Improving quality function deployment analysis with the cloud MULTIMOORA method

• Date: 01 May 2020
• Author: Xiao‐Yue You,Hu‐Chen Liu,Song‐Man Wu,Li‐En Wang
• DOI: http://doi.org/10.1111/itor.12484
• Published date: 01 May 2020

Intl. Trans. in Op. Res. 27 (2020) 1600–1621

DOI: 10.1111/itor.12484

INTERNATIONAL

TRANSACTIONS

IN OPERATIONAL

RESEARCH

Improving quality function deployment analysis with the cloud

MULTIMOORA method

Song-Man Wua, Xiao-Yue Youb,c, Hu-Chen Liua,b and Li-En Wanga

aSchool of Management, Shanghai University, Shanghai 200444, PR China

bSchool of Economics and Management, TongjiUniversity, Shanghai 200092, PR China

cInstitute for Manufacturing, University of Cambridge,Cambridge CB3 0FS, UK

E-mail: songmanwu@foxmail.com [Wu]; youxiaoyue@gmail.com[You]; huchenliu@foxmail.com [Liu];

lienwang@foxmail.com [Wang]

Received 18 August2016; received in revised form 6 August 2017; accepted 12 October 2017

Abstract

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 in-

volves multiple customer requirements (CRs). In this paper, a modified multi-objectiveoptimization 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 assess-

ments provided bydecision 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.

Keywords:quality function deployment (QFD); cloud model; MULTIMOORA method; incomplete weight information;

electric vehicle

1. Introduction

As a systematic manner to develop new products, the quality function deployment (QFD) proposed

in 1972 in Japan is beneficial for learning about customer requirements (CRs) throughout the

programming and implementation stages of a product (Li et al., 2014c). It utilizes a well-structured

and interfunctional group to identify CRs and translate them into engineering characteristics

(ECs) so that product designers can prioritize the ECs and decide which ones should be improved

during the production process. Hence, it is capable of helping working groups and management

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S.-M. Wu et al. / Intl. Trans.in Op. Res. 27 (2020) 1600–1621 1601

departments reach a final decision in planning new or improved products. Extensive analysis of the

literature revealsthat QFD has been applied in various industries during the past several years, such

as automobiles (Chakraborty and Prasad, 2016; Wu et al., 2017a), electronics (Wang, 2017), and

textiles (Chowdhury and Quaddus, 2015).

Specifically, building the house of quality (HOQ) is a crucial step in completing QFD and it con-

sists of the following components: CRs (WHATs), ECs (HOWs), relative importance of WHATs,

interrelations between WHATsand HOWs, inner dependence among CRs, inner dependence among

ECs, and competitiveassessment matrix of customer and technology.With the design-oriented char-

acter of HOQ, CRs are easily converted into ECs to strengthen vertical and horizontal communi-

cation between customers and product designers. Therefore, QFD serves as a valuable technique

for product designers, and has been used by top executives to identify strategic opportunities and

challenges. But there areseveral critical shortcomings of the traditional QFD method. For example,

the input information is described by precise values in the evaluation process of the conventional

QFD (Zaim et al., 2014; Chen et al., 2017), which, however, are not adequate to express the fuzzy

human language. Besides, the weights of CRs are given directly in the traditional QFD, but the

weight information regarding CRs may be incompletely known in practical product designs (Wang

et al., 2016b). What is more, the weighted average method is commonly used in the traditional QFD,

which is not suitable for ranking ECs exactly (Dat et al., 2015; Wu et al., 2017a).

Toovercome the deficiencies of the classical QFD, an approach using cloud model theory and the

MULTIMOORA (multiobjective optimization by ratio analysis plus the full multiplicative form)

method is developed for QFD in this paper. This study makes several significant contributions to

the existing QFD literature: (a) cloud model theory is applied to reflect the uncertainty, ambiguity,

and randomness of judgments provided by experts; (b) a maximizing deviation method is used to

compute the weights of CRs when the weight information is incompletely known; (c) a modified

MULTIMOORA method, called C-MULTIMOORA, is adopted to obtain the sort orders of ECs

in the QFD analysis. As a result, the improved QFD method can capture the uncertainty of experts’

assessment information and derive an accurate and credible ranking of ECs. Furthermore, the new

QFD is appropriate for handling the product development problems with partially known CR

weight information. Finally, a case study regarding the product design of electric vehicles is applied

to elucidate the presented QFD framework.

The remainder of this paper is structured as follows. Section 2 reviews briefly the improvements

of QFD as well as the applications of the cloud model and the MULTIMOOORA method. Section

3 presents the preliminaries of cloud model theory. In Section 4, we develop an improved QFD

approach by integratingthe cloud model and the MULTIMOORA method. An illustrativeexample

is given in Section 5 to show the effectiveness of the proposed QFD framework. In Section 6,

conclusions and future research directions are provided.

2. Literat ure review

2.1. Improvement of QFD

Over the past years, QFD has received intense attention from both academic and practical circles.

Researchers have applied several multicriteria decision making (MCDM) approaches to improve

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2017 The Authors.

International Transactionsin Operational Research C

2017 International Federation of OperationalResearch Societies