Hesitant fuzzy integrated MCDM approach for quality function deployment: a case study in electric vehicle

Quality function deployment (QFD) is a product planning management instrument which has been used in a broad range of industries. However, the traditional QFD method has been criticised much for its deficiencies in acquiring experts’ opinions, weighting customer requirements (CRs) and ranking engineering characteristics (ECs). To overcome the limitations, an integrated analytical model is presented in this study for obtaining the importance ratings of ECs in QFD by integrating decision-making trial and evaluation laboratory (DEMATEL) technique and Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method under hesitant fuzzy environment. In particular, the hesitant fuzzy DEMATEL is used to analyse the interrelationships among CRs and determine their weights, and the hesitant fuzzy VIKOR is utilised to prioritise ECs. Finally, the feasibility and practicality of the proposed method are verified by an example regarding the product development of electric vehicle.     

Prioritising technical attributes in QFD under vague environment: a rough-grey relational analysis approach

This study aims at improving the effectiveness of Quality function deployment (QFD) in handling the vague, subjective and limited information. QFD has long been recognised as an efficient planning and problem-solving tool which can translate customer requirements (CRs) into the technical attributes of product or service. However, in the traditional QFD analysis, the vague and subjective information often lead to inaccurate priority. In order to solve this problem, a novel group decision approach for prioritising more rationally the technical attributes is proposed. Basically, two stages of analysis are described: the computation of CR importance and the prioritising the technical attributes with a hybrid approach based on a rough set theory (RST) and grey relational analysis (GRA). The approach integrates the strength of RST in handling vagueness with less priori information and the merit of GRA in structuring analytical framework and discovering necessary information of the data interactions. Finally, an application in industrial service design for compressor rotor is presented to demonstrate the potential of the approach.     

Systematical decision-making approach for quality function deployment based on uncertain linguistic term sets

A systematical decision-making approach is constructed for quality function deployment (QFD) in uncertain linguistic situations. The mathematical expression and operation of linguistic terms play important roles in the proposed approach in terms of customer requirements (CRs) and design requirements (DRs) in QFD. First, hesitant fuzzy linguistic term sets are designed to conveniently express uncertain linguistic terms and compute with words after the data derived from customers are pretreated and integrated in the decision-making process. Second, the tolerance deviation is defined to restrict innovatively the deviation range of fuzzy linguistic terms in the assessment stage of relative importance for CRs. Third, information entropy is originally designed to determine the final importance of DRs. Moreover, an empirical study on the research project called vortex recoil hydraulic retarder is conducted to demonstrate the performance of the systematical decision-making approach. The proposed approach can be applied to a wide variety of new product development problems in uncertainty settings.     

A novel fuzzy linguistic model for prioritising engineering design requirements in quality function deployment under uncertainties

Quality function deployment (QFD) is a planning and problem-solving tool gaining wide acceptance for translating customer requirements (CRs) into the design requirements (DRs) of a product. Deriving the priority order of DRs from input variables is a crucial step in applying QFD. Due to the inherent vagueness or impreciseness in QFD, the use of fuzzy linguistic variables for prioritising DRs has become more and more important in QFD applications. Existing approaches make use of the associated fuzzy membership functions of linguistic labels based on the fuzzy extension principle. However, an inherent limitation of such fuzzy linguistic approaches is the information loss caused by approximation processes, which eventually implies a lack of precision in the final results. This paper proposes an alternative approach to prioritising engineering DRs in QFD based on the order-based semantics of linguistic information and fuzzy preference relations of linguistic profiles, under random interpretations of customers, design team and CRs. Ultimately, this approach enhances the fuzzy-computation-based models proposed in the previous studies by overcoming the mentioned limitations. A case study taken from the literature is used to illuminate the proposed technique and to compare with the previous techniques based on fuzzy computation.     

Improved approach to quality function deployment based on Pythagorean fuzzy sets and application to assembly robot design evaluation

Quality function deployment (QFD) is an effective method that helps companies analyze customer requirements (CRs). These CRs are then turned into product or service characteristics, which are translated to other attributes. With the QFD method, companies could design or improve the quality of products or services close to CRs. To increase the effectiveness of QFD, we propose an improved method based on Pythagorean fuzzy sets (PFSs). We apply an extended method to obtain the group consensus evaluation matrix. We then use a combined weight determining method to integrate former weights to objective weights derived from the evaluation matrix. To determine the exact score of each PFS in the evaluation matrix, we develop an improved score function. Lastly, we apply the proposed method to a case study on assembly robot design evaluation.     

An integrated decision-making approach for designing and selecting product concepts based on QFD and cumulative prospect theory

In this paper, both unbalanced linguistic terms and a risk decision-making problem with developers’ bounded rationality are considered; an integrated approach of Quality Function Deployment (QFD) and Cumulative Prospect Theory (CPT) is proposed to help increase customer satisfaction and facilitate product concepts selection. Firstly, QFD is employed to provide a customer-driven tool for developers, which can be used to generate product concept alternatives. Subsequently, enhanced information entropy is utilised to prioritise competing Customer requirements (CRs) based on unbalanced linguistic terms. These terms can be directly processed without being translated into fuzzy numbers, where the risk of information loss in fuzzification can be minimised. Product concept alternatives can be generated based on the outcomes of the subsequent QFD process. Moreover, CPT can be considered as a novel method by incorporating the developers’ psychological characteristics under risk, which can help identify the most relevant product concepts. The cost prospect values of each alternative can be calculated by the function based on the cost reference point. The deficit and profit prospect values can be obtained by aggregating the values and weights of potential results, where functions from the enhanced CPT are used. The order of all alternatives can be ranked based on their overall prospect values. Finally, the proposed approach can be evaluated by a case study concerning the development of a new hydraulic breaker. The deliverables of this study are used to evaluate the relative advantages of the proposed approach over existing multi-attribute utility ones.     

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.     

Determining the importance ratings of customer requirements in quality function deployment based on interval linguistic information

Quality function deployment (QFD) is a customer-oriented tool and is widely applied to design and improve products and services. Determining the importance ratings (IRs) of customer requirements (CRs) is an essential step in QFD application and will affect the quality of product design and improvement. In this study, a group decision-making method is proposed to obtain realistic IRs. Low-carbon environment is considered in recognising CRs. Interval linguistic information (ILI) is used to express the vague evaluations in product improvement. In addition, an interval linguistic weighted arithmetic averaging operator, a normalised formula, and an expected value operator are integrated to deal with evaluation matrices expressed by ILI. A relationship matrix is used reversely to acquire accurate basic IRs (BIRs). The improved cosine method based on ILI is also employed to derive BIRs. The modified entropy method based on ILI is proposed to determine a competitive priority rating (CPR) of a CR. The final IR of every CR can be obtained by integrating its BIR and CPR. Finally, a practical product improvement of turbine engine is provided to illustrate the validity and feasibility of the proposed approach.     

Fuzzy multiple objective decision making approach to prioritize design requirements in quality function deployment

Quality function deployment (QFD) is a customer-oriented design tool for developing new or improved products to increase customer satisfaction by integrating marketing, design engineering, manufacturing, and other related functions of an organization. QFD focuses on delivering value by taking into account customer needs and then deploying this information throughout the development process. Although QFD aims to maximize customer satisfaction, technology and cost considerations limit the number and the extent of the possible design requirements that can be incorporated into a product. This paper presents a fuzzy multiple objective programming approach that incorporates imprecise and subjective information inherent in the QFD planning process to determine the level of fulfilment of design requirements. Linguistic variables are employed to represent the imprecise design information and the importance degree of each design objective. The fuzzy Delphi method is utilized to achieve the consensus of customers in determining the importance of customer needs. A pencil design example illustrates the application of the multiple objective decision analysis.     

Modelling customer and technical requirements for sustainable supply chain planning

Sustainable supply chains are the need of modern times. In order to make them successful, listening to voice of the customer and integrating them in the design and development phases of supply chains are very important. In this paper, we propose a technique called sustainability function deployment (SFD) developed on the concept of quality function deployment (QFD) to model customer and technical requirements, establish relationship between them and prioritise them for developing sustainable supply chains. SFD gathers customer and technical requirements for sustainable supply chain planning using C-REQ and T-REQ surveys, generates priority of customer requirements using Priority Matrix, performs screening of technical requirements using gap analysis, and generates requirements weights using QFD, thereby providing a structured approach to the decision-makers to select the right areas of improvement. Both the customer and technical requirements are captured from a socio-economic-environmental perspective. Results of application of the proposed technique are provided. The strength of the proposed approach is its novelty in integration of priority matrix and gap analysis in prioritising requirements based on their importance and performance in SFD. Besides, SFD facilitates modelling of complex, interrelated requirements in sustainable supply chain planning.     

A combined QFD and integer programming framework to determine attribute levels for conjoint study

Conjoint Analysis (CA) and Quality Function Deployment (QFD) are two popular tools for new product design; marketers frequently use the former and engineers the latter. Typically, in a conjoint study, the attributes and their levels are determined through focus group discussions or market surveys. Sometimes, the market researchers exclude some critical features or include unrealistic attribute levels resulting in infeasible product profiles. Inappropriate selection of attribute levels may render the conjoint study less useful. In QFD, the New Product Development team attempts to identify the technical characteristics (TCs) to be improved (included) to meet the customer requirements (CRs) through a subjective relationship matrix between CRs and TCs. At present there is no methodology that uses the output of QFD to generate feasible product profiles to be used in CA and therefore improve its usefulness. In this paper, QFD is used along with an integer programming (IP) model to determine the appropriate TCs and consequently the right attribute levels. These attribute levels are then used in a conjoint study. It is also proposed to measure the elements of the so-called relationship matrix in QFD in a way so that the right levels of the attributes can be generated from the IP solution. The proposed method is illustrated through a commercial vehicle design problem with hypothetical data.     

Determining importance ratings of patients’ requirements with multi-granular linguistic evaluation information

Determination of patients’ requirements (PRs) importance is a critical issue of medical service design. Quality function deployment (QFD) is one of the very effective customer-driven quality system tools typically applied to fulfil PRs. Often the patients cannot easily express their judgements on PRs’ importance with exact numerical values, and they usually present their judgements in different scales. Therefore, this paper aims at providing a systematic method to simultaneously deal with PRs’ fuzziness and multi-granularity in the QFD. Compared to previous research, its contribution is threefold. First, it proposes use of the 2-tuple linguistic model which can effectively manage the imprecise and vague evaluation information in QFD because of its accuracy and no information loss. Second, it develops a new 2-tuple transformation function to solve the unification problem of multi-granular linguistic judgements in QFD. Third, it proposes a qualitative approach to deal with uncertainty existed extensively in establishing modified factors. Finally, a practical case of hospital PRs is provided to illustrate its feasibility and effectiveness of the proposed methodology.     

Controlling Factor Weights in Data Envelopment Analysis

Data Envelopment Analysis (DEA) is a mathematical programming approach to assessing relative efficiencies within a group of Decision Making Units (DMUs). An important outcome of such an analysis is a set of virtual multipliers or weights accorded to each (input or output) factor taken into account. These sets of weights are, typically, different for each of the participating DMUs. A version of the DEA model is offered where bounds are imposed on weights, thus reducing the variation in the importance accorded to the same factor by the various DMUs. Techniques for locating appropriate bounds are suggested and the notion of a common set of weights is examined. Possible interpretations to differences in efficiency ratings obtained with the various models developed are discussed.     

A fuzzy QFD program modelling approach using the method of imprecision

The use of quality function deployment (QFD) to aid decision making in product planning has gained extensive international attention, but current QFD approaches are unable to cope with complex product planning (CPP) characterized by involving multiple engineering characteristics (ECs) associated with significant uncertainty. To tackle this difficulty, in this paper, fuzzy set theory is embedded into a QFD framework and a novel fuzzy QFD program modelling approach to CPP is proposed to optimize the values of ECs by taking the design uncertainty and financial considerations into account. In the proposed methodology, fuzzy set theory is used to account for design uncertainty, and the method of imprecision (MoI) is employed to perform multiple-attribute synthesis to generate a family of synthesis strategies by varying the value of s, which indicates the different compensation levels among ECs. The proposed methodology will allow QFD practitioners to control the distribution of their development budget by presetting the value of s to determine the compensation levels among ECs. An illustrative example of the quality improvement of the design of a motor car is provided to demonstrate the application and performance of the modelling approach.     

A novel fuzzy group decision-making approach to prioritising engineering characteristics in QFD under uncertainties

In new product development, design teams commonly need to define engineering characteristics (ECs) in a quality function deployment (QFD) planning process. Prioritising the engineering characteristics in QFD is essential to properly plan resource allocation. However, the inherent vagueness or impreciseness in QFD presents a special challenge to the effective calculation of the importance of ECs. Generally, there are two types of uncertain input in the QFD process: human perception and customer heterogeneity. Many contributions have been made on methods to prioritise ECs. However, most previous studies only address one of the two types of uncertainties that could affect the robustness of prioritising ECs. To address the two types of uncertainties simultaneously, a novel fuzzy group decision-making method that integrates a fuzzy weighted average method with a consensus ordinal ranking technique is proposed. An example is presented to illustrate the effectiveness of the proposed approach. Results of the implementation indicate that the robustness of prioritising ECs based on the proposed approach is better than that based on the method of Chen et al. (Chen, Y., Fung, R.Y.K., Tang, J.F., 2006. Rating technical attributes in fuzzy QFD by integrating fuzzy weighted average method and fuzzy expected value operator. European Journal of Operational Research, 174 (3), 1553–1556).     

Fuzzy expected value modelling approach for determining target values of engineering characteristics in QFD

Quality function deployment (QFD) is a planning and problem-solving tool that is renowned for translating customer requirements into the technical attributes of a product. To deal with the imprecise elements in the development process, fuzzy set theory is incorporated into QFD methodology. A novel fuzzy expected value operator approach is proposed in this paper to model the QFD process in a fuzzy environment, and two fuzzy expected value models are established to determine the target values of engineering characteristics in handling different practical design scenarios. Analogous to stochastic programming, the underlying philosophy in the proposed approach is based on selecting the decision with maximum expected returns. Furthermore, the proposed approach considers not only the inherent fuzziness in the relationships between customer requirements and engineering characteristics, but also the correlation among engineering characteristics. These two kinds of fuzzy relationships are aggregated to give the fuzzy importance of individual engineering characteristics. Finally, an example of a quality improvement problem of a motor car design is given to demonstrate the application and performance of the proposed modelling approach.     

基于熵的QFD在航空武器研制中的应用

由于在航空武器研发过程中很难得到准确的用户需求信息,针对这种信息的不完整和不确定性,采用了对称的模糊数据来获取用户含糊表达中的信息,并且在传统的质量功能展开(QFD)模型中引入熵的方法来确定指标权重及其相互之间的关系,建立了基于熵的QFD模型,并以某航空武器装备为例说明了该模型的实用性.     

Defining Characteristics for Product Development Using Quality Function Deployment: A Case Study on Indian Bikes

The success of the company lies in understanding the customer preferences and tastes and anticipating the changes required in existing or new products being offered. This study uses a heuristic approach to formulate the problem of product development using a combination of analytical hierarchy process (AHP) with quality function deployment (QFD) to evaluate the most satisfying design for customer. A case study for selection of a bike has been presented here to illustrate the proposed approach.

The contributions of the article are (a) structuring of the decision problem for assessment of impact of decisions after identification of customer attributes and preferences; (b) assessing strategies to synthesize qualitative and quantitative factors in decision-making, keeping checks on consistency; (c) using the additive synthesis of priorities to accommodate a variety of interactions and transform multidimensional measurements to unidimensional ratio scale; and (d) assessing the impact of the engineering characteristics weights on the priority of the criteria and overall project (v) Validation of the model using case study.