A grey approach to assess the challenges to adopting sustainable production practices in the apparel manufacturing industry: Implications for sustainability

Abstract

The apparel manufacturing industry (AMI) significantly contributes to the economic growth of various developing nations. However, unsustainable production practices remain a major concern in this sector. Therefore, this study investigated the most significant challenges to adopting sustainable production practices in the apparel manufacturing sector from the perspective of an emerging economy like Bangladesh. Initially, the study identified the significant challenges through an extensive literature review and expert validation. Later, by utilizing a Grey Decision-Making Trial and Evaluation Laboratory (Grey DEMATEL) framework, the challenges were analyzed, and the interrelations among them were explored. The findings revealed that the ‘slow return on investment’, ‘lack of proper waste management systems’, and ‘reluctance to adopt advanced manufacturing processes’ are the top most significant challenges to adopting sustainable production practices in the apparel manufacturing sector. A sensitivity assessment was also performed to appraise the robustness of the obtained result. The outcomes of this research are expected to assist managers and practitioners in the AMI and other relevant industrial sectors in strategic planning to achieve various sustainable development goals (SDGs).

Full text

Results in Engineering 22 (2024) 102006
Available online 14 March 2024
2590-1230/© 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-
nc/4.0/).
A grey approach to assess the challenges to adopting sustainable production
practices in the apparel manufacturing industry: Implications
for sustainability
Binoy Debnath
a
, Muntaha Rauf Taha
b
, Md. Tanvir Siraj
a
, Md. Fahmid Jahin
c
,
Sazzadul Islam Ovi
a
, A.B.M. Mainul Bari
a
,
*
, Abu Reza Md. Towqul Islam
d
, Asif Raihan
e
a
Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1000, Bangladesh
b
Department of Sociology, Shahjalal University of Science and Technology, Sylhet, 3114, Bangladesh
c
School of Business and Economics, North South University, Dhaka, 1229, Bangladesh
d
Department of Disaster Management, Begum Rokeya University, Rangpur, 5404, Bangladesh
e
Institute of Forestry and Environmental Sciences, University of Chittagong, Chittagong, 4331, Bangladesh
ARTICLE INFO
Keywords:
Sustainable production practices
Emerging economy
Apparel manufacturing industry
Grey DEMATEL
Sustainable development goals
ABSTRACT
The apparel manufacturing industry (AMI) signicantly contributes to the economic growth of various devel-
oping nations. However, unsustainable production practices remain a major concern in this sector. Therefore,
this study investigated the most signicant challenges to adopting sustainable production practices in the apparel
manufacturing sector from the perspective of an emerging economy like Bangladesh. Initially, the study iden-
tied the signicant challenges through an extensive literature review and expert validation. Later, by utilizing a
Grey Decision-Making Trial and Evaluation Laboratory (Grey DEMATEL) framework, the challenges were
analyzed, and the interrelations among them were explored. The ndings revealed that the ‘slow return on in-
vestment’, ‘lack of proper waste management systems’, and ‘reluctance to adopt advanced manufacturing pro-
cesses’ are the top most signicant challenges to adopting sustainable production practices in the apparel
manufacturing sector. A sensitivity assessment was also performed to appraise the robustness of the obtained
result. The outcomes of this research are expected to assist managers and practitioners in the AMI and other
relevant industrial sectors in strategic planning to achieve various sustainable development goals (SDGs).
1. Introduction
Countries and organizations worldwide have implemented new
ecological guidelines to conserve and sustain natural resources and
minimize greenhouse gas emissions. In line with that vision, the United
Nations’s Fashion Industry Charter for Climate Action (FICCA) issued
plans for a carbon-neutral fashion industry by 2050 [1]. As a signatory of
FICCA, the Bangladeshi apparel manufacturing industry (AMI), which
caters primarily to the Western markets, must adopt sustainable tech-
nologies and practices to ensure responsible and eco-friendly production
[2]. Incorporating sustainable production practices can boost economic
productivity while ensuring the betterment of the workforce, con-
sumers, and the environment [3].
The Bangladeshi apparel manufacturing sector has experienced rapid
growth in the past decade [4]. Bangladesh occupies second place glob-
ally in terms of apparel exports, where China has held the top spot for
decades [5]. Bangladesh’s export revenues surged to a record-breaking
USD 52.08 billion in Fiscal Year 2022, primarily driven by the apparel
manufacturing sector, which generated a substantial USD 42.61 billion,
constituting 9.25% of the nation’s GDP [6]. This sector employs an
estimated 4.22 million people [7]. Despite all its accomplishments, AMI
faces various environmental and sustainability issues in its
manufacturing, production, and supply chain operations.
Bangladesh’s apparel manufacturing sector is under intense inter-
national pressure to adopt and implement sustainable production
practices in its conventional production processes [4]. Due to
* Corresponding author.
E-mail addresses: binoydebnath15@gmail.com (B. Debnath), tahamuntaharauf@gmail.com (M.R. Taha), tanvir25392@gmail.com (Md.T. Siraj), jahin.dipto@
gmail.com (Md.F. Jahin), ovhi99bd@gmail.com (S.I. Ovi), mainul.ipe@gmail.com (A.B.M.M. Bari), towq_dm@brur.ac.bd (A.R.Md.T. Islam), asifraihan666@
gmail.com (A. Raihan).
Contents lists available at ScienceDirect
Results in Engineering
journal homepage: www.sciencedirect.com/journal/results-in-engineering
https://doi.org/10.1016/j.rineng.2024.102006
Received 8 January 2024; Received in revised form 19 February 2024; Accepted 8 March 2024

Results in Engineering 22 (2024) 102006
2
government regulations, consumer expectations, and buyer demands for
sustainable products, the topics of environmental sustainability, green
concerns, and social sustainability have gained increasing popularity
among scholars and supply chain managers [8]. While Bangladesh’s
textile and apparel manufacturing industry generates the lion’s share of
the country’s export revenues, this sector is also blamed for generating
the largest amount of industrial waste [4]. The unsustainable use of
various dyes and chemicals, most of which often contain caustic in-
gredients and heavy metals, especially contributes to the generation of
various toxic wastes and other pollutants during the apparel
manufacturing process.
Several recent studies have been conducted on developing econo-
mies’ sustainable production, manufacturing, and supply chain prac-
tices. According to Liu et al. [9], the external pressures from the Chinese
government to comply with environmental regulations have motivated
Chinese companies to achieve green supply chain practices for sustain-
able production and internal capacity building.. Ara et al. [10] provided
practitioners with empirical proof of green marketing’s contribution to
sustainability performance. Taqi et al. [11] indicated that
“manufacturing exibility,” “diversifying the source of supply,” and
“creating backup suppliers” practices have considerable benecial out-
comes for mitigating the effects of the recent pandemic on the apparel
manufacturing supply chain.
Sustainable practices address environmental issues and consider
social and economic dimensions. To achieve environmental, social, and
economic sustainability, this research identies challenges to adopting
sustainable production practices. This research work intends to address
the following research questions (RQs):
RQ1. What are the crucial challenges hindering the adoption of sus-
tainable production practices in the AMI in emerging economies?
RQ2. How can the interrelationships between the identied challenges
be evaluated?
RQ3. How can resolving these challenges benet AMI in terms of
practicality and achieving SDGs?
This study intends to answer these RQs by achieving the following
research objectives (ROs):
RO1. To identify and prioritize the specic challenges that hinder the
adoption of sustainable production practices in the AMI, drawing from
existing literature and expert feedback.
RO2. To examine the relationships and interdependence among the
identied challenges using an appropriate methodological framework.
RO3. To develop actionable strategies and recommendations that can
assist managers and stakeholders in the AMI in resolving the identied
challenges and advancing sustainable production practices.
To achieve these ROs, this study will apply a Grey theory-based
Decision-Making Trial and Evaluation Laboratory (Grey DEMATEL)
approach to analyze the identied challenges. Initially, the challenges
will be identied through an extensive literature review and expert
validation. Later, the Grey DEMATEL method will be utilized to analyze
them. The Grey DEMATEL method has been chosen here for various
reasons. For instance, it is a more effective decision-making approach
compared to the conventional DEMATEL method, especially in envi-
ronments where decision-making is complex due to multiple layers of
variables and dynamic relationships among them [4]. The conventional
DEMATEL method considers expert opinions or judgments as absolute
values without considering ambiguity in decision-making [12]. How-
ever, when taking subjective judgments from humans using categorical
variables or linguistic scales, there is always a certain level of ambiguity
that needs to be considered during analysis. Grey DEMATEL can over-
come this limitation of the conventional DEMATEL, as the Grey theory
accounts for the vagueness of human decision-making, making it a more
reliable tool in complex decision-making environments [12].
The Grey DEMATEL provides unique benets in the analysis of
intricate systems as opposed to alternative decision-making methods.
For example, techniques like the Step-Wise Weight Assessment Ratio
Analysis (SWARA), Analytic Hierarchy Process (AHP), or Best-Worst
Method (BWM) can rank factors but do not possess the capability to
evaluate the causal connections between them [13]. Another popular
MCDM method, TOPSIS, also excels at ranking alternatives based on
multiple criteria [14]. However, it has to rely on predened criteria for
ranking, and it does not show how the factors relate to each other. Grey
DEMATEL, on the other hand, offers rankings based on the visibility or
importance of alternatives and also shows how the factors are inter-
connected among themselves. Again, alternative approaches, like
Interpretive Structural Modeling (ISM), can only show the direct in-
uences between factors, ignoring the indirect associations [12], unlike
Grey DEMATEL. Hence, it is obvious that Grey DEMATEL outperforms
AHP, SWARA, BWM, TOPSIS, and ISM methods due to its ability to
account for the intricate interconnections among factors, offering a more
comprehensive analysis. Therefore, the Grey-based DEMATEL approach
has been utilized in various research on decision-making that deals with
complex decision-making environments [4,15].
Three distinct issues motivated this study. First, as an emerging
economy, Bangladesh often grapples with resource constraints while
attempting to balance sustainable practices in its most income-
generating industrial sectors: the apparel manufacturing industry. Sec-
ond, empirical studies on sustainable production practices in the apparel
manufacturing sector are not very common, as they are still in the initial
stages of being introduced in the Bangladeshi industries. Third, as the
leading export-earning sector in Bangladesh, studies related to sustain-
able production practices in this sector could have broader implications
for other industries and other emerging economies.
The novelty of this research lies in exploring the challenges to
adopting sustainable production practices in Bangladesh’s AMI by uti-
lizing a Grey theory-based DEMATEL method. Distinguishing from
previous studies, this work focuses on the complex interrelations be-
tween sustainability challenges, an issue less explored in the existing
literature. Here, the Grey DEMATEL method reduces the impact of
vagueness in human judgment, offering a more nuanced approach
suitable for the intricate dynamics of sustainable production systems.
This study aims to provide a comprehensive framework for enhancing
environmental, social, and economic sustainability in Bangladesh’s AMI
by prioritizing challenges, examining their interdependencies, and
developing actionable strategies. This methodological innovation lls a
gap in empirical research on sustainable production practices in
emerging economies and presents a more reliable tool for complex
decision-making environments, thereby contributing signicantly to the
advancement of SDGs.
The remainder of the article has been organized into the following
segments- Section 2 presents the review of the literature and identies
the challenges. The methodological approach, including the collection
and evaluation of data, has been discussed in Section 3. Section 4 rep-
resents the obtained results. Section 5 presents the discussion of the
study’s ndings and the implications from a theoretical, practical, and
sustainability perspective. Finally, Section 6 concludes the study.
2. Literature review
Fashion industries emitted approximately 2.1 billion metric tons of
greenhouse gases in 2018 [16]. AMI, a key player in Bangladesh’s in-
dustrial sector, is one of the major energy consumers. Notably, the
majority of this energy is generated from fossil fuels [17]. Focusing on
global contributions, Peters and Lenzen [18] have shown that countries
like China, India, the United States, and Brazil have signicant climatic
effects in terms of apparel and footwear consumption. Their study
revealed increased emissions from 1.0 to 1.3 Gt CO
2
equivalent from
2000 to 2015. Complementing this research, Shamsuzzaman et al. [19]
delved into the environmental impacts of efuents from Bangladesh’s
B. Debnath et al.

Results in Engineering 22 (2024) 102006
3
denim garment washing plants, discovering that many of them have
adequate ltering systems but still fall short in generating clean
wastewater.
On a managerial level, Salman et al. [20] focused on the difculties
in implementing lean manufacturing in Bangladesh’s apparel industry,
exacerbated by the COVID-19 pandemic, uncovering challenges such as
a limited comprehension of lean concepts and inadequate support from
the top management. The recent COVID-19 pandemic has also high-
lighted the vulnerability of social sustainability in the South Asian
garment supply chain [21]. Identication of the relevant challenges is
needed to facilitate new industrial advancements and to achieve sus-
tainable production [22]. Adding another layer to this discourse, Islam
et al. [23] evaluated the readiness for circular economy in the AMI of
emerging economies using a revised theory of planned behavior model
to reveal vital elements like environmental commitment, green eco-
nomic incentives, and rm maturity, which are critical for adopting
circular economy practices. Hoque et al. [2] found that adopting sus-
tainable technology is considerably and favorably impacted by customer
pressure, top management, competition between companies, and gov-
ernment support. Sabuj et al. [24] addressed the lack of thorough
research on critical factors of implementing environmentally sustainable
supply chains in emerging economies like Bangladesh’s ready-made
garments (RMG) sector. The study identied proper government pol-
icies as the dominant and inuential factor, offering valuable insights for
RMG industry managers. Several studies have also identied potential
challenges and proposed solutions for achieving sustainability.
Sarkar et al. [25] discovered the signicance of green business ini-
tiatives as a driver for sustainable development, particularly in Ban-
gladesh’s booming RMG industry. They highlighted the need for
environmentally conscious strategies to maintain competitiveness while
addressing ecological concerns, presenting a framework to measure the
impact of green business strategy on competitive advantages within
Bangladesh’s RMG sector. Green human resource management may be
effectively implemented in Bangladesh’s apparel manufacturing busi-
ness by establishing strict rules and legislation, monitoring, training
programs, incentives, and mandated courses in the education system
[26]. Kazancoglu et al. [27] offered a comprehensive conceptual
framework addressing CSC sustainability challenges in the textile sector.
Research on additive manufacturing, such as the works of Sitotaw et al.
[28] and Khajavi [29], suggested that embracing newer technologies
could signicantly alter the traditional manufacturing paradigms. They
explored how additive manufacturing offers benets like increased
product diversity and more straightforward production stages,
providing insights into new business models that could reshape the
fashion industry and deliver more sustainable solutions.
The landscape of sustainable production is complex and affected by
various factors, from environmental degradation to technological limi-
tations. There is an urgent need for concerted efforts, from policy
changes to technological innovation, to achieve long-term sustainability
goals across industries. A summary of the literature that is closely
related to this present study context can be found in Table 1.
2.1. Research gap, study contribution, and identication of the challenges
Based on the discussion above, no research has been conducted to
determine and analyze the challenges to adopting sustainable produc-
tion practices in the AMI from an emerging economy perspective.
Although there are some studies on the challenges of sustainable oper-
ations in the manufacturing sector from a generalized industrial
perspective [32–34], to the best of the author’s knowledge, no study has
yet specically focused on adopting sustainable production practices in
the apparel manufacturing sector in emerging economies like
Bangladesh.
Because Bangladesh’s social and economic situations are so diverse
from those in other countries, the challenges to the sustainable pro-
duction of the Bangladesh apparel manufacturing industry must be
identied through extensive research. Also, no study has used multi-
criteria decision-making (MCDM) tools such as Grey DEMATEL to
explore the obstacles to sustainable production in the AMI. It indicates
an obvious research gap that needs to be explored. Therefore, this
research aims to contribute to the existing body of literature in the
following avenues:
Table 1
Summary of the closely related literature.
Source Objectives Applied method Outcomes
Salman et al.
[20]
Investigate
challenges in
implementing lean
manufacturing in
Bangladesh’s
apparel industry.
Delphi method,
Fuzzy total
interpretive
structural
modeling (TISM)
Identied
challenges like
inadequate
planning
synchronization
and limited
understanding of
lean concepts
Hoque et al. [2] Study the impact
of different factors
on sustainable
technology.
Empirical
statistics
Found the factors
that favorably
impact the adoption
of sustainable
technology.
Islam et al. [23] Study circular
economy
readiness in
Bangladesh’s RMG
sector.
Revised Theory of
Planned Behavior
Commitment to the
environment, rm
maturity, and green
economic
incentives enhance
CE readiness.
Debnath et al.
[4]
Assess factors for
adopting a green
supply chain in
Bangladesh’s AMI.
Grey DEMATEL Identied ‘demand
from buyers,’
‘economic benets,’
and ‘government
rules’ as top CSFs
Azad et al. [26] Investigate
challenges to
Green Human
Resource
Management in
Bangladesh’s RMG
sector.
Expert Opinions,
Surveys
Identied
challenges and
suggested strict
rules, monitoring,
training programs,
and incentives.
Shamsuzzaman
et al. [19]
Examine efuents
discharged by
Bangladeshi
denim washing
plants.
Environmental
Analysis
Found limitations
in wastewater
treatment
techniques in
Bangladeshi plants
Sabuj et al. [24] Investigate factors
for implementing
a sustainable
supply chain in
Bangladesh’s RMG
sector.
TISM Identied “Effective
government
policies” as the
dominant factor
Islam et al. [30] Examine
challenges in
implementing
green HRM
practices in
Dhaka’s apparel
manufacturing
factories.
Qualitative
surveys and
interviews
Highlighted issues
like limited
knowledge,
managerial
disinterest, and
proposed remedies
Sarkar et al.
[25]
Investigate the
signicance of
green business
initiatives in
Bangladesh’s RMG
industry.
Case Studies Presented a
framework to
measure the impact
of green business
strategy.
Ma et al. [31] Propose an
innovative
resource-sharing
method for a
sustainable
production system
in the garments
industry.
Analytical
Network Process
Proposed an overall
sustainable
indicator based on
four production
performance
indicators
B. Debnath et al.

Results in Engineering 22 (2024) 102006
4
•Identication of Key Challenges: The study highlights critical chal-
lenges that hinder the adoption of sustainable production practices in
the AMI of emerging economies like Bangladesh.
•Stratied Ranking and Categorization: The study develops a structured
ranking and categorization system for the identied challenges. This
approach will offer professionals and decision-makers a practical
tool to mitigate the negative effects caused by these challenges.
•Application of Grey DEMATEL-based Method: The study employs a
Grey DEMATEL framework to analyze the causal relationships
among the identied challenges. This will uncover the complex in-
terconnections among the challenges.
•Guidance for Strategic Action: The insights generated by this study will
benet managers and policymakers in emerging economies and
guide the formulation of sustainable operational strategies in the
apparel manufacturing sector.
Following a comprehensive examination of pertinent articles
through search engine databases and adhering to a rigorous research
protocol for a systematic literature review, we initially compiled a list of
seventeen challenges related to sustainable production in the AMI of
emerging economies. This preliminary list of challenges underwent a
validation process by experts, as provided in Table A1 ofAppendix A, in
the supplementary materials le. After this validation, experts removed
one barrier (‘Social compliance auditing’), while incorporating four
additional relevant challenges (‘Associated high maintenance cost of
sustainable technologies’, ‘Supply chain complexity’, ‘Absence of a
conducive business environment’, and ‘Lack of standardized metrics for
performance benchmarking’) to the list. The nalized list of twenty
challenges is provided in Table 2. A concise description of the nalized
challenges is provided in Table B1 ofAppendix B, in the supplementary
materials le.
3. Research methodology
This research introduces a paradigm that combines both the Grey
theory and the DEMATEL method to assess the obstacles that impede the
adoption of sustainable production practices within the apparel
manufacturing industry. Fig. 1 visually represents this framework. The
following subsections elaborate on the data collection process and the
computational steps involved in implementing this methodological
framework.
3.1. Survey and data collection
This research conducted a two-phase survey to investigate the
challenges to sustainable manufacturing in the AMI. Initially, the chal-
lenges to sustainable production within the AMI were identied from
existing literature using the snowballing technique [17,53]. These
challenges were sought in the Scopus and Google Scholar databases,
focusing on the period from 2012 to 2023. A research protocol was
formulated while reviewing the literature, encompassing database
search criteria, inclusion and exclusion criteria, key phrases, and the
research timeline, as detailed in Table 3. Initially, over 200 papers
containing the relevant keywords were identied in this thorough
literature assessment. After that, using a rigorous process of full-text
evaluation and strict adherence to the research protocol, only 85 of
the most pertinent papers were ultimately examined to conform to the
specied RQs.
To assess and develop a model for the challenges to sustainable
production in the AMI, experts in various AMI areas collaborated to
exhibit the proposed framework. We used purposive sampling, also
known as judgmental sampling, to choose the experts for our study as it
enables a more precise alignment of the sample with the aims and tar-
gets of the research, hence enhancing the accuracy of the study and the
Table 2
List of the nalized challenges.
Code Challenges Source
A1 Limited availability of recycled raw
materials
Masi et al. [35]; Filho et al.
[36]
A2 Lack of environmental impact assessment
(EIA) implementation
Sharpe et al. [37]
A3 Lack of proper waste management
systems
Kazancoglu et al. [27]
A4 Lack of green product awareness among
local customers
Papadopoulou et al. [38]
A5 Unstable market demand Harsanto et al. [39]
A6 Lack of consumer pressure and concern Abdelmeguid et al. [40];
Abbate et al. [41]
A7 Extreme competition among vendors Guo et al. [42]
A8 Reluctance to adopt advanced
manufacturing processes
Javaid et al. [43]; Tavares
et al. [44]
A9 Lack of sustainable product design Yadav et al. [45]
A10 Lack of technological knowledge and
expertise
Gardas et al. [46]
A11 Insufcient renewable energy integration Siraj et al. [47]
A12 Low productivity due to production
complicacy
Bhunia. A [48]; Hoque et al.
[2]
A13 Associated high maintenance cost of
sustainable technologies
Expert Feedback
A14 Supply chain complexity Expert Feedback
A15 Absence of a conducive business
environment
Expert Feedback
A16 Sunk investments in production processes Hübel & Schaltegger [49]
A17 Lack of monitoring and auditing of supply
chain partners
Govindan et al. [50]
A18 Lack of standardized metrics for
performance benchmarking
Expert Feedback
A19 Slow return on investment Mani et al. [51]
A20 Lack of government support and policy Prakash & Barua [52]
Fig. 1. Methodological framework of the study.
B. Debnath et al.

Results in Engineering 22 (2024) 102006
5
dependability of its results [54,55]. Moreover, it is a non-random,
non-probabilistic approach that does not need any guiding concepts or
a set number of participants [56]. So, instead of using a random selec-
tion technique, this approach chooses samples depending on the re-
searcher’s subjective opinion [57,58]. An overview of the participating
experts is presented in Table B2 ofAppendix B, in the supplementary
materials le.
Following identifying challenges, a questionnaire was circulated to
the decision experts through Google Forms, urging them to assess the
signicance of the selected critical challenges for developing the com-
parison matrix. The survey was performed online from February 1, 2022
to July 15, 2023, and the participation rate of experts was 54.35% (25
responded, out of 46 invited experts). The necessary calculations were
conducted utilizing the data gathered through the questionnaires to
determine the signicance and relationships of the nally selected list of
challenges using the Grey DEMATEL method.
3.2. Grey DEMATEL method
The DEMATEL approach, originally developed by the Battelle Me-
morial Institute in Geneva, offers a sophisticated methodology that
employs relationship matrices and diagraphs to meticulously analyze
and reveal the intricate cause-and-effect relationships among various
factors in a given context [59]. In practical situations, researchers have
utilized the combination of Grey theory coupling with the DEMATEL
technique to analyze complex factors within real-life systems, particu-
larly in decision-making contexts where ambiguity and subjective
evaluations play a signicant role. Moreover, Grey theory and the
DEMATEL technique are combined to consider ambiguous and unreli-
able information inherent in human judgments [60]. Therefore, Grey
DEMATEL offers the potential to increase the efciency and success of
the estimation process by introducing unpredictability into the estima-
tion procedure [61]. Table 4 provides the Grey-scale linguistic scale
used for this purpose.
The step-wise procedure of the Grey DEMATEL approach [12,62] is
as follows:
a) Developing the initial relation matrix: A developed questionnaire
was distributed to the decision experts seeking their opinions to
develop the initial relation matrix. To collect data, 25 professionals
in diverse roles from apparel manufacturing companies were
selected. A 20 ×20 pairwise comparison matrix was formed with
expert feedback that indicates the signicance of every barrier over
other challenges. In this context, we regard “n” as the number of
obstacles and “K” as the sample size of participants chosen for the
study. Every participant is assigned to appraise the direct impact of
facilitator “i” relative to facilitator “j”.
⊗Zl
ij =⊗Zl
ij,⊗Zl
ij(1)
b) Formulating the Grey relation matrix: Based on the evaluations
for the Grey-scale, as determined in Table 4, the expert feedback
scores were transformed into Grey numbers. According to the Likert
scale, 0 to 5 represents no inuence and very high inuence,
respectively.
where, 1 ≤l≤k;1≤i≤n;1≤j≤n.
c) Calculate the average or aggregated Grey relation matrix: The
aggregated or average of the transformed Grey relation matrices for
all expert feedback was used to develop this matrix. The average
Grey relational matrix [⊗ ˙
Zij]is calculated based on “K" Grey rela-
tionship matrices,
⊗Zl
ij;l=1−kas,
⊗˙
Zij =l⊗Zl
ij
K,l⊗Zl
ij
K(2)
The developed average Grey relation matrix is given in Table C1
ofAppendix C, in the supplementary le.
d) Calculate the crisp relation matrix: The crisp matrices from
average Grey matrices are computed in this step. The crisp matrices
are produced using a three-step approach:
1. Lower normalized values and upper normalized values.
⊗
Zl
ij =⊗Zl
ij −min
j⊗Zl
ijΔmax
min (3)
where ⊗Zl
ij signies the normalized lower bound value of the Grey
number, ⊗Dl
ij.
⊗
Zl
ij =⊗Zl
ij −min
j⊗Zl
ijΔmax
min (4)
where ⊗Zl
ij signies the normalized lower bound value of the Grey
number, ⊗Dl
ij.
Δmax
min =max
j⊗Zl
ij −min
j⊗Zl
ij(5)
2. Compute the normalized crisp value:
Xl
ij =
⊗
Zl
ij1−⊗
Zl
ij+⊗
Zl
ij ×⊗
Zl
ij
1−⊗
Zl
ij +⊗
Zl
ij
(6)
Table 3
Applied research protocol for the systematic review of the literature.
Applied
Protocol
Brief description
Databases Google Scholar, Scopus
Timeline 2012 to 2023
Language English
Keywords “Challenges” OR “Impediments” AND “Sustainable production” OR
“Environmentally friendly production” OR “Sustainable
operations,” AND “Apparel manufacturing industry” OR “Garment
industry” OR “Fashion industry” etc.
Inclusion
criteria
(i) Articles based on the existing challenges or challenges to
sustainable production in the apparel manufacturing sector; (ii)
Articles align with the proposed ROs and RQs
Exclusion
criteria
(i) Articles not indexed in Scopus or Google Scholar; (ii) Articles not
published in the English language; (iii) Research articles that can’t
address the mentioned RQs or ROs; (iv) Ineffective research
methodological design
Table 4
Linguistic scale of converting expert opinion into Grey numbers.
Attribute Grey Numbers
No inuence (0) (0.0, 0.1)
Very low inuence (1) (0.1, 0.3)
Low inuence (2) (0.2, 0.5)
Medium Inuence (3) (0.4, 0.7)
High inuence (4) (0.6, 0.9)
Very high inuence (5) (0.9, 1)
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3. Compute nal crisp values:
Ul
ij =min
j⊗Dl
ij +Xl
ij ×Δmax
min  (7)
and
U=Ul
ij(8)
The developed crisp relation matrix can be found in Table C2
ofAppendix C, in the supplementary materials le.
e) Calculate the normalized direct crisp relation matrix: This stage
involves calculating Q and multiplying it by the average relation
matrix Z to obtain the normalized direct crisp relation matrix (P).
Q=1
max
1≤i≤n
n
j=1
Z∗
ij
(9)
And,P=U×Q(10)
Each element in matrix P falls between zero and one.
The normalized direct crisp relation matrix is provided in Table C3 of
Appendix C of in the supplementary materials le.
f) Compute the total relation matrix: The total relation matrix (T)is
calculated in this step using the following equation,
T=P× (I−P)−1(11)
where, I is the identity matrix.
Ri represents the sum of rows, and Cj denotes the column sum. Using
equations (12) and (13), Ri and Cj can be computed as follows:
Ri =n
j=1Tij(i=1,2,….., n)(12)
Cj =n
i=1Tij(j=1,2,….., n)(13)
Summing the rows and columns yields the cause and effect param-
eters. Table C4 ofAppendix C, in the supplementary materials le, includes
the nalized total relation matrix, T. g) Calculate the threshold value
and depict the causal diagram: Calculate the threshold value from the
total relation matrix, T, and plot the cause-effect and causal relationship
diagrams to demonstrate how one barrier affects another. A threshold
value must be dened to prevent adding excessive complexity to the
digraph charting process. Greater values are predicted to have a greater
impact on adopting sustainable manufacturing processes. Typically,
threshold values are determined by adding the mean and standard de-
viation of the matrix, T. The threshold value (mean +standard devia-
tion) was found to be 0.1120.
4. Results
By employing the Grey DEMATEL method, Table 5 provides the hi-
erarchy of the signicant challenges to sustainable production practices
in the AMI. The relative weights (RWs) of the challenges are determined
to emphasize the ranking of the signicant challenges, and the sum of
the relative weights is equal to 1.0. The hierarchy of challenges based on
inuence level (R +C) is as follows: A19 >A3 >A8 >A20 >A15 >A7
>A5 >A12 >A17 >A16 >A18 >A9 >A14 >A10 >A2 >A4 >A13 >
A6 >A11 >A1.
Moreover, Table 6 provides the ranking of challenges categorized
into cause and effect groups. There are 12 challenges identied in the
cause group (R-C >0) and 5 challenges in the effect group (R-C <0).
Here, the cause group challenges are found in the following sequence:
A20 >A9 >A4 >A2 >A18 >A10 >A19 >A17 >A6 >A5 >A11 >
A15. Meanwhile, the effect group challenges follow this sequence: A8 >
A3 >A7 >A12 >A16 >A1 >A13 >A14.
Fig. 2 depicts a cause-effect diagram of the challenges. In this dia-
gram drawn on the Cartesian Plane, Ri þCi is the X-axis, and Ri-Ci is the
Y-axis. The challenges are clustered into four categories: driving chal-
lenges, independent challenges, impact challenges, and critical chal-
lenges. The driving and the critical challenges represent the cause group,
whereas the independent and the impact challenges represent the effect
group. The critical challenges are the most inuencing as this category
lies in the cause group and the highly inuential region. Conversely,
impact challenges are the most affected challenges placed in the effect
group. Therefore, decision-makers must focus rst on the critical chal-
lenges to address the unsustainability issues in the apparel
manufacturing sector.
Fig. 3 displays the cause-and-effect interrelationships between the
challenges. When dealing with 20 challenges, there can be a maximum
of 20 ×20 potential relationships. Depicting such a vast number of
Table 5
Ranking of challenges based on inuence scores.
Rank Code Barrier Name Ri +
Ci
Relative
weights
1 A19 Slow return on investment 4.439 0.067
2 A3 Lack of proper waste management
systems
4.064 0.061
3 A8 Reluctance to adopt advanced
manufacturing processes
4.049 0.061
4 A20 Lack of government support and policy 3.976 0.060
5 A15 Absence of a conducive business
environment
3.882 0.058
6 A7 Extreme competition among vendors 3.790 0.057
7 A5 Unstable market demand 3.790 0.057
8 A12 Low productivity due to production
complicacy
3.704 0.055
9 A17 Lack of monitoring and auditing of
supply chain partners
3.399 0.051
10 A16 Sunk investments in production
processes
3.377 0.051
11 A18 Lack of standardized metrics for
performance benchmarking
3.305 0.050
12 A9 Lack of sustainable product design 3.199 0.048
13 A14 Supply chain complexity 3.137 0.047
14 A10 Lack of technological knowledge and
expertise
3.107 0.047
15 A2 Lack of environmental impact
assessment (EIA) implementation
3.106 0.047
16 A4 Lack of green product awareness among
local customers
2.791 0.042
17 A13 Associated high maintenance cost of
sustainable technologies
2.629 0.039
18 A6 Lack of consumer pressure and concern 2.351 0.035
19 A11 Insufcient renewable energy
integration
2.342 0.035
20 A1 Limited availability of recycling
materials
2.313 0.035
Table 6
Categorized challenges in the cause-and-effect group.
Cause Group Effect Group
Ranking Factors Ri-Ci Ranking Factors Ri-Ci
1 A20 0.947 1 A14 −0.137
2 A9 0.813 2 A13 −0.144
3 A4 0.577 3 A1 −0.169
4 A2 0.503 4 A16 −0.425
5 A18 0.444 5 A12 −0.438
6 A10 0.282 6 A7 −0.888
7 A19 0.276 7 A3 −1.058
8 A17 0.237 8 A8 −1.211
9 A6 0.219
10 A5 0.144
11 A11 0.028
12 A15 0.001
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Results in Engineering 22 (2024) 102006
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relationships in one diagram can be challenging to grasp. Hence, this
study opted to use a threshold value of 0.1120 to highlight the most
important relationships.
5. Discussion
5.1. Interpretation of the results
According to Table 5, in the context of Bangladesh’s apparel
manufacturing industry, a slow return on investment (A19) is the most
signicant hurdle to adopting sustainable production practices. High
operational costs, technical complexities associated with green tech-
nologies, and challenges in adopting sustainable practices contribute to
this sluggish return on investment [63]. This slow protability dissuades
current investors from further involvement and deters potential in-
vestors from entering the industry. Consequently, manufacturers nd
themselves caught in a vicious cycle: they face these challenges without
the necessary nancial backing to overcome them. Furthermore, the
reduced investment means fewer resources for manufacturers to inno-
vate and implement sustainable solutions.
The lack of proper waste management systems (A3) ranks Bangla-
desh’s AMI’s second most signicant barrier. This issue is particularly
pressing given the SDGs, particularly Goal 12, which necessitates pro-
moting responsible consumption and production. Inadequate waste
management systems contribute to environmental degradation and un-
dermine sustainability efforts. The key challenges stem from the
increasing waste disposal costs and the limited availability of designated
spaces [64]. Since efcient waste management is integral to sustainable
development, the Bangladeshi apparel manufacturing industry must
revamp its approach [65]. Adopting a culture and mindset focused on
recycling could offer a practical solution. In addition, using wastewater
treatment sludge to produce sustainable construction materials offers
Fig. 2. Cause-effect group of challenges to sustainable production practices.
Fig. 3. Causal relationship diagram.
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Results in Engineering 22 (2024) 102006
8
environmental benets [66]. By doing so, the industry addresses its
waste management issues and aligns itself with global sustainability
objectives.
In the Bangladeshi apparel manufacturing industry, reluctance to
adopt advanced manufacturing processes (A8) emerges as the third most
impactful challenge. While additive manufacturing has its merits, other
techniques like Computer Numerical Control (CNC) machining, laser
cutting, and smart manufacturing using the Internet of Things (IoT) also
offer signicant eco-friendly advantages [67]. Robotics and automation
further provide efciency gains and reduce waste [68]. Despite the
potential of these diverse technologies, Bangladesh’s apparel
manufacturing sector remains cautious, which negatively impacts the
adoption of sustainable production practices.
Lack of government support and policy (A20) stands as the fourth
most signicant obstacle. Government backing is critical for adopting
sustainable practices as it can provide the necessary regulatory frame-
work, incentives, and subsidies that encourage businesses to transition
toward greener operations [69]. Without adequate policies and gov-
ernment support, AMI manufacturers face many challenges—from high
investment costs for eco-friendly technologies to the absence of clear
guidelines on waste management and energy efciency. This lack of
governmental intervention results in a policy vacuum, making it easier
for companies to overlook sustainability in favor of short-term economic
gains. Consequently, the AMI struggles to make substantial progress
toward sustainability, falling short of both national and international
ecological goals.
The absence of a conducive business environment (A15) poses a
considerable hindrance to achieving sustainability in Bangladesh’s AMI,
thus positioning fth in our prominence ranking of the barrier. A
favorable business setting is essential for driving innovation, adopting
new technologies, and facilitating the transition to greener practices
[25]. Businesses face signicant roadblocks when attempting to imple-
ment sustainable practices in an environment that lacks proper infra-
structure, streamlined regulations, and access to capital. These
conditions often discourage investments in eco-friendly technologies or
processes, as the perceived risks and challenges outweigh the potential
long-term gains. Such a non-conducive business environment not only
hampers individual companies but also undermines the collective ability
of the apparel manufacturing sector to meet sustainability goals. The
absence of a supportive ecosystem thus becomes a self-perpetuating
cycle, making it exceedingly difcult for industry to break free from
unsustainable practices and align with broader environmental
objectives.
Extreme competition among vendors (A7) poses signicant chal-
lenges to adopting sustainable production practices, ranking sixth in
prominence. The race to offer the lowest prices often pressurizes man-
ufacturers to cut corners, compromising sustainable initiatives [42].
With tight margins, many companies nd investing in advanced,
resource-efcient technologies nancially prohibitive. This is even
though sustainable investments often yield protability in the long-term
perspective rather than a short-term one.
In the AMI, unstable market demand (A5) is a signicant challenge
and ranks as the seventh barrier to adopting sustainable production
practices. The root of this instability can be traced back to uctuating
consumer preferences and global economic uncertainties. Despite the
apparel sector being Bangladesh’s primary revenue source for over three
decades, the industry has grappled with such demand uncertainties from
the export market. Moreover, the supply chain becomes even more
vulnerable due to unexpected global disruptions, such as pandemics or
geopolitical crises like wars [70]. Such unstable market demand often
hinders the continuation of implementing sustainable production prac-
tices in the AMI.
Upon close examination of Fig. 2, we see that challenges A5, A15,
A17, A19, and A20 fall into the ‘critical’ category. This implies that these
challenges are both highly inuential and highly causal. In a complex
decision-making environment where decision-makers face multiple
constraints, such as resource shortages, budget limitations, and time
constraints, these critical challenges should be addressed rst. Suc-
cessfully tackling these issues will have a signicant positive impact on
resolving other challenges consequently. Similarly, A2, A4, A6, A9, A10,
A11, and A18 fall into the ‘driving’ category. This indicates that these
challenges should be given second-level priority compared to those in
the ‘critical’ category. While these challenges remain in the cause group,
their inuence over other challenges is low. On the other hand, chal-
lenges A1, A13, and A14 lie in the ‘independent’ category, meaning that
these challenges have very little relationship with the other challenges
studied in this research. They exert minimal inuence and have little
causal impact; therefore, these challenges can be addressed indepen-
dently after tackling the top-tier challenges discussed earlier. In
contrast, challenges A3, A7, A8, A12, and A16 fall into the ‘impact’
category, signifying their high inuence but limited causal effect on
others. These challenges should be addressed as a consequence of
resolving the ‘critical’ and ‘driving’ challenges.
In Fig. 3, the relationships between the challenges are denoted by
arrows directed from one barrier to another. For example, an arrow is
drawn from A19 to A3, indicating that addressing barrier A19 positively
impacts the resolution of barrier A3. Similar relationships can be
inferred for the remaining lines. Although as many as 400 such re-
lationships might be possible, Fig. 3 was designed with a threshold value
to lter out less signicant relationships, thereby highlighting the most
important ones. For industrial decision-makers, these one-to-one re-
lationships between each pair of challenges are signicant for under-
standing various mitigation strategies or for addressing the challenges
effectively.
Table 6 lists 12 causal challenges and 8 effect challenges for sus-
tainable production practices in the AMI. Causal challenges were A20,
A9, A4, A2, A18, A10, A19, A17, A6, A5, A11, and A15. Whereas effect
challenges were A8, A3, A7, A12, A16, A1, A13, and A14.
In the AMI, the causal challenges interact intricately with the effect
challenges to form a complex web of challenges hindering the adoption
of sustainable production practices. At the root of the issues, the lack of
government support and policy (A20) presents a foundational problem.
The absence of robust guidelines or incentives leaves companies in a
quandary. Industries often default to traditional, non-sustainable prac-
tices without a clear policy direction. This governmental void indirectly
propels the supply chain complexity (A14), making it harder for com-
panies to trace sustainable raw materials, leading to their limited
availability (A1).
Furthermore, the lack of green product awareness among local cus-
tomers (A4) and the absence of environmental impact assessment (EIA)
implementation (A2) indicate a wider societal and institutional knowl-
edge gap. When consumers aren’t demanding sustainable products and
industries aren’t mandated to assess their environmental impact, there’s
little motivation for change. This lack of motivation and demand results
in a reluctance to adopt advanced manufacturing processes (A8) that
further hamper sustainability.
Technical challenges also emerge as signicant causal challenges.
The lack of technological knowledge and expertise (A10) in the industry
paves the way for low productivity due to production complicacy (A12).
When the industry doesn’t fully understand or embrace sustainable
technologies, it’s bound to face inefciencies and complications.
Economic challenges cannot be overlooked. The slow return on in-
vestment (A19) for sustainable practices and the absence of a conducive
business environment (A15) make companies wary. This economic
hesitation often results in sunk investments in production processes
(A16) and is exacerbated by the associated high maintenance costs of
sustainable technologies (A13).
Additionally, internal industry practices like the lack of monitoring
and auditing of supply chain partners (A17) and the absence of stan-
dardized metrics for performance benchmarking (A18) contribute to the
extreme competition among vendors (A7). This extreme competition, in
turn, intensies the supply chain complexity (A14), making
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Results in Engineering 22 (2024) 102006
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sustainability even harder to achieve.
Understanding these nuanced interactions between causal and effect
challenges can signicantly assist managers in prioritizing in-
terventions. For instance, addressing causal challenges like government
support or investor pressure could have a domino effect, making tack-
ling waste management or sunk costs in technology easier. This multi-
faceted understanding can be instrumental in implementing sustain-
able manufacturing techniques in the apparel manufacturing industry.
This research presents a holistic map of the challenges to sustain-
ability in Bangladesh’s AMI, offering a granularity and interconnected
analysis that sets it apart from other existing studies. For instance,
Vishwakarma et al. [71] identied communication gaps among stake-
holders, the absence of training regarding sustainability, capacity limi-
tations, and the absence of reverse logistics approaches as the most
important challenges to sustainable production in the AMI. In compar-
ison, the ndings of this research appear more substantial. They go
beyond merely identifying organizational-level challenges to highlight
systemic issues, such as the lack of government support and policy,
which exert a more signicant inuence over other obstacles. Moreover,
this study extends its scope to include the role of investor pressure,
thereby addressing external stakeholders who can drive the changes.
The ndings of this study are pretty unique for several reasons. First,
whereas the existing research focuses primarily on operational and
process-oriented challenges such as collecting, sorting, and recycling,
our study broadens the scope to include systemic issues that extend
beyond the immediate operations of the apparel industry. Specically,
we highlight the slow return on investment and governmental support as
overarching issues exerting a profound inuence on the entire industry.
Second, our research pinpoints the lack of proper waste management
systems and the reluctance to adopt advanced manufacturing processes
as critical factors for the long-term sustainability and competitiveness of
the textile sector. By addressing these systemic and advanced
production-related issues, our study provides a more holistic view of the
challenges to sustainability, offering potentially more impactful insights
for driving substantial change in the industry.
5.2. Sensitivity analysis
A sensitivity analysis was carried out in this study to check the
robustness of the obtained results. To achieve this goal, several scenarios
were conducted to check whether the results were consistent across all
scenarios. Similar results across different scenarios indicate that the
ndings are robust to varying conditions, making the results more
reliable for replication and application in real-world scenarios. One
expert’s input was given distinct weighting in the scenario process,
while the other experts’ inputs were assigned equal weight. It can be
accomplished in various ways, such as adjusting the weightings assigned
to experts or different challenges. This study employed traditional
sensitivity assessment by allocating distinct weightings to decision ex-
perts. For instance, in the rst scenario (case 1), Expert 1 was given a
weight of 0.4, while the rest were assigned a weight of 0.2. For the
second scenario (case 2), Expert 2 was given a weight of 0.4, and the rest
were assigned a weight of 0.2. This approach was similarly applied
across 5 cases (see Table C5 ofAppendix C, in the supplementary ma-
terials le).
The pattern of the results obtained from these 5 cases can be seen in
Table C6 ofAppendix C, in the supplementary materials le. It is evident
from the table that there was no signicant change in the rankings of the
challenges, even after varying the decision experts’ weightage in the
sensitivity analysis. Each expert’s ranking of the cause-effect challenges
remained consistent, allowing for only minor variations in rank order.
This indicates that the results obtained in our study are quite stable and
robust.
5.3. Implications
5.3.1. Theoretical implications
This study signicantly enriches existing literature on sustainable
production in the AMI, particularly in developing economies like
Bangladesh. Using the Grey-based DEMATEL methodology, we unearth
a complex interplay between causal and effect challenges to sustainable
production. The ndings extend theoretical frameworks in several di-
mensions. For instance, the preeminence of economic considerations,
such as the slow return on investment, highlights the pivotal role that
nancial imperatives play in industry decision-making. Despite the
broader global emphasis on sustainability, the immediate economic
motivations often overshadow the long-term environmental and social
benets. This suggests a critical need for re-evaluating how sustain-
ability’s value proposition is presented to industries.
Operational challenges, like the “absence of proper waste manage-
ment systems” and “reluctance to embrace advanced manufacturing
processes”, emerged as signicant impediments. Such results support
the theory that industries, even when aware of the benets of change,
often resist it due to the inertia of established systems and practices. The
prominence of challenges related to the lack of government support and
policy underlines the inextricable link between governmental action and
industry practices. Theoretically, this reinforces the idea that govern-
ments can serve as either catalysts or hindrances to shifts toward sus-
tainable practices in major industries. Market dynamics, such as extreme
competition among vendors and unstable market demand, bring to the
forefront the challenges posed by consumer behavior and market vola-
tility. It indicates that industries might be deterred from sustainable
investments even with the best intentions if they don’t perceive an im-
mediate competitive advantage or foresee market instability.
Further down the list, technical challenges, including the lack of
technological knowledge and the absence of environmental impact as-
sessments, while essential, seem to be secondary concerns for the in-
dustry. It suggests that the AMI perceives these as challenges that can be
addressed once immediate economic and operational concerns are
managed. The study aligns with systems theory by elucidating the causal
relationships among these challenges, stressing that challenges are
interconnected rather than isolated. Therefore, this research is a
cornerstone for future theoretical and empirical investigations to
develop effective strategies for overcoming challenges to sustainable
apparel manufacturing.
5.3.2. Managerial implications
The ndings of this study offer pivotal insights for managers in the
AMI, particularly in emerging economies. A primary concern is the lack
of supportive governmental policies. Managers should advocate for
favorable regulations; public-private partnerships could be vital to sus-
tainable production. Additionally, investment in employee training and
development is crucial to lling the existing technical knowledge gap
and developing sustainable product designs. Consumer awareness is
another signicant barrier to sustainable manufacturing. Brands should
engage in educational campaigns to enlighten consumers about the
environmental implications of their choices, thereby inuencing
behavior and enhancing brand reputation. Environmental Impact As-
sessments (EIA) must be incorporated into all project planning phases to
ensure eco-friendly production practices are used. In the absence of
industry-wide sustainability standards, it is imperative for companies to
create internal benchmarks. It will help set realistic sustainability goals
and offer a clear roadmap for measuring progress. Understanding the
causal relationships between the identied challenges, as revealed by
the Grey-based DEMATEL analysis, can help prioritize resources ef-
ciently. Resolving key issues in the ‘cause group’ can cascade down to
reduce the ‘effect group’ challenges. Beyond the apparel manufacturing
sector, these insights hold value for managers in other industries
struggling with similar sustainability roadblocks. Collaborative efforts
across sectors could amplify the efcacy of best practices. Managers
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Results in Engineering 22 (2024) 102006
10
should also seek to attract investors committed to sustainability by
generating comprehensive sustainability reports. Particular attention
should be paid to waste management and advanced manufacturing, as
addressing these challenges can bring substantial sustainability im-
provements. These strategies can serve as a foundation for overcoming
sustainability challenges and bolstering long-term protability and
environmental responsibility.
5.3.3. Implications for achieving SDGs
This study directly contributes to the SDGs, specically SDG 9, which
centers on developing robust infrastructure, encouraging inclusive and
sustainable industrial growth, and promoting innovation [72]. By
identifying and prioritizing the challenges to sustainable production in
Bangladesh’s AMI, the study sets a roadmap for overcoming challenges
that hinder sustainable industrialization. Furthermore, this research
touches on SDG 12, which aims for responsible consumption and pro-
duction [72]. It addresses challenges like waste management, environ-
mental impact assessments, and the need for standardized sustainability
metrics, thereby providing actionable insights to improve resource ef-
ciency and reduce waste.
The study also has implications for SDG 8: Decent Work and Eco-
nomic Growth [72]. If not addressed, challenges such as low produc-
tivity due to production complicacy and extreme competition among
vendors could lead to unfair labor practices and poor working condi-
tions. The study helps guide policy and decision-making towards more
decent work conditions and sustained economic growth by pinpointing
these challenges and suggesting countermeasures. Finally, the focus on
investor pressure and government policies relates to SDG 17, which
advocates for partnerships between governments, the private sector, and
civil society. Engaging investors and the government in sustainable
production strategies can create a multi-stakeholder approach that
propels the apparel manufacturing sector closer to meeting SDGs.
6. Conclusion
The transition to sustainable production practices is not merely an
option but an imperative, especially for industries in emerging econo-
mies undergoing rapid industrialization. Bangladesh’s apparel
manufacturing sector is a quintessential example, representing a
cornerstone of the country’s economy and a signicant contributor to
environmental degradation. As global awareness rises around sustain-
ability and the enforcement of international environmental regulations
tightens, the AMI nds itself at a crossroads. The urgency to shift toward
sustainable practices has never been more pronounced, and failure to
adapt not only risks global market share but also places an untenable
burden on the country’s ecological balance.
This study is set against this backdrop of urgent necessity and
operational complexity. Utilizing the Grey theory-based DEMATEL
methodological approach, the research offers a sophisticated framework
for comprehending the multi-faceted obstacles to adopting sustainable
production practices in an uncertain environment and with limited data
availability. This methodology provides a robust platform for analyzing
the nuanced interrelationships between various challenges that stem
from investor behavior, policy gaps, or technological constraints. The
study highlighted the “slow return on investment” and “lack of proper
waste management systems” as the most signicantly correlated chal-
lenges. In addition, challenges like “lack of government support and
policy”, “lack of sustainable product design”, and “lack of green product
awareness among local customers” were identied as the most causal
challenges that drive other challenges to adopt sustainable production
practices.
This research offers practical implications that are instrumental for
managers and planners involved in the AMI, offering a hierarchical
understanding of challenges to sustainable production. By identifying
specic areas for resource allocation and effort, such as enhancing
investor engagement and improving waste management systems, this
research suggests that addressing these focal points could trigger
widespread enhancements in sustainable practices throughout the
industry.
In direct alignment with our research objectives, this study has not
only identied and ranked the major challenges to sustainable produc-
tion within the AMI but also dissected the intricate relationships and
dependencies among these challenges through the innovative use of the
Grey DEMATEL method. The outcomes directly address RO1 by pin-
pointing specic hurdles based on literature and expert insights. RO2
has been fullled by mapping out the complex web of interdependencies
among these challenges. The study achieved RO3 by proposing targeted
strategies and recommendations for industrial decision-makers, policy-
makers, and stakeholders. These strategies are designed to tackle the
root causes of resistance to sustainable practices, offering a clear
pathway towards achieving sustainable development goals for a more
sustainable and environmentally responsible future.
While the Grey-based DEMATEL methodology proved effective, it
relies heavily on expert opinions, underscoring the need for cautious
interpretation. Additionally, the study is limited by examining only
twenty challenges, despite the intricate reality of sustainable production
likely involving a greater number of inuencing factors. The study sets
the stage for extending its insights into other sectors, such as footwear,
plastic, and food processing industries, which also bear signicant
environmental impacts. Future research could also benet from
employing additional multi-criteria decision-making techniques, such as
triangular fuzzy DEMATEL, intuitionistic fuzzy DEMATEL, Q-rung
ortho-pair fuzzy theory-based DEMATEL, to conduct a comparative
analysis to evaluate how these different numerical approaches affect the
outcomes’ precision.
CRediT authorship contribution statement
Binoy Debnath: Writing – original draft, Software, Methodology,
Investigation, Formal analysis, Data curation, Conceptualization.
Muntaha Rauf Taha: Writing – original draft, Investigation, Formal
analysis, Conceptualization. Md. Tanvir Siraj: Writing – original draft,
Methodology, Formal analysis, Conceptualization. Md. Fahmid Jahin:
Writing – original draft, Formal analysis. Sazzadul Islam Ovi: Writing –
original draft, Formal analysis, Data curation. A.B.M. Mainul Bari:
Writing – review & editing, Visualization, Validation, Supervision,
Conceptualization. Abu Reza Md. Towqul Islam: Writing – review &
editing, Validation. Asif Raihan: Writing – review & editing, Validation.
Declaration of competing interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Data availability
All data used in this research are provided either in the manuscript or
in the supplementary materials le
Acknowledgments
The authors would like to thank the experts who took their valuable
time to provide feedback for this study.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.rineng.2024.102006.
B. Debnath et al.

Results in Engineering 22 (2024) 102006
11
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