A Neuro-fuzzy Inference System for the Evaluation of New Product Development Projects

  • Orhan Feyzioğlu
  • Gülçin Büyüközkan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4304)


As a vital activity for companies, new product development is also a very risky process due to the high uncertainty degree encountered at every development stage and the inevitable dependence on how previous steps are successfully accomplished. Hence, there is an apparent need to evaluate new product initiatives systematically and make accurate decisions under uncertainty. Another major concern is the time pressure to launch a significant number of new products to preserve and increase the competitive power of the company. In this work, we propose an integrated decision-making framework based on neural networks and fuzzy logic to make appropriate decisions and accelerate the evaluation process. We are especially interested in the two initial stages where new product ideas are selected and the implementation order of the corresponding projects are determined. We show that this two-staged intelligent approach allows practitioners to roughly and quickly separate good and bad product ideas by making use of previous experiences, and then, analyze a more shortened list rigorously.


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  1. 1.
    Bezdek, J.C., Dubois, D., Prade, H. (eds.): Fuzzy sets approximate reasoning and information systems. Kluwer Academic Publishers, Netherlands (1999)MATHGoogle Scholar
  2. 2.
    Brown, S.L., Eisenhardt, K.M.: Product development: past research, present findings and future directions. Academy of Management Review 20, 343–378 (1995)CrossRefGoogle Scholar
  3. 3.
    Carbonell-Foulquie, P., Munuera-Aleman, J.L., Rodriguez-Escudero, A.I.: Criteria employed fo go/no-go decisions when developing successful highly innovative products. Industrial Marketing Management 33, 307–316 (2004)CrossRefGoogle Scholar
  4. 4.
    Cooper, R.G.: Perspective: third generation new product processes. Journal of Product Innovation Management 11, 3–14 (1994)CrossRefGoogle Scholar
  5. 5.
    Danneels, E., Kleinschmidt, E.J.: Product innovativeness from the firm’s perspective: its dimensions and their relation with project selection and performance. Journal of Product Innovation Management 18, 357–373 (2001)CrossRefGoogle Scholar
  6. 6.
    Ernst, H.: Success factors of new product development: a review of the empirical literature. International Journal of Management Reviews 4, 1–40 (2002)CrossRefGoogle Scholar
  7. 7.
    Fox, J., Gann, R., Shur, A., Glahn, L., Zaas, B.: Process uncertainty: a new dimension for new product development. Engineering Management Journal 10, 19–27 (1998)Google Scholar
  8. 8.
    Grabisch, M.: k-order additive discrete fuzzy measures and their representation. Fuzzy Sets and Systems 92, 167–189 (1997)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Grabisch, M., Roubens, M.: Application of the Choquet integral in multi-criteria decision making. In: Grabisch, M., Murofushi, T., Sugeno, M. (eds.) Fuzzy Measures and Integrals: Theory and Applications, pp. 348–374. Physica, New York (2000)Google Scholar
  10. 10.
    Hammerstrom, D.: Neural networks at work. IEEE Spectrum Computer Applications 30, 26–32 (1993)CrossRefGoogle Scholar
  11. 11.
    Hillson, D.: Extending the risk process to manage opportunities. International Journal of Project Management 20, 235–240 (2002)CrossRefGoogle Scholar
  12. 12.
    Hultink, E.J., Hart, S., Robben, H.S.J., Griffin, A.: Launch decisions and new product success: an empirical comparison of consumer and industrial products. Journal of Product Innovation Management 17, 5–23 (2000)CrossRefGoogle Scholar
  13. 13.
    Jaafari, A.: Management of risks, uncertainties and opportunities on projects: time for a fundamental shift. International Journal of Project Management 19, 89–101 (2001)CrossRefGoogle Scholar
  14. 14.
    Jang, J.S.R.: ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transaction on Systems, Man and Cybernetics 23, 665–685 (1993)CrossRefGoogle Scholar
  15. 15.
    Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice Hall, USA (1997)Google Scholar
  16. 16.
    Klir, G.J., Yuan, B.: Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice-Hall, Englewood Cliffs (1995)MATHGoogle Scholar
  17. 17.
    Lin, C., Lee, C.: Neural Fuzzy Systems. Prentice Hall, Englewood Cliffs (1996)Google Scholar
  18. 18.
    Maffin, D., Braiden, P.: Manufacturing and supplier roles in product development. International Journal of Production Economics 69, 205–213 (2001)CrossRefGoogle Scholar
  19. 19.
    Matlab Fuzzy Logic Toolbox. The Math Works Inc., Natick, MA (1997)Google Scholar
  20. 20.
    Miller, R., Lessard, D.: Understanding and managing risks in large engineering projects. International Journal of Project Management 19, 437–443 (2001)CrossRefGoogle Scholar
  21. 21.
    Montoya, M.M., Calantone, R.: Determinants of new product performance: a review and meta-analysis. Journal of Product Innovation Management 11, 397–417 (1994)CrossRefGoogle Scholar
  22. 22.
    Mullins, J.W., Sutherland, D.J.: New product development in rapidly changing markets: an exploratory study. Journal of Product Innovation Management 15, 224–236 (1998)CrossRefGoogle Scholar
  23. 23.
    Rao, S.S., Nahm, A., Shi, Z., Deng, X., Syamil, A.: Artificial intelligence and expert systems applications in new product development–a survey. Journal of Intelligent Manufacturing 10, 231–244 (1999)CrossRefGoogle Scholar
  24. 24.
    Riek, R.F.: From experience: Capturing hard-won NPD lessons in checklists. Journal of Product Innovation Management 18, 301–313 (2001)CrossRefGoogle Scholar
  25. 25.
    Schmidt, J.B., Calantone, R.J.: Are really new product development projects harder to shut down? Journal of Product Innovation Management 15, 111–123 (1998)CrossRefGoogle Scholar
  26. 26.
    Sun, H., Wing, W.C.: Critical success factors for new product development in the Hong Kong toy industry. Technovation 25, 293–303 (2005)CrossRefGoogle Scholar
  27. 27.
    Tzeng, G.H., Yang, Y.P.O., Lin, C.T., Chen, C.B.: Hierarchical MADM with fuzzy integral for evaluating enterprise intranet web sites. Information Sciences 169, 409–426 (2005)CrossRefMathSciNetGoogle Scholar
  28. 28.
    Özer, M.: Factors which influence decision making in new product evaluation. European Journal of Operational Research 163, 784–801 (2005)CrossRefGoogle Scholar
  29. 29.
    Zadeh, L.A.: Fuzzy sets. Information and control 8, 338–353 (1965)MATHCrossRefMathSciNetGoogle Scholar
  30. 30.
    Zaremba, M.B., Morel, G.: Integration and control of intelligence in distributed manufacturing. Journal of Intelligent Manufacturing 14, 25–42 (2003)CrossRefGoogle Scholar
  31. 31.
    Zimmermann, H.J.: Practical Applications of Fuzzy Technologies. Kluwer Academic Publishers, Massachusetts (1999)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Orhan Feyzioğlu
    • 1
  • Gülçin Büyüközkan
    • 1
  1. 1.Department of Industrial EngineeringGalatasaray UniversityİstanbulTurkey

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