Skip to main content
SpringerLink
Log in

A Naïve Bayes approach to map customer requirements to product variants

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

Abstract

A company develops product positioning strategy to make each product cover certain market segmentation and meet a group of customers’ requirements. In this sense, customer requirements can be mapped to a product variant. This paper addresses the issue of mapping customer requirements to existing product offerings. We treat the mapping task as a classification problem. Product variants are used as the class label for customer requirements. Considering that customer requirements are usually expressed in ambiguous language and contain uncertain information, a probabilistic Naïve Bayes based classifier is built by using existing customer choices data. The classifier takes new customer requirements as input and the output is the product variant which the customer may be satisfied with. In addition, the probabilistic classifier leverages on the flexibility of customer requirements and classifies the requirements based on the probability of relevance of each product variant. Case study shows that the approach can achieve good performance in terms of classification accuracy and F-measure.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. BLADES is the abbreviation of Bell Lab Analog Design Expert System and MICON is for MIcroprocessor CONfigurator.

References

  • Aldanondo, M., & Vareilles, E. (2008). Configuration for mass customization: How to extend product configuration towards requirements and process configuration. Journal of Intelligent Manufacturing, 19, 521–535.

    Article  Google Scholar 

  • Berger, C., Blauth, R., Boger, D., Bolster, C., Burchill, G., DuMouchel, W., Pouliot, F., Richter, R., Rubinoff, A., Shen, D., Timko, M., & Walden, D. (1993). Kano’s method for understanding customer-defined quality. Center for Quality of Management Journal, 2(4), 3–35.

    Google Scholar 

  • Birmingham, W., Brennan, A., & Siewiorek, D. (1988). MICON: A single board computer synthesis tool. IEEE Circuits and Devices Magazine, 4(1), 37–46.

    Google Scholar 

  • Chen, C., & Wang, L. (2008). Multiple-platform based product family design for mass customization using a modified genetic algorithm. Journal of Intelligent Manufacturing, 19(5), 577–589.

    Article  Google Scholar 

  • Chen, S. L., Wang, Y., & Tseng, M. M. (2009). Mass customization as a collaborative engineering effort. International Journal of Collaborative Engineering, 1(2), 152–167.

    Article  Google Scholar 

  • Domingos, P., & Pazzani, M. (1997). On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29(1), 103–137.

    Article  Google Scholar 

  • Elturky, F., & Nordin, R. (1986). BLADES: An expert system for analog circuit design. In Proceeding of the IEEE symposium on circuit and system (pp. 552–555). San Jose, CA.

  • Fleischanderl, G., Friedrich, G., Haselbock, A., Schreiner, H., & Stumptner, M. (1998). Configuring large systems using generative constraint satisfaction. IEEE Intelligent Systems, 13(4), 59–68.

    Google Scholar 

  • Franke, N., & Von Hippel, E. (2003). Satisfying heterogeneous user needs via innovation toolkits: The case of Apache security software. Research Policy, 32(7), 1199–1215.

    Article  Google Scholar 

  • Frayman, F., & Mittal, S. (1987). Cossack: A constraint-based expert system for configuration tasks. Knowledge-Based Expert Systems in Engineering, Planning, and Design, 631–636.

  • Gelle, E., & Faltings, B. (2003). Solving mixed and conditional constraint satisfaction problems. Constraint, 8(2), 107–141.

    Article  Google Scholar 

  • Green, P. E., & Rao, V. R. (1971). Conjoint measurement for quantifying judgemental data. Journal of Marketing Research, 8, 355–63.

    Article  Google Scholar 

  • Green, P., & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5(2), 103–123.

    Article  Google Scholar 

  • Griffin, A., & Hauser, J. (1993). The voice of the customer. Marketing Science, 12(1), 1–27. (Winter).

    Article  Google Scholar 

  • Hauser, J.R., & Clausing, D. (1988). House of quality. Harvard Business Review Article, May 01.

  • Henderson, N. R. (2009). Managing moderator stress: Take a deep breath. You can do this!. Marketing Research, 21(1), 28–29.

    Google Scholar 

  • Huffman, C., & Kahn, B. E. (1998). Variety for sale: Mass customization or mass confusion? Journal of Retailing, 74(4), 491– 513.

    Google Scholar 

  • Jannach, D., & Zanker, M. (2013). Modeling and solving distributed configuration problems: A CSP-based approach. IEEE Transactions on Knowledge and Data Engineering (accepted).

  • Kamakura, W. A., & Wedel, M. (1995). Life-style segmentation with tailored interviewing. Journal of Marketing Research, 32, 308–317.

    Article  Google Scholar 

  • Kano, N., Seraku, N., Takahashi, F., & Tsuji, S. (1984). Attractive quality and must-be quality. The Journal of the Japanese Society for Quality Control, 14(2), 39–48.

    Google Scholar 

  • Kim, K.-J., Moskowitz, H., Dhingra, A., & Evans, G. (2000). Fuzzy multicriteria models for quality function deployment. European Journal of Operational Research, 121(3), 504–518.

    Article  Google Scholar 

  • Lilien, G. L., Kotler, P., & Moorthy, K. S. (1992). Marketing model. NJ: Prentice-Hall Inc.

    Google Scholar 

  • McDermott, J. (1980). R1: An expert in the computer systems domain In: Proceedings of the 1st annual national conference on artificial intelligence (pp. 269–271). California: Stanford University.

  • Mittal, S., & Falkenhainer, B. (1990). Dynamic constraint satisfaction problems In Proceedings of the AAAI’90 (pp. 25–32). Boston, Massachusetts

  • Mizuno, S., & Akao, Y. (Eds.) (1994). QFD: The customer-driven approach to quality planning and deployment. (Translated by Glenn H. Mazur) Asian Productivity Organization.

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

    Article  Google Scholar 

  • Nagamachi, M. (2002). Kansei engineering as a powerful consumer-oriented technology for product development. Applied Ergonomics, 33(3), 289–294.

    Article  Google Scholar 

  • Özgür, A., Özgür, L., & Güngör, T. (2005). Text categorization with class-based and corpus-based keyword selection. Lecture Notes in Computer Science, 3733/2005, 606–615.

    Article  Google Scholar 

  • Picard, R. W. (1995). Affective computing. M.I.T Media Laboratory Perceptual Computing Section Technical Report No. 321. Cambridge, MIT.

  • Rahmer, J., & Voss, A. (1996). Case-based reasoning in the configuration of telecooperation systems. AAAI technical report FS-96-03 (pp. 93–98) AAAI Press.

  • Riviere, P., Monrozier, R., Rogeaux, M., Pages, J., & Saporta, G. (2006). Adaptive preference target: Contribution of Kano’s model of satisfaction for an optimized preference analysis using a sequential consumer test. Food Quality and Preference, 17, 572–581.

    Article  Google Scholar 

  • Risdiyono, R., & Koomsap, P. (2013). Design by customer: Concept and applications. Journal of Intelligent Manufacturing, 24, 295–311.

    Google Scholar 

  • Russell, S., & Norvig, P. (2003). Artificial intelligence: A modern approach. NJ: Prentice Hall.

    Google Scholar 

  • Sabin, M., & Freuder, E. (1998). Detecting and resolving inconsistency and redundancy in conditional constraint satisfaction problems [online]. Available from: http://citeseer.ist.psu.edu/sabin98detecting.html

  • Sabin, D., & Weigel, R. (1998). Product configuration frameworks: A survey. IEEE Intelligent Systems, 13(4), 42–49.

    Google Scholar 

  • Salvador, F., & Forza, C. (2004). Configuring products to address the customization-responsiveness squeeze: A survey of management issues and opportunities. International Journal of Production Economics, 91(3), 273–291.

    Article  Google Scholar 

  • Schwartz, B. (2004). The paradox of choice: Why more is less. New York: ECCO.

    Google Scholar 

  • Shen, X. X., Tan, K. C., & Xie, M. (2001). The implementation of quality function deployment based on linguistic data. Journal of Intelligent Manufacturing, 12(1), 65–75.

    Article  Google Scholar 

  • Stumptnerm, M., & Haselbock, A. (1993). A generative constraint formalism for configuration problems. In Proceedings of the AI*IA 1993, (pp. 302–313). Torino, Italy.

  • Stumptner, M., Friedrich, G., & Haselbock, A. (1998). Generative constraint-based configuration. AI EDAM, 12(4), 307–320.

    Google Scholar 

  • Suh, N. P. (1990). The principles of design. New York: Oxford University Press.

  • Temme, D., Paulssen, M., & Dannewald, T. (2008). Incorporating latent variables into discrete choice models: A simultaneous estimation approach using SEM software. BuR-Business Research, 1(2), 220–237. URN: urn:nbn:de:0009–20-16598.

    Google Scholar 

  • Tseng, M. M., Jiao, R. J., & Wang, C. (2010). Design for mass personalization. CIRP Annals-Manufacturing Technology, 59(1), 175– 178.

    Google Scholar 

  • Urban, G. L., & Hauser, J. R. (2004). ’Listening-In’ to find and explore new combinations of customer needs. Journal of Marketing, 68, 72–87.

    Article  Google Scholar 

  • Von Hippel, E. (2005). Democratizing innovation. Cambridge: MA, MIT Press.

    Google Scholar 

  • Wang, Y., & Tseng, M. M. (2011a). Adaptive attribute selection for configurator design via Shapley value. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 25, 185–195.

    Google Scholar 

  • Wang, Y., & Tseng, M. M. (2011b). Integrating comprehensive customer requirements into product design. Annals of the CIRP, 60(1), 175–178.

    Article  Google Scholar 

  • Wang, Y., & Tseng, M. M. (2012). Customized products recommendation based on probabilistic relevance model. Journal of Intelligent Manufacturing (accepted). doi:10.1109/TEM.2013.2248729.

  • Wang, Y., & Tseng, M. M. (2013). Identifying emerging customer requirements in early design stage by applying Bayes factor based sequential analysis. IEEE Transactions on Engineering Management (accepted). doi:10.1007/s10845-012-0644-7.

  • Wielinga, B., & Schreiber, G. (1997). Configuration-design problem solving. IEEE Expert: Intelligent Systems and Their Applications, 12(2), 49–56.

    Article  Google Scholar 

  • Xu, Q., Jiao, R. J., Yang, X., & Helander, M. (2009). An analytical Kano model for customer need analysis. Design Studies, 30, 87–110.

    Article  Google Scholar 

  • Zhai, L. Y., Khoo, L. P., & Zhong, Z. W. (2008). A rough set enhanced fuzzy approach to quality function deployment. The International Journal of Advanced Manufacturing Technology, 37(5–6), 613–624.

    Article  Google Scholar 

  • Zadeh, L. A. (1996). Fuzzy sets, fuzzy logic, fuzzy systems. Singapore: World Scientific Press.

    Book  Google Scholar 

  • Zhou, F., Ji, Y., & Jiao, R. (2012). Affective and cognitive design for mass personalization: Status and prospect. Journal of Intelligent Manufacturing (accepted).

Download references

Acknowledgments

This research is supported by Hong Kong Research Grants Council (RGC CERG HKUST 620609).

Author information

Authors and Affiliations

  1. Advanced Manufacturing Institute, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China

    Yue Wang & Mitchell M. Tseng

Authors
  1. Yue Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mitchell M. Tseng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yue Wang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wang, Y., Tseng, M.M. A Naïve Bayes approach to map customer requirements to product variants. J Intell Manuf 26, 501–509 (2015). https://doi.org/10.1007/s10845-013-0806-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-013-0806-2

Keywords

Search

Navigation