Abstract
Mass customization, which aims at satisfying individual customer needs with near mass production efficiency, has become a major trend in industry. Adopting the mass customization paradigm, customer preferences have a significant impact on the product design process. Thus, it is important for companies to make proper decisions in translating the voice of customers to product specifications. To facilitate this process, a learning-based hybrid method named KBANN-DT is proposed, which combines knowledge-based artificial neural network (KBANN) and CART decision tree (DT). In this method, the KBANN algorithm is applied to modeling the relationship between customer needs and product specifications. With prior domain theory, KBANN can provide a high generalization performance even if the data set is small. Based on the trained KBANN network, the CART DT algorithm is employed to extract rules from it. To illustrate the effectiveness of the proposed method, a case study in an elevator company is reported. The results show that the proposed method can be a promising tool for product definition.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Pine BJ (1993) Mass customization: the new frontier in business competition. Harvard Business School Press, Boston MA
Tseng MM, Jiao J (1997) A variant approach to product definition by recognizing functional requirement patterns. Comput Ind Eng 33(3–4):629–633
Pugh S, Gardiner KM (1991) Total design: integrated methods for successful product engineering. Addison Wesley, Wokingham
Tseng MM, Jiao J (2001) Mass customization. In: Salvendy G (ed) Handbook of industrial engineering, 3rd edn. Wiley, New York, pp 684–709
Tseng MM, Jiao J (1998) Computer-aided requirement management for product definition: a methodology and implementation. Concurr Eng Res Appl 6(2):145–160
Du X, Jiao J (2001) Architecture of product family: fundamentals and methodology. Concurr Eng Res Appl 9(4):309–325
Tseng MM, Du X (1998) Design by customers for mass customization products. CIRP Ann - Manuf Technol 47(1):103–106
Du X, Jiao J, Tseng MM (2003) Identifying customer need patterns for customization and personalization. Integr Manuf Syst 14(5):387–396
Jiao J, Chen C-H (2006) Customer requirement management in product development: a review of research issues. Concurr Eng Res Appl 14(3):173–185
Wu H-H, Liao AYH, Wang P-C (2005) Using grey theory in quality function deployment to analyse dynamic customer requirements. Int J Adv Manuf Technol 25(11–12):1241–1247
Wu H-H, Shieh J-I (2006) Using a Markov chain model in quality function deployment to analyse customer requirements. Int J Adv Manuf Technol 30(1–2):141–146
Chen C-H, Khoo LP, Yan W (2002) A strategy for acquiring customer requirement patterns using laddering technique and ART2 neural network. Adv Eng Inf 16(3):229–240
Du X, Jiao J, Tseng MM (2006) Understanding customer satisfaction in product customization. Int J Adv Manuf Technol 31(3–4):396–406
Nagamachi M (2002) Kansei engineering in consumer product design. Ergon Des 10(2):5–9
Yan W, Chen C-H, Shieh M-D (2006) Product concept generation and selection using sorting technique and fuzzy c-means algorithm. Comput Ind Eng 50(3):273–285
Chen Z, Wang L (2006) Product definition in mass customization adopting neural network. Accepted by The 32nd Annual Conference of the IEEE Industrial Electronics Society, Paris, FRANCE, 7-10 November
Le Riche R, Gualandris D, Thomas JJ, Hemez F (2001) Neural identification of non-linear dynamic structures. Sound Vibr 248(2):247–265
Jiao J, Zhang Y (2005) Product portfolio identification based on association rule mining. Comput Aided Des 37(2):149–172
Shao X-Y, Wang Z-H, Li P-G, Feng C-XJ (2006) Integrating data mining and rough set for customer group-based discovery of product configuration rules. Int J Prod Res 44(14):2789–2811
Towell GG, Shavlik JW (1994) Knowledge-based artificial neural networks. Artif Intell 70(1–2):119–165
Sordo M, Buxton H, Watson D (2001) A hybrid approach to breast cancer diagnosis. ftp://acl.icnet.uk/pub/PUBLICATIONS/sordo/chapter2001.pdf
Li C, Xu J, Xue L (2001) Knowledge-based artificial neural network models for finline. Int J Infrared Millim Waves 22(2):351–359
Towell GG, Shavlik JW (1993) Extracting refined rules from knowledge-based neural network. Mach Learn 13(1):71–101
Breiman L (1993) Classification and regression trees. Chapman & Hall, Boca Raton
Schmitz GPJ, Aldrich C, Gouws FS (1999) ANN-DT: an algorithm for extraction of decision trees from artificial neural networks. IEEE Trans Neural Netw 10(6):1392–1401
Boz O (2002) Extracting decision trees from trained neural networks. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 456–461
Andrews R, Diederich J, Tickle AB (1995) Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowl-Based Syst 8(6):373–384
Yao JT (2005) Knowledge extracted from trained neural networks -What’s next? In: Proceedings of SPIE - The International Society for Optical Engineering, vol 5812, Data mining, intrusion detection, information assurance, and data networks security 2005, pp 151–157
Krishnan R, Sivakumar G, Bhattacharya P (1999) Extracting decision trees from trained neural networks. Pattern Recogn 32(12):1999–2009
Towell GG (1991) Symbolic knowledge and neural networks: insertion, refinement and extraction. Ph.D. thesis, University of Wisconsin, Madison
van Zyl J, Omlin CW (2001) Knowledge-based neural networks for modelling time series. In: Proc 6th International Work-Conference on artificial and natural neural networks: bio-inspired applications of connectionism, pp 579–586
Haddawy P, Ha V, Restificar A, Geisler B (2004) Preference elicitation via theory refinement. J Mach Learn Res 4(3):317–337
Srivastava L, Singh SN, Sharma J (1999) Knowledge-based neural networks for voltage contingency selection and ranking. IEE Proc. Gen Transm Distrib 146(6):649–656
Krishnan V, Gupta S (2001) Appropriateness and impact of platform-based product development. Manag Sci 47(1):52–68
Blecker T, Abdelkafi N, Kreutler G, Friedrich G (2004) An advisory system for customers’ objective needs elicitation in mass customization. In: The 4th International ICSC Symposium on Engineering of Intelligent Systems, University of Madeira, Funchal, Portugal
Osorio FS, Amy B (1999) INSS: a hybrid system for constructive machine learning. Neurocomputing 28:191–205
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. Parallel Distributed Processing: Explorations in the Microstructure of Cognition 1:318–362
Sen PK (2005) Gini diversity index, hamming distance, and curse of dimensionality. Metron - Int J Stat LXIII(3):329–349
Zhou Z-H, Jiang Y (2003) Medical diagnosis with C4.5 Rule preceded by artificial neural network ensemble. IEEE Trans Inf Technol Biomed 7(1):37–42
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yu, L., Wang, L. & Yu, J. Identification of product definition patterns in mass customization using a learning-based hybrid approach. Int J Adv Manuf Technol 38, 1061–1074 (2008). https://doi.org/10.1007/s00170-007-1152-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-007-1152-3