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Analytical affective design with ambient intelligence for mass customization and personalization

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Abstract

The fulfillment of individual customer affective needs may award the producer extra premium in gaining a competitive edge. This entails a number of technical challenges to be addressed, such as the elicitation, evaluation, and fulfillment of affective needs, as well as the evaluation of affordability of producers to launch the planned products. Mass customization and personalization have been recognized as an effective means to enhance front-end customer satisfaction while maintaining back-end production efficiency. This paper proposes an affective design framework to facilitate decision-making in designing customized product ecosystems. In particular, ambient intelligence techniques are applied to elicit affective customer needs. An analytical model is proposed to support affective design analysis. Utility measure and conjoint analysis are employed to quantify affective satisfaction, while the producer affordability is evaluated using an affordability index. Association rule mining techniques are applied to model the mapping of affective needs to design elements. Configuration design of product ecosystems is optimized with a heuristic genetic algorithm. A case study of Volvo truck cab design is reported with a focus on the customization of affective features. It is demonstrated that the analytical affective design framework can effectively manage the elicitation, analysis, and fulfillment of affective customer needs. Meanwhile, it can account for the manufacturer’s capabilities, which is vital for ensuring a profit margin in the mass customization and personalization endeavor.

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Acknowledgement

This research is partially sponsored by the European Commission (DG Information Society and Media) in the framework of the 6th Research Program (FP6) under grant IST-5-035030-STREP (http://www.cater-ist.org). Also acknowledged is support from Singapore Agency for Science, Technology and Research (A*STAR) Science and Engineering Research Council (SERC) Thematic Strategic Research Programme grant on human factors engineering (#062 131 0066).

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Authors and Affiliations

  1. School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore

    Roger J. Jiao, Qianli Xu, Jun Du, Yiyang Zhang & Martin Helander

  2. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Roger J. Jiao

  3. Damai Sciences Sdn Bhd, Kuala Lumpur, Malaysia

    Halimahtun M. Khalid

  4. Faculty of Technology, University of Vaasa, Vaasa, Finland

    Petri Helo

  5. IC:IDO GmbH, Stuttgart, Germany

    Cheng Ni

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  1. Roger J. Jiao
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  2. Qianli Xu
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  3. Jun Du
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  4. Yiyang Zhang
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  6. Halimahtun M. Khalid
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  8. Cheng Ni
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Correspondence to Roger J. Jiao.

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Jiao, R.J., Xu, Q., Du, J. et al. Analytical affective design with ambient intelligence for mass customization and personalization. Int J Flex Manuf Syst 19, 570–595 (2007). https://doi.org/10.1007/s10696-008-9032-1

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  • DOI: https://doi.org/10.1007/s10696-008-9032-1

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