Title: Complex data structures in product design: a sequential approach to elicit customer perceptions
Authors: Mauricio Camargo; Christian Fonteix; Francois Delmotte
Addresses: ERPI-Université de Lorraine, 8 rue Bastien Lepage, B.P. 647 F-54010 Nancy Cedex, France ' LRGP-CNRS UPR 6811 Université de Lorraine, 1 rue Grandville, B.P. 451, 54001 Nancy Cedex, France ' LGI2A Faculté des Sciences Appliquées, Université d'Artois, Techno parc Futura 62400 Bethune Cedex, France
Abstract: The present paper proposes a new methodology to integrate expert judgement in new product development decision process. In particular, within the garment industry product evaluation data come mainly from judges or consumer panels. Treatment of aggregate data is difficult as some measures could seem to be contradictory. To deal with this issue the present paper proposes the application of a sequential fitting (SEFIT) approach to exploit information from the whole set of data. SEFIT methods, proposed originally by (Mirkin, 1990) attempt to explain the variability in the initial data (commonly defined by a sum of squares) through an additive decomposition terms in the model. In this case, data from expert's evaluation of a set of garment products, concerning six predetermined fashion themes (judge perception), are treated to determine the importance level of each criterion.
Keywords: decision making; product design; regression analysis; sequential algorithms; weighting functions; membership functions; complex data structures; customer perceptions; expert judgement; new product development; NPD; garment industry; apparel industry; clothing industry; product evaluation; sequential fitting; fashion themes.
International Journal of Advanced Operations Management, 2013 Vol.5 No.1, pp.45 - 57
Available online: 05 Jan 2013 *Full-text access for editors Access for subscribers Purchase this article Comment on this article