Modeling customer satisfaction for new product development using a PSO-based ANFIS approach

When developing new products, it is important to understand customer perception towards consumer products. It is because the success of new products is heavily dependent on the associated customer satisfaction level. If customers are satisfied with a new product, the chance of the product being successful in marketplaces would be higher. Various approaches have been attempted to model the relationship between customer satisfaction and design attributes of products. In this paper, a particle swarm optimization (PSO) based ANFIS approach to modeling customer satisfaction is proposed for improving the modeling accuracy. In the approach, PSO is employed to determine the parameters of an ANFIS from which better customer satisfaction models in terms of modeling accuracy can be generated. A notebook computer design is used as an example to illustrate the approach. To evaluate the effectiveness of the proposed approach, modeling results based on the proposed approach are compared with those based on the fuzzy regression (FR), ANFIS and genetic algorithm (GA)-based ANFIS approaches. The comparisons indicate that the proposed approach can effectively generate customer satisfaction models and that their modeling results outperform those based on the other three methods in terms of mean absolute errors and variance of errors.     

ANFIS modeling for predicting affective responses to tactile textures

The Adaptive Neuro‐Fuzzy Inference System (ANFIS) is proposed to simulate and analyze the mapping between the physical properties of tactile textures and people's affective responses. People were asked to rate the tactile feeling of 37 tactile textures against six pairs of adjectives on a semantic differential questionnaire. The friction coefficient, average roughness, compliance, and a thermal parameter of each tactile texture were measured. ANFIS models were built to predict the affective responses to tactile textures. The resulting ANFIS models demonstrated a good match between predicted and actual responses, and always yielded better performance when compared to linear and exponential regression models. The effects of physical properties of textures on affective responses were also analyzed by simulating the synthetic data with ANFIS. © 2011 Wiley Periodicals, Inc.     

Risk evaluation of customer integration in new product development under uncertainty

This study mainly focuses on the risk evaluation of customer integration in new product development. Customer integration in product innovation projects has been widely recognized a best practice to enhance innovation success rate and reduces the development cycle time, but it also has many potential risks including loss of know-how, much dependence on customer, and limitation to incremental innovations, etc. Unfortunately, there are few researches about risk evaluation for customer integration which is important to the risk management of the co-innovation process. Further, evaluating customer integration risk involves much subjectivity and vagueness. To manipulate this problem, a novel evaluation approach for assessing customer integration risk under uncertainty is proposed. The novel approach integrates the merit of rough set theory in handling vagueness and the strength of group analytic hierarchy process (GAHP) in modeling hierarchy evaluation. Finally, an application in a project of mobile phone development is provided to demonstrate the application and potential of the methodology.     

A multi-objective genetic algorithm approach to rule mining for affective product design

A novel multi-objective genetic algorithm (GA)-based rule-mining method for affective product design is proposed to discover a set of rules relating design attributes with customer evaluation based on survey data. The proposed method can generate approximate rules to consider the ambiguity of customer assessments. The generated rules can be used to determine the lower and upper limits of the affective effect of design patterns. For a rule-mining problem, the proposed multi-objective GA approach could simultaneously consider the accuracy, comprehensibility, and definability of approximate rules. In addition, the proposed approach can deal with categorical attributes and quantitative attributes, and determine the interval of quantitative attributes. Categorical and quantitative attributes in affective product design should be considered because they are commonly used to define the design profile of a product. In this paper, a two-stage rule-mining approach is proposed to generate rules with a simple chromosome design in the first stage of rule mining. In the second stage of rule mining, entire rule sets are refined to determine solutions considering rule interaction. A case study on mobile phones is used to demonstrate and validate the performance of the proposed rule-mining method. The method can discover rule sets with good support and coverage rates from the survey data.     

Rough set based bilateral filter design for denoising brain MR images

A study on bilateral filter for denoising reveals that more informative the filters are, better is the result expected. Moreover, getting precise information of the image with noise is a difficult task. In the current work, a rough set theory (RST) based approach is used to derive pixel level edge map and class labels which in turn are used to improve the performance of bilateral filters. RST handles the uncertainty present in the data even under noise. The basic structure of existing bilateral filter is not changed much, however, boosted up by prior information derived by rough edge map and rough class labels. The filter is extensively applied to denoise brain MR images. The results are compared with that of the state-of-the-art approaches. The experiments have been performed on two real (normal and pathological disordered) human MR databases. The performance of the proposed filter is found to be better, in terms of benchmark metrics.     

Optimal concurrent design based upon distributed product development life-cycle modeling

This research introduces an optimal concurrent design approach based upon a previously developed distributed product development life-cycle modeling method. In this approach, the product realization process alternatives and relevant activities are modeled at different locations that are connected through the Internet. Relations among these alternative activities are described by an AND/OR graph. The optimal product realization process alternative and its parameter values are identified using a multi-level optimization method. Genetic programming (GP) and particle swarm optimization (PSO) are employed for identifying the optimal product realization process alternative and the optimal parameter values of the feasible alternatives, respectively.     

Rough set based 1-v-1 and 1-v-r approaches to support vector machine multi-classification

Support vector machines (SVMs) are essentially binary classifiers. To improve their applicability, several methods have been suggested for extending SVMs for multi-classification, including one-versus-one (1-v-1), one-versus-rest (1-v-r) and DAGSVM. In this paper, we first describe how binary classification with SVMs can be interpreted using rough sets. A rough set approach to SVM classification removes the necessity of exact classification and is especially useful when dealing with noisy data. Next, by utilizing the boundary region in rough sets, we suggest two new approaches, extensions of 1-v-r and 1-v-1, to SVM multi-classification that allow for an error rate. We explicitly demonstrate how our extended 1-v-r may shorten the training time of the conventional 1-v-r approach. In addition, we show that our 1-v-1 approach may have reduced storage requirements compared to the conventional 1-v-1 and DAGSVM techniques. Our techniques also provide better semantic interpretations of the classification process. The theoretical conclusions are supported by experimental findings involving a synthetic dataset.     

Dynamic modelling of customer preferences for product design using DENFIS and opinion mining

Previous studies mainly employed customer surveys to collect survey data for understanding customer preferences on products and developing customer preference models. In reality, customer preferences on products could change over time. Thus, the time series data of customer preferences under different time periods should be collected for the modelling of customer preferences. However, it is difficult to obtain the time series data based on customer surveys because of long survey time and substantial resources involved. In recent years, a large number of online customer reviews of products can be found on various websites, from which the time series data of customer preferences can be extracted easily. Some previous studies have attempted to analyse customer preferences on products based on online customer reviews. However, two issues were not addressed in previous studies which are the fuzziness of the sentiment expressed by customers existing in online reviews and the modelling of customer preferences based on the time series data obtained from online reviews. In this paper, a new methodology for dynamic modelling of customer preferences based on online customer reviews is proposed to address the two issues which mainly involves opinion mining and dynamic evolving neural-fuzzy inference system (DENFIS). Opinion mining is adopted to analyze online reviews and perform sentiment analysis on the reviews under different time periods. With the mined time series data and the product attribute settings of reviewed products, a DENFIS approach is introduced to perform the dynamic modelling of customer preferences. A case study is used to illustrate the proposed methodology. The results of validation tests indicate that the proposed DENFIS approach outperforms various adaptive neuro-fuzzy inference system (ANFIS) approaches in the dynamic modelling of customer preferences in terms of the mean relative error and variance of errors. In addition, the proposed DENFIS approach can provide both crisp and fuzzy outputs that cannot be realized by using existing ANFIS and conventional DENFIS approaches.     

PSO-based intelligent integration of design and control for one kind of curing process

In this paper, a PSO-based intelligent integration of design and control is proposed for one kind of nonlinear curing process. This method combines the merits of both fuzzy modeling/control and PSO method, where fuzzy modeling/control is proposed to approximate/control the nonlinear process in a large operating region and the PSO-based intelligent optimization method is developed to solve non-convex and non-differential integration problem with design and control optimized simultaneously. Finally, the proposed method is compared with the traditional sequential method on controlling the temperature profile of a nonlinear curing process.     

Development,analysis and applications of a quantitative methodology for assessing customer satisfaction using evolutionary optimization

Consumer-oriented companies are getting increasingly more sensitive about customer's perception of their products, not only to get a feedback on their popularity, but also to improve the quality and service through a better understanding of design issues for further development. However, a consumer's perception is often qualitative and is achieved through third party surveys or the company's recording of after-sale feedback through explicit surveys or warranty based commitments. In this paper, we consider an automobile company's warranty records for different vehicle models and suggest a data mining procedure to assign a customer satisfaction index (CSI) to each vehicle model based on the perceived notion of the level of satisfaction of customers. Based on the developed CSI function, customers are then divided into satisfied and dissatisfied customer groups. The warranty data are then clustered separately for each group and analyzed to find possible causes (field failures) and their relative effects on customer's satisfaction (or dissatisfaction) for a vehicle model. Finally, speculative introspection has been made to identify the amount of improvement in CSI that can be achieved by the reduction of some critical field failures through better design practices. Thus, this paper shows how warranty data from customers can be utilized to have a better perception of ranking of a product compared to its competitors in the market and also to identify possible causes for making some customers dissatisfied and eventually to help percolate these issues at the design level. This closes the design cycle loop in which after a design is converted into a product, its perceived level of satisfaction by customers can also provide valuable information to help make the design better in an iterative manner. The proposed methodology is generic and novel, and can be applied to other consumer products as well.     

Enhanced customer requirement classification for product design using big data and improved Kano model

The classification of customer requirements (CRs) has a significant impact on the solution of product design. Existing CRs classification methods such as the Kano model and IPA model are time-consuming and inaccurate. This paper proposes a CRs classification method for product design using big data of online customer reviews of products to classify CRs accurately and efficiently. Comments of customer reviews are matched to CRs using a hierarchical semantic similarity method. Customer satisfaction degrees are defined based on emotional levels of adjectives and adverbs of customer comments using word vectors. The function implementation degree of each product is determined by specifications crawled from online products. Fitting curves are formed by defined customer satisfaction and function implementation of CRs using polynomial modeling and least square methods. Based on the slope of the fitted curves, CRs are classified to provide the minimum and maximum function implementations of CRs in each CR group to guide a product design process. The proposed method is applied in a case study of defining CRs classifications for design of upper limb rehabilitation devices. For verifying the proposed method, CRs defined by the existing methods are compared with CRs from the proposed method in design of an upper limb rehabilitation device.     

A computerized team approach for concurrent product and process design optimization

Concurrent product and process design (CPPD) can be handled by developing a computerized team approach. In the team approach, CPPD at the top-level abstraction is treated as a two-objective optimization problem with a view to two teams: design and manufacture. The design team aims to optimize the overall product functionality (or performance) and the manufacture team to minimize the total manufacturing cost. Satisfaction metrics are used to characterize the degree of how each team prefers a design in seeking the most favorite that best fulfills the goal of a team. Using min and geometric mean operators as a baseline, the satisfaction levels of two teams can be aggregated with respect to strategic team paradigms derived from game theory. With satisfaction metrics, three dual-team based CPPD models are developed, along with the computational algorithms, in an attempt to reveal team activities in the context of design. The procedures to simulate and implement various team design models are demonstrated through a design example.     

粗糙集及PSO优化BP网络的故障诊断研究

针对PP神经网络故障诊断存在网络结构复杂、训练时间长、精度不高的问题,将粗糙集、微粒群算法、遗传算法引入到柴油机故障诊断中,提出了基于粗糙集理论与改进PP神经网络相结合的柴油机故障诊断算法。算法采用自组织映射方法对连续属性离散化,利用粗糙集理论对特征参数进行属性约简,使用微粒群算法优化PP网络结构,从而缩短训练时间,有效提高故障诊断的准确度。最后用柴油机的实际诊断结果验证了该算法的可行性、快速性和准确性。     

Modular and platform methods for product family design: literature analysis

After the industrial revolution, the literature has mentioned different principles to allow a better management of the production and product life cycle activities. For example the principle of standardization was first mentioned in the literature by an automobile engineer and placed in a real context by Henry Ford. Standardization has made possible the configuration of different products using a large set of common components. Another strategy called modularization was first mentioned in the literature in the 60s. The modularity proposed to group components of products in a module for practical production objectives. Today, modularity and standardization are promising tools in product family development because they allow to design a variety of products using the same modules of components called platforms. Using platforms allows important family design savings and easy manufacturing. In this paper we give a literature review of the platform concept with a special interest on the efficient product family development. This paper is organized as follows. Section 1 mentions the general context of modularity to develop product variety. Section 2 details the importance of product architectures in the literature for a modular design. Section 3 points on some important works that apply some modular and platform methodologies.This revised version was published in June 2005 with corrected page numbers.     

Facial configuration and BMI based personalized face and upper body modeling for customer-oriented wearable product design

To realize truly customer-oriented wearable products, individual users’ unique characteristics and features should be properly captured and represented. This research focuses on an efficient methodology to generate low polygonal virtual human face models, which overcome the limitation of existing high polygonal models. To determine individuals’ characteristics in the conceptual design stage of wearable products, a computerized and personalized 3D face model should be efficiently generated and be able to interact with wearable products. This research formulates a computerized 3D face via a 3D feature-based transformation. The developed algorithm is able to concisely and efficiently create a 3D face by using frontal and lateral pictures of users. The performance of this algorithm is well adapted both to typical PCs and to mobile devices. The generated virtual face models can serve as communication media in a multi-device based collaborative design environment. Through experiments, the validity of the proposed modeling method is considerably acceptable with respect to the quality of the similarity between 3D faces and individual pictures. Finally, this paper discusses how the developed personalized face modeling can be successfully utilized for customer-oriented wearable product design by showing compatible matching of a hairstyle product as a user study.     

Customer satisfaction evaluation method for customized product development using Entropy weight and Analytic Hierarchy Process

Providing customized products with high customer satisfaction (CS) has become an inevitable trend for modern customized companies to remain competitive. This study proposes a CS evaluation method for customized product development using Entropy weight and Analytic Hierarchy Process (AHP). Firstly, customer requirements are identified based on Voice-of-Customer (VoC), and classified into four categories (positive, negative, must-be and fuzzy attributes) according to the characteristics of CS criteria. Then, CS evaluation model and its solution method are introduced in detail. This study especially focuses on the quantitative methods for each category of the evaluation criteria through formulating several mathematical models, and criteria weights are obtained through integrating Entropy weight and AHP method. Finally, a case study of applying the proposed methodology to customized portrait-based product industry demonstrates the functionality of the proposed methodology.     

Space exploration and global optimization for computationally intensive design problems: a rough set based approach

Modern engineering design problems often involve computation-intensive analysis and simulation processes. Design optimization based on such processes is desired to be efficient, informative and transparent. This work proposes a rough set based approach that can identify multiple sub-regions in a design space, within which all of the design points are expected to have a performance value equal to or less than a given level. The rough set method is applied iteratively on a growing sample set. A novel termination criterion is also developed to ensure a modest number of total expensive function evaluations to identify these sub-regions and search for the global optimum. The significance of the proposed method is twofold. First, it provides an intuitive method to establish the mapping from the performance space to the design space, i.e. given a performance level, its corresponding design region(s) can be identified. Such a mapping could be potentially used to explore and visualize the entire design space. Second, it can be naturally extended to a global optimization method. It also bears potential for more broad application to problems such as metamodeling-based design and robust design optimization. The proposed method was tested with a number of test problems and compared with a few well-known global optimization algorithms.     

一种基于模糊聚类和粗糙集的产品族设计知识约简方法*

针对产品族设计中产品族共性与产品个性化之间相互冲突的问题,提出了一种基于模糊聚类和粗糙集的产品族设计知识约简方法。通过模糊聚类对设计参数进行离散化和分类,以粗糙集进行知识约简区分出产品族设计参数和个性化产品设计参数,实现产品族综合性能损失最小。通过对通用电机产品族的实例分析,并经与PPCEM方法比较,验证了该方法的高效性和可行性。     

基于犹豫模糊语言术语集和粒子群优化算法的产品造型设计感性评价方法

产品造型感性评价反映了用户的意象感知,具有模糊性与不确定性,用户常难以准确描述其感性偏好而表现出犹豫.针对该问题,引入犹豫模糊语言术语集(HFLTSs)描述用户感性评价,基于其数学算子构建犹豫模糊语言共识模型以测度用户认知一致性程度,借助粒子群优化算法(PSO)实现非共识条件下用户评价矩阵的优化与共识达成,通过逼近理想...