A rough set based data mining approach for house of quality analysis

As the first phase of quality function deployment (QFD) and the only interface between the customers and product development team, house of quality (HOQ) plays the most important role in developing quality products that are able to satisfy customer needs. No matter in what shape or form HOQ can be built, the key to this process is to find out the hidden relationship between customers’ requirements and product design specifications. This paper presents a general rough set based data mining approach for HOQ analysis. It utilises the historical information of customer needs and the design specifications of the product that was purchased, employs the basic rough set notions to reveal the interrelationships between customer needs and design specifications automatically. Due to the data reduction nature of the approach, a minimal set of customer needs that are crucial for the decision on the correlated design specifications is derived. The end result of the approach is in the form of a minimal rule set, which not only fulfils the goal of HOQ, but can be used as supporting data for marketing purposes. A case study on the product of electrically powered bicycles is included to illustrate the approach and its efficiency.     

Attribute selection for product configurator design based on Gini index

Customer needs play a critical role in product design. Matching diversified customer needs with a company’s product offerings has been a challenge for academia and industry. To this end, product configurator systems have been accepted as important tools to elicit customer needs to meet the challenge. However for complex products with many product possibilities and intricate inter-product relationships, current product configuration systems may become tedious and time consuming for customers’ choice navigation process. They cannot adapt to each individual customer’s preferences by leveraging on the attribute specification information captured in previous configuration steps. This paper presents a Gini index-based attribute selection approach for configurator design. Product configuring is modelled as a sequential query–answer process. In each configuring step, the Gini index is deployed to quantify the clarity of the designer’s beliefs about the customer’s needs. The attribute which contributes most to the clarity will be selected for the customer to configure. A product recommendation module is also integrated with the configurator to further improve the efficiency. As a result, designers obtain clarification on the customer’s needs and preferences in an accelerated manner. An example is presented to test the viability of the method.     

Integration of rough set and neural network ensemble to predict the configuration performance of a modular product family

Configuration performance prediction (CPP) is critical in the whole process of configuration design for a modular product family. Its aim is to estimate the key performance parameter values in advance, thus evaluating if the product variant can satisfy the customers’ personalised requirements or not. In this paper, we propose a novel prediction approach based on the integration of rough set and neural network ensemble through discovering the knowledge from the historical configuration information table. The minimal hitting set is introduced and its equivalence relationship with the minimal attribute reduction is proven. A genetic algorithm is designed to perform the approximate reduction of the condition attributes. A neural network ensemble model used for regression prediction is constituted by means of the variant bagging method based on error clustering. This methodology can reuse the discovered configuration rules and knowledge efficiently, as well as reduce the effort of experimental measurement to some extent. Finally, the applicability of this prediction method is verified on a newly developed refrigerator family.     

Proceedings Reviews

Abstract:

Quality Function Deployment (QFD) is a systematic process for capturing and integrating the voice of the customer into every aspect of the design and delivery of products and services. Understanding customer wants or needs is crucial to the successful design and development of new products and services. QFD is a system that utilizes customer demand information to design products or services that will meet a client's mission. In addition, the process prioritizes and deploys these customer-driven characteristics throughout the product or service development to meet the customer needs, wants, and expectations. QFD determines effective development targets for the prioritized product and service characteristics. The QFD process has been used and documented extensively in product development. The service industry, however, lacks in the application of this process. The purpose of this article is to show practitioners and researchers how this process, in its entirety, can be used as a planning process to link customer requirements and service characteristics in the service industry. A case study was developed in which QFD was applied to develop recommendations for the American Society of Engineering Management (ASEM) in an effort to increase customer satisfaction and to identify opportunities to improve member benefits. The results of this study are applicable to any organization to improve the design and delivery of products and service regardless of industry.     

Integrating Kansei engineering with conjoint analysis to fulfil market segmentation and product customisation for digital cameras

Diverse customer desires coupled with technological advances have forced companies to manufacture products with ultimate performance, low cost, high quality and much shorter time-to-market. Recently, the popularity of smart phones has given rise to seriously declined product sales of digital cameras. In this paper, a two-phase framework is presented to offer decision supports on developing next-generation cameras. In the phase of market segmentation, Kansei engineering is employed to capture customer perceptions of affective features. Then, rough set theory is conducted to generate decision rules for partitioning the whole market into the consumer segment and the professional segment, respectively. In the phase of product customisation, conjoint analysis is applied to extract customer preferences for functional features. Furthermore, Grey relational analysis is conducted to select the top three varieties with regard to two distinct segments. In particular, this paper is capable to help brand companies or camera manufacturers better capture customer perceptions and preferences for digital cameras, effectively perform market segmentation (based on affective features) and efficiently conduct product customisation (based on functional features).     

Prioritising technical attributes in QFD under vague environment: a rough-grey relational analysis approach

This study aims at improving the effectiveness of Quality function deployment (QFD) in handling the vague, subjective and limited information. QFD has long been recognised as an efficient planning and problem-solving tool which can translate customer requirements (CRs) into the technical attributes of product or service. However, in the traditional QFD analysis, the vague and subjective information often lead to inaccurate priority. In order to solve this problem, a novel group decision approach for prioritising more rationally the technical attributes is proposed. Basically, two stages of analysis are described: the computation of CR importance and the prioritising the technical attributes with a hybrid approach based on a rough set theory (RST) and grey relational analysis (GRA). The approach integrates the strength of RST in handling vagueness with less priori information and the merit of GRA in structuring analytical framework and discovering necessary information of the data interactions. Finally, an application in industrial service design for compressor rotor is presented to demonstrate the potential of the approach.     

Mapping gender and marital roles on customer delight by value perception for mobile technology in India

The Indian telephony industry is a diverse platform for product and service in customer value perception. The present study organizes and synthesizes the varied research streams for developing a customer value delight (CVD) framework for marketing decisions like segmentation and targeting. The conceptual framework is developed by using ten-value predictor variables and customer delight through divergent viewpoints identified from previous studies. The significance of the segregation proposition is tested through discriminant analysis in three different dimension runs i.e. delight, gender, and marital category. The scholarship behind the study provides insight into an imperative issue of segregating customer delight by perceived value predictors in categories. The perceptual mapping and vector magnitude analysis of value predictors were performed to club customers in two categories of Mid-Town (low/moderate delight) and Extremes (high delight). The analysis rigorously supports the discriminating power of perceived price, perceived benefit, and self-congruity. The salience of study unlocks psychographic and demographic perceptual maps of customer delight with its perceived value predictors for strategic and tactful planning in the mobile industry.     

A framework for cost evaluation in product service system configuration

Configuration systems are increasingly used as a means for efficient design of customised product service systems (PSS) to satisfy diverse customer needs. Cost evaluation is thereby important to assist the configuration engineers in making decisions on feasible configuration solutions. However, little research attention has been received until recently. To fill this gap, this paper contributes in developing a framework for cost evaluation in PSS configuration. A holistic view of PSS configuration, the three-dimensional PSS cost element, and a life cycle-oriented cost evaluation approach are successively proposed. The framework is thereby established with a number of parts, including the preparatory stage, the evaluation stage and the configuration stage. A pump PSS is illustrated to validate the developed framework. Four feasible configuration solutions in one configuration activity are evaluated and compared. The configuration engineers are thus assisted with the decision on selecting the one with the least cost.     

Optimisation approach to target costing under uncertainty with application to ICT-service

Target costing is a modern approach applied during product development that defines cost targets for products and its components. These cost targets are driven by customer requirements and achievable revenues. The intention of this paper is the integration of target costing with modern concepts of modelling uncertainty and management of risk based on optimisation. Contrary to the traditional focus of target costing on cost targets, this paper prefers a strategy for achieving a target profit. Moreover, in this paper target costing is understood as a continuous process with incremental changes of cost drivers, product and component design as well as product prices. Therefore, the change in costs and profit with respect to aforementioned control parameters is modelled by linear approximations. Hence, improved decisions concerning design and prices are derived by linear programming models. In practice, information concerning product and component costs, demand or customer preferences are not given with certainty. Therefore, we apply a stochastic programming approach to manage the risk inherent in the target costing process. After a general presentation, we apply our approach to the provision of an information and communication technology service where the level of uncertainty is considerable.     

Quality and reliability assessment of hardware and software during the total product life cycle

A major challenge for the computer industry is the need for the development and implementation of an engineering languge which provides continuous linkages and measurement between the customer and all engineering functions internal to the company. Such a language would allow information solution developers and information service providers to be able to continuously monitor the quality and reliability performance of integrated hardware and software products during the complete product life cycle. A quantitative engineering language needs to be developed to provide seamless and continuous linkages between the customer—the user of integrated computer systems—and all of the engineering development and manufacturing functions tasked with designing and building solutions which meet customer needs. A methodology is proposed which addresses this challenge by the implementation of two metrics: total defects per unit (TDU) and the annual rate of events (ARE). These two metrics can measure all hardware, software and computer integration events during the total product life cycle. A methodology is presented which provides a rigorous translation of the ARE metric, monitored at the customer site, into the traditional reliability metrics used by engineering and manufacturing. Algorithms are presented which directly translate AREs into mean time between failures (MTBF), mean time between parts replacement (MTBPR) and mean time between system interruptions (MTBSI). The ARE metric meets the customer requirement of being able to clearly focus on the reliability and availability performance of total systems as a result of hardware and software components, the user interface, and environmental factors. The paper discusses the development and application of integrated TDU and ARE metrics and shows how the total product life cycle quality and reliability of a complex integrated computer and communications solution can be efficiently monitored and managed for improvement during design, manufacturing, and installation performance in an integrated customer environment.     

Identifying environmental improvement options by combining life cycle assessment and fuzzy set theory

Great efforts have recently resulted in increasing the environmental performance of industrial products. However, to obtain more sustainable solutions, environmental properties must meet customer requirements. This paper introduces a novel approach for identifying environmental improvement options by taking into account customer preferences. The Life Cycle Assessment methodology is applied to evaluate the environmental profile of a product, using eco-Indicator99 as the impact assessment method. A fuzzy approach based on the House of Quality in the Quality Function Deployment methodology provides a more quantitative method for evaluating the imprecision of the customer preferences. Experimental membership functions have been obtained directly from users and design team members through an opinion poll. The proposed methodology has been applied to identify environmental improvement options by taking into account customer opinions of an office table and identifying three product components where the environmental performance of the table could be improved.     

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.     

基于客户感知价值的客户细分研究

基于客户感知价值进行了客户细分的理论和实证研究,以物流公共信息平台为例提出依据客户感知价值进行客户细分的一般流程：首先利用文献查阅与问卷调查方法确定客户感知价值的驱动因素,接着利用因子分析法对驱动因素变量降维处理,然后利用聚类方法识别客户,再利用决策树方法寻找客户分类规则并分析各类客户特征。最后依据客户特征将物流公共信息平台的客户分为4类：高价值需求客户、中等价值需求客户、低价值需求客户与高性能需求客户,研究结果可以为不同企业实施客户细分提供借鉴和参考。     

以顾客需求导向的产品评价体系构建研究

针对产品评价体系构建的问题,提出了一种通过对顾客评论信息的挖掘进行顾客需求的识别,进而应用数据聚类分析,自下而上地构建产品评价体系的方法。有别于传统多属性评价分析中大多通过自上而下（从概括到具体）方式进行评价体系构建的思路,该方法以顾客评价信息为导向,使最终构建出来的产品评价体系更具客观性,能有效为产品评价体系优化设计提供帮助,相关案例演算展示了方法的可行性。     

考虑竞争性分析的改进模糊重要度绩效分析方法

重要度绩效分析（IPA）是顾客满意度评价的常用方法,分析结果是产品/服务改进设计的重要依据。）为处理顾客评价信息的不确定性,采用模糊集方法表达并处理重要度和绩效评价信息。针对传统IPA方法中,属性绩效值的获取没有考虑市场竞争因素的问题,提出了考虑竞争性分析的改进模糊IPA方法。灰色关联分析法用来计算属性的市场竞争性系数,以修正顾客群决策得到的初始属性绩效评价值。考虑到属性之间存在影响关系,DEMATEL方法用来分析属性的影响和被影响程度,以修正顾客群决策得到的初始属性重要度评价值。以某电子产品的顾客满意度分析为例,验证了所提方法的有效性。     

Quality control for a detected an appropriate queue from K-independent M/M/C/N queueing models

In this paper, we consider a system of K-independent Markovian queues such that each one of them has a Poisson arrival process and exponential service time. We assume that every server has some characteristics such as the speed of the service performance or the service cost. To find an appropriate queue, which meets customer needs for the service performance, we present a new approach that gives a suitable decision to choose an appropriate queue from our system. This allows the customer to deal with minimum cost and faster server under steady state. We solve an interesting discrete stochastic optimization problem where the paid cost by the customer is bounded by a Gaussian distribution. Using these hypotheses, we perform a simulation study by generating the paid cost random values and choosing the minimum value between them. This minimum cost gives the highest service rate, which is used to obtain the optimum values of the system effectiveness measures.     

企业职工健康体检车产品服务系统的概念设计

目的 针对企业职工健康体检流程复杂、耗时长、检后沟通不便等问题,深入研究职工医疗体检服务流程,构建企业职工健康体检车服务系统并输出相应的方案设计。方法 首先,通过调研与分析目标人群的健康体检现状、需求以及行为,运用产品服务系统设计的理论与方法,归纳凝练体检车服务系统的设计策略。其次,结合服务设计中的服务蓝图、系统图、服务触点等方法,整合服务系统中利益相关者的需求,进行体检车产品服务系统构建,指导体检车硬件和信息系统设计。结论 基于前期研究结果进行体检车空间布局与规划、人机工效学分析、体检APP的设计输出,在一定程度上优化了企业职工健康体检服务流程,探索了医疗体检的优化模式,提升了用户的体检体验。     

Customer-oriented Configuration Model for Modular Mechatronic Products: Application in Industrial Robot Design

The contradiction between manufacturing costs and customer demand of mechatronic products can be balanced by configuration design. The article proposes a customer-oriented configuration model for modular mechatronic products which makes up the shortfall in meeting customer needs for the traditional configuration de- sign mode. The elements of the modular mechatronic products configuration model including module,module connection and configuration knowledge are analyzed. And a formal representation method for configuration model elements combining with their characteristics is given. Based on the above research,an abstract configuration model of modular mechatronic products is designed. Finally,an industrial robot is used as an application exam- ple to build a customer-oriented industrial robot configuration model.     

基于RIR-MOO的定制产品优化设计方法研究

大规模定制是未来产品制造的发展方向,以低成本快速响应客户的个性化需求是定制产品设计的关键。以往对定制产品设计的研究缺少从个性化需求到产品参数的流程化设计指导方法,无法从定制产品个性化的客户需求出发,快速地关联设计目标并指导产品参数的设计。为此,提出了基于RIR-MOO （relative importance ratings and multi-objective optimization,相对重要性等级-多目标优化）的定制产品优化设计方法,从客户需求出发,将关键需求映射到相关的设计目标,再通过多目标优化（multi-objective optimization,MOO）方法快速求解产品参数,实现定制产品的快速响应和优化设计。首先,针对模糊、动态变化的客户需求,利用加权区间粗糙集分析方法对客户需求进行客观分析,排除争议性大的模糊需求,同时计算不同客户需求的相对权重,得到其相对重要性等级（relative importance ratings,RIR）;然后,提出一种基于转换矩阵的客户需求与设计目标关联的方法,根据关键需求与设计目标的关联度以及关键需求的RIR,对关键需求与设计目标进行匹配,实现关键需求到设计目标的转换;最后,基于MOO方法对设计目标对应的产品参数进行优化设计,实现约束条件下定制产品的多目标优化。以汽车发动机活塞机构优化设计为例,对基于RIR-MOO的优化设计方法的可行性进行验证。结果表明,所提出的方法可以快速响应定制产品的客户需求并指导其优化设计,可为定制化产品制造企业提供指导。