp. 199–212, Chicago, IL.
Dobson, G. & Kalish, S. (1993). Heuristics for pricing and positioning a product line using conjoint analysis and cost data. Management Science 39(2), 160–175.Google Scholar
Du, X., Jiao, J., & Tseng, M. (2000). Architecture of product family for mass customization. IEEE Int. Conf. Management of Innovation and Technology, pp. 437–443, Singapore, November 12–15, 2000.
Frazell, E. (1985). Suggested techniques enable multi-criteria evaluation of material handling alternatives. Industrial Engineering 17(2).Google Scholar
Friedman–Hill, E.J. (1999). The Java Expert System Shell. Sandia National Laboratories. http://herzberg.ca.sandia.gov/jess.
Fujita, K. & Ishii, K. (1997). Task structuring toward computational approaches to product variety design. Proc. 1997 ASME Design Engineering Technical Conf., Paper No. 97DETC/DAC-3766. New York: ASME.
Fujita, K., Akagi, S., Yoneda, T., & Ishikawa, M. (1998). Simultaneous optimization of product family design sharing system structure and configuration. CD-ROM Proc. 1998 ASME Design Engineering Technical Conf., Atlanta, GA.
Gaithen, N. (1980). Production and Operations Management: A Problem-Solving and Decision-Making Approach. New York: Dryden Press.
Gonzalez–Zugasti, J.P. (2000). Models for platform-based product family design. PhD Thesis.
Green, P.E. & Krieger, A.M. (1985). Models and heuristics for product line selection. Marketing Science 4(1), 1–19.Google Scholar
Gui, J.K. (1993). Methodology for modeling complete product assemblies. PhD Dissertation. Helsinki University of Technology.
Huang, P. & Ghandforoush, P. (1984). Procedures given for evaluating, selecting robots. Industrial Engineering 16(4).Google Scholar
Ishii, K., Juengel, C., & Eubanks, F. (1995). Design for product variety: key to product line structuring. ASME Design Theory and Methodology Conf., DE-Vol. 83, pp. 499–506, Boston.
Jiao, J.X. & Tseng, M.M. (1998a). Fuzzy ranking for concept evaluation in configuration design for mass customization. Concurrent Engineering: Research and Application 6(3), 189–206.Google Scholar
Jiao, J.X. & Tseng, M.M. (1998b). Design for mass customization by developing product family architecture. Proc. 1998 ASME Design Engineering Technical Conf., Paper No. DETC98/DFM-5717.
Kandel, A. (1982). Fuzzy Techniques in Pattern Recognition. New York: Wiley.
Kickert, W.J.M. (1978). Fuzzy Theories on Decision Making: A Critical Review. Boston: Martinus Nijhoff Social Sciences Division.
Knosala, R. & Pedrycz, W. (1992). Evaluation of design alternatives in mechanical engineering. Fuzzy Sets and Systems 47(3), 269–280.Google Scholar
Kohli, R. & Sukumar, R. (1990). Heuristics for product-line design using conjoint analysis. Management Science 36(12), 1464–1477.Google Scholar
Kotler, P. (1989). From mass marketing to mass customization. Planning Review 17(5), 10–15.Google Scholar
Krishnan, V. & Gupta, S. (2001). Appropriateness and impact of platform-based product development. Management Science 47(1), 52–68.Google Scholar
Kusiak, A. & Huang, C.C. (1996). Development of modular products. IEEE Transactions on Components, Packaging, and Manufacturing Technology Part-A 19(4), 523–538.Google Scholar
Lee, H.L. & Billington, C. (1994). Designing products and processes for postponement. In Management of Design: Engineering and Management Perspectives (Dasu, S. & Eastman, C., Eds.), pp. 105–122. Boston: Kluwer.
Li, H. & Azarm, S. (2000). Product design selection under uncertainty and with competitive advantage. Journal of Mechanical Design, Transactions of the ASME 122, 411–418.Google Scholar
Li, H. & Azarm, S. (2002). An approach for product line design selection under uncertainty and competition. Journal of Mechanical Design, Transactions of the ASME 124, 385–392.Google Scholar
Maimon, O. & Fisher, E. (1985). Analysis of robotic technology alternatives. Proc. 1985 Annual Industrial Engineering Conf., pp. 227–236.
Martin, M. & Ishii, K. (1996). Design for variety: A methodology for understanding the costs of product proliferation. 1996 Design Theory and Methodology Conf., Paper No. 96-DETC/DTM-1610 (Wood, K., Ed.). Irvine, CA: ASME.
McKay, A., Erens, F., & Bloor, M.S. (1996). Relating product definition and product variety. Research in Engineering Design 8(2), 63–80.Google Scholar
Meyer, M.H. & Lehnerd, A.P. (1997). The Power of Product Platforms. New York: Free Press.
Meyer, M.H. & Utterback, J.M. (1993). The product family and the dynamics of core capability. Sloan Management Review 34(Spring), 29–47.Google Scholar
Meyer, M.H., Tertzakian, P., & Utterback, J.M. (1997). Metrics for managing research and development in the context of the product family. Management Science 43(1), 88–111.Google Scholar
Mistree, F., Hughes, O.F., & Bras, B.A. (1992). The compromise decision support problem and the adaptive linear programming algorithm. In Structural Optimization: Status and Promise (Kamatt, M.P., Ed.), Chapter 11, pp. 247–286. Washington, DC: AIAA.
Mistree, F., Bras, B., Smith, W.F., & Allen, J.K. (1995). Modeling design processes: A conceptual, decision-based perspective. Engineering Design & Automation 1(4), 209–321.Google Scholar
Nelson, S.A., Parkinson, M.B., & Papalambros, P.Y. (1999). Multi-criteria optimization in product platform design. CD-ROM Proc. DETC99, 1999 ASME Design Engineering Technical Conf., Las Vegas, NV, September 12–15.
Nielsen, E.H., Dixon, J.R., & Simmons, M.K. (1986). GERES: A knowledge-based material selection program for injection molded resins. Proceedings of the ASME 1986 Computers in Engineering Conference, pp. 255–262, Chicago.
National Research Council of Canada. (2003). Fuzzy Logic in Integrated Reasoning. http://www.iit.nrc.ca/IR_public/fuzzy.
Nutt, G.J. (1992). Open Systems. Englewood Cliffs, NJ: Prentice Hall.
Pahl, G. & Beitz, W. (1996). Engineering Design—A Systematic Approach. New York: Springer.
Pine, J.B. (1993). Mass Customization: The New Frontier in Business Competition. Boston: Harvard Business School Press.
Prasad, B. (1996). Concurrent Engineering Fundamentals, Vols. 1–2. Englewood Cliffs, NJ: Prentice Hall.
Pugh, S. (1991). Total Design: Integrating Methods for Successful Product Engineering. Reading, MA: Addison–Wesley.
Rothwell, R. & Gardiner, P. (1990). Robustness and product design families. In Design Management: A Handbook of Issues and Methods (Oakley, M., Ed.), pp. 279–292. Cambridge, MA: Basil Blackwell.
Saaty, T.L. (1991). The Analytic Hierarchy Process. New York: McGraw–Hill.
Samuel, A.K. & Bellam, S. (2000). http://www.glue.umd.edu/∼sbellam.
Sanderson, S.W. (1991). Cost models for evaluating virtual design strategies in multi-cycle product families. Journal of Engineering and Technology Management 8, 339–358.Google Scholar
Simpson, T.W. (1998). A concept exploration method for product family design. Ph.D Dissertation. Atlanta, GA: Georgia Institute of Technology, System Realization Laboratory, Woodruff School of Mechanical Engineering.
Simpson, T.W., Maier, J.R.A., & Mistree, F. (2001). Product platform design: method and application. Research in Engineering Design 13, 2–22.Google Scholar
Simpson, T.W., Umapathy, K., Nanda, J., Halbe, S., & Hodge, B. (2003). Development of a framework for web-based product platform customization. Journal of Computing and Information Science in Engineering, Transactions of the ASME 3, 119–129.Google Scholar
Siskos, J., Lochard, J., & Lombard, J. (1984). A multi-criteria decision-making methodology under fuzziness: application to the evaluation of radiological protection nuclear power plants. In TIMS/Studies in Management Sciences (Zimmermann, H.J., Ed.), pp. 261–283. Amsterdam: North–Holland.
Sriram, R.D. (1997). Intelligent Systems for Engineering: A Knowledge-based Approach. London: Springer–Verlag.
Sriram, R.D. (2002). Distributed and Integrated Collaborative Engineering Design. Glenwood, MD: Sarven.
Stadzisz, P.C. & Henrioud, J.M. (1995). Integrated design of product families and assembly systems. In IEEE Int. Conf. Robotics and Automation, Vol. 2, pp. 1290–1295. Aichi, Japan: Nagoya.
Suh, N.P. (1990). The Principles of Design. New York: Oxford University Press.
Sullivan, W. (1986). Models IEs can be used to include strategic, non-monetary factors in automation decisions. Industrial Engineering 18, 42–50.Google Scholar
Taguchi, G. (1986). Introduction to Quality Engineering. Tokyo: Asian Productivity Organization.
Thurston, D.L. (1991). A formal method for subjective design evaluation with multiple attributes. Research in Engineering Design 3(2), 105–122.Google Scholar
Thurston, D.L. & Carnahan, J.V. (1992). Fuzzy rating and utility analysis in preliminary design evaluation of multiple attributes. Journal on Mechanical Design, Transaction of the ASME 114(4), 648–658.Google Scholar
Thurston, D.L. & Crawford, C.A. (1994). A method for integrating end-user preferences for design evaluation in rule-based systems. Journal of Mechanical Design, Transactions of the ASME 116(2), 522–530.Google Scholar
Thurston, D.L. & Locascio, A. (1994). Decision theory for design economics. Engineering Economist 40(1), 41–72.Google Scholar
Tong, C. & Sriram, D., Eds. (1991a). Artificial Intelligence in Engineering Design: Volume I—Representation: Structure, Function and Constraints; Routine Design. New York: Academic Press.
Tong, C. & Sriram, D., Eds. (1991b). Artificial Intelligence in Engineering Design: Volume III—Knowledge Acquisition, Commercial Systems; Integrated Environments. New York: Academic.
Tseng, M. & Jiao, J. (2001). Mass customization. In Industrial Engineering Handbook (Salvendy, G., Ed.), 3rd ed. New York: Wiley.
Tseng, M.M. & Jiao, J.X. (1996). Design for mass customization. CIRP Annals 45(1), 153–156.Google Scholar
Tseng, M.M. & Jiao, J.X. (1998). Product family modeling for mass customization. Computers in Industry 35(3–4), 495–498.Google Scholar
Tseng, T.Y. & Klein, C.M. (1989). New algorithm for the ranking procedure in fuzzy decision-making. IEEE Transactions on Systems, Man and Cybernetics 19(5), 1289–1296.Google Scholar
Wang, J. (1997). A fuzzy outranking method for conceptual design evaluation. International Journal of Production Research 35(4), 995–1010.Google Scholar
Wheelwright, S.C. & Clark, K.B. (1992). Creating project plans to focus product development. Harvard Business Review 70(March–April), 70–82.Google Scholar
Wheelwright, S.C. & Sasser, W.E. (1989). The new product development map. Harvard Business Review 67(May–June), 112–125.Google Scholar
Wortmann, H.C., Muntslag, D.R., & Timmermans, P.J.M. (1997). Customer-Driven Manufacturing. London: Chapman & Hall.
Zadeh, L.A. (1965). Fuzzy sets. Information and Control 8, 338–353.Google Scholar
Zha, X.F. (2001). Neuro-fuzzy comprehensive assemblability and assembly sequence evaluation. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 15(5), 367–384.Google Scholar
Zha, X.F. & Lu, W.F. (2002a). Knowledge support for customer-based design for mass customization. In AID'02 (Gero, J.S., Ed.), pp. 407–429. Dordrecht: Kluwer Academic.
Zha, X.F. & Lu, W.F. (2002b). Knowledge intensive support for product family design. Proc. 2002 ASME DETC02, Paper No. DETC/DAC 34098.
Zha, X.F. & Sriram, R.D. (2003). Platform-based product design and development: Knowledge support strategy and implementation. In Business and Technology in the New Millennium (Leondes, C.T., Ed.). Norwell, MA: Kluwer Academic.
Zhang, W.Y., Tor, S.B., & Britton, G.A. (2002). A heuristic state-space approach to the functional design of mechanical systems. International Journal of Advanced Manufacturing Technology 19, 235–244.Google Scholar
Zimmermann, H.J. (1987). Fuzzy Sets, Decision Making, and Expert Systems. Boston: Kluwer Academic.
Zimmermann, H.J. (1996). Fuzzy Set Theory and Its Applications, 3rd ed. Boston: Kluwer Academic.