基于联合分析和模糊聚类的产品族客户需求模型研究

客户需求信息的准确获取与表达是产品族规划的首要环节,针对客户需求模型构建过程中需求信息具有抽象性、模糊性等特点,基于公理设计构建描述客户需求的模型,运用联合分析法对客户需求进行量化,将模糊的客户需求转换为具体的产品设计指标和参数.通过对指标和参数的相关性分析,建立产品设计关联矩阵.采用模糊最大树聚类算法对设计关联矩阵进行聚类分析,以聚类有效性指标为依据,得到最佳聚类阈值,从而获得最优聚类结果.在此基础上,利用最优聚类结果对设计关联矩阵进行解耦,最终得到解耦设计关联矩阵,为参数化产品族规划提供理论依据.最后,以圆柱齿轮减速器产品族规划为实例,验证了该方法的有效性和可行性.     

MC模式下基于本体的客户需求映射方法

针对目前大规模定制模式下客户需求映射方法中存在的问题,用一组类客户需求(GCN)来表达面向产品族的客户群体需求,以期在满足众多客户需求的前提下降低客户需求的多样化程度.基于本体映射方法,建立了类客户需求与类功能要求之间的映射关系,实现了类客户需求到类功能要求的映射转换,以完善客户需求到产品族的映射过程.通过一个实例说明了此方法的应用性.     

面向产品族的模块化设计方法研究

为使模块化设计中模块之间的功能独立,在基于功能相关与结构相关的模块划分准则的基础上进行模糊聚类,通过设定阀值确定模块划分结果,并在此基础上对划分的模块进行变型设计,组成产品族,为客户的个性化需求提供产品设计方法．     

开放式结构产品的模块规划

 产品的多元化和市场竞争使客户对产品的个性化需求不断增加,传统的产品结构难以满足低成本和个性化的需求．开放式结构产品使得产品既拥有大批量定制的优点又能满足客户个性化的需求．开放式结构产品将产品的模块分为通用模块、定制模块和个性化模块．模块划分和模块类型规划是开放式结构产品设计的重要步骤．针对开放式结构产品的设计,提出了一种以公理设计为分析框架、以质量功能展开（QFD）为基本模型的模块划分和分类方法．该方法采用公理设计建立功能需求与概念结构之间的映射关系,通过分析概念结构之间的功能、空间和信息流关系建立了设计关联矩阵,运用系统聚类法对设计关联矩阵进行聚类模块划分,利用QFD规划产品中零部件的类型并建立模块类型规划准则,最后根据零部件类型确定了开放式结构产品的模块类型．应用所提出的方法对玩具喷漆机进行了模块规划,得出了开放式结构玩具喷漆机的结构方案,验证了该方法的有效性和可行性．     

面向客户需求的产品优化设计方法研究

面向客户需求是企业在当前竞争激烈的市场环境下成功的关键。本文提出了一种快速响应客户需求的产品优化设计方法,通过基于模糊相似理论的需求信息模糊聚类方法,对具有模糊性和多样性的原始客户需求信息进行精简分类处理,以获取客户需求集合;然后构建了一种综合联合分析法(CA)和质量功能展开(QFD)的全新CA-QFD需求转化方法,从客户需求到产品设计属性进行精确转化,并通过对实例产品的优化设计,实现快速响应客户需求;最后,通过锻压机主工作液压缸的优化设计实例,验证了本文方法的可行性和适用性。     

基于多目标和聚类的产品功能疲劳健壮性分析

研究了产品功能疲劳问题中产品功能组合的健壮性分析问题,提出了基于NSGA-Ⅱ和客户聚类的综合分析方法.具体包括建立了基于NSGA-Ⅱ算法的产品功能疲劳问题的多目标优化模型,并利用K-Means算法对客户评价数据进行了聚类分析,然后结合多目标模型和聚类结果进行健壮性分析,为产品开发人员选择健壮性的产品功能组合提供决策支持.最后通过智能手机产品的实际案例说明了所提出方法的有效性.     

基于多目标和聚类的产品功能疲劳健壮性分析北大核心CSSCI

研究了产品功能疲劳问题中产品功能组合的健壮性分析问题,提出了基于NSGA-II和客户聚类的综合分析方法。具体包括建立了基于NSGA-II算法的产品功能疲劳问题的多目标优化模型,并利用K-Means算法对客户评价数据进行了聚类分析,然后结合多目标模型和聚类结果进行健壮性分析,为产品开发人员选择健壮性的产品功能组合提供决策支持。最后通过智能手机产品的实际案例说明了所提出方法的有效性。     

基于需求层次模型的产品发展趋势预测方法

提出了一种从用户产品需求进化规律来定位目前产品发展阶段,寻找产品改进方向,预测未来发展趋势的方法。该方法建立了用户需求层次模型和用户需求到产品特性之间的映射关系,采用模糊聚类分析法对产品特征进行聚类分析,并用分析结果进行产品预测。最后以插线板特征分析为例,验证了该方法的可行性。     

基于优化的产品平台参数规划方法

 为满足客户化和全球竞争的需求,企业要实现大规模定制(mass customization,MC).基于公共产品平台的产品族设计是实现大规模定制的一种有效方式,而平台规划是面向产品族设计方法学的核心内容,也是目前研究中的一个热点问题.基于模型参数的平台设计是其方法之一.针对基于一系列标准可变参数的产品平台,用优化方法对产品平台参数进行规划,以满足各种客户需求.该规划方法无需事先人为指定,而是在满足客户需求的前提下,尽可能提高产品族中设计变量的共性,从而确定最好的产品平台的公共参数及其最优值,以及个性参数及其变化值,并以带式输送机为例验证了该方法.     

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。     

考虑客户满意度的物流末端节点选址模型及算法

为了解决自取货的物流末端单一节点选址问题，在服务顾客数量和地理位置已知的前提下，考虑最远取货距离的约束，建立以客户满意度模糊隶属度为目标函数的数学模型。为了求解该模型，运用均值聚类给出初始可行节点，以满意度较小的客户为顶点，在分析其合理性的基础上，设计基于三角形外心和等分点的启发式算法优化初始解，算法允许接受一次次优解以避免陷入局部最优，并通过记忆数组来跟踪搜索过程，最终输出过程最优解。最后给出算例分析，证明了所提出的图上寻优算法优于均值聚类和点密度聚类算法。     

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.     

An approach to rule extraction for product service system configuration that considers customer perception

At present, manufacturers tend to bundle a product with its related services as a product service system, to meet customer needs and achieve competitive advantage. Configuring a product service system involves selecting and combining appropriate product and service components, to satisfy individual customer needs. One crucial step to rapidly configure a product service system is to develop formalised configuration rules, which show the relationships between product service components and customer needs, including those expressed as perception needs. In this study, a rough set-based approach is proposed to acquire configuration rules. First, an information table is built by combining the results of factor analysis and questionnaire survey. Then, customer segmentation is accomplished by clustering. Based on the information table and customer segmentation, a dominance-based rough set approach is used to extract the configuration rules. Finally, a weighbridge is selected for the case study to validate the proposed approach.     

Fuzzy multiple objective decision making approach to prioritize design requirements in quality function deployment

Quality function deployment (QFD) is a customer-oriented design tool for developing new or improved products to increase customer satisfaction by integrating marketing, design engineering, manufacturing, and other related functions of an organization. QFD focuses on delivering value by taking into account customer needs and then deploying this information throughout the development process. Although QFD aims to maximize customer satisfaction, technology and cost considerations limit the number and the extent of the possible design requirements that can be incorporated into a product. This paper presents a fuzzy multiple objective programming approach that incorporates imprecise and subjective information inherent in the QFD planning process to determine the level of fulfilment of design requirements. Linguistic variables are employed to represent the imprecise design information and the importance degree of each design objective. The fuzzy Delphi method is utilized to achieve the consensus of customers in determining the importance of customer needs. A pencil design example illustrates the application of the multiple objective decision analysis.     

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.     

数据挖掘在电信客户细分中的应用研究

把数据挖掘中聚类分类技术应用于基于客户价值矩阵的客户价值细分中,建立一种电信客户细分方法,为电信公司客户保持和营销提供决策依据.用样本进行实验,建立了各类价值客户的分类模型,得出结论.     

Prediction of manufacturing resource requirements from customer demands in mass-customisation production

Mass-customisation production is a new manufacturing approach to produce customised products based on requirements of individual customers while maintaining the quality and efficiency of mass production. Due to the large variations of customised products, the traditional methods for planning manufacturing resources based on volumes of mass produced products are not effective for mass-customization production. In this research, a new manufacturing resource planning method is developed by studying the relations between customer demands and manufacturing resource requirements based on the true data from a mass-customisation production company—Gienow Windows and Doors. In this research, first the relations between the customer demands, modeled by sales data at levels of whole company, sales branches, and markets in sales branches, and the manufacturing resource requirements, modeled by labour requirements of different production lines are studied. Fuzzy pattern clustering method is employed for classifying the resource requirements into patterns to further understand the relations. Based on this study, linear regression and neural network are used to model the linear and non-linear relations between customer demands and manufacturing resource requirements, and to predict the manufacturing resource requirements from available customer demands. A manufacturing resource planning system was developed to demonstrate the effectiveness of this introduced approach.