Evaluation of New Product Development Projects using Artificial Intelligence and Fuzzy Logic
As a vital activity for companies, new product
development (NPD) 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 (go/no go decision) 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.
[1] S. L. Brown and K. M. Eisenhardt, "Product development: past research,
present findings and future directions," Academy of Management
Review, vol. 20, pp. 343-378, 1995.
[2] D. Maffin and P. Braiden, "Manufacturing and supplier roles in product
development," International Journal of Production Economics, vol. 69,
pp. 205-213, 2001.
[3] R. G. Cooper, "Perspective: third generation new product processes.,"
Journal of Product Innovation Management, vol. 11, pp. 3-14, 1994.
[4] J. B. Schmidt and R. J. Calantone, "Are really new product development
projects harder to shut down?," Journal of Product Innovation
Management, vol. 15, pp. 111-123, 1998.
[5] M. Özer, "Factors which influence decision making in new product
evaluation," European Journal of Operational Research, vol. 163, pp.
784-801, 2005.
[6] D. Hillson, "Extending the risk process to manage opportunities.,"
International Journal of Project Management, vol. 20, pp. 235-240,
2002.
[7] S. S. Rao, A. Nahm, Z. Shi, X. Deng and A. Syamil, "Artificial
intelligence and expert systems applications in new product
development-a survey," Journal of Intelligent Manufacturing, vol. 10,
pp. 231-244, 1999.
[8] M. B. Zaremba and G. Morel, "Integration and control of intelligence in
distributed manufacturing," Journal of Intelligent Manufacturing, vol.
14, pp. 25-42, 2003.
[9] D. Hammerstrom, "Neural networks at work," IEEE Spectrum Computer
Applications, vol. 30, pp. 26-32, 1993.
[10] C. Lin and C. Lee, Neural Fuzzy Systems. New Jersey: Prentice Hall,
1996.
[11] G. J. Klir and B. Yuan, Fuzzy Sets and Fuzzy Logic: Theory and
Applications. New Jersey: Prentice-Hall, 1995.
[12] Z. A. Zadeh, "Fuzzy sets," Information and Control, vol. 8, pp. 338-
353, 1965.
[13] H. J. Zimmermann, Practical Applications of Fuzzy Technologies.
Massachusetts: Kluwer Academic Publishers, 1999.
[14] J. C. Bezdek, D. Dubois and H. Prade, Fuzzy sets approximate reasoning
and information systems. Netherlands: Kluwer Academic Publishers
Group, 1999.
[15] J. S. R. Jang, "ANFIS Adaptive-Network-Based Fuzzy Inference
System," IEEE Transaction on Systems, Man and Cybernetics, vol. 23,
pp. 665-685, 1993.
[16] M. Grabisch and M. Roubens, "Application of the Choquet integral in
multi-criteria decision making," in Fuzzy Measures and Integrals:
Theory and Applications, M. Grabisch, T. Murofushi and M. Sugeno,
M., Eds. New York: Physica, 2000, pp. 348-374.
[17] M. Grabisch, "k-order additive discrete fuzzy measures and their
representation," Fuzzy Sets and Systems, vol. 92, pp. 167-189, 1997.
[18] Matlab Fuzzy Logic Toolbox Manual, The Math Works Inc., Natick,
MA, 1997.
[19] J. S. R. Jang, C. T. Sun and E. Mizutani, Neuro-Fuzzy and Soft
Computing: A Computational Approach to Learning and Machine
Intelligence. Prentice Hall, USA, 1997.
[20] E. Danneels and E. J. Kleinschmidt, "Product innovativeness from the
firm-s perspective: its dimensions and their relation with project
selection and performance," Journal of Product Innovation
Management, vol. 18, pp. 357-373, 2001.
[21] H. Ernst, "Success factors of new product development: a review of the
empirical literature," International Journal of Management Reviews, vol.
4, pp. 1-40, 2002.
[22] P. Carbonell-Foulquie, J. L. Munuera-Aleman and A. I. Rodriguez-
Escudero, "Criteria employed fo go/no-go decisions when developing
successful highly innovative products," Industrial Marketing
Management, vol. 33, pp. 307-316, 2004.
[23] M. M. Montoya and R. Calantone, "Determinants of new product
performance: a review and meta-analysis," Journal of Product
Innovation Management, vol. 11, pp. 397-417, 1994.
[24] H. Sun and W. C. Wing, "Critical success factors for new product
development in the Hong Kong toy industry," Technovation, vol. 25, pp.
293-303, 2005.
[25] G. H. Tzeng, Y. P. O. Yang, C. T. Lin and C. B. Chen, "Hierarchical
MADM with fuzzy integral for evaluating enterprise intranet web sites.,"
Information Sciences, vol. 169, pp. 409- 426, 2005.
[1] S. L. Brown and K. M. Eisenhardt, "Product development: past research,
present findings and future directions," Academy of Management
Review, vol. 20, pp. 343-378, 1995.
[2] D. Maffin and P. Braiden, "Manufacturing and supplier roles in product
development," International Journal of Production Economics, vol. 69,
pp. 205-213, 2001.
[3] R. G. Cooper, "Perspective: third generation new product processes.,"
Journal of Product Innovation Management, vol. 11, pp. 3-14, 1994.
[4] J. B. Schmidt and R. J. Calantone, "Are really new product development
projects harder to shut down?," Journal of Product Innovation
Management, vol. 15, pp. 111-123, 1998.
[5] M. Özer, "Factors which influence decision making in new product
evaluation," European Journal of Operational Research, vol. 163, pp.
784-801, 2005.
[6] D. Hillson, "Extending the risk process to manage opportunities.,"
International Journal of Project Management, vol. 20, pp. 235-240,
2002.
[7] S. S. Rao, A. Nahm, Z. Shi, X. Deng and A. Syamil, "Artificial
intelligence and expert systems applications in new product
development-a survey," Journal of Intelligent Manufacturing, vol. 10,
pp. 231-244, 1999.
[8] M. B. Zaremba and G. Morel, "Integration and control of intelligence in
distributed manufacturing," Journal of Intelligent Manufacturing, vol.
14, pp. 25-42, 2003.
[9] D. Hammerstrom, "Neural networks at work," IEEE Spectrum Computer
Applications, vol. 30, pp. 26-32, 1993.
[10] C. Lin and C. Lee, Neural Fuzzy Systems. New Jersey: Prentice Hall,
1996.
[11] G. J. Klir and B. Yuan, Fuzzy Sets and Fuzzy Logic: Theory and
Applications. New Jersey: Prentice-Hall, 1995.
[12] Z. A. Zadeh, "Fuzzy sets," Information and Control, vol. 8, pp. 338-
353, 1965.
[13] H. J. Zimmermann, Practical Applications of Fuzzy Technologies.
Massachusetts: Kluwer Academic Publishers, 1999.
[14] J. C. Bezdek, D. Dubois and H. Prade, Fuzzy sets approximate reasoning
and information systems. Netherlands: Kluwer Academic Publishers
Group, 1999.
[15] J. S. R. Jang, "ANFIS Adaptive-Network-Based Fuzzy Inference
System," IEEE Transaction on Systems, Man and Cybernetics, vol. 23,
pp. 665-685, 1993.
[16] M. Grabisch and M. Roubens, "Application of the Choquet integral in
multi-criteria decision making," in Fuzzy Measures and Integrals:
Theory and Applications, M. Grabisch, T. Murofushi and M. Sugeno,
M., Eds. New York: Physica, 2000, pp. 348-374.
[17] M. Grabisch, "k-order additive discrete fuzzy measures and their
representation," Fuzzy Sets and Systems, vol. 92, pp. 167-189, 1997.
[18] Matlab Fuzzy Logic Toolbox Manual, The Math Works Inc., Natick,
MA, 1997.
[19] J. S. R. Jang, C. T. Sun and E. Mizutani, Neuro-Fuzzy and Soft
Computing: A Computational Approach to Learning and Machine
Intelligence. Prentice Hall, USA, 1997.
[20] E. Danneels and E. J. Kleinschmidt, "Product innovativeness from the
firm-s perspective: its dimensions and their relation with project
selection and performance," Journal of Product Innovation
Management, vol. 18, pp. 357-373, 2001.
[21] H. Ernst, "Success factors of new product development: a review of the
empirical literature," International Journal of Management Reviews, vol.
4, pp. 1-40, 2002.
[22] P. Carbonell-Foulquie, J. L. Munuera-Aleman and A. I. Rodriguez-
Escudero, "Criteria employed fo go/no-go decisions when developing
successful highly innovative products," Industrial Marketing
Management, vol. 33, pp. 307-316, 2004.
[23] M. M. Montoya and R. Calantone, "Determinants of new product
performance: a review and meta-analysis," Journal of Product
Innovation Management, vol. 11, pp. 397-417, 1994.
[24] H. Sun and W. C. Wing, "Critical success factors for new product
development in the Hong Kong toy industry," Technovation, vol. 25, pp.
293-303, 2005.
[25] G. H. Tzeng, Y. P. O. Yang, C. T. Lin and C. B. Chen, "Hierarchical
MADM with fuzzy integral for evaluating enterprise intranet web sites.,"
Information Sciences, vol. 169, pp. 409- 426, 2005.
@article{"International Journal of Business, Human and Social Sciences:62792", author = "Orhan Feyzioğlu and Gülçin Büyüközkan", title = "Evaluation of New Product Development Projects using Artificial Intelligence and Fuzzy Logic", abstract = "As a vital activity for companies, new product
development (NPD) 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 (go/no go decision) 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.", keywords = "Decision Making, Neural Networks, Fuzzy Theory
and Systems, Choquet Integral, New Product Development.", volume = "1", number = "11", pages = "736-7", }
