Skip to main content
SpringerLink
Log in

Customer Oriented Product Design and Intelligence

  • Chapter
  • First Online:
Customer Oriented Product Design

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 279))

  • 944 Accesses

Abstract

The aim of customer oriented product design is to develop products on the basis of an understanding of customers’ expectations and requirements. Customer orientation is based on both the experiences of users and customers. This is a crucial issue for the product to be accepted in the market by customers. The main steps of customer oriented product design consist of data collection, definition of customer expectations, integration of customer requirements to design characteristics, implementation of the design and production of the prototype. Under vague and imprecise environment, definition of customer expectations and integration of customer requirements to design characteristics require fuzzy and intelligent techniques to be employed. In this chapter, we summarize data collection methods for product design and fuzzy and intelligent design approaches.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Matzler, K., Hinterhuber, H.-H., Bailom, F., Sauerwein, E.: How to delight your customers. J. Prod. Brand. Manag. 5(2), 6–18 (1996)

    Article  Google Scholar 

  2. Lager, T.: The industrial usability of quality function deployment: a literature review and synthesis on a meta-level. R&D Manag. 35(4), 409–426 (2005)

    Article  Google Scholar 

  3. Ulrich, K., Eppinger, S.: Product Design and Development, 5th edn. Mcgraw Hill International edition (2012)

    Google Scholar 

  4. Lin, M.-C., Wang, C.-C., Chen, T.-C.: A strategy for managing customer-oriented product design. Concurr. Eng. 14(3), 231–244 (2006)

    Article  Google Scholar 

  5. Gill, P., Stewart, K., Treasure, E., Chadwick, B.: Methods of data collection in qualitative research: interviews and focus groups. Br. Dent. J. 204, 291–295 (2008). https://doi.org/10.1038/bdj.2008.192

    Article  Google Scholar 

  6. McGrath, C., Palmgren, P.J., Liljedahl, M.: Twelve tips for conducting qualitative research interviews. Med. Teach. 41(9), 1002–1006 (2019). https://doi.org/10.1080/0142159X.2018.1497149

    Article  Google Scholar 

  7. Breen, Rosanna L.: A practical guide to focus-group research. J. Geogr. High. Educ. 30(3), 463–475 (2006). https://doi.org/10.1080/03098260600927575

    Article  Google Scholar 

  8. Karen, L., James, A., Ellena, A.: Focus group research: what is it and how can it be used? Can. J. Cardiovasc. Nurs. 24(1), 16–22 (2014)

    Google Scholar 

  9. Nielsen, J.: Heuristic evaluation. Usability Inspection Methods, pp. 25–62. Wiley (1994)

    Google Scholar 

  10. Randolph, G.: Use-cases and personas: a case study in light-weight user interaction design for small development projects. Informing Sci. Int. J. Emerg. Transdiscipl. 7, 105–116 (2004)

    Article  Google Scholar 

  11. Zimmermann, G., Vanderheiden, G.: Accessible design and testing in the application development process: considerations for an integrated approach. Univ. Access Inf. Soc. 7(1–2), 117–128 (2008)

    Article  Google Scholar 

  12. Kjeldskov, J., Stage, J.: New techniques for usability evaluation of mobile systems. Int. J. Hum. Comput. Stud. 60(5–6), 599–620 (2004)

    Article  Google Scholar 

  13. Akao, Y.: In: Mazur, G.H. (trans) Quality Function Deployment: Integrating Customer Requirements into Product Design. Cambridge, Productivity Press, MA (1990)

    Google Scholar 

  14. Suh, N.P.: The Principles of Design. Oxford Series on Advanced Manufacturing (1990)

    Google Scholar 

  15. Suh, H.P.: Axiomatic Design: Advances and Applications MIT-Pappalardo Series in Mechanical Engineering. Oxford University Press, USA (2001)

    Google Scholar 

  16. Kano, N.: Attractive quality and must-be quality. Hinshitsu Qual. J. Jpn. Soc. Qual. Control 14, 39–48 (1984)

    Google Scholar 

  17. Mikulić, J., Prebežac, D.: A critical review of techniques for classifying quality attributes in the Kano model. Manag. Serv. Qual. Int. J. 21(1), 46–66 (2011)

    Article  Google Scholar 

  18. Green, P., Rao, V.: Conjoint measurement for quantifying judgmental data. J. Mark. Res. 8(3), 355–363 (1971). https://doi.org/10.2307/3149575

    Article  Google Scholar 

  19. Nagamachi, M.: Kansei Engineering: a new ergonomic consumer-oriented technology for product development. Int. J. Ind. Ergon. 15, 3–11 (1995)

    Article  Google Scholar 

  20. Nagamachi, M.: Kansei Engineering as a powerful consumer-oriented technology for product development. Appl. Ergon. 33, 273–278 (2002)

    Article  Google Scholar 

  21. Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  MATH  Google Scholar 

  22. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning—I. Inf. Sci. 8(3), 199–249 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  23. Sambuc, R.: Fonctions φ-floues: application a l’aide au diagnostic en pathologie thyroidienne. Ph.D. Thesis, University of Marseille, France (1975)

    Google Scholar 

  24. Grattan-Guiness, I.: Fuzzy membership mapped onto interval and many-valued quantities. Z. Math. Logik. Grundladen, Math. 22, 149–160 (1975)

    Google Scholar 

  25. Jahn, K.U.: Intervall-wertige Mengen. Math. Nach. 68, 115–132 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  26. Yager, R.R.: On the theory of bags. Int. J. Gen. Syst. 13(1), 23–37 (1986). https://doi.org/10.1080/03081078608934952

    Article  MathSciNet  Google Scholar 

  27. Miyamoto, S.: Fuzzy multisets and their generalizations. In: Calude, C.S., Pǎun, G., Rozenberg, G., Salomaa, A. (eds.) Multiset Processing. Lecture Notes in Computer Science, WMC 2000, pp. 225–235. Springer, Berlin, Heidelberg (2000)

    Google Scholar 

  28. Riesgo, Á., Alonso, P., Díaz, I., Montes, S.: Basic operations for fuzzy multisets. Int. J. Approx. Reason. 101, 107–118 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  29. Atanassov, K.T.: Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20(1), 87–96 (1986)

    Article  MATH  Google Scholar 

  30. Smarandache, F.: Neutrosophy: Neutrosophic Probability, Set, and Logic: Analytic Synthesis & Synthetic Analysis. American Research Press (1998). ISBN-10: 1879585634

    Google Scholar 

  31. Smarandache, F.: Neutrosophy, a new branch of philosophy. Mult. Valued Log. 8(3), 297–384 (2002)

    MathSciNet  MATH  Google Scholar 

  32. Smarandache, F.: Neutrosophic set—a generalization of the intuitionistic fuzzy set. Int. J. Pure Appl. Math. 24(3), 287–297 (2005)

    MathSciNet  MATH  Google Scholar 

  33. Ozen, T., Garibaldi, J.M., Musikasuwan, S.: Preliminary investigations into modelling the variation in human decision making. Uncertainty in Knowledge Based System in Perugia, Italy, July 2004

    Google Scholar 

  34. Garibaldi, J.M., Ozen, T.: Uncertain fuzzy reasoning: a case study in modelling expert decision making. IEEE Trans. Fuzzy Syst. 15(1), 16–30 (2007)

    Article  Google Scholar 

  35. Garibaldi, J.M., Jaroszewski, M., Musikasuwan, S.: Nonstationary fuzzy sets. IEEE Trans. Fuzzy Syst. 16(4), 1072–1086 (2008)

    Article  Google Scholar 

  36. Torra, V.: Hesitant fuzzy sets. Int. J. Intell. Syst. 25(6), 529–539 (2010)

    MATH  Google Scholar 

  37. Atanassov, K.T.: Intuitionistic Fuzzy Sets, Theory and Applications. Springer (1999)

    Google Scholar 

  38. Yager, R.R.: Pythagorean fuzzy subsets. In: IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013 Joint, pp. 57–61. IEEE (2013)

    Google Scholar 

  39. Cuong, C.B.: Picture fuzzy sets. J. Comput. Sci. Cybern. 30(4), 409–420 (2014)

    Google Scholar 

  40. Yager, R.R.: Generalized orthopair fuzzy sets. IEEE Trans. Fuzzy Syst. 25(5), 1222–1230 (2017)

    Article  Google Scholar 

  41. Kutlu Gundogdu, F., Kahraman, C.: Spherical fuzzy sets and spherical fuzzy TOPSIS method. J. Intell. Fuzzy Syst. 36(1), 337–352 (2019)

    Article  Google Scholar 

  42. Senapati, T., Yager, R.R.: Fermatean fuzzy weighted averaging/geometric operators and its application in multi-criteria decision-making methods. Eng. Appl. Artif. Intell. 85, 112–121 (2019). https://doi.org/10.1016/j.engappai.2019.05.012

    Article  Google Scholar 

  43. Senapati, T., Yager, R.R.: Some new operations over Fermatean fuzzy numbers and application of Fermatean fuzzy WPM in multiple criteria decision making. Informatica 30(2), 391–412 (2019). https://doi.org/10.15388/informatica.2019.211

    Article  Google Scholar 

  44. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI (1975)

    Google Scholar 

  45. Holland, J.H.: Genetic algorithms. Sci. Am. 267, 66–72 (1992). https://doi.org/10.1038/scientificamerican0792-66

    Article  Google Scholar 

  46. Miranda, V., Srinivasan, D., Proença, A.M.: Evolutionary computation in power systems. Electr. Power Syst. Res. 20(2), 89–98 (1998). https://doi.org/10.1016/S0142-0615(97)00040-9

    Article  Google Scholar 

  47. Won, J.R., Park, Y.M.: Economic dispatch solutions with piecewise quadratic cost functions using improved genetic algorithm. Int. J. Electr. Power Energy Syst. 25(5), 355–361 (2003). https://doi.org/10.1016/S0142-0615(02)00098-4

    Article  Google Scholar 

  48. Dorigo, M.: Optimization, learning and natural algorithms. PhD thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy (in Italian) (1992)

    Google Scholar 

  49. Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge, MA & London, UK (2004)

    Google Scholar 

  50. Yang, J., Zhuang, Y.: An improved ant colony optimization algorithm for solving a complex combinatorial optimization problem. Appl. Soft Comput. 10(2), 653–660 (2010). https://doi.org/10.1016/j.asoc.2009.08.040

    Article  Google Scholar 

  51. Macura, W.K.: Ant Colony Algorithm. From MathWorld–A Wolfram Web Resource, Created by Eric W. Weisstein. http://mathworld.wolfram.com/AntColonyAlgorithm.html

  52. Pincus, M.: A Monte Carlo method for the approximate solution of certain types of constrained optimization problems. Oper. Res. 18, 1225–1228 (1970)

    Google Scholar 

  53. Kirkpatrick, S., Gelatt, S., Vecchi, M.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  54. Cerny, V.: Thermodynamical approach to the travelling salesman problem. J. Optim. Theory Appl. 45(1), 41–51 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  55. Özdağoğlu, G.: Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi. 22(2), 357–377 (2010)

    Google Scholar 

  56. Jarraya, B., Bouri, A.: Metaheuristic optimization backgrounds: a literature review. Int. J. Contemp. Bus. Stud. 3(12), 31–44 (2012)

    Google Scholar 

  57. Glover, F.: Future paths for integer programming and links to artificial intelligence. Comput. Oper. Res. 13, 533–549 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  58. Gendreau, M.: An Introduction to Tabu Search. In: Glover, F., Kochenberger, G.A. (eds.) Handbook of Metaheuristics. Kluwer Academic Publishers, New York (2003)

    Google Scholar 

  59. McCulloch, W.S., Pitts, W.A.: A logical calculus of the ideas immanent in neural nets. Bull. Math. Biophys. 5, 115–133 (1943). https://doi.org/10.1007/BF02478259

    Article  MathSciNet  MATH  Google Scholar 

  60. Cebi, S., Kahraman, C., Kaya, I.: Soft computing and computational intelligent techniques in the evaluation of emerging energy technologies. In: Vasant, P., Barsoum, N., Webb, J. (eds.) Innovation in Power, Control, and Optimization: Emerging Energy Technologies, pp. 164–197. IGI Global (2012)

    Google Scholar 

  61. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of Conference on Evolutionary Computation (CEC), pp. 1942–1948 (1995)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Yildiz Technical University, Istanbul, Turkey

    Selcuk Cebi

  2. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Cengiz Kahraman

Authors
  1. Selcuk Cebi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cengiz Kahraman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Selcuk Cebi .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, Faculty of Management, Istanbul Technical University, Macka, Istanbul, Turkey

    Cengiz Kahraman

  2. Department of Industrial Engineering, Faculty of Mechanical Engineering, Yildiz Technical University, Besiktas, Istanbul, Turkey

    Selcuk Cebi

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Cebi, S., Kahraman, C. (2020). Customer Oriented Product Design and Intelligence. In: Kahraman, C., Cebi, S. (eds) Customer Oriented Product Design. Studies in Systems, Decision and Control, vol 279. Springer, Cham. https://doi.org/10.1007/978-3-030-42188-5_1

Download citation

Publish with us

Policies and ethics

Search

Navigation