From 1998 to 2007 he was an Assistant Professor at the University of Valenciennes, France.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 experts evaluation of a set of garment products, concerning six predetermined fashion themes (judge perception), are treated to determine the importance level of each criterion. Application of the SEFIT algorithm: improvement of the SDD values Maximum values of judges for fashion driven products Membership functions of the weights for the price driven products (see online version for colours) Membership functions characteristics for the fashion driven products 1 Membership functions characteristics for the price driven products Figures - uploaded by Mauricio Camargo Author content All figure content in this area was uploaded by Mauricio Camargo Content may be subject to copyright. Sequential Approach To Product Design Free Public FullDiscover the worlds research 17 million members 135 million publications 700k research projects Join for free Public Full-text 1 Content uploaded by Mauricio Camargo Author content All content in this area was uploaded by Mauricio Camargo on Feb 07, 2015 Content may be subject to copyright. ![]() SEFIT methods, proposed originally by (Mirkin, 1990) attempt to explain the vari ability in the initi al d ata (commonly defined by a sum of squares) through an additive decomposition terms in the model. In this case, data from experts evaluation of a set of garment products, concerning six predeterm ined fashion themes (judge perc eption), are treated to determine the im portance level of each criterion. Keywords: decision making; product design; regression analysis; sequential algorithm; weighting functions; membership functions. Reference to this paper should be made as follows: Camargo, M., Fonteix, C. Delmotte, F. (2013) Complex da ta structures in product design: a sequential approach to elicit cus tomer perceptions, Int. J. Advanced Operations Management, Vol. Sequential Approach To Product Design Trial Engineering SchoolNo. 1, pp.4557. Biographical notes: Mauricio Camargo is an Associate Professor on Management of Technology and Innovati on at the Eco le Nationale en Gnie des Systmes Industriels of Nancy (The Industrial Engineering School of th e University of Lorraine France ). His main research interests are new product development, cost estimation models, and design-to-c ost. His late researches concerns application of multi-objective evolution ary techniques to evaluate product performances and innovativene ss at the design stage. Christian Fonteix is a Full Professor in th e Univ ersity of Lorraine. His field of research is modelling, multi criteria optimisation and dec ision aid of polymerisations and composites producti ons. He was Associate Professor from 1973 to 2001 in the University of Lorraine. He obtained his PhD in 1978, on the study of scrubbers dynamics. Between 1982 and 1998, he worked on automatic control of bioprocess es. His actual research takes place in the Reactions and Process Engineering Labor atory, a component o f the French National Centre of the Scientific Research. Francois Delmotte obtained his PhD in Control Theory from the Universit y of Lille, France, in 1997. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |