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A CBR System to New Product Development: An Application for Hearing Devices Design

Authors: J.L. Castro, K. Benghazi, M.V. Hurtado, M. Navarro, J.M. Zurita


Nowadays, quick technological changes force companies to develop innovative products in an increasingly competitive environment. Therefore, how to enhance the time of new product development is very important. This design problem often lacks the exact formula for getting it, and highly depends upon human designers- past experiences. For these reasons, in this work, a Casebased reasoning (CBR) system to assist in new product development is proposed. When a case is recovered from the case base, the system will take into account not only the attribute-s specific value and how important it is. It will also take into account if the attribute has a positive influence over the product development. Hence the manufacturing time will be improved. This information will be introduced as a new concept called “adaptability". An application to this method for hearing instrument new design illustrates the proposed approach.

Keywords: Case based reasoning, Fuzzy logic, New product development, Retrieval stage, Similarity.

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[1] D. Arias-Aranda, J.L. Castro, M. Navarro, J.M. Zurita, A CBR system for knowing the relationship between flexibility and operations strategy, ISMIS, Lectures Notes in Artificial Intelligence, Springer Berlin, 2009, pp. 463-472.
[2] M.-Y. Cheng, H.-C. Tsai, Y.-H. Chiu, Fuzzy case-based reasoning for coping with construction disputes, Expert Systems with Applications, 36(2), 2009, pp. 4106-4113.
[3] Y.J. Park, B.C. Kim, S.H. Chum, New knowledge extraction technique using probability for case-based reasoning: application to medical diagnosis, Expert Systems, 23(1), 2006, pp. 2-20.
[4] H. Li, J. Sun, Gaussian case-based reasoning for business failure prediction with empirical data in China, Information Sciences, 179(1-2), 2009, pp. 89-108.
[5] H. Li, J. Sun, Ranking-order case-based reasoning for financial distress prediction, Knowledge-Based Systems, 21(8), 2008, pp. 868-878.
[6] S. Negny, J.M. Le Lann, Acceleration of the Retrieval of past experiences in Case Based Reasoning: application for preliminary design in chemical engineering, Computer Aided Chemical Engineering, 25, 2008, pp. 1009- 1014.
[7] M.S Suh, W.C. Jhee, Y.K. Ko, A. Lee, A case-based expert system approach for quality design, Expert Systems with Applications, 15, 1998, pp. 181-190.
[8] M.-C. Wu, Y.-L. Lo, S.-H. Hsu, A fuzzy CBR technique for generating product ideas, Expert Systems with Applications, 34, 2008, pp. 530-540.
[9] A. Aamodt, E. Plaza, Case-based reasoning: foundational issues, methodological variations and system approaches, AI Communications, 7(1), 1994, pp. 39-59.
[10] T. Cover, P. Hart, Nearest neighbor pattern classification, Information Theory, IEEE Transactions on, 13(1), 1967, pp. 21-27.
[11] J.W. Schaaf, Fish and shrink: a next step towards efficient case retrieval in large scaled case bases, In: Advances in Case-based Reasoning: Third European Workshop, Lausanne, Switzerland, 1996, pp. 362-377.
[12] K.H. Im, S.C. Park, Case-based reasoning and neural network based expert system for personalization, Expert Systems with Applications, 32(1), 2007, pp. 77-85.
[13] P.-C. Chang, C.-Y. Fan, W.-Y. Dzan, A CBR-based fuzzy decision tree approach for database classification, Expert Systems with Applications, 37(1), 2010, pp. 214-225.
[14] J.L. Castro, M. Navarro, J.M. S'anchez, J.M. Zurita, Similarity local adjustment: Introducing attribute risk into the case, In: Proceedings of the European and Mediterranean Conference on Information Systems, Alicante, Spain, 2006.
[15] J.L. Castro, M. Navarro, J.M. S'anchez, J.M. Zurita, Global risk attribute in case-based reasoning, In: Proceedings of the 7th International Conference on Case-Based Reasoning, Belfast, Ireland, 2007, pp. 21-30.
[16] J.L. Castro, M. Navarro, J.M. S'anchez, J.M. Zurita, An automatic method to assign local risk, In: Proceedings of the IADIS multi conference on computer science and information systems Amsterdam,IADIS-08, The Netherlands, 2008, pp. 151-157.
[17] J.L. Castro, M. Navarro, J.M. S'anchez, J.M. Zurita, Loss and Gain Functions for CBR Retrieval, Information Sciences, 179(11), 2009, pp. 1738-1750.
[18] A. Khurana, S.R. Rosenthal, Integrating the Fuzzy Front End of New Product Development, Sloan management review, 38(2), 1997, pp. 103- 120.
[19] F.J. Miranda, T.M. Baegil, The effect of new product development techniques on new product success in Spanish firms, Industrial Marketing Management, 31(3), 2002, pp. 261-271.
[20] P.R. Carlille, A pragmatic view of knowledge and boundaries: boundary objects in new product development, Organization Science, 13(4), 2002, pp. 442-455.
[21] T. Takagi, M. Sugeno, Fuzzy identification of systems and its application to modelling and control, IEEE Transaction on Systems, Man, and Cybernetics 15(1), 1985, pp. 116-132.
[22] T.W. Liao, Z. Zhang, C.R. Mount, Similarity measures for retrieval in Case-based reasoning systems, Applied Artificial Intelligence, 12, 1998, pp. 267-288.
[23] H. N'u˜nez, M. S'anchez-Marr'e, U. Cort'es, J. Comas, M. Mart'ınez, I. Rodr'ıguez-Roda, M. Poch, A comparative study on the use of similarity measures in case-based reasoning to improve the classification of environmental system situations, Environmental Modelling & Software, 19(9), 2004, pp. 809-819.
[24] D.R. Wilson, R. Martinez, Improved heterogeneous distance functions, Journal of Artificial Intelligence Research, 6, 1997, pp. 1-34.