A hybrid methodology of fuzzy grey relation for determining multi attribute customer preferences of edible oil

The main idea behind this study is to introduce a fuzzy grey relational methodology (FGRM) to determine the importance of customer attributes’ (CAs) for edible oil, particularly for defining the opportunity of competitiveness which has recently become one of an important concern of manufacturing companies. It was also aimed to seek high quality level of product and process characteristics for meeting the desirability of attributes and for health care. A fuzzy grey inference system was employed with the support of fuzzy quality function (QFD) deployment to interpret the qualitative and imprecise customer attributes. Fuzzy QFD is relatively a recent methodology offering a more in-depth analysis and investigation into the handling of customer requirements and engineering characteristics. The attributes then were defuzzified into quantitative values by amalgamating the knowledge of the customers and the product designers.Customer needs (CNs) are the voice of customers and tend to be linguistic naturally. In this study, fuzzy inference system (FIS) along with fuzzy QFD was employed to determine the desirability of edible oil by consumers. Fuzzy QFD is a powerful tool for improving product design and quality, and procuring a customer-driven quality system. The integrated framework based on FIS, fuzzy QFD and FGRM determined the CAs relations, and desirability level of edible oil by consumers. The findings are not only meaningful for customers, but also important for market position of product. The methodology significantly reduces the complexity of decision making and increase the production efficiency and overall competitiveness.     

A new mixed integer linear programming model for product development using quality function deployment

Quality function deployment (QFD) is a product development process performed to maximize customer satisfaction. In the QFD, the design requirements (DRs) affecting the product performance are primarily identified, and product performance is improved to optimize customer needs (CNs). For product development, determining the fulfillment levels of design requirements (DRs) is crucial during QFD optimization. However, in real world applications, the values of DRs are often discrete instead of continuous. To the best of our knowledge, there is no mixed integer linear programming (MILP) model in which the discrete DRs values are considered. Therefore, in this paper, a new QFD optimization approach combining MILP model and Kano model is suggested to acquire the optimized solution from a limited number of alternative DRs, the values of which can be discrete. The proposed model can be used not only to optimize the product development but also in other applications of QFD such as quality management, planning, design, engineering and decision-making, on the condition that DR values are discrete. Additionally, the problem of lack of solutions in integer and linear programming in the QFD optimization is overcome. Finally, the model is illustrated through an example.     

A new approach to quality function deployment planning with financial consideration

Quality function deployment (QFD) is becoming a widely used customer-oriented approach and tool in product design. Taking into account the financial factors and uncertainties in the product design process, this paper deals with a fuzzy formulation combined with a genetic-based interactive approach to QFD planning. By introducing new concepts of planned degree, actual achieved degree, actual primary costs required and actual planned costs, two types of fuzzy optimisation models are discussed in this paper. These models consider not only the overall customer satisfaction, but also the enterprise satisfaction with the costs committed to the product. With the interactive approach, the best balance between enterprise satisfaction and overall customer satisfaction can be obtained, and the preferred solutions under different business criteria can be achieved through human–computer interaction.Scope and PurposeQuality function deployment (QFD) that originated in Japan in the late 1960s is a concept and mechanism for translating the ‘voice of customer’ into product through various stages of product planning, engineering and manufacturing. It has become a widely used customer-oriented approach to facilitating product design by analysing customer requirements (CRs). Determination of the target levels for the technical attributes (TAs) of a product with a view to achieving a high level of overall customer satisfaction is an important activity in product design and development.Traditional methods for QFD planning are mainly subjective, ad hoc and heuristic. They can hardly achieve global optimisation, and most of these models barely take into consideration the correlation between TAs. Moreover, most of these methods are technically one-sided without considering the design budget. However, the financial factor is also an important factor and should not be neglected in QFD planning. In addition, owing to uncertainties involved in the decision process, these deterministic methods could not formulate and solve it effectively.Taking into consideration the financial factors and uncertainties in the product design process, this paper deals with fuzzy formulation combined with a genetic-based interactive approach to QFD planning. By introducing new concepts of planned degree, actual achieved degree, actual primary costs required and actual planned costs, two types of fuzzy optimisation models are discussed in this paper. These models consider not only the overall customer satisfaction, but also the enterprise satisfaction with the costs committed to the product. With the interactive approach, the best balance between enterprise satisfaction and overall customer satisfaction can be obtained, and the preferred solutions under different business criteria can be achieved through human–computer interaction.     

Fuzzy group decision-making for multi-format and multi-granularity linguistic judgments in quality function deployment

As a customer-driven tool, quality function deployment (QFD) is widely used in product planning or improvement to achieve higher product performance and customer satisfaction. QFD uses a matrix called the house of quality (HoQ) to translate customer requirements (CRs) into engineering characteristics (ECs). Constructing the HoQ, which includes determining the importance weights of CRs, the correlation matrix among ECs and the relationship matrix between CRs and ECs, is an important issue in the application of QFD. However, decision-makers (DMs) participating the construction of HoQ tend to give their individual judgments in multi-format or multi-granularity depending on their different knowledge, experience, culture and circumstance. Furthermore, these judgments are more difficult to assess with the precise quantitative forms due to the vagueness and uncertainty existed in the early stage of new product development. In this paper, a group decision-making approach incorporating with two optimization models (i.e. logarithmic least squares model and weighted least squares model) is proposed to aggregate these multi-format and multi-granularity linguistic judgments. Fuzzy set theory is utilized to address the uncertainty in the decision-making process. The proposed method is illustrated with a real-world case of horizontal directional drilling machine. The application indicates that the group decision-making method may be a promising tool for constructing the HoQ.     

A combined fuzzy linear regression and fuzzy multiple objective programming approach for setting target levels in quality function deployment

Quality function deployment (QFD) is a systematic process for translating customer needs into engineering characteristics, and then communicating them throughout the enterprise in a way to ensure that details are quantified and controlled. The inherent fuzziness of relationships in QFD modeling justifies the use of fuzzy regression for estimating the relationships between both customer needs and engineering characteristics, and among engineering characteristics. Albeit QFD aims to maximize customer satisfaction, requirements related to enterprise satisfaction such as cost budget, extendibility, and technical difficulty also need to be considered. This paper presents a fuzzy multiple objective decision framework that includes not only fulfillment of engineering characteristics to maximize customer satisfaction, but also maximization of extendibility and minimization of technical difficulty of engineering characteristics as objectives subject to a financial budget constraint to determine target levels of engineering characteristics in product design. A real-world quality improvement problem is presented to illustrate the application of the decision approach.     

An integrated approach for rating engineering characteristics’ final importance in product-service system development

Product-service system (PSS) approach has emerged as a competitive strategy to impel manufacturers to offer a set of product and services as a whole. A three-domain PSS conceptual design framework based on quality function deployment (QFD) is proposed in this research. QFD is a widely used design tool considering customer requirements (CRs). Since both product and services influence satisfaction of customer, they should be designed simultaneously. Identification of the critical parameters in these domains plays an important role. Engineering characteristics (ECs) in the functional domain include product-related ECs (P-ECs) and service-related ECs (S-ECs). ECs are identified by translating customer requirements (CRs) in the customer domain. Rating ECs’ importance has a great impact on achieving an optimal PSS planning. The rating problem should consider not only the requirements of customer, but also the requirements of manufacturer. From the requirements of customer, the analytic network process (ANP) approach is integrated in QFD to determine the initial importance weights of ECs considering the complex dependency relationships between and within CRs, P-ECs and S-ECs. In order to deal with the vagueness, uncertainty and diversity in decision-making, the fuzzy set theory and group decision-making technique are used in the super-matrix approach of ANP. From the requirements of manufacturer, the data envelopment analysis (DEA) approach is applied to adjust the initial weights of ECs taking into account business competition and implementation difficulty. A case study is carried out to demonstrate the effectiveness of the developed integrated approach for prioritizing ECs in PSS conceptual design.     

A fuzzy QFD approach to determine supply chain management strategies in the dairy industry

The aim of this study is to identify the crucial logistics requirements and supply chain management (SCM) strategies for the dairy industry. For product or service development, quality function deployment (QFD) is a useful approach to maximize customer satisfaction. The determination of design requirements and supply chain management strategies are important issues during QFD processes for product or service design. For this reason, a fuzzy QFD methodology is proposed in this study to determine these aspects and to improve customer satisfaction. Qualitative information is converted firstly into quantitative parameters, and then this data is combined with other quantitative data to parameterize two multi-objective mathematical programming models. In the first model, the most important logistic requirements for the company are determined based on total technical importance, total cost, total feasibility and total value increment objectives, and in the second model, based on these objectives, appropriate supply chain management strategies are determined. Finally, a case study from the Turkish dairy industry is given to illustrate the proposed approach.     

Integrating customer's preferences in the QFD planning process using a combined benchmarking and imprecise goal programming model

Quality function deployment (QFD) is a customer-oriented design tool for developing new or improved products to achieve higher customer satisfaction by integrating various functions of an organization. The engineering characteristics (ECs) affecting the product performances are designed to match the customer attributes (CAs). However, from the viewpoint of the QFD team, product design processes are performed in imprecise environments, and more than one factor must be taken into account in determining the target levels of ECs, especially the limited resources and increased market competition. This paper presents an imprecise goal programming (GP) approach to determine the optimum target levels of ECs in QFD for maximizing customer satisfaction under resource limitation and considerations of market competition. Based on benchmarking data of CAs, the concept of satisfaction functions is utilized to formulate explicitly the customer's preferences and to integrate the competitive analysis of target market into the modelling and solution process. In addition, the relationships linking CAs and ECs and the ECs to each other are integrated by functional relationships. The proposed approach will be illustrated through a car door design example.     

Cost engineering with quality function deployment

Conventional Quality Function Deployment (QFD) is technically one-sided. Prioritization of technical attributes, if carried out at all, attempts to maximize customer satisfaction without considering the costs incurred. However, product design is usually a techno-economic process, hence there is always a tradeoff between quality goals and limited budgets. Based on a prioritization method suggested by Wasserman [1], this paper integrates design costs into the QFD framework. This proposed approach enables designers to optimize product development resources towards customer satisfaction and conduct analytical investigations to facilitate decision making in product design and development.     

A linear goal programming approach to determining the relative importance weights of customer requirements in quality function deployment

Quality function deployment (QFD) is a planning tool used in new product development and quality management. It aims at achieving maximum customer satisfaction by listening to the voice of customers. To implement QFD, customer requirements (CRs) should be identified and assessed first. The current paper proposes a linear goal programming (LGP) approach to assess the relative importance weights of CRs. The LGP approach enables customers to express their preferences on the relative importance weights of CRs in their preferred or familiar formats, which may differ from one customer to another but have no need to be transformed into the same format, thus avoiding information loss or distortion. A numerical example is tested with the LGP approach to demonstrate its validity, effectiveness and potential applications in QFD practice.     

How the Kano model contributes to Kansei engineering in services

Recent studies show that products and services hold great appeal if they are attractively designed to elicit emotional feelings from customers. Kansei engineering (KE) has good potential to provide a competitive advantage to those able to read and translate customer affect and emotion in actual product and services. This study introduces an integrative framework of the Kano model and KE, applied to services. The Kano model was used and inserted into KE to exhibit the relationship between service attribute performance and customer emotional response. Essentially, the Kano model categorises service attribute quality into three major groups (must-be [M], one-dimensional [O] and attractive [A]). The findings of a case study that involved 100 tourists who stayed in luxury 4- and 5-star hotels are presented. As a practical matter, this research provides insight on which service attributes deserve more attention with regard to their significant impact on customer emotional needs. STATEMENT OF RELEVANCE: Apart from cognitive evaluation, emotions and hedonism play a big role in service encounters. Through a focus on delighting qualities of service attributes, this research enables service providers and managers to establish the extent to which they prioritise their improvement efforts and to always satisfy their customer emotions beyond expectation.     

Application of quality function deployment in the semiconductor industry: A case study

In quality function deployment (QFD), the voice of the customer (VOC) is the critical factor in developing and producing a product that will meet or exceed customer requirements. This study integrates quality function deployment (QFD), management techniques to optimise product-design investment, process improvement, and phase-into meet customer requirements and company goals. QFD uses systematic multi-level development and evaluation to translate customer requirements into the design of product characteristics and manufacturing processes that will satisfy the customer and minimise potential failure costs. ‘Process management’, which is one of the pivotal Six Sigma implementation criteria, is used to construct an ‘integrated product and process development’ (IPPD) model by product type to enhance the effectiveness of QFD. This model, combined with a company’s customer satisfaction strategy and QFD techniques, provides process management through built-in IPPD and appropriate changes in organisational culture. This paper presents a case study from the semiconductor industry to demonstrate the model’s applicability and suitability.     

How the Kano model contributes to Kansei engineering in services

Recent studies show that products and services hold great appeal if they are attractively designed to elicit emotional feelings from customers. Kansei engineering (KE) has good potential to provide a competitive advantage to those able to read and translate customer affect and emotion in actual product and services. This study introduces an integrative framework of the Kano model and KE, applied to services. The Kano model was used and inserted into KE to exhibit the relationship between service attribute performance and customer emotional response. Essentially, the Kano model categorises service attribute quality into three major groups (must-be [M], one-dimensional [O] and attractive [A]). The findings of a case study that involved 100 tourists who stayed in luxury 4- and 5-star hotels are presented. As a practical matter, this research provides insight on which service attributes deserve more attention with regard to their significant impact on customer emotional needs.

Statement of Relevance: Apart from cognitive evaluation, emotions and hedonism play a big role in service encounters. Through a focus on delighting qualities of service attributes, this research enables service providers and managers to establish the extent to which they prioritise their improvement efforts and to always satisfy their customer emotions beyond expectation.     

Kano integrated robust design approach for aesthetical product design: a case study of a car profile

Visual shape parameters and aesthetic aspects of a product are one of the crucial factors for the success of a product in the market. The type and the value of the shape parameters plays an important role in visual appearance of a product and designers tends to be critical while deciding these parameters. The aesthetic aspect of a product has been matter of concern for researchers with its electromechanical design. The Kano model has been found to be a useful tool to establish the relationship between performance criteria and customer satisfaction. To achieve the desired customer satisfaction weight of each product criteria is determined by using Kano model. This study presents an integrative design approach combining the Kano model, Taguchi method and grey relation analysis to obtain the optimal combination of shape parameters and aesthetic aspects. Prioritized criteria of aesthetic attributes have been abstracted through proposed methodology. A case study has been presented to evolve a profile of a car.     

A fuzzy AHP approach to the determination of importance weights of customer requirements in quality function deployment

Quality function deployment (QFD) is an important tool in product planning that could contribute to increase in customer satisfaction and shorten product design and development time. During the QFD process, determination of the importance weights of customer requirements is a crucial and essential step. The analytic hierarchy process (AHP) has been used in weighting the importance. However, due to the vagueness and uncertainty existing in the importance attributed to judgement of customer requirements, the crisp pairwise comparison in the conventional AHP seems to be insufficient and imprecise to capture the degree of importance of customer requirements. In this paper, fuzzy number is introduced in the pairwise comparison of AHP. An AHP based on fuzzy scales is proposed to determine the importance weights of customer requirements. The new approach can improve the imprecise ranking of customer requirements which is based on the conventional AHP. Finally, an example of bicycle splashguard design is used to illustrate the proposed approach.     

Fuzzy quality function deployment: determining the distributions of effort dedicated to technical change

A model is presented that develops the Quality Function Deployment (QFD) House of Quality tool into a fuzzy‐set based multi‐criteria decision‐making process to determine the distributions of effort directed toward technical changes. When customers are polled for desired product‐specific attributes, the responses are typically defined by linguistic variables that represent a fuzzy set distribution. Fuzzy sets also define customer perceptions of the product attributes and technical expert opinions about product design criteria relative to marketplace competitors. Therefore, for each technical criterion, the following factors have an effect on the decision to implement change(s): (1) fuzzy sets providing evidence of customer need for attributes; (2) motivation to change to satisfy desired customer attributes; (3) motivation for technical criterion change; and (4) the strength of the relationships between the attributes and the technical criterion. Priority rankings are linked to the distributions of effort to apply in fulfilling continuous product improvement.     

A fuzzy regression and optimization approach for setting target levels in software quality function deployment

With the rapid development of the software industry, improving the quality of software development has gained increasing importance. Software manufacturers have recently applied quality improvement techniques to software development to respond to the needs for software quality. Software quality function deployment (SQFD), as a technique for improving the quality of the software development process to create products responsive to customer expectations, is used to maximize customer satisfaction. This paper presents a fuzzy regression and optimization approach to determine target levels in SQFD. The inherent fuzziness of relationships in SQFD modeling justifies the use of fuzzy regression. Fuzzy regression is used to identify the functional relationships between customer requirements and technical attributes, and among technical attributes. Then, a mathematical programming model is developed to determine target levels of technical attributes using the functional relationships obtained by fuzzy regression. A search engine quality improvement problem is presented to illustrate the application of the proposed approach.     

基于模糊回归的质量屋决策模型

基于带有对称三角形模糊系数的模糊回归及模糊规划理论，提出关联函数及自 相关函数的数学模型，并在系统考虑资源约束影响的基础上，分别建立了基于质量屋的产品 规划精确模型及模糊模型．仿真研究表明，这些模型适合于各种工程设计问题，尤其是在不 确定的、模糊的条件下，能够有效地确定关联函数及自相关函数，帮助开发人员优化顾客需 求的满意水平，在资源约束下使产品的顾客满意度最大．     

Combining fuzzy AHP and fuzzy Kano to optimize product varieties for smart cameras: A zero-one integer programming perspective

In an era of global customization, dominating the majority market with a single product has become increasingly difficult and almost impossible for most companies. In contrast, they must provide various product varieties that attract diverse customers, particularly when acquiring distinct market segments. In practice, however, most companies cannot effectively reduce the gap between customer requirements and design characteristics, although this impacts the profitability and future growth of companies. Meanwhile, companies often get stuck in the trade-offs between enhancing product varieties and controlling manufacturing costs. Accordingly, this paper proposes a hybrid framework that combines fuzzy analytical hierarchy process (AHP), fuzzy Kano model with zero-one integer programming (ZOIP) to incorporate customer preferences and customer perceptions into the decision-making process of product configuration. Specifically, fuzzy AHP is used to extract customer preferences for core attributes while fuzzy Kano model is utilized to elicit customer perceptions of optional attributes. Finally, by virtue of ZOIP, the optimal product varieties (smart cameras) for distinct segments are determined by maximizing overall customer utility (OCU) and taking a firm's pricing policy into account.