Theme-Based Comprehensive Evaluation in New Product Development Using Fuzzy Hierarchical Criteria Group Decision-Making Method

One of the features of the digital ecosystem is the integration of human cognition and socio-economic themes into the process of new product development (NPD). In a socio-economic theme-based NPD, ranking a set of product prototypes that have been designed always requires the participation of multiple evaluators and consideration of multiple evaluation criteria. Using the well-being theme-based garment NPD as a background, this paper first presents a fuzzy hierarchical criteria group decision-making (FHCGDM) method which can effectively calculate final ranking results through fusing all assessment data from human beings and machines. It then presents a garment NPD comprehensive evaluation model with hierarchical criteria under the well-being theme through identifying a set of marketing tactics from a consumer acceptance survey. It further provides an establishment process for an NPD evaluation model under the digital ecosystem framework. Finally, a garment NPD case study further demonstrates the proposed well-being NPD comprehensive evaluation model and the FHCGDM method. The advantages of the proposed evaluation method include successfully handling criteria in a hierarchical structure, automatically processing both objective measurements from machines and subjective assessments from human evaluators, and using the most suitable type of fuzzy numbers to describe linguistic terms.

[1]  Jelka Geršak Development of the system for qualitative prediction of garments appearance quality , 2002 .

[2]  Matthew J. Liberatore,et al.  Expert Support Systems for New Product Development Decision Making: A Modeling Framework and Applications , 1995 .

[3]  Edward N. Wolff,et al.  What has happened to the quality of life in the advanced industrialized nations , 2004 .

[4]  Ren C. Luo,et al.  Multilayered fuzzy behavior fusion for real-time reactive control of systems with multiple sensors , 1996, IEEE Trans. Ind. Electron..

[5]  Juite Wang,et al.  A fuzzy multicriteria group decision making approach to select configuration items for software development , 2003, Fuzzy Sets Syst..

[6]  Ching-Lai Hwang,et al.  Multiple attribute decision making : an introduction , 1995 .

[7]  James A. Stover,et al.  A fuzzy-logic architecture for autonomous multisensor data fusion , 1996, IEEE Trans. Ind. Electron..

[8]  Cornelis Cristo Van Waveren,et al.  NEW PRODUCT DEVELOPMENT WITH DYNAMIC DECISION SUPPORT , 2009 .

[9]  Francisco Herrera,et al.  Theory and Methodology Choice functions and mechanisms for linguistic preference relations , 2000 .

[10]  Thomas G. Habetler,et al.  A Survey on Testing and Monitoring Methods for Stator Insulation Systems of Low-Voltage Induction Machines Focusing on Turn Insulation Problems , 2008, IEEE Transactions on Industrial Electronics.

[11]  Mikihiko Matsui,et al.  Fuzzy-Logic-Based $V/f$ Control of an Induction Motor for a DC Grid Power-Leveling System Using Flywheel Energy Storage Equipment , 2009, IEEE Transactions on Industrial Electronics.

[12]  Abbie Griffin,et al.  The PDMA toolbook 2 for new product development , 2004 .

[13]  Lida Xu,et al.  A decision support system for product design in concurrent engineering , 2007, Decis. Support Syst..

[14]  Edson Bim Fuzzy optimization for rotor constant identification of an indirect FOC induction motor drive , 2001, IEEE Trans. Ind. Electron..

[15]  Da Ruan,et al.  Multi-Objective Group DSS , 2007 .

[16]  Henry J. Bruton,et al.  On the search for well-being , 1997 .

[17]  Da Ruan,et al.  Intelligent multi-criteria fuzzy group decision-making for situation assessments , 2007, Soft Comput..

[18]  Elizabeth Chang,et al.  A framework for discovering and classifying ubiquitous services in digital health ecosystems , 2011, J. Comput. Syst. Sci..

[19]  Da Ruan,et al.  Multi-Objective Group Decision Making - Methods, Software and Applications with Fuzzy Set Techniques(With CD-ROM) , 2007, Series in Electrical and Computer Engineering.

[20]  Jie Lu,et al.  An Integrated Group Decision-Making Method Dealing with Fuzzy Preferences for Alternatives and Individual Judgments for Selection Criteria , 2003 .

[21]  Michel Grabisch,et al.  Using the transferable belief model and a qualitative possibility theory approach on an illustrative example: The assessment of the value of a candidate * , 2001, Int. J. Intell. Syst..

[22]  Martin West,et al.  Digital ecosystems and comparison to existing collaboration environment , 2006 .

[23]  Nikos I. Karacapilidis,et al.  Computer-supported collaborative argumentation and fuzzy similarity measures in multiple criteria decision making , 2000, Comput. Oper. Res..

[24]  Xinghuo Yu,et al.  Sliding-Mode Control With Soft Computing: A Survey , 2009, IEEE Transactions on Industrial Electronics.

[25]  Pao-Long Chang,et al.  A fuzzy multi-criteria decision making method for technology transfer strategy selection in biotechnology , 1994 .

[26]  Kuo-Shen Chen,et al.  Development of a Digital-Convolution-Based Process Emulator for Three-Dimensional Microstructure Fabrication Using Electron-Beam Lithography , 2009, IEEE Transactions on Industrial Electronics.

[27]  Itsuo Hatono,et al.  Linguistic labels for expressing fuzzy preference relations in fuzzy group decision making , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[28]  Jie Lu,et al.  Decider: A fuzzy multi-criteria group decision support system , 2010, Knowl. Based Syst..

[29]  Christer Carlsson,et al.  Fuzzy multiple criteria decision making: Recent developments , 1996, Fuzzy Sets Syst..

[30]  Edward E. Jones The New Technology and Human Values. , 1966 .

[31]  Kulwant S. Pawar,et al.  Performance evaluation of new product development from a company perspective , 2001 .

[32]  Fateh Krim,et al.  Fuzzy-Logic-Based Switching State Selection for Direct Power Control of Three-Phase PWM Rectifier , 2009, IEEE Transactions on Industrial Electronics.