Ludovic Koehl

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.

A fuzzy model of customer satisfaction index in e-commerce

Customer satisfaction index (CSI) is an important concept for evaluating the quality of service in e-commerce. It permits to evaluate the validity of an e-commerce operation from the point of view of consumers. In this paper, we present a model of CSI in e-commerce using fuzzy techniques and provide a method for calculating CSI, expressed in a five levels quantity table.

Intelligent sensory evaluation: Concepts, implementations, and applications

Sensory evaluation has been widely applied in different industrial fields especially for quality inspection, product design and marketing. Classically, factorial multivariate methods are the only tool for analyzing and modeling sensory data provided by experts, panelists or consumers. These methods are efficient for solving some problems but sometimes cause important information lost. In this situation, new methods based on intelligent techniques such as fuzzy logic, neural networks, data aggregation, classification, clustering have been applied for solving uncertainty and imprecision related to sensory evaluation. These new methods can be used together with the classical ones in a complementary way for obtaining relevant information from sensory data. This paper outlines the general background of sensory evaluation and the corresponding industrial interests and explicitly indicates some orientations for further development by IT researchers.

Textile, Physiological, and Sensorial Parameters in Sock Comfort

This paper explores the influence of textile, physiological, and sensorial parameters on sock comfort. Comfort parameters of sport socks were evaluated in two different sport exercises by a non-trained panel. The criteria of the sensory evaluation were perceived foot temperature, fabric dampness, and pain. No rating scale or common lexicon was needed for the sensory evaluation. Foot temperature and humidity were measured during the sport exercises by sensors integrated in the footwear, while coefficients of friction between sock fabric samples and a mechanical skin model were measured using a previously developed Textile Friction Analyzer. The influence of textile, physiological, and sensorial parameters on the sock comfort was statistically investigated. Significant correlations were found between physiological and sensorial parameters as well as between the fabric friction and perceived comfort. Perceived comfort depended on the fibre content of sock fabrics and on the perceived dampness and temperature of the feet. Surface roughness and water content of the textiles had no influence on sock comfort. The new approach of sensory evaluation using a non-trained panel is suitable for the assessment of sock comfort. Using test parameters simulating the contact conditions between foot and sock in sport activities, the Textile Friction Analyzer is an appropriate device to determine the fabric friction which is related to the sock comfort. The results of this research represent a first step towards a better understanding of the influence of different parameters on sock comfort.

A linguistic multi-criteria group decision support system for fabric hand evaluation

Fabric hand evaluation (FHE) is the main measure in textile material selection for fashion design and development. Fabric hand evaluation requires considering multiple evaluation aspects/criteria by a group of evaluators. Some fabric features can also be measured using instruments. The evaluation often uses linguistic terms in the weights of criteria, and the weights and judgments of evaluators. To support a FHE-based material selection, this study first develops a fabric hand-based textile material evaluation model. It then proposes a human-machine measure integrated fuzzy multi-criteria group decision-making method. A software tool is also developed, which implements the proposed method and is applied in fabric hand-based textile material evaluation.

Integration of human knowledge and measured data for optimization of fabric hand

This paper presents a new method for modeling the relationship between objective and subjective fabric hand evaluations and between adjusting parameters of fabric production and objective fabric hand features. In this method, fuzzy techniques, principal component analysis and other data analysis techniques are applied and human knowledge on fabric production and numerical data measured on instruments are used in a complementary way for selecting relevant physical features and extracting fuzzy rules. The effectiveness of this method has been shown through a number of knitted cotton samples.

A 2-Tuple Fuzzy Linguistic Model for Sensory Fabric Hand Evaluation

This chapter presents two fuzzy logic based procedures for formalizing and analyzing sensory data provided by different panels on fabric hand evaluation. In the first procedure, the sensory data are transformed into fuzzy sets e ach representing the evaluation result of one individual. A 2-tuple linguistic model is used to aggregate multi-granular data provided by different individuals of the same panel. In the second procedure, we develop a method permitting to compute a dissimilarity criterion between panels and linguistic terms in different evaluation spaces of fabric hand. This dissimilarity criterion is defined according to the coherence of internal data variation in different evaluation spaces. The proposed procedures permit to analyze the expert’s and consumer’s behaviors on fabric hand on French and Chinese textile markets in order to make an adaptive design of textile products.

Design and implementation of an estimator of fractal dimension using fuzzy techniques

Abstract This paper presents a new method for estimating the fractal dimension of one-dimensional profiles. In this approach, the real fractal dimension D is considered as an implicit continuous function of the estimated fractal dimension De, the resolution and several other elements. By approximating this function from a number of experimental data, we can obtain more precise estimates of the fractal dimension D. This approximation is done using a fuzzy logic controller and an averaging procedure, permitting to, respectively, decrease two kinds of estimation errors: (1) systematic errors, which are associated with values of D, resolution, trends of profiles, and etc. (2) stochastic errors, which are mainly caused by the choice of the sequence {ek} representing the sizes of structuring elements corresponding to different scales. The effectiveness of this method is shown by estimating fractal dimensions for two sample functions and a number of natural and synthetic fibers.

Control and optimization of human perception on virtual garment products by learning from experimental data

This paper proposes an original study for controlling, in an Internet and virtual reality-based collaborative design platform, human perception on 3D virtual garments by adjusting fabric parameters, constituting the inputs to the garment CAD software. This study will permit the designer to determine the most relevant product through a number of interactions with the consumer. For this purpose, two sensory experiments have been realized on a small number of fabric samples. In the first experiment, we propose an active learning-based experimental design in order to find the most appropriate values of the fabric technical parameters permitting to minimize the overall perceptual difference between real and virtual fabrics in static and dynamic scenarios. The second sensory experiment aims to extract normalized tactile and visual sensory descriptors characterizing human perception on the concerned fabric samples. In the collaborative design process, these normalized descriptors will be used for communications between the designer and the consumer on perceptual quality of virtual products. By learning from the experimental data on identified inputs (fabric parameters of the CAD software) and outputs (sensory descriptors), we model the relationship between fabric technical parameters and human perception on finished virtual products. The method of fuzzy ID3 decision tree has successfully been applied in this modeling procedure. This model, combined with the corresponding garment CAD software and the learning data acquired from the two sensory experiments, constitutes the main components of the learning data-driven collaborative design platform. Using this platform, we have realized a number of garments meeting consumers personalized perceptual requirements.

Intelligent sensory evaluation of industrial products for exploiting consumer's preference

This paper presents an intelligent technique based method for analyzing and interpreting sensory data provided by consumers and experts in order to predict consumers preference on industrial products. In this study, we first introduce the concept of equilibrium consumption for estimating consumers data variation between two different samples. This method can effectively solve the multi-peaks problem and the order correlation problem existing in consumers data. According to the degree of consistency of data variation, we define the dissimilarities or distances between different evaluation terms used by consumers. Based on these dissimilarities, we develop a procedure for interpreting consumers terms using normalized experts evaluation terms. This procedure can also be used for predicting consumers evaluation and preference from experts evaluation on a collection of industrial products. The effectiveness of this method has been validated in one collection of cosmetic protection creams.