Quality improvement pilot program selection based on dynamic hybrid MCDM approach
| Date | 05 February 2018 |
| DOI | https://doi.org/10.1108/IMDS-11-2016-0498 |
| Published date | 05 February 2018 |
| Pages | 144-163 |
| Author | Fuli Zhou,Xu Wang,Avinash Samvedi |
| Subject Matter | Information & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems |
Quality improvement pilot
program selection based on
dynamic hybrid MCDM approach
Fuli Zhou and Xu Wang
Department of Industrial Engineering, Chongqing University,
Chongqing, China, and
Avinash Samvedi
The Logistics Institute –Asia Pacific, National University of Singapore,
Kent Ridge, Singapore
Abstract
Purpose –Driven by motivation of quality enhancement and brand reputation promotion, automotive
industries try to improve product quality and customer satisfaction by performing quality pilot programs
continuously. The purpose of this paper is to develop a dynamic model to select the improvementquality pilot
program from competitive candidates based on dynamic customers’feedback.
Design/methodology/approach –An extended dynamic multi-criteria decision-making method is
developed by embedding dynamic triangular fuzzy weighting average operators into fuzzy
VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, and the novel evaluation
indicator “ζ”is introduced to reflect prioritization performance.
Findings –The two evaluation indicators (Qand “ζ”) assist quality managers to identify the best program
with respect to multiple conflicting criteria and the best choice based on these two indexes shows high
conformity. Besides, ranking sequences obtained by “ζ”can avoid the dilemma that there are several
candidates with top priority calculated by comprehensive group utility value Q.
Practical implications –The dynamic MCDM method has been applied into the quality improvement
procedure in Chinese domestic auto factories and contributes to highly efficient promotion.
Originality/value –Few dynamic models on pilot program selection for quality improvement based on
dynamic customers’feedback, this study deals with the dynamic promotion by an extended fuzzy VIKOR
method and presents a case application.
Keywords Automotive industry, Dynamic multi-criteria decision-making (MCDM) method,
Dynamic triangular fuzzy weighting average (DTFWA) operator, Pilot program,
Quality improvement procedure
Paper type Research paper
1. Introduction
With increasing fierce competition on the automotive industry around the world, excellent
quality and customer-oriented strategy become the competences of auto factories and
continuous quality improvement programs tend to be drivers that increase successes and
reduce failures (Bhuiyan and Baghel, 2005; Donauer et al., 2014). The Chinese domestic auto
factory shows soaring interests on quality improvement activities and continuous
improvement methodologies on products and production processes (Bilgen and Şen, 2012;
Zhou, Wang, Lin, He and Zhou, 2016), which leads to positive advantages on business
competitive position and customer satisfaction (Sharma and Gadenne, 2008). Quality
practices based on Six Sigma, 8D, total quality management (TQM) and other quality
management techniques are employed and developed by researchers and practitioners to
promote product performance, service quality and brand reputation (Talib et al., 2011;
Kumar et al., 2009). The concentration of customers’feedback becomes valuable references
to stimulate quality improvement initiatives (Putri and Yusof, 2008), especially within the
warranty period (Zuo et al., 2000; Murthy and Blischke, 2006). Automotive industries that
Industrial Management & Data
Systems
Vol. 118 No. 1, 2018
pp. 144-163
© Emerald PublishingLimited
0263-5577
DOI 10.1108/IMDS-11-2016-0498
Received 18 November 2016
Revised 27 May 2017
9June2017
Accepted 4 July 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
144
IMDS
118,1
can convert unconformity and deficiency to success by learning from past experience
through quality improvement pilot program will be the triumphs and pioneers.
Quality is a term that has a variety of specific definitions and can be reflected by
utilization information and client’feedback. Managerial decisions, before product delivery
including procurement, technological level and assembly abilities, contribute to product
performance. It is un-conformance and imperfect performance reflected by the warranty
system that stimulates the continuous improvement by quality pilot programs (Donauer
et al., 2014). Generally, the quality indexes like R/1000 and TGW/1000 in Chinese and Indian
auto industries have been established to describe and reflect quality performances of part,
subsystem and vehicle (Teli et al., 2013; Fuli et al., 2015), which have been employed by
quality managers to scrutinize and evaluate the consequents of quality initiatives and pilot
programs. In addition, roadmap performance of the crucial quality indicators can reflect the
quality variation and improvement result which provide some operational guidance for
managers to perform quality activities.
In order to apply continuous improvement with high efficiency, domestic auto
industries in China target the non-conformity, and deficient part or system as pilot
program to perform quality practices by Pareto chart based on failure frequency (R/1000)
and maintenance record. With increasing focus on vehicle users, the voice of consumers is
becoming one crucial indicator to be considered (Rahman and Ali, 2015; Putri and Yusof,
2008). How to select an appropriate alternative as the quality pilot program from various
competitive candidates is an urgent challenge for quality department due to the limitation
of resources and significance on successful quality improvement implementation
and movement (Cagliano and Spina, 2000; Bacdayan, 2001). Many practical industries
select the best improvement object based on thumb criterion failure frequency (R/1000) for
its convenient access, some researchers proposed multi-criteria decision-making method
to deal with this problem by considering multiple conflicting criteria and it has been
adopted by quality engineers and managers. Bilgen applied fuzzy analytical hierarchy
process method into the Six Sigma project selection and proposed the implementation
stages of improvement actions (Bilgen and Şen, 2012). define, measure, analyze,
improve and control procedures are conducted to analyze the object and improve quality
performance. In addition, other pilot programs like TQM project or 8D program
selection by multi-criteria decision-making approaches for pilot program selection were
employed and developed to deal with the issue considering specific attributes
(Büyüközkan and Öztürkcan, 2010; Hu et al., 2007, 2008; Mawby, 2007; Kumar et al., 2009;
Mohammadshahi, 2013; Ahire and Rana, 1995). Donauer proposed MCDM method of
prioritization matrix to track nonconformities and prioritize the best candidate for quality
improvement (Donauer et al., 2014). Since the increasing attention on vehicle users, the
customers’feedback and customers’perception are becoming much more significant in
quality project selection matter. Zhou proposed a hybrid fuzzy VlseKriterijumska
Optimizacija I Kompromisno Resenje (VIKOR) method to identify the unconformity with
respect to established multiple criteria by rough set-based attribute reduction (RSAR)
technique, and the best improvement part is obtained from competitive candidates
(Zhou, Wang, Lin, He and Zhou, 2016). Water employed AHP method to prioritize the
quality improvement project and addressed the customer satisfaction improvement
(van de Water and de Vries, 2006). However, all these literatures highlighted the static
quality performance with ignoring the dynamic variation of competitive candidates, and
the quality improvement is a continuous process for competence advancement.
Cagliano thought that the practice-driven program selection only based on static
performance is unwise, and a continuous monitoring of the candidates’performance is the
only way to make sure consistency (Cagliano and Spina, 2000). To fill the gap, the dynamic
multi-criteria decision-making (DMCDM) modelisexploredandtheextensionofthefuzzy
145
Quality
improvement
pilot program
selection
To continue reading
Request your trial