Technical importance ratings in fuzzy QFD by integrating fuzzy normalization and fuzzy weighted average

Fuzzy quality function deployment (QFD) has been extensively used for translating customer requirements (CRs) into product design requirements (DRs) in fuzzy environments. Existing approaches, however, for rating technical importance of DRs in fuzzy environments are found problematic, either incorrect or inappropriate. This paper investigates how the technical importance of DRs can be correctly rated in fuzzy environments. A pair of nonlinear programming models and two equivalent pairs of linear programming models are developed, respectively, to rate the technical importance of DRs. The developed models are examined and illustrated with two numerical examples.     

Designing a sustainable supply chain using an integrated analytic network process and goal programming approach in quality function deployment

Sustainable supply chain management (SSCM) provides economic, social end environmental requirements in material and service flows occurring between suppliers, manufacturers and customers. SSCM structure is considered as a prerequisite for a sustainable success. Thus designing an effective SCM structure provides competitive advantages for the companies. In order to achieve an effective design of this structure, it is possible to apply quality function deployment (QFD) approach which is successfully applied as an effective product and system development tool. This study presents a decision framework where analytic network process (ANP) integrated QFD and zero-one goal programming (ZOGP) models are used in order to determine the design requirements which are more effective in achieving a sustainable supply chain (SSC). The first phase of the QFD is the house of quality (HOQ) which transforms customer requirements into product design requirements. In this study, after determining the sustainability requirements named customer requirements (CRs) and design requirements (DRs) of a SSC, ANP is employed to determine the importance levels in the HOQ considering the interrelationships among the DRs and CRs. Furthermore ZOGP approach is used to take into account different objectives of the problem. The proposed method is applied through a case study and obtained results are discussed.     

Construction of house of quality for new product planning: A 2-tuple fuzzy linguistic approach

A house of quality (HOQ) diagram is used to analyze the critical factors involved in the quality function deployment (QFD) processes for the new product planning (NPP). The principal tasks of the QFD acting process comprise describing and scoring customer requirements (CRs); determining design requirements (DRs), the relationship between CRs and DRs, the correlations among CRs, and the correlations among DRs. Finally, the DRs can be scored by these assessments in NPP. This study proposes various methods of scoring the requirements of current and potential customers to reflect the knowledge and preference differences among different customers regarding CRs. The CR scores provided by different customers can be assessed by using linguistic, numerical, and interval values, or can be assessed using linguistic label sets with different granularity. A 2-tuple fuzzy linguistic computational approach is adopted to aggregate the CR importance scores obtained from customers by using various methods. In addition, to accurately rate the DRs, a modified relationship between CRs and DRs is proposed. The proposed HOQ construction model is practical because it prevents the loss of information during the QFD process for NPP. An example is used to demonstrate the applicability of the proposed model.     

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.     

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.     

Determining the importance weights for the design requirements in the house of quality using the fuzzy analytic network approach

Quality function deployment (QFD) has been used to translate customer needs (CNs) and wants into technical design requirements (DRs) in order to increase customer satisfaction. QFD uses the house of quality (HOQ), which is a matrix providing a conceptual map for the design process, as a construct for understanding CNs and establishing priorities of DRs to satisfy them. This article uses the analytic network process (ANP), the general form of the analytic hierarchy process (AHP), to prioritize DRs by taking into account the degree of the interdependence between the CNs and DRs and the inner dependence among them. In addition, because human judgment on the importance of requirements is always imprecise and vague, this work concentrates on a fuzzy ANP approach in which triangular fuzzy numbers are used to improve the quality of the responsiveness to CNs and DRs. A numerical example is presented to show the proposed methodology. © 2004 Wiley Periodicals, Inc.     

Fuzzy cognitive map based quality function deployment approach for dishwasher machine selection

The traditional quality function deployment (QFD) approach deals with the weights of customer requirements (CRs), relationships among CRs and design requirements (DRs), correlation among DRs by using crisp values. This paper uses fuzzy numbers to improve the drawbacks of the traditional QFD method because fuzzy numbers enable to make consistent decisions in uncertain environment to decision makers. The existing papers handle a simple multiplication operation to calculate the correlation among CRs and the correlation among DRs. This study proposes fuzzy cognitive map (FCM) approach to calculate these correlations so that FCM is a successful method to handle the interactions among criteria. This paper contributes to the literature by integrating QFD approach and fuzzy cognitive map approach. The weights of DRs are defined in the result of the proposed QFD approach. These weights are used to evaluate the dishwasher machine alternatives in intuitionistic fuzzy VIKOR method. Intuitionistic fuzzy number (IFS) ensures to handle more information than type-1 fuzzy number to describe the fuzziness and the uncertainty of the real life world. Finally, the proposed approach has been implemented to a dishwasher machine selection in order to test its validity.     

Hybrid optimization and simulation to design a logistics network for distributing perishable products

Dynamics in product quality complicate the design of logistics networks for perishable products, like flowers and other agricultural products. Complications especially arise when multiple products from different origins have to come together for processes like bundling. This paper presents a new MILP model and a hybrid optimization–simulation (HOS) approach to identify a cost-optimal network design (i.e. facility location with flow and process allocation) under product quality requirements. The MILP model includes constraints on approximated product quality. A discrete event simulation checks the feasibility of the design that results from the MILP assuming uncertainties in supply, processing and transport. Feedback on product quality from the simulation is used to iteratively update the product quality constraints in the MILP. The HOS approach combines the strengths of strategic optimization via MILP and operational product quality evaluation via simulation. Results, for various network structures and varying degrees of dynamics and uncertainty, show that if quality decay is not taken into account in the optimization, low quality products are delivered to the final customer, which results in not meeting service levels and excess waste. Furthermore, case results show the effectiveness of the HOS approach, especially when the change from one iteration to the next is in the choice of locations rather than in the number of location. It is shown that the convergence of the HOS approach depends on the gap between the product quality requirements and the quality that can be delivered according to the simulation.     

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.     

A new hesitant fuzzy QFD approach: An application to computer workstation selection

Computer workstation selection is a multiple criteria decision making problem that is generally based on vague linguistic assessments, which represent human judgments and their hesitancy. In this paper, a new fuzzy quality function deployment (QFD) approach is used to effectively determine the design requirements (DRs) of a computer workstation. Hesitant fuzzy linguistic term sets (HFLTS) are innovatively employed to capture the hesitancy of the experts in this approach. More precisely, the proposed new QFD approach is the first study that determines the importance of customer requirements (CRs), the relations between CRs and DRs and the correlations among DRs via HFLTS. Additionally, HFLTS based Analytic Hierarchy Process (AHP) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) methods are utilized in the computational steps to select the best computer workstation. A real industrial application is carried out to validate the implementation of the proposed approach.     

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.     

Quality function deployment implementation based on Fuzzy Kano model: An application in PLM system

Product lifecycle management (PLM), a strategic business system allows more effective communication among different groups at dispersed locations to share ideas and access information needed for developing new products and executing innovative processes. The main function of PLM is to develop an attractive system which ensures customer satisfaction. Therefore, one of the important topics of the PLM system developments is to take customer requirements into consideration. Quality function deployment (QFD) has been widely used for numerous years; it is one of the structured methodologies that are used to translate customer needs into specific quality development. However, in the traditional QFD approach, each element’s interdependence and customer requirements are usually not systematically treated. Additionally, the Kano model can effectively classify customer demand attributes, but to make Kano model more objective in the course of weighing, we have also included Fuzzy mode in our discussion. This study presents an integrative approach by incorporating the Kano model with Fuzzy mode into the matrix of QFD and adjusting customer requirement weights. This approach can fulfill two objectives, First, through the Kano model with the Fuzzy mode, it will not only discriminate out options for the required attributes in much more breadth but also simultaneously render the discretions on the linguistic implications much more accurate with the aid of the ambiguous questionnaire response method. Second, combining the Kano model and QFD, can not only provide a new way to optimize the product design but can also enhance customer satisfaction and loyalty, and minimize dissatisfaction. The proposed methods can be useful to both practitioners and researchers. To illustrate our findings, we have incorporated an example which suggests that the proposed approach can contribute to the creation of attractive PLM attributes and PLM innovation.     

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.     

Evaluation of design requirements using fuzzy outranking methods

To increase customer satisfaction, quality function deployment is used to translate customer needs into technical design requirements (DRs). Determination of DRs for product development is very important because these requirements are the vital keys to successful products. The methods used to evaluate DRs in the literature can be categorized into multicriteria evaluation methods such as scoring methods, the analytic hierarchy method, analytic network process, and so forth. There are few papers using fuzzy multi‐attribute outranking methods to evaluate DRs. This article aims to compare the results of three different fuzzy outranking methods to evaluate the DRs in the PVC windows industry. A sensitivity analysis is also made by using the software, FOuR. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1229–1250, 2007.     

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．     

Optimal sizing,scheduling and shift policy of the grinding section of a ceramic tile plant

This paper addresses the optimal design of the grinding section of a ceramic tile plant operating in a cyclic mode with the units (mills) following a batch sequence. The optimal design problem of this single product plant is formulated with a fixed time horizon of one week, corresponding to one cycle of production, and using a discrete-time resource task network (RTN) process representation. The size of the individual units is restricted to discrete values, and the plant operates with a set of limited resources (workforce and equipment). The goal is to determine the optimal number and size of the mills to install in the grinding section, the corresponding production schedule, and shift policy. This problem involves labor/semi-labor intensive (LI/SLI) units with a depreciation cost of the same order as that of the operation cost. The optimal design of the grinding section comprises the trade-off between these two costs. The resulting optimization formulation is of the form of a mixed integer linear programming (MILP) problem, solved using a branch and bound solver (CPLEX 9.0.2). The optimal solution is analyzed for various ceramic tile productions and different shift policies.     

Determining optimal levels of engineering characteristics in quality function deployment under multi-segment market

Customers often have various requirements and preferences on a product. A product market can be partitioned into several market segments, each of which contains a number of customers with homogeneous preferences. In this paper, a methodology which mainly involves a market survey, fuzzy clustering, quality function deployment (QFD) and fuzzy optimization, is proposed to achieve the optimal target settings of engineering characteristics (ECs) of a new product under a multi-segment market. An integrated optimization model for partitioned market segments based on QFD technology is established to maximize the overall customer satisfaction (OCS) for the market considering the weights of importance of different segments. The weights of importance of market segments and development costs in the model are expressed as triangular fuzzy numbers in order to describe the imprecision caused by human subjective judgement. The solving approach for the fuzzy optimization model is provided. Finally, a case study is provided for illustrating the proposed methodology.     

Reducing Two-Stage Probabilistic Optimization Problems with Discrete Distribution of Random Data to Mixed-Integer Programming Problems*

We consider two-stage stochastic programming models with quantile criterion as well as models with a probabilistic constraint on the random values of the objective function of the second stage. These models allow us to formalize the requirements for the reliability and safety of the system being optimized and to optimize system’s performance under extreme conditions. We propose a method of equivalent transformation of these models under discrete distribution of random parameters to mixed-integer programming problems. The number of additional integer (Boolean) variables in these problems equals to the number of possible values of the vector of random parameters. The obtained mixed optimization problems can be solved by powerful standard discrete optimization software. To illustrate the approach, the results of numerical experiment for the problem of small dimension are presented.     

Improving quality function deployment analysis with the cloud MULTIMOORA method

Quality function deployment (QFD) is a quality guarantee method extensively used in various industries, which can help enterprises shorten the product design period and enhance the manufacturing and managing work. The task of selecting important engineering characteristics (ECs) in QFD is crucial and often involves multiple customer requirements (CRs). In this paper, a modified multi‐objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) method based on cloud model theory (called C‐MULTIMOORA) is developed to determine the ranking order of ECs in QFD. First, the linguistic assessments provided by decision makers are transformed into normal clouds and aggregated by the cloud weighted averaging operator. Then, the weights of CRs are determined based on a maximizing deviation method with incomplete weight information. Finally, the importance of ECs is obtained using the C‐MULTIMOORA method. An empirical case conducted in an electric vehicle manufacturing organization is provided together with a comparative analysis to validate the advantages of our proposed QFD model.     

Group decision making to better respond customer needs in software development

Quality Function Deployment (QFD) is a well-known planning methodology for translating customer needs into relevant design and production requirements. The intent of applying QFD is to incorporate the voice of the customer into the various phases of the product development cycle for a new product, or a new version of an existing product. The traditional QFD structure requires individuals to express their preferences in a restricted scale without exceptions. In practice, people contributing to the process tend generally to give information about their personal preferences in many different ways, numerically or linguistically, depending on their background. Moreover, collaborative decision-making is not an emphasized issue in QFD even though it requires several people's involvement. In this study, we extend the QFD methodology by introducing a new group decision making approach that takes into account multiple preference formats and fusing different expressions into one uniform group decision by means of fuzzy set theory. An application on software development is supplied to illustrate the approach.