A Naïve Bayes approach to map customer requirements to product variants

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, access via your institution.

Fig. 1
Fig. 2

Notes

  1. 1.

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

References

  1. 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 

  2. 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 

  3. 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 

  4. 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 

  5. 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 

  6. 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 

  7. 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.

  8. 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 

  9. 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 

  10. 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.

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

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

    Google Scholar 

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

    Google Scholar 

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

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

    Article  Google Scholar 

  20. 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 

  21. 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 

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

    Google Scholar 

  23. 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.

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

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

  26. 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 

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

    Article  Google Scholar 

  28. 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 

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

  30. 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.

  31. 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 

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

    Google Scholar 

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

    Google Scholar 

  34. 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

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

    Google Scholar 

  36. 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 

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

    Google Scholar 

  38. 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 

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

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

    Google Scholar 

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

  42. 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 

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

    Google Scholar 

  44. 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 

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

    Google Scholar 

  46. 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 

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

    Article  Google Scholar 

  48. 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.

  49. 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.

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

    Article  Google Scholar 

  51. 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 

  52. 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 

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

    Book  Google Scholar 

  54. 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

Affiliations

Authors

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

Keywords

  • Product design
  • Naïve Bayes
  • Customer requirements