Identification of the to-be-improved product features based on online reviews for product redesign

Acquisition of customer needs usually serves as the basis for the identification of to-be-improved features for the product redesign process. However, the customer's true needs tend to be non-obvious and are difficult to extract from the data source like interviews or market survey. In the era of Big Data, with the advances in e-commerce, the customer's online review has become one of the most important data source to reveal the insight of customer's preference. In this paper, an online-review-based approach is introduced to identify the to-be-improved product features. The product features and corresponding opinions are extracted and reduced based on the semantic similarity. A structured preference model based on the semantic orientation analysis is constructed. A redesign index is subsequently introduced to measure the priority of redesign for each feature, and a target feature selection model is created to identify the to-be-improved features from candidate features considering engineering cost, redesign lead time and technical risk. A case study for smartphones is developed to demonstrate the effectiveness of the developed approach. In the future study, the online reviews may be combined with the traditional survey data to provide a more effective and reliable identification on the to-be-improved product features.     

Bridging customer knowledge to innovative product development: a data mining approach

In the big data era, firms are inundated with customer data, which are valuable in improving services, developing new products, and identifying new markets. However, it is not clear how companies apply data-driven methods to facilitate customer knowledge management when developing innovative new products. Studies have investigated the specific benefits of applying data-driven methods in customer knowledge management, but failed to systematically investigate the specific mechanics of how firms realised these benefits. Accordingly, this study proposes a systematic approach to link customer knowledge with innovative product development in a data-driven environment. To mine customer needs, this study adopts the Apriori algorithm and C5.0 in addition to the association rule and decision tree methodologies for data mining. It provides a systematic and effective method for managers to extract knowledge ‘from’ and ‘about’ customers to identify their preferences, enabling firms to develop the right products and gain competitive advantages. The findings indicate that the knowledge-based approach is effective, and the knowledge extracted is shown as a set of rules that can be used to identify useful patterns for both innovative product development and marketing strategies.     

Iterative product design through group opinion evolution

More and more industrial products have begun to adopt iterative design modes. Opinions collected from all sides in each round of iterations are used as bases to make products that are able to capture the changes of customer demand dynamically and to respond to these changes in a timely manner. In this context, it becomes important for product designers and manufacturers to study the evolution of opinions as well as the product scheme along with design iteration to determine its regularity. This paper integrates the Hegselmann–Krause model into product improvement process, and develops a dynamic model from the view of opinion dynamics. The dynamic model simultaneously converges the evolution of customers’ opinions, professional designers’ opinions and the product scheme. The paper also analyses the interaction mechanism among different opinions as well as the mechanism between opinions and the product scheme. The experimental results determine that the product scheme, customers’ opinions and professional designers’ opinions can demonstrate differently but regular evolution patterns along with the design iteration according to the difference in confidence intervals or the difference caused by tending to adopt a more customer-driven design strategy or a more professional designer-driven design strategy. Finally, reasonable suggestions are provided to help manufacturers make product design strategies.     

A review of systematic evaluation and improvement in the big data environment

The era of big data brings unprecedented opportunities and challenges to management research. As one of the important functions of management decision-making, evaluation has been given more functions and application space. Exploring the applicable evaluation methods in the big data environment has become an important subject of research. The purpose of this paper is to provide an overview and discussion of systematic evaluation and improvement in the big data environment. We first review the evaluation methods based on the main analytic techniques of big data such as data mining, statistical methods, optimization and simulation, and deep learning. Focused on the characteristics of big data (association feature, data loss, data noise, and visualization), the relevant evaluation methods are given. Furthermore, we explore the systematic improvement studies and application fields. Finally, we analyze the new application areas of evaluation methods and give the future directions of evaluation method research in a big data environment from six aspects. We hope our research could provide meaningful insights for subsequent research.     

Analysing semiconductor manufacturing big data for root cause detection of excursion for yield enhancement

With the shrinking feature size of integrated circuits driven by continuous technology migrations for wafer fabrication, the control of tightening critical dimensions is critical for yield enhancement, while physical failure analysis is increasingly difficult. In particular, the yield ramp up stage for implementing new technology node involves new production processes, unstable machine configurations, big data with multiple co-linearity and high dimensionality that can hardly rely on previous experience for detecting root causes. This research aims to propose a novel data-driven approach for Analysing semiconductor manufacturing big data for low yield (namely, excursions) diagnosis to detect process root causes for yield enhancement. The proposed approach has shown practical viability to efficiently detect possible root causes of excursion to reduce the trouble shooting time and improve the production yield effectively.     

An integrated framework for product line design for modular products: product attribute and functionality-driven perspective

The purpose of this research is to facilitate original equipment manufacturers operating in a single market segment to frame their product line design strategy that pertains to offering right product attributes with right attribute level in the right product profile within a market segment. Through this research, we attempt to establish a link between functional level design of product attributes with commercial objectives of the enterprise. Initially, by deriving the functional importance of product attribute levels of individual product attributes within a product profile, demand and functional importance data are generated. Utilising the function-based cost estimating framework and multi-linear regression methodology, we determine the cost and product development time coefficients for respective product attributes. Finally, a mixed integer quadratic programming-based mathematical formulation is developed that includes maximisation of product premium and minimisation of various costs as major objectives under the assumption that manufacturer seeks to offer optimal number of product profiles within the market segment. Employing the commercial solver LINGO, the integrated framework is solved. The entire framework is illustrated using the operator cabin of heavy construction machinery.     

基于功能矩阵空间和变换法的产品设计需求功能获取

 针对产品概念设计阶段获取的用户需求功能,构建了设计需求功能获取原理框架和用户知识空间定性模型,并在功能范畴下以基本功能集为组成单元,以包含运动、能量、控制、关联、综合这5种类型基本功能集的用户需求功能矩阵表达为表现形式,建立了空间变换和运算法则及其判定准则,并在变换运算过程中采用三维模型二维化表达方法,将三维空间和变换运算转换为二维平面和变换运算,使得从用户需求功能到设计需求功能的变换可以伴随用户知识空间到设计者知识空间的变换实现,从而得到了包含各项基本功能集子空间的设计需求功能。通过一个实例贯穿在各个部分中来验证理论和方法的可行性,最后给出了相应的结论和未来需要进一步研究的内容。     

Using big data to make better decisions in the digital economy

The question this special issue would like to address is how to harvest big data to help decision-makers to deliver better fact-based decisions aimed at improving performance or to create better strategy? This special issue focuses on the big data applications in supporting operations decisions, including advanced research on decision models and tools for the digital economy. Responds to this special issue was great and we have included many high-quality papers. We are pleased to present 13 of the best papers. The techniques presented include data mining, simulation and expert system with applications span across online reviews, food retail chain to e-health.     

A data-driven approach for the optimisation of product specifications

In order to develop the profit-maximising, market share-maximising or cost-minimising bundle of product engineering specifications with proper performance levels, an optimisation model driven by operating data is proposed. The operating data are input as the sources to conduct the optimisation and a data-based customer satisfaction function can be formed. Then, a customer choice model developed from the customer satisfaction is constructed to estimate the customer choice probability. The expected market share (EMS) then can be derived from the choice probability. After all, a multi-objective model is constructed to maximise the EMS and minimise the total engineering cost. The candidate Pareto-optimal solutions can be obtained by solving the optimisation model. Then a membership function is defined to select the optimal solution from the Pareto-optimal solutions. A case study for optimising the smartphone’s specifications is conducted to demonstrate the effectiveness of the newly developed approach. Compared with the commonly used Conjoint Analysis (CA) method in determining the most desired levels for product specifications, the proposed data-driven method can avoid the situation where the user’s preferences are irrational, making the proposed method be more practical in measuring customer preferences than the utility-based model.     

产品基因及其在能量转换功能求解中的应用

 提出了能量转换型原理解的产品基因模型,构造了能量型产品基因知识库,建立了基于产品基因的启发式原理方案求解模型,并给出了求解实例。     

机构特征挖掘与创新概念设计方法

传统机构设计方法过度仰赖设计者本身专业素养与创意构思,缺乏有系统地利用现有信息,基础回避设计层级中往往无法有效掌握技术的创意概念,导致陷入低阶设计修改的窠臼之中。若能运用专利分析挖掘出机构设计概念,从大量know-how的分析中获得功能设计的know-why,则能于专利中获取相关机构之设计精神。设计方法则导入系统化机构概念设计,提高设计层次,以挖掘出的大量信息为设计基础,汇整出机构设计规范,并将设计规范导入概念设计方法,获得可行机构设计。如此能充分运用现有机构获得创新机构设计,能更有效率且更能掌握机构设计内涵,对于设计者能有效地获得可行设计、避免设计产生雷同而浪费资源,并可缩短研发时程。     

Seeing things: consumer response to the visual domain in product design

This paper discusses consumer response to product visual form within the context of an integrated conceptual framework. Emphasis is placed on the aesthetic, semantic and symbolic aspects of cognitive response to design. The accompanying affective and behavioural responses are also discussed and the interaction between cognitive and affective response is considered. All aspects of response are presented as the final stage in a process of communication between the design team and the consumer. The role of external visual references is examined and the effects of moderating influences at each stage in the process of communication are discussed. In particular, the personal, situational and cultural factors that moderate response are considered. In concluding the paper, implications for design practice and design research are presented.     

Creative marketing and innovative consumer product design—some case studies

This paper uses three case studies to discuss the importance of a total integration between marketing research and design at the concept stages of new consumer product development and throughout the design development and marketing process of the product     

A guided search genetic algorithm using mined rules for optimal affective product design

Affective design is an important aspect of new product development, especially for consumer products, to achieve a competitive edge in the marketplace. It can help companies to develop new products that can better satisfy the emotional needs of customers. However, product designers usually encounter difficulties in determining the optimal settings of the design attributes for affective design. In this article, a novel guided search genetic algorithm (GA) approach is proposed to determine the optimal design attribute settings for affective design. The optimization model formulated based on the proposed approach applied constraints and guided search operators, which were formulated based on mined rules, to guide the GA search and to achieve desirable solutions. A case study on the affective design of mobile phones was conducted to illustrate the proposed approach and validate its effectiveness. Validation tests were conducted, and the results show that the guided search GA approach outperforms the GA approach without the guided search strategy in terms of GA convergence and computational time. In addition, the guided search optimization model is capable of improving GA to generate good solutions for affective design.     

A framework for affective customer needs in product design

A systematic framework is proposed to conceptualize customer needs in product design. Customer needs were derived for current and future electronic devices in automobiles. Subjects rated their preferences for 15 product attributes on 10-point semantic differential scales. Using factor analysis, three generic factors were extracted, namely holistic attributes, styling and functional design. Depending upon the familiarity of the device, there were clear differences among potential customers. Unknown devices such as a navigation map were assessed first hand by using holistic attributes. Familiar designs such as car radio and cell phone were assessed using styling and functionality attributes. Customer reactions and preferences may be caused by product design parameters that operate either through their perceptual attributes or from the experience they acquire in using the artifacts or interfaces. There are both functional and affective needs. Functional (or cognitive) customer needs can be derived top-down, using product design features. Affective customer needs are difficult to derive top-down—typically they are evaluated by looking at several design propositions.     

Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects

Big data has recently been recognised as one of the most important areas of future technology. It has attracted the attention of many industries, since it has the potential to provide companies with high business value. This paper examines the forms of business value that companies can create from big data analytics investments, the direct impacts it has on the financial performance of a firm, and the mediating effects of market performance and customer satisfaction. Drawing on the resource-based view theory, this study demonstrates that the business value achieved from investments in big data analytics leads to advantages in terms of the financial performance of a firm. The results offer evidence of the existence of a customer satisfaction mediation effect and of the absence of a market performance mediation effect. Theoretical and practical implications are discussed at the end of the paper.     

基于保质设计方法的产品开发决策研究

讨论了保质设计思想指导下的产品开发设计策略,并以产品开发设计的关键阶段——概念设计为对象,进一步研究了产品概念设计决策过程,建立了产品概念设计方案的评价指标体系和基于模糊优选理论的决策模型.     

A method for identifying customer orientations and requirements for product–service systems design

For manufacturers, developing product–service systems (PSSs) is getting more important because of the trends of servitisation and creating social value. A PSS is a social system where multiple actors mutually provide products and services. A PSS design, therefore, must take into account various actors as customers. However, existing methods provide an insufficient solution as to how various customers should be handled in an analysis to identify and accommodate various customer preferences and requirements. To tackle this issue, this article proposes a new method of identifying customers’ orientations and requirements for PSS design. The proposed method employs a combination of topic analysis, persona and scenario approaches. The effectiveness of the method is demonstrated with its application to an urban development case. Through the demonstration, its practical benefits are concluded as follows: consistent and logical results of requirement analysis and insights into a new market for manufacturers.