Incorporating customer personalization preferences in open product architecture design

Open product architecture is a key enabler for product personalization, as it allows the integration of personalized modules in a product architecture to satisfy individual customer needs and preference. A critical challenge for integrating personalized modules into a product architecture is determining the optimal assembly architecture when considering market expectations and manufacturing constraints. In this paper, an optimization method is proposed for determining the personalized product design architecture that incorporates individual customer preferences. First, a decision hierarchy is presented to describe the integrated design decisions of the product architecture, including product variety determination, module variant selection, and personalized module configuration. Next, a profit model is formulated as an overall performance metric that incorporates customer preferences and manufacturing cost. The systematic patterns and randomness of diverse customer preferences are modeled by combining conjoint analysis and market segmentation with a multivariate normal mixture model. Individual customer product utilities in the target market and their product purchase intent probability are estimated through Monte-Carlo simulation, which is incorporated into the profit calculation. Manufacturing limitations on processes and materials are included as they influence manufacturer’s planning on candidate module variants and production strategies of personalized modules. These models are used to determine a product family architecture that maximizes profit by optimally determining its offering of product variants, module combinations, and personalized module configuration through a genetic algorithm. The proposed method is demonstrated by a personalized bicycle architecture design example.     

Rough set and PSO-based ANFIS approaches to modeling customer satisfaction for affective product design

Facing fierce competition in marketplaces, companies try to determine the optimal settings of design attribute of new products from which the best customer satisfaction can be obtained. To determine the settings, customer satisfaction models relating affective responses of customers to design attributes have to be first developed. Adaptive neuro-fuzzy inference systems (ANFIS) was attempted in previous research and shown to be an effective approach to address the fuzziness of survey data and nonlinearity in modeling customer satisfaction for affective design. However, ANFIS is incapable of modeling the relationships that involve a number of inputs which may cause the failure of the training process of ANFIS and lead to the ‘out of memory’ error. To overcome the limitation, in this paper, rough set (RS) and particle swarm optimization (PSO) based-ANFIS approaches are proposed to model customer satisfaction for affective design and further improve the modeling accuracy. In the approaches, the RS theory is adopted to extract significant design attributes as the inputs of ANFIS and PSO is employed to determine the parameter settings of an ANFIS from which explicit customer satisfaction models with better modeling accuracy can be generated. A case study of affective design of mobile phones is used to illustrate the proposed approaches. The modeling results based on the proposed approaches are compared with those based on ANFIS, fuzzy least-squares regression (FLSR), fuzzy regression (FR), and genetic programming-based fuzzy regression (GP-FR). Results of the training and validation tests show that the proposed approaches perform better than the others in terms of training and validation errors.     

Embracing diversity in user needs for affective design

To develop product portfolios and affective design we need to understand the diversity in user needs. The challenge is how to predict what users want and how they will behave. One approach is to understand user emotions and affective needs, and predict successful product design that can match the needs. This paper discusses affect and its link to cognition. To provide a context, several theories are presented. A framework is described that incorporates characteristics of users, tasks, products, and use environment. The goal is to highlight the importance of emotions in enhancing the value of products. This research field, which we call Hedonomics, is new. There are many challenges in developing valid and reliable measurements of affect, which can influence human factors research as well as design.     

Single users' affective responses models for product form design

This paper presents a neural network based approach to modeling consumers' affective responses for product form design. A theoretical framework for a single user's perception is developed. On the basis of this theoretical framework, a mathematical model which enables single users' responses to different products to be predicted was developed. The results obtained show that the mathematical models developed achieved highly accurate predictions.For the purpose of obtaining a global model various individual mathematical models were created, which were based on the opinions of users representing different groups of opinion. The results suggest that, under some conditions, the combined use of various models of individual users can perform as well as a single model generated on the basis of mean market responses.     

The marketing implications of affective product design

Emotions are compelling human experiences and product designers can take advantage of this by conceptualizing emotion-engendering products that sell well in the market. This study hypothesized that product attributes influence users’ emotions and that the relationship is moderated by the adherence of these product attributes to purchase criteria. It was further hypothesized that the emotional experience of the user influences purchase intention. A laboratory study was conducted to validate the hypotheses using mobile phones as test products. Sixty-two participants were asked to assess eight phones from a display of 10 phones and indicate their emotional experiences after assessment. Results suggest that some product attributes can cause intense emotional experience. The attributes relate to the phone's dimensions and the relationship between these dimensions. The study validated the notion of integrating affect in designing products that convey users’ personalities.     

基于顾客需求的产品概念设计方案评价

为了克服传统主成分分析法中权重确定过于客观,不能反映不同群体顾客需求的缺点,将主成分分析法和层次分析法相结合进行评价,提出一种改进的主成分分析法。通过对产品的市场调研提取了顾客需求评测指标,采用层次分析法来确定不同顾客群体需求的指标权重,对无量纲化后的数据加权,再用主成分分析法评价,改进后的方法可以针对不同的顾客群体需求进行评价。同时选用均值化法进行无量纲处理,减少了数据信息的损失。海尔冰箱概念设计方案的评价结果证明了该方法的可靠性。     

Incorporating anthropometry into design of ear-related products

To achieve mass customization and collaborative product design, human factors and ergonomics should play a key development role. The purpose of this study was to provide product designers with the anthropometic dimensions of outer ears for different demographic data, including gender and age. The second purpose was to compare the dimensions of various ear-related products (i.e., earphone, bluetooth earphone and ear-cup earphone) with the anthropometic database and recommend appropriate solutions for design. Two hundred subjects aged 20-59 was selected for this study and divided into four age stratifications. Further, three different dimensions of the outer ear (i.e., the earhole length, the ear connection length and the length of the pinna) were measured by superimposed grid photographic technique. The analysis of variance (ANOVA) was used to investigate the effects of gender, and age on ear dimensions. The results showed that all ear dimensions had significant gender effects. A comparison between the anthropometric dimensions and those of current products revealed that most current ear-related products need to be redesigned using anthropometric data. The shapes of earhole and pinna are not circular. Consequently, ear products need to be elongated so that users may feel more comfortably and not have the product slip off easily.     

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.     

Innovative product design based on customer requirement weight calculation model

In the processes of product innovation and design, it is important for the designers to find and capture customer's focus through customer requirement weight calculation and ranking. Based on the fuzzy set theory and Euclidean space distance, this paper puts forward a method for customer requirement weight calculation called Euclidean space distances weighting ranking method. This method is used in the fuzzy analytic hierarchy process that satisfies the additive consistent fuzzy matrix. A model for the weight calculation steps is constructed; meanwhile, a product innovation design module on the basis of the customer requirement weight calculation model is developed. Finally, combined with the instance of titanium sponge production, the customer requirement weight calculation model is validated. By the innovation design module, the structure of the titanium sponge reactor has been improved and made innovative.     

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.     

Data-driven multi-objective affective product design integrating three-dimensional form and color

Neural Computing and Applications - Three-dimensional (3D) form and color are the main appearance elements that arouse product emotion. To use more complete data of appearance and emotion and their...     

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.     

Incorporating codes of practice into reinforced concrete design programs

Changing the Code of Practice embodied in a reinforced concrete design program usually needs much expensive programming effort. A solution to this problem is described in which the Code-dependent sections of design programs are isolated and specified in a new problem-oriented language, developed for this task. Changing Codes in this program merely means changing the file of Code provisions. A simple beam design program, already run under five Codes, is described and some comparative design results are also shown.     

A support vector regression based prediction model of affective responses for product form design

In this paper, a state-of-the-art machine learning approach known as support vector regression (SVR) is introduced to develop a model that predicts consumers’ affective responses (CARs) for product form design. First, pairwise adjectives were used to describe the CARs toward product samples. Second, the product form features (PFFs) were examined systematically and then stored them either as continuous or discrete attributes. The adjective evaluation data of consumers were gathered from questionnaires. Finally, prediction models based on different adjectives were constructed using SVR, which trained a series of PFFs and the average CAR rating of all the respondents. The real-coded genetic algorithm (RCGA) was used to determine the optimal training parameters of SVR. The predictive performance of the SVR with RCGA (SVR–RCGA) is compared to that of SVR with 5-fold cross-validation (SVR–5FCV) and a back-propagation neural network (BPNN) with 5-fold cross-validation (BPNN–5FCV). The experimental results using the data sets on mobile phones and electronic scooters show that SVR performs better than BPNN. Moreover, the RCGA for optimizing training parameters for SVR is more convenient for practical usage in product form design than the timeconsuming CV.     

Incorporating user satisfaction into the look-and-feel of mobile phone design

The look-and-feel of the mobile phone was investigated using a consumer survey. Seventy-eight participants evaluated the design of 50 different mobile telephones on the perceived scale of image/impression characteristics, including: luxuriousness, simplicity, attractiveness, colourfulness, texture, delicacy, harmoniousness, salience, rigidity, and overall satisfaction. Stepwise multiple linear regression analysis were used to evaluate results. The results showed that the image and impression characteristics of the products were closely related to the human-product interface specifications as well as overall shape of the product. Design variables such as texture, use of surface curvature, surface treatment, operating sound, and control response ratio were perceived as important by customers. This study also suggested a series of statistical processes for selecting and screening the critical design variables closely related to the customer's impression of a product. The product evaluation and analysis methods could be generalized to other consumer products.     

Incorporating user satisfaction into the look-and-feel of mobile phone design

The look-and-feel of the mobile phone was investigated using a consumer survey. Seventy-eight participants evaluated the design of 50 different mobile telephones on the perceived scale of image/impression characteristics, including: luxuriousness, simplicity, attractiveness, colourfulness, texture, delicacy, harmoniousness, salience, rigidity, and overall satisfaction. Stepwise multiple linear regression analysis were used to evaluate results. The results showed that the image and impression characteristics of the products were closely related to the human-product interface specifications as well as overall shape of the product. Design variables such as texture, use of surface curvature, surface treatment, operating sound, and control response ratio were perceived as important by customers. This study also suggested a series of statistical processes for selecting and screening the critical design variables closely related to the customer's impression of a product. The product evaluation and analysis methods could be generalized to other consumer products.     

Decision theoretic modeling of affective and cognitive needs for product experience engineering: key issues and a conceptual framework

User experience (UX) design plays a critical role in product experience engineering. To create a theoretical foundation of UX design, it is imperative to develop mathematical and computational models for elicitation, quantification, evaluation and reasoning of affective–cognitive needs that are inherent in the fulfillment of user experience. This paper explores the key research issues for understanding how human users’ subjective experience and affective prediction impact their choice behavior under uncertainty. A conceptual framework is envisioned by extending prospect theory in the field of behavioral economics to the modeling of user experience choice behavior, in which inference of affective influence is enacted through the shape parameters of prospect value functions.     

Incorporating shape into histograms for CBIR

This paper describes an indexing system for use in Content Based Image Retrieval. The standard colour histogram approach is simple, efficient, and robust. However, it does not include shape information, which leads to problems (e.g., many-to-many mappings). To remedy this, we use additional features in an attempt to incorporate shape and textual information to the index key. Our experiments showed that the combination of colour, texture, distance and orientation histograms gave approximately 10% improvement of recall over the standard colour histogram.     

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.