Customer Requirements In Quality Function Deployment Research Articles

A novel approach to relative importance ratings of customer requirements in QFD based on probabilistic linguistic preferences

Quality function deployment (QFD) is a customer-oriented product/service design tool with diversified team members reaching a consensus in developing a new or an improved product/service to maximize customer satisfaction. Determining the relative importance ratings (RIRs) of customer requirements (CRs) is actually a multiple attribute decision making (MADM) problem, which is regarded as an essential step in QFD application. Although many decision making approaches have been developed to rate the CRs, few of them refer to derive the weights of evaluators, and there is paucity of literature which combines the probabilistic linguistic term sets (PLTSs) with QFD methodology. Therefore, PLTSs are introduced in our study, to simultaneously reflect the hesitancy and preference degrees of evaluators. A novel MADM technique based on probabilistic linguistic preference (PLP) is explored to calculate the RIRs among CRs under the PLP environment. Concretely, some operators, such as the normalization formulas, the probabilistic linguistic expected value (PLEV) operator as well as the standard formula, are applied to weight the decision makers; a modified grey relational analysis-technique for order preference by similarity to ideal solution (GRA-TOPSIS) method called probabilistic linguistic-based GRA-TOPSIS is also proposed to rate the relative importance over CRs. Finally, application of product improvement of a turbine engine is given to see the validity and feasibility of the proposed approach.

A spectral clustering method to improve importance rating accuracy of customer requirements in QFD

Quality function deployment (QFD) is a method commonly used in manufacturing industries to identify customer requirements (CRs) and the manufacturing capacity to meet CRs. Importance rate (IR) of CRs plays an important role in the QFD method. The existing methods in determining IR of CRs cannot consider all related factors of customer satisfaction, need importance, personal information, and relationship between customer satisfaction and function implementation. This paper proposes a method to improve the IR accuracy. Based on comments of customers for a product, importance rates of CRs are defined using integrated importance-performance analysis (IPA) and Kano models. IPA and Kano models are integrated by spectral clustering where a similarity matrix W is formed to balance the influence proportion between the IPA and Kano models considering comments of different customers for the product. IR of CRs is used in the QFD method to define functions and structures of the product. The proposed method is compared with several existing methods in case studies of designing an upper limb rehabilitation device and a feed drive system of the CNC machine. Results show that the proposed method has improved accuracy of IR of CRs for the product design and manufacturing.

Development of Data Mining Driven Software Tool to Forecast the Customer Requirement for Quality Function Deployment

Quality Function Deployment (QFD) is widely used customer driven process for product development. Thus, Customer Requirements (CRs) play a key role in QFD process. However, the diversification in marketplace makes these CRs more dynamic and changing, giving rise the need to forecast CRs to improve competitiveness and increase customer satisfaction. The purpose can be served by using Data Mining techniques of forecasting. With the pool of forecasting techniques available, it is important to evaluate a suitable one for more effective results. To this end, the paper presents a novel software tool to efficiently forecast CRs in QFD. The tool allows for forecasting using various data mining based time series analysis techniques that strongly assists in doing comparative analysis and evaluating out the most apt technique for forecasting of CRs. The tool is developed using VB.Net and MS-Access. Finally, an example is presented to demonstrate the practicability of proposed software tool.

Using fuzzy non-linear regression to identify the degree of compensation among customer requirements in QFD

As an effective customer-driven approach, the quality function deployment (QFD) takes numbers of customer requirements (CRs) into account in the process of the initial product design and the competitive analysis. It is a traditional multi-attribute decision making problem, and the trade-off strategy among CRs which is interpreted as decision parameters, is crucial for resulting the overall customer satisfaction. Although the general trade-off strategies concern about the importance weights of CRs, which are specified with a variety of methods, they ignore the influence of the degree of compensation among them. In this paper, we embed the degree of compensation among CRs into QFD, which is expressed as a symmetric triangular fuzzy number, and develop a fuzzy non-linear regression model using the minimum fuzziness criterion to identify it. Furthermore, an illustrative example is provided to demonstrate the application and the performance of the modeling approach. It can be verified from the experimental results that the overall customer satisfaction as well as the prioritization of products are affected by the degree of compensation among CRs. Meanwhile, against to the products in example, the overall customer satisfaction obtained with the traditional weighted-sum method is confirmed to be underestimated.

A novel approach to prioritize customer requirements in QFD based on customer satisfaction function for customer-oriented product design

In the highly competitive marketplace, customer satisfaction is one of the critical success factors to many leading companies. Quality function deployment (QFD) has been regarded as a powerful customer-oriented design tool for developing new or improved products to achieve higher customer satisfaction by integrating various functions of an organization. In the QFD process, correctly rating the importance of customer requirements (CRs) is a crucial and essential step since it largely affects the target value setting of design requirements (DRs). This paper proposes a novel approach to prioritize CRs from the viewpoint of company’s competitiveness (i.e., competitive priority ratings). The key characteristic of proposed approach is to model the customer’s preference structure as the form of customer satisfaction function by combining the competitive benchmarking analysis with Kano’s analysis. Finally, the proposed approach is illustrated with a numerical example of car door design problem.

Assessing the relative importance weights of customer requirements using multiple preference formats and nonlinear programming

Quality function deployment (QFD) is a methodology to ensure that customer requirements (CRs) are deployed through product planning, part development, process planning and production planning. The first step to implement QFD is to identify CRs and assess their relative importance weights. This paper proposes a nonlinear programming (NLP) approach to assessing the relative importance weights of CRs, which allows customers to express their preferences on the relative importance weights of CRs in their preferred or familiar formats. The proposed NLP approach does not require any transformation of preference formats and thus can avoid information loss or information distortion. Its potential applications in assessing the relative importance weights of CRs in QFD are illustrated with a numerical example.

New rating methods to prioritize customer requirements in QFD with incomplete customer preferences

Customer satisfaction is one of the critical success factors to many leading companies over the world. Quality function deployment (QFD) has gained extensive international support as one of the powerful techniques to increase the customer satisfaction. In the QFD, correctly rating the final importance of customer requirements (CRs) is a crucial and essential process since it largely affects the target value setting of design requirements. The final importance ratings of CRs are generally determined by combining relative importance ratings and competitive priority ratings. However, determining the final importance ratings is very difficult due to the typical uncertainty or imprecision of customer’s judgment (or perceptions). This paper proposes a novel approach to prioritize CRs in QFD process by developing two sets of new rating methods, called customer preference rating (CPR) method and customer satisfaction rating (CSR) method, for relative importance ratings and competitive priority ratings, respectively. The CPR method provides a simple and intuitive technique to capture the customers’ incomplete or uncertain perceptions on the relative importance of CRs based on their own preferences, allowing them to give a partial ordering of CRs. The CSR method constructs the customer satisfaction model based on the competitive benchmarking analysis and then evaluates the performance quality of company product using our satisfaction and uncertainty measure. Furthermore, the CSR method is integrated with the Kano’s model to capture the different impacts of CRs on customer satisfaction. Finally, the proposed approach is illustrated with a numerical example of car door design problem.

Ranking Customer Requirements in QFD by Factoring in Their Interrelationship Values

Quality function deployment (QFD) is a methodology used to achieve higher customer satisfaction. The engineering characteristics (ECs) affecting product performance are designed to match the customer requirements (CRs). Computing raw weights of CRs and priority scores of ECs from various input variables is one of the most critical phases in QFD application. This study deals with the most neglected and often-omitted part of the QFD process—the interrelationship matrix among customer requirements. This article presents a mathematical solution to the correlation triangle problem that incorporates both CRs and ECs. An attempt is made to address the issue by considering the impact of these interrelationships on the overall calculations, using weighted average method for the CR interrelationship value operator in order to prioritize customers' requirements in QFD. The outcome of the study is a mathematical solution to the perennial correlation triangle problem in the form of a framework that factors in the correlation triangle values in conjunction with computed raw weights for customer requirements. The model fine tunes and adds precision to an otherwise qualitative strategic decision process. The applicability of the authors' proposed model is demonstrated with a real-life example.

Determining the Importance Weights for the Customer Requirements in QFD Using a Fuzzy AHP with an Extent Analysis Approach

In the Quality Function Deployment (QFD) process, determining the importance weights for the customer requirements is an essential and crucial process. The Analytic Hierarchy Process (AHP) has been used to determine the importance weights for product planning, but this has occurred mainly in crisp (non-fuzzy) decision applications. However, human judgment on the importance of customer requirements is always imprecise and vague. To make up for this deficiency in the AHP, a fuzzy AHP with an extent analysis approach is proposed to determine the importance weights for the customer requirements. In the method, triangular fuzzy numbers are used for the pairwise comparison of a fuzzy AHP. By using the extent analysis method and the principles for the comparison of fuzzy numbers, one can derive the weight vectors. The new approach can improve the imprecise ranking of customer requirements inherited from studies based on the conventional AHP. Furthermore, the fuzzy AHP with extent analysis is simple and easy to implement to prioritize customer requirements in the QFD process compared with the conventional AHP. This paper uses an example of a hair dryer design to illustrate the proposed approach.