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In previous studies, customer survey data were commonly adopted to perform the modelling of customer satisfaction (CS). However, it could be time-consuming to conduct surveys and obtain their data. On the other hand, respondents' responses are quite often confined by preset questions. Nowadays, a huge number of customer online reviews on products can be found on various websites. The reviews can be extracted easily in a very short time. Customers can freely express their concerns and views of products in their online reviews. Those reviews provide a valuable source of information for manufacturers to improve their existing products and develop their new products. Previous studies have attempted to develop CS models based on survey data by using various computational intelligence techniques. However, no attempt at developing explicit CS models based on online reviews was reported in the literature. In this paper, a methodology for the modelling of CS based on customer online reviews and a multigene genetic programming-based fuzzy regression (MGGP-FR) approach is proposed. In the proposed methodology, relevant textual reviews of products are extracted from e-commerce websites. Then, opinion mining is conducted on the reviews and sentiments scores of customer concerns are derived. A MGGP-FR approach is then introduced to develop CS models based on the derived sentiment scores. A case study on developing CS models for electronic hairdryers is conducted to illustrate the proposed methodology and validate the effectiveness of MGGP-FR in the modelling of CS. The validation results show MGGP-FR outperforms the other three modelling approaches, fuzzy regression, genetic programming, and genetic programming-based fuzzy regression, in the CS modelling in terms of prediction accuracy. [ABSTRACT FROM AUTHOR]

In recent years, remanufacturing has received increased attention as a sustainable and profitable product recovery strategy. To facilitate the remanufacturing of used product returns, factors which affect remanufacturability should be considered during the product design stage. The selection of fastening method during the product design stage is one of the critical decisions which affects the remanufacturability as well as the total cost of disassembly and re-assembly of used products. Hence, both product assembly and disassembly issues should be considered in the product design stage for the selection of fastening methods. Simultaneous consideration of product assembly and disassembly in the product design stage for the fastening method selection has not been properly addressed in previous studies. In this paper, a methodology for selecting appropriate fastening method from a remanufacturing perspective is proposed in which both product assembly and disassembly are addressed. In the proposed methodology, an optimization model is formulated with the objective of minimizing the total cost of product assembly and disassembly. The genetic algorithm is employed to solve the model. A case study on the selection of fastening method for a laptop computer is conducted to illustrate the proposed methodology and to evaluate its effectiveness. The effect of the degree of product disassembly and the demand size for remanufactured products on the total cost of product assembly and disassembly was also investigated. The results showed the proposed methodology provide significant cost savings in the total product assembly and disassembly cost. [ABSTRACT FROM AUTHOR]

Affective design is an important aspect of new product development that can enhance customer satisfaction of new products. Previous studies generally conducted customer surveys based on questionnaires and interviews to collect customers’ views and preferences of affective design of products. However, the process could be time-consuming and the survey data does not contain much sentiment expression. Presently, a large number of online customer reviews on products can be found on various websites that contain rich information of customer opinions and expectations. However, the generation of useful information based on online customer reviews for affective design has not been addressed in previous studies. In this paper, a methodology for generating association rules for supporting affective design based on online customer reviews is proposed which mainly involves opinion mining of affective dimensions from online customer reviews and association rule mining based on multi-objective particle swarm optimisation (PSO). Opinion mining is adopted to analyze online reviews and conduct sentiment analysis for affective dimensions. Based on the mined information and morphological analysis of products, a multi-objective PSO approach is proposed to generate association rules that depict the relationships between affective dimensions and design attributes. A case study was conducted to illustrate the proposed methodology. [ABSTRACT FROM AUTHOR]

Managing product returns has been considered essential in the production planning and control of remanufacturing, as well as in the inventory control management of product returns. Uncertainties in the quantity and quality of returned products collected for remanufacturing could largely affect the profit generated from remanufactured products. However, determining the optimal quantity and quality levels of used products to be collected for remanufacturing in multiple periods under uncertain quality of the returns has not been properly addressed in previous studies. On the other hand, the effects of new product sales and demand for remanufactured products on used product returns, as well as the effect of quality of returns on the takeback and remanufacturing costs, have not been properly considered in previous studies. In this paper, a novel methodology is proposed to determine the optimal product returns for remanufacturing with consideration of the uncertainty in the quantity and quality of returns and study the effects of new product sales and demand for remanufactured products on used product returns, as well as the effect of quality of returns on the remanufacturing cost. A case study of determining the optimal returns of tablet PCs is conducted to illustrate the proposed methodology. [ABSTRACT FROM AUTHOR]

Previous studies conducted customer surveys based on questionnaires and interviews, and the survey data were then utilized to analyze product features. In recent years, online customer reviews on products became extremely popular, which contain rich information on customer opinions and expectations. However, previous studies failed to properly address the determination of the importance of product features and prediction of their future importance based on online reviews. Accordingly, a methodology for predicting future importance weights of product features based on online customer reviews is proposed in this paper which mainly involves opinion mining, a fuzzy inference method, and a fuzzy time series method. Opinion mining is adopted to analyze the online reviews and extract product features. A fuzzy inference method is used to determine the importance weights of product features using both frequencies and sentiment scores obtained from opinion mining. A fuzzy time series method is adopted to predict the future importance of product features. A case study on electric irons was conducted to illustrate the proposed methodology. To evaluate the effectiveness of the fuzzy time series method in predicting the future importance, the results obtained by the fuzzy time series method are compared with those obtained by the three common forecasting methods. The results of the comparison show that the prediction results based on fuzzy time series method are better than those based on exponential smoothing, simple moving average, and fuzzy moving average methods. [ABSTRACT FROM AUTHOR]

Market demand estimation is an important process to assess the financial feasibility of new product development (NPD) projects. The development of models for market demand estimation involves market potential estimation and choice modeling. Previous studies commonly used conjoint analysis to develop utility functions which were then used in discrete choice models to generate market share models. The jury of executive opinion method is commonly used in industries wherein a number of experts and/or consultants are always involved in the market potential estimation. However, a high degree of fuzziness always exists in the data obtained from conjoint surveys and the market potential estimation because of the subjective judgments of respondents and experts. However, ignorance of the fuzziness would lead to the over-estimation of market demands. This research aims to tackle the fuzziness associated with market potential estimation and survey data in the development of market demand models. In this paper, a new methodology of developing fuzzy market demand models for NPD is proposed to address the fuzziness by which market demands can be estimated for the worst, normal, and best scenarios. The proposed methodology involves fuzzy choice modeling based on fuzzy regression and discrete choice analysis, and fuzzy estimate generation of market potential. To evaluate the effectiveness of the proposed methodology, a case study of market demand estimation of a new tablet PC is conducted based on the proposed methodology. The results of the implementation are compared with those based on a popular multinominal logit (MNL) based demand model. From the comparison, it can be noted that the estimated market demand based on the MNL model is very close to that for the normal scenario based on the proposed fuzzy demand model. However, the MNL model cannot provide estimates for other scenarios. [ABSTRACT FROM AUTHOR]

Faced with fierce competition in marketplaces, manufacturers need to determine the appropriate settings of engineering characteristics of the new products so that the best customer preferences of the products can be obtained. To achieve this, functional models relating customer preferences to engineering characteristics need to be developed. As information regarding functional relationships between customer preferences are generally subjective or heuristic in nature, development of the customer preference models involve two uncertainties, namely fuzziness and randomness. Existing approaches use only fuzzy-based technologies to address the uncertainty caused by fuzziness. They are not designed to address the randomness of the observed data which is caused by a limited knowledge of the variability of influences between customer preferences and engineering characteristics. In this article, a fuzzy ordinary regression method is proposed to develop the customer preference models which are capable of addressing the two uncertainties of crispness and fuzziness of the customer preferences. A case study of a tea maker design which involves both uncertainties is used to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Product line design is a key decision area that a product development team has to deal with in the early stages of product development. Previous studies of product line design have focused on single-objective optimization. However, several optimization objectives may be simultaneously pursued, and the solutions that can address the objectives are required in many practical scenarios. In this research, we propose a one-step multiobjective optimization approach for product line design. The proposed optimization model has three objectives: 1) maximizing the market share of a company's products; 2) minimizing the total product development cost of a product line; and 3) minimizing the total product development cycle time. A curve-fitting method is introduced into the part-worth utility models so that the optimization model can be applied to products with level-based attributes and attributes that have continuous values. A multiobjective genetic algorithm is adopted to solve the optimization model, obtaining a set of nondominated solutions. With the solutions, a new product development team can select a preferred solution interactively in a 2-D graph. An example of the optimal design of a product line of digital cameras is used to illustrate the proposed approach. [ABSTRACT FROM PUBLISHER]

Fluid dispensing is a popular process in semiconductor manufacturing industry which is commonly used in die-bonding as well as microchip encapsulation for electronic packaging. Modelling the fluid dispensing process is important to understand the process behaviour as well as determine optimum operating conditions of the process for a high-yield, low cost and robust operation. Previous studies of fluid dispensing mainly focus on the development of analytical models. However, an analytical model for fluid dispensing, which can provide accurate results, is very difficult to develop because of the complex behaviour of fluid dispensing and high degree of uncertainties of the process in a real world environment. In this project, an empirical approach to modelling fluid dispensing was attempted. Two common empirical modelling techniques, statistical regression and neural networks, were introduced to model fluid dispensing process for electronic packaging. Development of neural network based process models using genetic algorithm (GA) and Levenberg−Marquardt algorithm are presented. Validation tests were performed to evaluate the effectiveness of the developed process models from which a multiple regression model and a GA trained neural network with the architecture of 3-15-1 were identified to be the process models of the fluid dispensing respectively for the encapsulation weight and encapsulation thickness. [ABSTRACT FROM AUTHOR]

Process modelling refers to the development of a process model that serves to provide the input-output relationship of a process, while process optimisation provides the optimum operating conditions of a process for a high-yield, low cost and robust operation. Normally, process modelling is a starting point of process optimisation. In this paper, a method of integrating artificial neural networks with a gradient search method for process modelling and optimisation is presented. Artificial neural networks are used to develop process models while a gradient search method is used in process optimisation. Application of the method to the modelling and optimisation of epoxy dispensing for microchip encapsulation is described. Results of the validation tests indicate that good quality of encapsulation can be obtained based on the proposed method. [ABSTRACT FROM AUTHOR]

Epoxy dispensing is a popular way to perform microchip encapsulation for chip-on-board (COB) packages. However, the determination of the proper process parameters setting for a satisfactory encapsulation quality is difficult due to the complex behaviour of the encapsulant during the dispensing process and the inherent fuzziness of epoxy dispensing systems. Sometimes, the observed values from the process may be irregular. In conventional regression models, deviations between the observed values and the estimated values are supposed to have a probability distribution. However, when data is scattered, the obtained regression model has too wide of a possibility range. These deviations in processes such as epoxy dispensing can be regarded as system fuzziness that can be dealt with satisfactorily using a fuzzy regression method. In this paper, the fuzzy linear regression concept with fuzzy intervals and its application to the process modelling of epoxy dispensing for microchip encapsulation are described. Two fuzzy regression models, expressing the correlation between various process parameters and the two quality characteristics, respectively, were developed. Validation experiments were performed to demonstrate the effectiveness of the method for process modelling. [ABSTRACT FROM AUTHOR]

In the Quality Function Deployment (QFD) process, determining the importance weights for the customer requirements is an essential and crucial process. The Analytic Hierarchy Process (AHP) has been used to determine the importance weights for product planning, but this has occurred mainly in crisp (non-fuzzy) decision applications. However, human judgment on the importance of customer requirements is always imprecise and vague. To make up for this deficiency in the AHP. a fuzzy AHP with an extent analysis approach is proposed to determine the importance weights for the customer requirements. In the method, triangular fuzzy numbers are used for the pairwise comparison of a fuzzy AHP. By using the extent analysis method and the principles for the comparison of fuzzy numbers, one can derive the weight vectors. The new approach can improve the imprecise ranking of customer requirements inherited from studies based on the conventional AHP. Furthermore, the fuzzy AHP with extent analysis is simple and easy to implement to prioritize customer requirements in the QFD process compared with the conventional AHP. This paper uses an example of a hair dryer design to illustrate the proposed approach. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports published within the issue which include the use of particle swarm optimization in customer reviews, the use of machine learning framework to collect product information, and the affective properties of a new product.

By providing after-sale services, manufacturing companies can gain more chances to interact with customers, improve brand preference and loyalty, occupy new bases in the value chain, and create more cross-selling opportunities. Considering after-sale service, we propose a new method for product family configuration optimisation in this study. Mathematical models for product family configuration optimisation are established with the objective of maximising the overall profit of the family of products and after-sale services. An adaptive quantum evolutionary algorithm (AQEA) is developed to solve the established optimisation models. An example of an e-book reader product is used to illustrate the proposed method in a case study. An industry case study shows that: (1) the proposed method outperforms the traditional method; (2) the proposed AQEA has better performance when compared with the other three meta-heuristic algorithms; and (3) the proposed method is more useful for industrial cases where the proportion of service profit is relatively large. [ABSTRACT FROM AUTHOR]

In order to incorporate the customer into the early phase of the product development cycle and to better satisfy customers' requirements, this article adopts quality function deployment (QFD) for optimal design of a scalable product platform. A five-step QFD-based method is proposed to determine the optimal values for platform engineering characteristics (ECs) and non-platform ECs of the products within a product family. First of all, the houses of quality (HoQs) for all product variants are developed and a QFD-based optimization approach is used to determine the optimal ECs for each product variant. Sensitivity analysis is performed for each EC with respect to overall customer satisfaction (OCS). Based on the obtained sensitivity indices of ECs, a mathematical model is established to simultaneously optimize the values of the platform and the non-platform ECs. Finally, by comparing and analysing the optimal solutions with different number of platform ECs, the ECs with which the worst OCS loss can be avoided are selected as platform ECs. An illustrative example is used to demonstrate the feasibility of this method. A comparison between the proposed method and a two-step approach is conducted on the example. The comparison shows that, as a kind of single-stage approach, the proposed method yields better average degree of customer satisfaction due to the simultaneous optimization of platform and non-platform ECs. [ABSTRACT FROM AUTHOR]

Quality function deployment (QFD) is a widely adopted customer-oriented methodology to assist in product design and development. In a traditional QFD-based product planning process, the importance weights of customer requirements (CRs) in house of quality are obtained by analyzing and generalizing the requirements of various customers, and the purpose of the subsequent optimal setting of engineering characteristics (ECs) and process parameters is to achieve a higher level of overall customer satisfaction (OCS). However, nowadays customers’ requirements are increasingly diversified and customers usually have heterogeneous preferences. Consequently, customers in a product market may have different purchasing choice behaviors and satisfaction criteria toward a new product. Therefore, heterogeneity of CRs should be considered in QFD-based optimization models to describe the relationship between CRs and ECs and to model the OCS. In this paper, a novel QFD-based product planning approach is proposed for a product market with diversified CRs by integrating consumer choice behavior analysis. The contributions of this paper include: 1) customers’ purchase choice rules are introduced into QFD-based product planning process to simulate the purchase behavior of a customer toward a product; 2) two new QFD-based optimization models under deterministic and multinomial logit consumer choice rules are proposed to help firms improve the product quality under environment of diversified CRs; 3) the established model under deterministic consumer choice rule is transformed into an equivalent linear model to facilitate the solving process; and 4) the established optimization models are further extended for products with both continuous and discrete target values of ECs. [ABSTRACT FROM PUBLISHER]

For the sake of gaining competitive advantages, it is important to evaluate the satisfaction level of a product or service from the users? perspective. This can be done by investigating the relationship among customer attributes (customer requirements) and design attributes (product configurations). However, such relationship would be highly non-linear in nature. In this regard, many approaches have been proposed over traditional linear methods. Particularly, the Adaptive Neuro-Fuzzy Inference System (ANFIS) method has been prevalently utilized in modeling such vague and complex relationship among these attributes and evaluating user satisfaction towards certain products or services. Despite the fact that the ANFIS method can explicitly model the non-linear relation among these attributes, it may be restricted if uncertain information can be observed due to subjectivity and incompleteness. To overcome these limitations, a belief rule base (BRB) approach with evidential reasoning (ER) is applied in this paper. For justification purpose, both the ANFIS and BRB methods are applied to the same case. Comparison results indicate that the BRB is capable of minimizing the human biases in evaluating user satisfaction and rectifying the inappropriateness associated with the ANFIS method. Also, the BRB method can generate more rational and informative evaluation results. [ABSTRACT FROM AUTHOR]