[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Perceived feature utility-based product family design: a mobile phone case study

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

To assure profit maximization through mass customization and personalization, effectively eliciting consumer needs across different market segments is critical. Although functional performance specifications and adequacy of various design forms can be measured directly and objectively, many designers and engineers struggle with clearly evaluating product criteria requiring subjective consumer input; the fact that these inputs change over time further complicates the process. To appropriately evaluate product criteria, an effective design decision-making analysis is required. In this study, we propose a methodology to assure effective elicitation of needs and their inclusion in design decision making and illustrate it using a mobile phone product family design scenario. First, consumer perceived utility of design features is gathered using a questionnaire (500+ responses) and then modeled using multi- attribute utility theory to facilitate the evaluation of a product family while responding to needs across customer clusters shaped by demographics. The methodology goal is to determine the relative goodness of a product family in comparison to its competition. We also compare and evaluate the application of the proposed method to conjoint analysis.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Akao, Y. (1997). QFD: Past, present and future, international symposium of QFD, Linkoping.

  • Andree P., Daniel B., Gerard M., Louise C. (1990) Pink or blue environmental gender stereotypes in the first two years of life. Sex Roles 22(5–6): 359–367

    Google Scholar 

  • Barone S., Lombardo A., Tarantino P. (2007) A weighted logistic regression for conjoint analysis and kansei engineering. Quality and Reliability Engineering International 23((6): 689–706

    Article  Google Scholar 

  • Carulli, M., Bordegoni, M., & Cugini, U. (2012). An approach for capturing the voice of the customer based on virtual prototyping. Journal of Intelligent Manufacturing. doi:10.1007/s10845-012-0662.

  • Cattinm, P., & Wittink, D. R. (1981). Commercial use of conjoint analysis: A survey. Research Paper, Graduate School of Business, Stanford University.

  • Cleaver, T. (2011). Economics: The basics. Routledge, NY. ISBN: 9780415571081.

  • Dittmar M. (2001) Changing color preference with aging: a comparative study on younger and older native Germans aged 19-90 years. Gerontology 47(4): 219–226

    Article  Google Scholar 

  • Du X., Jiao J., Tseng M. M. (2006) Understanding customer satisfaction in product customization. International Journal of Advanced Manufacturing Technology 31(3–4): 396–406

    Article  Google Scholar 

  • Green P. E., Carroll D., Goldberg S. M. (1981) A general approach to product design via conjoint analysis. Journal of Marketing 45: 17–37

    Article  Google Scholar 

  • Gustafsson A., Ekdahl F., Bergman B. (1999) Conjoint analysis: A useful tool in the design process. Total Quality Management 10(3): 327–343

    Article  Google Scholar 

  • Hwang C. L., Yoon K. (1981) Multiple attribute decision making methods and applications. Springer, Heidelberg

    Book  Google Scholar 

  • Isiklar G., Buyukozkan G. (2007) Using a multi-criteria decision making approach to evaluate mobile phone alternatives. Computer Standards and Interfaces 29(2): 265–274

    Article  Google Scholar 

  • Jiao J., Simpson T. W., Siddique Z. (2007) Product family design and platform-based product development: A state-of-the-art review. Journal of Intelligent Manufacturing 18(1): 5–29

    Article  Google Scholar 

  • Jiao J., Tseng M. M. (1999) A methodology of developing product family architecture for mass customization. Journal of Intelligent Manufacturing 10: 3–20

    Article  Google Scholar 

  • Keeney R., Raiffa H. (1976) Decisions with multiple objectives: Preferences and value tradeoffs. Wiley, New York

    Google Scholar 

  • Kim, T. (2009). An investigation on the importance of design form and function: Market success and consumer preferences. MS Thesis, Department of Industrial and Manufacturing Engineering, The Pennsylvania State University.

  • Kim, T., & Okudan, G. E. (2009a). Innovation in product form and function: How investments should be directed? In Proceedings of the IIE Annual Conference and Expo 2009, (IERC 2009), May 30–Jun 3, 2009, Miami, FL.

  • Kim,T., & Okudan, G. E. (2009b) Perceptions of innovation in product form and function: A comparison of historical and future oriented data mining. In Proceedings ASME 2009 international design engineering technical conference & computers and information in engineering conference, San Diego, CA, ASME Paper No. DETC2009-87694.

  • Kim, T., Okudan, G., & Chiu, M.-C. (2010). Product family design through customer perceived utility, design engineering technical conferences (IDETC), August 15–18, 2010, Montreal, QC.

  • Kohli R., Krishnamurti R. (1989) Optimal product design using conjoint analysis: Computational complexity and algorithms. European Journal of Operational Research 40: 186–195

    Article  Google Scholar 

  • Kohli R., Sukumar R. (1990) Heuristics for product-line design using conjoint analysis. Management Science 36(12): 1464–1478

    Article  Google Scholar 

  • Lee W. B., Lau H., Liu Z., Tam S. (2001) A fuzzy analytic hierarchy process approach in modular product design. Expert Systems 18(1): 32–42

    Article  Google Scholar 

  • Ling C., Hwang W., Salvendy G. (2007) A survey of what customers want in a cell phone design. Behaviour & Information Technology 26(2): 149–163

    Article  Google Scholar 

  • Liu C., Ramirez-Serrano A., Yin G. (2012) An optimum design selection approach for product customization development. Journal of Intelligent Manufacturing 23: 1433–1443

    Article  Google Scholar 

  • Nagamachi M. (1995) Kansei engineering: A new ergonomic consumer-oriented technology for product development. Journal of Industrial Ergonomics 15(1): 3–11

    Article  Google Scholar 

  • PCWorld: Cell Phones Getting Too Complicated: Poll Finds, http://www.pcworld.com/businesscenter/article/167079/cell_phones_getting_too_complicated_poll_finds.html, viewed on 2/9/2012 (2009).

  • Pugh S. (1991) Total design: Integrated methods for successful product engineering. Addison-Wesley, New York

    Google Scholar 

  • Pullman M. E., Moore W. L., Wardell D. G. (2002) A comparison of quality of functional deployment and conjoint analysis in the new product design. Journal of Product Innovation Management 19(5): 354–364

    Article  Google Scholar 

  • Renaud K., van Biljon J. (2010) Worth-centred mobile phone design for older users. Universal Access in the Information Society 9(4): 387–403

    Article  Google Scholar 

  • Saaty T. L. (1980) The analytic hierarchy process. McGraw-Hill, New York

    Google Scholar 

  • Simpson T. W. (2004) Product platform design and customization: Status and promise. Journal of Artificial Intelligence for Engineering Design, Analysis and Manufacturing 18: 3–20

    Google Scholar 

  • Smith, S., Smith, G. C., & Chen, Y.-R. (2012). A KE-LSA approach for user-centered design. Journal of Intelligent Manufacturing. doi:10.1007/s10845-012-0625.

  • Veryzer R. W. (1993) Aesthetic response and the influence of design principles on product preferences. Advances in Consumer Research 20: 224–228

    Google Scholar 

  • Wang J. (2002) Improved engineering design concept selection using fuzzy sets. International Journal of Computer Integrated Manufacturing 15(1): 18–27

    Article  Google Scholar 

  • Wang, J., Zhang, J., & Wei, X. (2006). Evolutionary muti-objective optimization algorithm with preference for mechanical design. In Advances in machine learning and cybernetics—4th international conference, Vol. 3930, pp. 497–506.

Download references

Author information

Authors and Affiliations

  1. School of Engineering Design, The Pennsylvania State University, University Park, PA, 16802, USA

    Gül E. Okudan

  2. Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA, 16802, USA

    Gül E. Okudan & Tae-Hyun Kim

  3. Department of Industrial Engineering and Engineering Management, National Tsing Hua University, HsinChu, 30001, Taiwan, ROC

    Ming-Chuan Chiu

Authors
  1. Gül E. Okudan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ming-Chuan Chiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tae-Hyun Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ming-Chuan Chiu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Okudan, G.E., Chiu, MC. & Kim, TH. Perceived feature utility-based product family design: a mobile phone case study. J Intell Manuf 24, 935–949 (2013). https://doi.org/10.1007/s10845-012-0699-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-012-0699-5

Keywords

Search

Navigation