Importance Weights Of Customer Requirements Research Articles

A customer-driven quality function deployment approach for intelligent product design: A case of automatic-dishwasher

Quality Function Deployment (QFD) is one of the very effective customer-driven product development tools. With automatic dishwashers entering the Chinese market, manufacturers can use QFD to develop products that meet the unique Chinese customer requirements and explore the Chinese market. To help manufacturers maximize profits by developing automatic dishwashers to China’s market situation and competitive environment, this paper proposes an improved QFD model considering competitive learning and cost analysis based on market information analysis with Product Life Cycle (PLC) theory. On the one hand, the improved QFD model determines the life cycle stage of the product by analyzing the characteristics of the market. The improvement rate of customer requirements (CRs) determined by competitive learning in different stages has different influence on the importance weights of CRs, so that the new products developed based on design requirements (DRs) converted from CRs can better adapt to the current market environment. On the other hand, the cost strategy will also change according to different PLC stages. In cost analysis, different cost criteria weights will be determined in different life cycle stages to deal with product development risks in different market environments and help enterprises achieve better sustainable development. Finally, we take Chinese HE company’s automatic-dishwasher design as an example to show the specific application of the proposed model. The proposed model identifies the important DRs and their relative importance ranking for the product development. Comparison with existing QFD models shows the ranking obtained through the model is better integrated with market information, enabling the development of products that are better suited to the market, and helping companies to achieve sustainable development.

Integrating Quantitative Techniques Into Quality Function Deployment For Determining Of Importance Weights Of Customer Requirements In The Design Of A Baby Diaper

industries such as baby diapers, would help to prevent design repetitions and consequently wasting time and resources. Quality Function Deployment (QFD) has turned out to be a key methodology to design products by getting the voice of the customer in the first place, as well as converting the customer’s voice into product engineering characteristics. In addition, Kano model emphasizes the degree to which a customer is satisfied when a particular customer need is met. In this study, an approach integrating the Kano model into the planning matrix of QFD using Fuzzy logic, was proposed. The importance weights of customer requirements of a baby diaper were calculated using both the proposed Adjusted Kano-Fuzzy-QFD (AKFQFD) and traditional Kano-QFD model. It was demonstrated that the AKFQFD could effectively prioritize the importance weights of customer requirements.

Quantification and integration of an improved Kano model into QFD based on multi-population adaptive genetic algorithm

Abstract In an effort to address the inherent deficiencies of traditional Kano model and quality function deployment (QFD), this paper proposes an improved Kano model named as importance-frequency Kano (IF-Kano) model and integrates IF-Kano model into QFD. Considering the interaction between frequencies and importance weights of customer requirements (CRs), the IF-Kano model adopts the logical Kano classification criteria to categorize CRs. Then, both qualitative and quantitative results derived from IF-Kano model are integrated into QFD with a non-linear programming model. The model aims to determine appropriate Kano categories of CRs and target values of engineering characteristics (ECs) with a view to achieving an optimal design solution under the best balance between enterprise satisfaction and customer satisfaction (CS). To solve the presented model, a multi-population adaptive genetic algorithm (MPAGA) is designed. Finally, an example of a home elevator design is given to demonstrate the feasibility and effectiveness of the developed approach and algorithm.

QFD-Based Product Planning With Consumer Choice Analysis

Quality function deployment (QFD) is a widely adopted customer-oriented methodology to assist in product design and development. In a traditional QFD-based product planning process, the importance weights of customer requirements (CRs) in house of quality are obtained by analyzing and generalizing the requirements of various customers, and the purpose of the subsequent optimal setting of engineering characteristics (ECs) and process parameters is to achieve a higher level of overall customer satisfaction (OCS). However, nowadays customers' requirements are increasingly diversified and customers usually have heterogeneous preferences. Consequently, customers in a product market may have different purchasing choice behaviors and satisfaction criteria toward a new product. Therefore, heterogeneity of CRs should be considered in QFD-based optimization models to describe the relationship between CRs and ECs and to model the OCS. In this paper, a novel QFD-based product planning approach is proposed for a product market with diversified CRs by integrating consumer choice behavior analysis. The contributions of this paper include: 1) customers' purchase choice rules are introduced into QFD-based product planning process to simulate the purchase behavior of a customer toward a product; 2) two new QFD-based optimization models under deterministic and multinomial logit consumer choice rules are proposed to help firms improve the product quality under environment of diversified CRs; 3) the established model under deterministic consumer choice rule is transformed into an equivalent linear model to facilitate the solving process; and 4) the established optimization models are further extended for products with both continuous and discrete target values of ECs.

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.

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.

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.

Customer requirements mapping method based on association rules mining for mass customisation

Customer requirements analysis is the key step for product variety design of mass customisation. Quality Function Deployment (QFD) is a widely used management technique for comprehending the 'Voice of the Customer' (VOC), however, QFD in excess depends on human subjective judgement during extracting customer requirements and determination of the importance weights of customer requirements. And also, QFD process and related problems are complicated. In this paper, based on generic data structure of product family (GBOM), association rules analysis is introduced to construct the classification mechanism between customer requirements and product architecture. The new method can map customer requirements to the items of product family architecture respectively, accomplish the mapping process from customer domain to physical domain directly, decrease mutual process between customer and designer, improve the product design quality, and thus furthest satisfy customer needs. Finally, an example of customer requirements mapping of the elevator cabin is used to illustrate the proposed method.

A new integrated decision-making approach for design alternative selection for supporting complex product development

Design alternative selection is regarded as a crucial activity in complex product development. The traditional decision-making methods barely consider the design alternative evaluation of complex products or integrate the voices of customers into the decision process systematically. The aim of this paper is to improve the effectiveness of decision making about multiple design alternatives in complex product development under uncertainty. Based on the integration of quality function deployment and group decision making, a new decision-making approach is proposed for assisting product designers in selecting the design alternatives of complex products. Meanwhile, fuzzy set theory is incorporated in order to capture the vagueness and uncertainty that exists in the decision process. In this study, a complex product is divided into multi-parts to form a hierarchical structure. Fuzzy quality function deployment is used for translating customer requirements into the priorities of these parts. Furthermore, a fuzzy multi-criteria group decision-making method is employed for evaluating the performance of part alternatives. All design alternatives are ranked and then selected according to the multiplied evaluation scores of parts with their weights. This proposed approach is applied in a real-world example of a horizontal directional drilling machine. In addition, how the importance weights of customer requirements and evaluation criteria change are analysed under various risk environments.

Customer requirements mapping method based on association rule mining for mass customization

Customer requirements analysis is the key step for product variety design of mass customization(MC). Quality function deployment (QFD) is a widely used management technique for understanding the voice of the customer (VOC), however, QFD depends heavily on human subject judgment during extracting customer requirements and determination of the importance weights of customer requirements. QFD process and related problems are so complicated that it is not easily used. In this paper, based on a general data structure of product family, generic bill of material (GBOM), association rules analysis was introduced to construct the classification mechanism between customer requirements and product architecture. The new method can map customer requirements to the items of product family architecture respectively, accomplish the mapping process from customer domain to physical domain directly, and decrease mutual process between customer and designer, improve the product design quality, and thus furthest satisfy customer needs. Finally, an example of customer requirements mapping of the elevator cabin was used to illustrate the proposed method.

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.