Abstract
Changing customer needs coupled with rapid technology advances has boosted stronger requirements for a greater variety of consumer electronics. This trend has forced global companies to reconsider their product-positioning strategies. To reduce design cost and shorten the time to market, portfolio analysis for product family design is usually adopted to acquire diverse but related market applications. This study presents a novel framework to implement product differentiation and product configuration. Initially, association rule mining is used to capture user perceptions to identify the significant portfolios of hedonic attributes. Secondly, cognitive pairwise rating is conducted to elicit user preferences for utilitarian attributes (UAs). Finally, the Technique for Order Preference by Similarity to Ideal Solution is used to prioritize the optimal portfolios of UAs. Experiment results show that “keyboard interface”, “body material”, and “screen size” are the most concerned HAs for differentiating the product family while “CPU performance” is the most important UA for configuring padbooks, ultrabooks and notebooks. In summary, this research allows companies to effectively and efficiently incorporate user perceptions or preferences into the entire decision-making process.
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References
Agard, B., & Kusiak, A. (2004). Data-mining based methodology for the design of product families. International Journal of Production Research, 42(15), 2955–2969.
Agrawal, R., Imielinski, T., & Swarmi, A. (1993). Mining association rules between sets of items in large databases. In Proceedings of the ACM SIGMOD Conference on Management of Data (pp. 207–216).
Askin, R. G., & Dawson, D. W. (2000). Maximizing customer satisfaction by optimal specification of engineering characteristics. IIE Transactions, 32(1), 9–20.
Ayağ, Z. (2005). A fuzzy AHP-based simulation approach to concept evaluation in a NPD environment. IIE Transactions, 37(4), 827–842.
Bae, J. W., & Kim, J. (2011). Product development with data mining techniques: A case on design of digital camera. Expert Systems with Applications, 38(8), 9274–9280.
Dhar, R., & Wertenbroch, K. (2000). Consumer choice between hedonic and utilitarian goods. Journal of Marketing Research, 37(1), 60–71.
Gershenson, J. K., Khadke, K. N., & Lai, X. (2007). A research roadmap for product family design. In International Conference on Concurrent Engineering Design (pp. 28–31).
Guo, F., Liu, W. L., Liu, F. T., Wang, H., & Wang, T. B. (2014). Emotional design method of product presented in multi-dimensional variables based on Kansei engineering. Journal of Engineering Design, 25(4–6), 194–212.
Hwang, C. L., & Yoon, K. (1981). Multiple criteria decision making: Methods and applications. New York: Springer.
Jahng, J., & Jain, H. K. (2006). An empirical study of the impact of product characteristics and electronic commerce interface richness on consumer attitude and purchase intentions. IEEE Transactions on Systems, Man, and Cybernetics, 36(6), 1185–1201.
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.
Jiao, J., & Tseng, M. M. (1999). A methodology of developing product family architecture for mass customization. Journal of Intelligent Manufacturing, 10(1), 3–20.
Jiao, J., & Zhang, Y. (2005). Product portfolio planning with customer–engineering intereaction. IIE Transactions, 37, 801–814.
Jiao, J., Zhang, Y., & Helander, M. (2006). A Kansei mining system for affective design. Expert Systems with Applications, 30(4), 658–673.
Jose, A., & Tollenaere, M. (2005). Modular and platform methods for product family design: Literature analysis. Journal of Intelligent Manufacturing, 16(3), 371–390.
Kano, N. (1984). Attractive quality and must-be quality. The Journal of Japanese Society for Quality Control, 14(2), 39–48.
Kotler, P. T., & Keller, K. L. (2011). Marketing management (14th ed.). New York: Pearson.
Kumar, D., Chen, W., & Simpson, T. W. (2009). A market-driven approach to product family design. International Journal of Production Research, 47(1), 71–104.
Liu, C., Ramirez-Serrano, A., & Yin, G. (2011). Customer-driven product design and evaluation method for collaborative design environments. Journal of Intelligent Manufacturing, 22(5), 751–764.
Luce, R. D., & Turkey, J. W. (1964). Simultaneous conjoint measurement. A new type of fundamental measurement. Journal of Mathematical Psychology, 1, 1–27.
Luo, X. G., Kwong, C. K., Tang, J., & Tu, P. (2012). Optimal product positioning with consideration of negative utility effect on consumer choice rule. Decision Support Systems, 54(1), 402–413.
Nagamachi, M. (1995). Kansei engineering: A new ergonomic consumer-oriented technology for product development. International Journal of Industrial Ergonomics, 15(1), 311–346.
Nayak, R. U., Chen, W., & Simpson, T. W. (2002). A variation-based method for product family design. Engineering Optimization, 34(1), 65–81.
Oküdan, G. E., Chiu, M. C., & Kim, T. H. (2013). Perceived feature utility-based product family design: A mobile phone case study. Journal of Intelligent Manufacturing, 24(5), 935–949.
Otten, S., Spruit, M., & Helms, R. (2015). Towards decision analytics in product portfolio management. Decision Analytics, 2, 4.
Pakkanen, J., Juuti, T., & Lehtonen, T. (2016). Brownfield process: A method for modular product family development aiming for product configuration. Design Studies, 45, 210–241.
Pirmoradi, Z., Wang, G., & Simpson, T. W. (2013). A review of recent literature in product family design and platform-based product development. In T. W. Simpson, J. R. Jiao, Z. Siddique & K. Hölttä-Otto (Eds.), Advances in product family and product platform design (pp. 1–46). New York: Springer.
Saaty, T. L. (1980). The analytic hierarchy process. New York: McGraw-Hill.
Shi, F., Sun, S., & Xu, J. (2012). Employing rough sets and association rule mining in KANSEI knowledge extraction. Information Sciences, 196, 118–128.
Simpson, T. W., Siddique, Z., & Jiao, J. R. (2006). Product platform and product family design: Methods and applications. New York: Springer.
Smith, G. C., & Smith, S. S. (2012). Latent semantic engineering—A new conceptual user-oriented design approach. Advanced Engineering Informatics, 26, 456–473.
Song, Z., & Kusiak, A. (2009). Optimising product configuration with a data-mining approach. International Journal of Production Research, 47(1), 1733–1751.
Tan, P. N., Steinbach, M., & Kumar, V. (2010). Introduction to data mining. New York: Pearson.
Ulrich, K., & Eppinger, S. (2008). Product and development. New York: McGraw-Hill.
Wang, C. H. (2013). Incorporating customer satisfaction into the decision-making process of product configuration: A fuzzy Kano perspective. International Journal of Production Research, 51(22), 6651–6662.
Wang, C. H. (2016). Integrating correspondence analysis with Grey relational model to implement a user-driven STP product strategy for smart glasses. Journal of Intelligent Manufacturing, 27(5), 1007–1016.
Wang, C. H., & Chen, J. N. (2012). Using quality function deployment for collaborative product design and optimal selection of module mix. Computers and Industrial Engineering, 63(4), 1030–1037.
Wang, C. H., & Hsueh, O. Z. (2013). A novel approach to incorporate customer preference and perception into product configuration: A case study on smart pads. Computer Standards and Interfaces, 35(5), 549–556.
Wang, C. H., & Shih, C. W. (2013). Integrating conjoint analysis with quality function deployment to carry out customer-driven concept development for ultrabooks. Computer Standards and Interfaces, 36(1), 89–96.
Xu, L., Li, Z., Li, S., & Tang, F. (2007). A decision support system for product design in concurrent engineering. Decision Support Systems, 42(4), 2029–2042.
Yang, C. C. (2011). Constructing a hybrid Kansei engineering system based on multiple affective responses: Application to product form design. Computers and Industrial Engineering, 60(4), 760–768.
Yang, C. C., & Shieh, M. D. (2010). A support vector regression based prediction model of affective responses for product form design. Computers and Industrial Engineering, 59(4), 669–682.
Yuen, K. K. F. (2012). Pairwise opposite matrix and its cognitive prioritization operators: Comparisons with pairwise reciprocal matrix and analytic prioritization operators. Journal of the Operational Research Society, 63(3), 322–338.
Yuen, K. K. F. (2014). Fuzzy cognitive network process: Comparisons with fuzzy analytical hierarchy process in new product development strategy. IEEE Transactions on Fuzzy Systems, 22(3), 597–610.
Zhai, L. Y., Khoo, L. P., & Zhong, Z. W. (2009). A rough set based decision support approach to improving consumer affective satisfaction in product design. International Journal of Industrial Ergonomics, 39(2), 295–302.
Zhang, Y., Jiao, J., & Ma, Y. (2009). Market segmentation for product family positioning. Journal of Engineering Design, 18(3), 227–241.
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The authors would like to thank two anonymous referees for their constructive suggestions and comments to improve the quality of this study.
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Wang, CH. Association rule mining and cognitive pairwise rating based portfolio analysis for product family design. J Intell Manuf 30, 1911–1922 (2019). https://doi.org/10.1007/s10845-017-1362-y
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DOI: https://doi.org/10.1007/s10845-017-1362-y