Fuzzy linear programming models for NPD using a four-phase QFD activity process based on the means-end chain concept

Quality function deployment (QFD) is a customer-driven approach in processing new product development (NPD) to maximize customer satisfaction. Determining the fulfillment levels of the “hows”, including design requirements (DRs), part characteristics (PCs), process parameters (PPs) and production requirements (PRs), is an important decision problem during the four-phase QFD activity process for new product development. Unlike previous studies, which have only focused on determining DRs, this paper considers the close link between the four phases using the means-end chain (MEC) concept to build up a set of fuzzy linear programming models to determine the contribution levels of each “how” for customer satisfaction. In addition, to tackle the risk problem in NPD processes, this paper incorporates risk analysis, which is treated as the constraint in the models, into the QFD process. To deal with the vague nature of product development processes, fuzzy approaches are used for both QFD and risk analysis. A numerical example is used to demonstrate the applicability of the proposed model.     

An evaluation approach to engineering design in QFD processes using fuzzy goal programming models

Quality function deployment (QFD) is a product development process used to achieve higher customer satisfaction: the engineering characteristics affecting the product performance are designed to match the customer requirements. From the viewpoint of QFDs designers, product design processes are performed in uncertain environments, and usually more than one goal must be taken into account. Therefore, when dealing with the fuzzy nature in QFD processes, fuzzy approaches are applied to formulate the relationships between customer requirements (CRs) and engineering design requirements (DRs), and among DRs. In addition to customer satisfaction, the cost and technical difficulty of DRs are also considered as the other two goals, and are evaluated in linguistic terms. Fuzzy goal programming models are proposed to determine the fulfillment levels of the DRs. Differing from existing fuzzy goal programming models, the coefficients in the proposed model are also fuzzy in order to expose the fuzziness of the linguistic information. Our model also considers business competition by specifying the minimum fulfillment levels of DRs and the preemptive priorities between goals. The proposed approach can attain the maximal sum of satisfaction degrees of all goals under each confidence degree. A numerical example is used to illustrate the applicability of the approach.     

Modeling customer satisfaction with new product design using a flexible fuzzy regression-data envelopment analysis algorithm

The success of new products depends greatly on customer satisfaction and meeting the customer needs is vital for new product development. By incorporating customer needs in the design and development process, organizations can improve productivity for their new products and reduce the risks associated with new product markets. Hence, design teams require methods to model customer satisfaction when setting the associated product design attributes. Thus, different approaches have been developed for modeling the relationship between customer satisfaction and product design parameters. In this study, 16 well-known fuzzy regression (FR) models are considered to understand the relationship between customer satisfaction and new product design. The design of FR models is based on the 4Ps marketing mix (product, price, place, and promotion) concept in fuzzy environments. A flexible algorithm is then presented based on the index of confidence, error measures, and data envelopment analysis for selecting the best FR model. The applicability and usefulness of the proposed algorithm is demonstrated experimentally based on an actual case study, where the flexible algorithm is employed to predict customer satisfaction with a new product design in the freezer/refrigerator industry.     

Rating technical attributes in fuzzy QFD by integrating fuzzy weighted average method and fuzzy expected value operator

Quality function deployment (QFD) is a planning and problem-solving tool that is gaining acceptance for translating customer requirements into the technical attributes of a product. Deriving the rating order of technical attributes from input variables is a crucial step in applying QFD. When the relative weights of customer requirements and the relationship measures between customer requirements and technical attributes are expressed as fuzzy numbers, calculating the importance of each technical attribute falls into the category of fuzzy weighted average, in which the derived membership function of the fuzzy importance of each technical attribute is not explicitly known. Thus, most ranking methods are not suitable under these circumstances. A method is proposed in this paper using fuzzy weighted average method in the fuzzy expected value operator in order to rank technical attributes in fuzzy QFD. An example of a flexible manufacturing system design is cited to demonstrate the application of the proposed approach.     

Product design and selection using fuzzy QFD and fuzzy MCDM approaches

Quality function deployment (QFD) is a useful analyzing tool in product design and development. To solve the uncertainty or imprecision in QFD, numerous researchers have applied the fuzzy set theory to QFD and developed various fuzzy QFD models. Three issues are investigated by examining their models. First, the extant studies focused on identifying important engineering characteristics and seldom explored the subsequent prototype product selection issue. Secondly, the previous studies usually use fuzzy number algebraic operations to calculate the fuzzy sets in QFD. This approach may cause a great deviation in the result from the correct value. Thirdly, few studies have paid attention to the competitive analysis in QFD. However, it can provide product developers with a large amount of valuable information. Aimed at these three issues, this study integrates fuzzy QFD and the prototype product selection model to develop a product design and selection (PDS) approach. In fuzzy QFD, the α-cut operation is adopted to calculate the fuzzy set of each component. Competitive analysis and the correlations among engineering characteristics are also considered. In prototype product selection, engineering characteristics and the factors involved in product development are considered. A fuzzy multi-criteria decision making (MCDM) approach is proposed to select the best prototype product. A case study is given to illustrate the research steps for the proposed PDS method. The proposed method provides product developers with more useful information and precise analysis results. Thus, the PDS method can serve as a helpful decision-aid tool in product design.     

An advanced quality function deployment model using fuzzy analytic network process

Quality function deployment (QFD) is a customer-oriented design tool used to ensure that the voice of customers is employed throughout the product planning and design stages. QFD uses the house of quality (HOQ), which is a matrix that provides a conceptual map for inter-functional planning and communication. In this paper, an advanced QFD model, based on fuzzy analytic network process (ANP) approach, is proposed to systematically take into account the interrelationship between and within the QFD components. The proposed method is aimed at expanding the current research scope from the product planning phase to the part deployment phase to provide product developers with more valuable information (ex. the importance and bottleneck level of part characteristics). Both customer requirements and the company’s production demands will be used as the inputs for the QFD process to enhance the completeness and accuracy of the QFD analysis results. A case study is presented to illustrate the application of the proposed method.     

Product concept and prototype flexibility in manufacturing: Implications for customer satisfaction

A rapidly changing competitive landscape and dynamic customer expectations require manufacturing firms to seek flexibility in product development. Product concept flexibility (i.e., developing design options) and product prototype flexibility (i.e., creating working models) emerge as effective ways to quickly develop new products that meet competitive challenges and satisfy customer demands. Product concept flexibility enables firms to fully explore various product definitions and ideas. Product prototype flexibility allows firms to gather customers’ feedback and investigate design feasibility. Using data from 273 manufacturing firms, this research tests mediating, moderating, and additive models that relate product concept flexibility, product prototype flexibility, and customer satisfaction. The results indicate that firms with high product concept flexibility are more likely to benefit from prototype flexibility than firms with low product concept flexibility, and that product concept flexibility and product prototype flexibility act independently and additively to predict customer satisfaction.     

Modern analysis of customer satisfaction surveys: comparison of models and integrated analysis

Customer satisfaction is a key dimension driving business outcomes and performance of processes in service and product organizations. Measuring customer satisfaction is typically based on self‐declared or interview‐based questionnaires where users or consumers are asked to express opinions on statements, or satisfaction scales, mapping out various interactions with the service provider or product supplier. The topic has gained importance in recent years with researchers proposing new models and methods for designing, implementing, and analyzing customer satisfaction surveys. This paper builds on material presented in a recent edited book entitled Modern Analysis of Customer Satisfaction Surveys (Kenett and Salini, 2011). The book provides a comprehensive exposition of a variety of models that have all been applied to the same data set by leading experts. These models generate a variety of management insights. Combining models opens up opportunities for further research and applications. Specifically, we suggest that an integrated analysis, aggregating several approaches to survey data analysis, may prove effective in increasing the information quality derived from of a customer satisfaction survey. Copyright © 2011 John Wiley & Sons, Ltd.     

A problem-solving approach to product design using decision tree induction based on intuitionistic fuzzy

Customer complaint problem is a product design used to understand customer requirements. Furthermore, product design corresponding to customer requirement does not feel adequately solved for a cause of problem. The cause of the problem affecting product design is solved to prevent customer complaint from reoccurring. However, the problems by customer may have observation uncertainty and fuzzy. Fuzzy concept considers not only the degree of membership to an accept set, but also the degree of non-membership to a rejection set. Therefore, we present a new approach for problem solving using decision tree induction based on intuitionistic fuzzy sets in this paper. Under this approach, we first develop the problem formulation for the symptoms and causes of the problem based on intuitionistic fuzzy sets. Next, we identify the cause of the problem using intuitionistic fuzzy decision tree by the problem formulation. We then provide the approach to find the optimal cause of the problem for the consideration of product design. A numerical example is used to illustrate the approach applied for product design.     

基于犹豫模糊偏好关系FMEA方法的改进

为有效解决产品在研发过程中存在的一系列质量可靠性问题,本文提出了一种新的基于犹豫模糊偏好关系的改进FMEA方法。考虑到专家小组对不同失效模式评估时主要依据相关标准和自身经验,存在犹豫模糊不确定或自身偏好问题。本文首先对风险因子的评分标准进行犹豫模糊化,并用犹豫模糊偏好关系对失效模式的相对风险矩阵进行处理;其次,将得到的具有犹豫模糊偏好关系的综合偏好值与犹豫模糊评价信息相结合,得到改进的风险优先数,从而得出新的失效模式风险评估顺序对FMEA进行改进;最后,利用改进的FMEA模型对产品研发过程中的质量风险进行分析验证,使得风险结果更接近实际情况,进而提高研发成功率,显示该方法可行、有效。     

A new model and solution method for product line design with pricing

Existing customer preference based product design models do not consider product prices and consumer budgets. These models assume that a purchase is based only on the satisfaction obtained from the product, irrespective of the product price and customer budget. However, when products are expensive relative to buyers' budgets, the effect of prices and budgets must be considered in addition to customer satisfaction. Most current models, moreover, assume that a low preference for one product characteristic is compensated by high preference for another, which may not hold for unacceptable levels of characteristics. For such products, we incorporate prices, budget constraints, and minimum acceptable thresholds in our model. To solve the model we develop a highly accurate, robust and efficient Beam Search (BS) based heuristic that identifies optimal or near optimal product lines. The heuristic is tested on 300 simulated problems and an application. It is also compared to a Genetic Algorithms (GA) based heuristic. We found that our heuristic worked better than the GA heuristic in identifying optimal and near optimal solutions quickly. We also give detailed examples that illustrate the heuristic and demonstrate a pricing analysis application of the model.     

服务管理目标优化配置的Fuzzy-QFD线性规划模型构建与求解

由于服务管理的复杂性和模糊性,现有方法难以有效解决基于主观语言评价的服务质量改进问题。本文拓展了质量功能展开(QFD)方法在服务业中的应用,通过构建一个模糊线性规划模型,以求解最大化提高顾客需求综合满意度的企业能力优化配置问题。首先基于顾客感知-期望差距的模糊评估确定顾客需求、需求权重和边界约束等模型参数,接着运用模糊线性回归和非对称三角模糊数的隶属函数,将含有模糊变量的模糊线性规划问题转化为经典线性规划问题,进而求得不同模糊条件下的模型解。最后通过网购平台的实例验证了模型的有效性和可行性。     

Impact of introducing make-to-order options in a make-to-stock environment

Firms engaged in consumer product sales often implement a strict make-to-stock approach, applying a single price to all customers. In such systems, customers can get the product at the given price upon availability on the shelf. However, consumers can often tolerate a delay between order placement and demand satisfaction under a price discount. Recognizing this phenomenon, a supplier may consider offering a menu of delivery-price options to consumers, where longer delay-time options imply lower prices. Demands from customers willing to wait provide advance demand information to the supplier. This paper studies strategies to exploit this additional information to improve profitability and service levels. Primarily assuming that delivery times are set exogenously, we determine optimal prices and stock levels under the new delayed demand satisfaction options. In addition, we develop analytical models to characterize the system performance gains under the new demand fulfillment option.     

考虑预算约束的旅游服务质量提升决策方法

通过对Kano模型的定量化分析,从最大化顾客满意视角提出考虑预算约束的旅游服务质量提升决策方法。采用问卷调查方式获取顾客旅游服务质量因素的评价信息;依据Kano模型,对评价信息进行处理并建立顾客满意度与旅游服务质量满足水平的关系函数(S-CR);基于S-CR函数构建考虑预算约束条件下、顾客满意最大化的旅游服务质量提升决策模型,通过求解确定最优的预算分配方案。最后,通过实证验证模型的可行性与有效性。     

Capturing and prioritizing students’ requirements for course design by embedding Fuzzy-AHP and linear programming in QFD

Customer requirements play a vital and important role in the design of products and services. Quality Function Deployment (QFD) is a popular, widely used method that helps translate customer requirements into design specifications. Thus, the foundation for a successful QFD implementation lies in the accurate capturing and prioritization of these requirements. This paper proposes and tests the use of an alternative framework for prioritizing students’ requirements within QFD. More specifically, Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) and the linear programming method (LP-GW-AHP) based on Data Envelopment Analysis (DEA) are embedded into QFD (QFD-LP-GW-Fuzzy AHP) in order to account for inherent subjectivity of human judgements. The effectiveness of the proposed framework is assessed in capturing and prioritizing students’ requirements regarding courses’ learning outcomes within the process of an academic course design. Sensitivity analysis evaluates the robustness of the prioritization solution and implications for course design specifications are discussed.     

顾客满意度测评的模糊集合论模型

基于国际通用的顾客满意度测评的计量经济学模型，应用模糊综合评判和模糊推理建立顾客满意度测评的模糊集合论模型，介绍“五一黄金周”旅游市场顾客满意度测评的实证研究。     

Fuzzy goal programming for inventory management: A bacterial foraging approach

An efficient inventory planning approach in today’s global trading regime is necessary not only for increasing the profit margin, but also to maintain system flexibility for achieving higher customer satisfaction. Such an approach should hence be comprised of a prudent inventory policy and clear satisfaction of stakeholder’s goals. Relative significance given to various objectives in a supply chain network varies with product as well as time. In this paper, a model is proposed to fill this void for a single product inventory control of a supply chain consisting of three echelons. A generic modification proposed to the membership functions of the fuzzy goal-programming approach is used to mathematically map the aspiration levels of the decision maker. The bacterial foraging algorithm has been modified with enhancement of the algorithms’ capability to map integer solution spaces and utilised to solve resulting fuzzy multi-objective function. An illustrative example comprehensively covers various decision scenarios and highlights the underlying managerial insights.     

A fuzzy linear programming based approach for tactical supply chain planning in an uncertainty environment

This paper models supply chain (SC) uncertainties by fuzzy sets and develops a fuzzy linear programming model for tactical supply chain planning in a multi-echelon, multi-product, multi-level, multi-period supply chain network. In this approach, the demand, process and supply uncertainties are jointly considered. The aim is to centralize multi-node decisions simultaneously to achieve the best use of the available resources along the time horizon so that customer demands are met at a minimum cost. This proposal is tested by using data from a real automobile SC. The fuzzy model provides the decision maker (DM) with alternative decision plans with different degrees of satisfaction.     

Social media and sensemaking patterns in new product development: demystifying the customer sentiment

Artificial intelligence by principle is developed to assist but also support decision making processes. In our study, we explore how information retrieved from social media can assist decision-making processes for new product development (NPD). We focus on consumers’ emotions that are expressed through social media and analyse the variations of their sentiments in all the stages of NPD. We collect data from Twitter that reveal consumers’ appreciation of aspects of the design of a newly launched model of an innovative automotive company. We adopt the sensemaking approach coupled with the use of fuzzy logic for text mining. This combinatory methodological approach enables us to retrieve consensus from the data and to explore the variations of sentiments of the customers about the product and define the polarity of these emotions for each of the NPD stages. The analysis identifies sensemaking patterns in Twitter data and explains the NPD process and the associated steps where the social interactions from customers can have an iterative role. We conclude the paper by outlining an agenda for future research in the NPD process and the role of the customer opinion through sensemaking mechanisms.

     

基于即时配送和收益激励的众包物流运力调度研究

基于参与众包物流配送模式的人员闲散、积极性不高,以及客户对即时配送要求并非完全刚性的特征,引入模糊时间窗,将客户满意度量化为众包物流配送人员到达客户位置时刻的模糊隶属度函数。在一定客户满意度下,以最大化众包物流配送人员收益为目标,构建了基于即时配送和收益激励的众包物流运力调度问题模型,考虑到机会、逾时、超载惩罚成本,利用带有动态权重的粒子群算法,通过算例验证分析,结果表明该模型在保证客户满意度和提高众包物流配送人员积极性方面具有可行性和有效性。