Mapping Product Design Specification for Mass Customization

Mass customization is an emerging field in manufacturing research community where customer satisfaction is achieved by complying with exact customer requirements at mass production efficiencies. Product varieties have mostly failed in the aspect of satisfying customer requirements, since knowing beforehand exactly what customer needs is beyond the scope of any manufacturing or marketing paradigm. So design to order and build to order is widely talked in the industry to capture heterogeneous market segments. In order to achieve the challenging task of customization at mass production efficiencies, a tight integration of the various entities involved is necessary. Gathering customer requirements, finalizing product design and eventual manufacturing need to be seamlessly integrated to achieve mass production efficiencies. Product designs are classified into customizable product platform families, and then searched based on customer requirements to identify the most conformal design family. Finally the design parameter transport is carried out from customer domain to product domain. Eventually a valid and realizable product specification is generated. The paper will address these issues of classification, selection and ultimately the mapping of parameters. A methodology to classify the product design information, which can easily accommodate design variations based on product platform architecture is proposed. Further, adaptive design customization, which relates most design parameters with the scalable platform design parameters using matrix formulation, is discussed. The proposed design parameter classification and adaptive synthesis of design parameters is applied to a spring design example.     

Forecasting of manufacturing cost in mobile phone products by case-based reasoning and artificial neural network models

The mobile phone manufacturers in Taiwan have made great efforts in proposing the rational quotations to the international phone companies with the ambition to win the bids by out beating other phone manufacturers. However, there are a lot of uncertainties and issues to be resolved in estimating the manufacturing costs for mobile phone manufacturers. As far as we know, there is no existing model which can be applied directly in forecasting the manufacturing costs. This research makes the first attempt to develop a hybrid system by integrated Case-Based Reasoning (CBR) and Artificial Neural Networks (ANN) as a Product Unit Cost (PUC) forecasting model for Mobile Phone Company. According to the cost formula of the mobile phone and experts’ opinions, a set of qualitative and quantitative factors are analyzed and determined. Qualitative factors are applied in CBR to retrieve a similar case from the case bases for a new phone product and ANN is used to find the relationship between the quantitative factors and the predicted PUC. Finally, intensive experiments are conducted to test the effectiveness of six different forecasting models. The model proposed in this research is compared with the other five models and the MAPE value of the proposed model is the smallest. This research provides a new prediction model with high accuracy for mobile phone manufacturing companies.     

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.     

Classification model for product form design using fuzzy support vector machines

Consumer preferences regarding product design are often affected by a large variety of form features. Traditionally, the quality of product form design depended heavily on designers’ intuitions and did not always prove to be successful in the marketplace. In this study, to help product designers develop appealing products in a more effective manner, an approach based on fuzzy support vector machines (fuzzy SVM) is proposed. This constructs a classification model of product form design based on consumer preferences. The one-versus-one (OVO) method is adopted to handle a multiclass problem by breaking it into various two-class problems. Product samples were collected and their form features were systematically examined. To formulate a classification problem, each product sample was assigned a class label and a fuzzy membership that corresponded to this label. The OVO fuzzy SVM model was constructed using collected product samples. The optimal training parameter set for the model was determined by a two-step cross-validation. A case study of mobile phone design is given to demonstrate the effectiveness of the proposed methodology. The performance of fuzzy SVM is also compared with SVM. The results of the experiment show that fuzzy SVM performed better than SVM.     

A Gestalt–Minimalism-based decision-making model for evaluating product form design

This paper presents a novel methodology for dealing with decision-making problems in product design fields. The purpose of this study is to evaluate product form design in terms of the perspectives of Gestalt psychology and Minimalist principles. Unlike traditional AHP methods, the proposed decision-making model uses distinct mathematical tools to establish priorities for the criteria and synthesize the evaluation results. A case study was conducted to illustrate the practicability of this proposed model. It has shown a credible result. In addition to product form design, this model can be applied to related design fields, such as plane design and other visual design.

Relevance to industry

Product form design is a creative process that involves complex visual perceptions. It is very important to develop an effective decision support system for designers to deal with problems concerning the consumers’ psychological preferences toward product forms. As Gestalt psychology and Minimalist principles provide an important perspective on visual perception, it is appropriate to apply these principles in assessing the quality of product form design.     

An integrated neuro-genetic approach incorporating the Taguchi method for product design

Product design is a multidisciplinary activity that requires the integration of concurrent engineering approaches into a design process that secures competitive advantages in product quality. In concurrent engineering, the Taguchi method has demonstrated an efficient design approach for product quality improvement. However, the Taguchi method intuitively uses parameters and levels in measuring the optimum combination of design parameter values, which might not guarantee that the final solution is the most optimal. This work proposes an integrated procedure that involves neural network training and genetic algorithm simulation within the Taguchi quality design process to aid in searching for the optimum solution with more precise design parameter values for improving the product development. The concept of fractals in computer graphics is also considered in the generation of product form alternatives to demonstrate its application in product design. The stages in the general approach of the proposed procedures include: (1) use of the Taguchi experimental design procedure, (2) analysis of the neural network and genetic algorithm process, and (3) generation of design alternatives. An electric fan design is used as an example to describe the development and explore the applicability of the proposed procedures. The results indicate that the proposed procedures could enhance the efficiency of product design efforts by approximately 7.8%. It is also expected that the proposed design procedure will provide designers with a more effective approach to product development.     

A quantitative aesthetic measurement method for product appearance design

Product appearance is one of the crucial factors that influence consumers’ purchase decisions. The attractiveness of product appearance is mainly determined by the inherent aesthetics of the design composition related to the arrangement of visual design elements. Hence, it is critical to study and improve the arrangement of visual design elements for product appearance design. Strategies that apply aesthetic design principles to assist designers in effectively arranging visual design elements are widely acknowledged in both academia and industry. However, applying aesthetic design principles relies heavily on the designer’s perception and experience, while it is rather challenging for novice designers. Meanwhile, it is hard to measure and quantify design aesthetics in designing artefacts when designers refer to existing successful designs. In this regard, this study aims to introduce a method that assists designers in applying aesthetic design principles to improve the attractiveness of product appearance. Furthermore, formulas for aesthetic measurement based on aesthetic design principles are also developed, and it makes an early attempt to provide quantified aesthetic measurements of design artefacts. A case study on camera design was conducted to demonstrate the merits of the proposed method where the improved strategies for the camera appearance design offer insights for concept generation in product appearance design based on aesthetic design principles.     

An in-process customer utility prediction system for product conceptualisation

Customer involvement in new product development, especially in the early stage of product conceptualisation, plays an important role for a successful product. In this study, a customer utility prediction system (CUPS) is proposed. The system comprises two modules, namely design knowledge acquisition module and customer utility evaluation module. In the design knowledge acquisition module, a knowledge acquisition technique called general sorting is utilised to establish a design knowledge hierarchy (DKH), in which design options can be generated. In the same module, customer voices towards diverse design options called customer-sensitive design criteria are solicited from customer requirements. Subsequently, in the customer utility evaluation module, a measurement for customer desirability, i.e. customer utility index (CUI), is formulated using conjoint analysis (CA) technique. Finally, the rated criteria are also used as inputs to a radial basis function (RBF) neural network for in-process customer utility prediction. A case study on cellular phone design is used to illustrate the proposed approach.     

A systematic data-mining-based methodology for product family design and product configuration

Product family design and product configuration based on data mining technology is identified as an intelligent and automated means to improve the efficiency of product development. However, few of previous literatures have proposed systematic product family design method based on data mining technology. To make up for this deficiency, this research put forward a systematic data-mining-based method for product family design and product configuration. First, the customer requirement information and product engineering information in the historical order are formatted into structural data. Second, principal component analysis is performed on historical orders to extract the customers' differentiated needs. Third, association rule algorithm is introduced to mine the rules between differentiated needs and module instances in the historical orders, thus obtained the configuration knowledge between customer needs and product engineer. Forth, the mined rules are used to construct association rule-based classifier (CBA) that is employed to sort out the best product configuration schemes as popular product variants. Fifth, sequence alignment technique is employed to identify modules for popular product variants, so that the module instances are divided into optional, common and special module, respectively, thereby the product platform is generated based on common modules. Finally, according to new customer needs, the CBA classifier is used to recommend the best configuration schemes, and then popular product variants are configured based on the product platform. The feasibility of the proposed method is demonstrated by the product family design example of desktop computer hosts.     

A method of constructing an inspiration library driven by user-perceived preference evaluation data for biologically inspired design

Biologically inspired design (BID) is one of the common methods for product design. To solve the problem of inaccurate acquisition of inspirational creatures due to the lack of user perception preference analysis, a data-driven intelligent service model for BID considering user perception needs is proposed based on Kansei engineering. Firstly, by extracting the perceptual features of creatures from the semantic source elements of products through mapping and encodes them, we proposed a data acquisition method based on intuitionistic fuzzy sets considering different customer preference distributions, bridging the gap caused by the asymmetry between designers and users. Secondly, the functional relationship between biometric features and user-perceived attributes is identified and predicted, and a predictive model of biodata considering user preferences is obtained by multiple linear regression analysis. Finally, based on the data clustering and reorganization theory to understand the organization and dynamics of the database, the construction of a BID library was completed, and the design resources in the library were used as analyzed knowledge for designers to plan design activities. Taking the bionic design of a UAV product as an example, a prototype of a computer-aided design service system was developed based on the theory proposed in the article, and the analyzed knowledge was used to improve the efficiency and science of the design, effectively verifying the usefulness of this study for design. To a certain extent, this study addresses the problem of cognitive limitations of designers and cognitive differences between designers and users, promotes the application of bioinspiration in product design, and improves the marketability of design solutions.     

基于日志的客户感知问题辅助分析系统的应用研究

智能手机的移动业务和移动互联网应用直接影响客户感知,通过挖掘网络日志与手机日志对客户感知问题分析的支撑作用,提出基于日志的客户感知问题辅助分析系统。该系统通过采集、分类和统计,提供异常事件告警和智能分析。通过对上网和彩倩业务的应用实践表明,该系统能提供准确、有效的日志信息,为分析和定位问题提供端到端的数据支持。该系统和思路可扩展应用于长期演进网络的客户感知问题辅助分析。     

Product platform two-stage quality optimization design based on multiobjective genetic algorithm

Product platform design (PFD) has been recognized as an effective means to satisfy diverse market niches while maintaining the economies of scale and scope. Numerous optimization-based approaches have been proposed to help resolve the tradeoff between platform commonality and the ability to achieve distinct performance targets for each variant. In this study, we propose a two-stage multiobjective optimization-based platform design methodology (TMOPDM) for solving the product family problem using a multiobjective genetic algorithm. In the first stage, the common product platform is identified using a nondominated sorting genetic algorithm II (NSGA-II); In the second stage, each individual product is designed around the common platform such that the functional requirements of the product are best satisfied. The design of a family of traction machine is used as an example to benchmark the effectiveness of the proposed approach against previous approachs.     

The influence of product aesthetics and user state in usability testing

An empirical study examined the effects of two influencing factors in usability tests on user performance and usability ratings. Product aesthetics (high vs. low) as the main independent factor and prior usage event (positive vs. negative) as a subsidiary independent factor were varied in a between-participants design. 60 participants took part in the experiment, completing a number of typical tasks of mobile phone users. The results showed that increased product aesthetics had a positive effect on perceived usability and led to longer task completion times. After a negative usage event had been experimentally induced, usability ratings dropped as expected but user performance on subsequent tasks remained unaffected. An important implication of the study is that the aesthetic properties of a product may have multiple effects that go beyond perceived product attractiveness.     

Quality function deployment implementation based on Fuzzy Kano model: An application in PLM system

Product lifecycle management (PLM), a strategic business system allows more effective communication among different groups at dispersed locations to share ideas and access information needed for developing new products and executing innovative processes. The main function of PLM is to develop an attractive system which ensures customer satisfaction. Therefore, one of the important topics of the PLM system developments is to take customer requirements into consideration. Quality function deployment (QFD) has been widely used for numerous years; it is one of the structured methodologies that are used to translate customer needs into specific quality development. However, in the traditional QFD approach, each element’s interdependence and customer requirements are usually not systematically treated. Additionally, the Kano model can effectively classify customer demand attributes, but to make Kano model more objective in the course of weighing, we have also included Fuzzy mode in our discussion. This study presents an integrative approach by incorporating the Kano model with Fuzzy mode into the matrix of QFD and adjusting customer requirement weights. This approach can fulfill two objectives, First, through the Kano model with the Fuzzy mode, it will not only discriminate out options for the required attributes in much more breadth but also simultaneously render the discretions on the linguistic implications much more accurate with the aid of the ambiguous questionnaire response method. Second, combining the Kano model and QFD, can not only provide a new way to optimize the product design but can also enhance customer satisfaction and loyalty, and minimize dissatisfaction. The proposed methods can be useful to both practitioners and researchers. To illustrate our findings, we have incorporated an example which suggests that the proposed approach can contribute to the creation of attractive PLM attributes and PLM innovation.     

An active styling platform for designing and developing product families

In recent years, fierce market competition has increased the need for incorporating manufacturing flexibility into product styling. The product styling platform approach proposed in this paper offers an insight into how this challenge can be dealt with by transforming an existing product fashion into an intangible styling module with the view to refreshing its appeal. This paper addresses the important issues of how the approach can be turned into a robust applicational tool to facilitate the design of product families to satisfy diverse customer needs. The paper investigates the nature and quantification of modular styling. By capturing and analysing design experts’ opinions on product styling, the evolution of a fashion can be quantified in the form of a schema of diffusion styles. The study has also led to the development of the ideas of “Prompt Time Market Segmentation Grid” and the “Active Style Diagnosis”. The concepts are applied in an “Active Product Styling Platform” with scalable and expandable capabilities to enhance the effectiveness of conventional product styling practices. This approach can also help the formation of product style families and facilitate the development of products particularly in a mass customisation environment.     

Product manual elaboration in product design phases: Behavioral and functional analysis based on user experience

In classical design methods, intuitive consideration is given to how the product, or the system that is the subject of the design, is used. How the product is used is defined in a product manual. It is taken into account in the final phase of the techno-centric process, which leads not only to modifications of the late design phases, but also to difficulties using the product. Product manual also helps businesses save significant staff training and customer service costs. Anthropocentric methods are rarely used by the general public, and are in fact too expensive. We propose to take product use into account by integrating behavioral analyses (the behavior of the product and of its user) with functional analyses. This will allow us to set out instructions for using the product right at the design stage. An analysis of techno- and anthropocentric approaches is presented in this work in a bid to position our approach. Next, a functional and behavioral analysis is proposed based on a car seat case study. The proposed method also calls for a phase dedicated to the collection of product use information and data on user feedback or legislatives data. These analyses are carried out using the QFD method in order to assist the designer in structuring knowledge, taking into account the functions of the product, the characteristics or criteria of its use, and the points to be developed when drawing up operating instructions.     

The influence of product aesthetics and usability over the course of time: a longitudinal field experiment

A longitudinal field experiment was carried out over a period of 2 weeks to examine the influence of product aesthetics and inherent product usability. A 2 × 2 × 3 mixed design was used in the study, with product aesthetics (high/low) and usability (high/low) being manipulated as between-subjects variables and exposure time as a repeated-measures variable (three levels). A sample of 60 mobile phone users was tested during a multiple-session usability test. A range of outcome variables was measured, including performance, perceived usability, perceived aesthetics and emotion. A major finding was that the positive effect of an aesthetically appealing product on perceived usability, reported in many previous studies, began to wane with increasing exposure time. The data provided similar evidence for emotion, which also showed changes as a function of exposure time. The study has methodological implications for the future design of usability tests, notably suggesting the need for longitudinal approaches in usability research. PRACTITIONER SUMMARY: This study indicates that product aesthetics influences perceived usability considerably in one-off usability tests but this influence wanes over time. When completing a usability test it is therefore advisable to adopt a longitudinal multiple-session approach to reduce the possibly undesirable influence of aesthetics on usability ratings.     

A study that applies aesthetic theory and genetic algorithms to product form optimization

In the past, designers developed new products by virtue of their own accumulation of aesthetic perception and experience. Because the information that designers could master was limited, it was difficult to develop quickly the capability of satisfying consumer-oriented markets. This limitation may consequently cause the enterprises unnecessary risks. Based on this, a set of aesthetic evaluations and an optimization system for form aesthetics are proposed in this study. Calculations of explicit equations were performed so as to assisting in measuring the aesthetic characteristics; next, a fuzzy judgment was invoked to calculate the perceptual aesthetic measures of a product style so as to establish the overall aesthetic standard for the product. Aesthetic measurement principles were combined with the genetic algorithm (GA) and applied to the optimization of the product’s shape. All-in-one stereos were chosen to serve as the target products as a case study. Further form optimization was conducted on two of the stereos and the questionnaire survey indicated that their aesthetic measures increased after the optimization. The errors that resulted from the equations for aesthetic measurements and judgments were also reduced accordingly. The precision and feasibility of the aesthetic theory that was constructed in this study were assessed, tested, and verified.     

Customized products recommendation based on probabilistic relevance model

Product customization is attracting more attentions in industry as a viable strategy to better meet customer requirements and gain more profit. However the vast number of product variants in product customization process often makes it difficult for consumers to make purchase decisions, a phenomenon referred to as information overload. In this paper we take a two-prong approach to tackle the issue of information overload in customized products recommendation. Basically, the method answers two questions, namely, which products to recommend and in what order to present the recommendations. Firstly, a probability relevance model is deployed to calculate the probability of relevance for each end product. Then a probability ranking principle is exploited to present the recommendations. The approach also takes customer flexibility into consideration and thus mitigates the effect of inconsistent specifications from customers. It does not require any prior knowledge about an active customer’s preference and can accommodate the new customers challenge facing by recommendation approaches. Analytical results show that the method is optimal in terms of customer’s utility and product recommendation efficiency. Numerical experiments are also conducted to test the presented approach.