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DRIVERS OF INDUSTRY 4.0-ENABLED SMART WASTE MANAGEMENT IN SUPPLY CHAIN
OPERATIONS: A CIRCULAR ECONOMY PERSPECTIVE IN CHINA
Citation: Zhang, A., Venkatesh, V. G., Wang, J. X., Venkatesh, M., Wan, M. & Qu, T. (Forthcoming,
Accepted in November 2019). Drivers of Industry 4.0-enabled smart waste management in supply chain
operations: A circular economy perspective in China. Production Planning & Control.
Abraham Zhanga, b
a Auckland University of Technology (AUT) Business School, AUT University, Private Bag
92006, Auckland 1142, New Zealand (E-mail: abraham.zhang@aut.ac.nz)
b Lumen Research Institute, Excelsia College and Indiana Wesleyan University, 69-71
Waterloo Road, Macquarie Park, NSW 2113 Australia
V G Venkateshc,
c Ecole de Management de Normandie, 30 Rue de Richelieu, 76087 Le Havre, France.
(E-mail: vgv1976@gmail.com)
Jason X. Wangd
d Huddersfield Business School, University of Huddersfield, Queensgate, Huddersfield, HD1
3DH, the United Kingdom (E-mail: J.Wang2@hud.ac.uk)
Mani Venkateshe
e Montpellier Business School, Montpellier, France. (E-mail: m.venkatesh@montpellier-
bs.com)
Ming Wanf, g
f School of Management, Jinan University, 510632, Guangzhou, PR China
g Institute of Physical Internet, Jinan University (Zhuhai Campus), 519070, Zhuhai, PR
China. (E-mail: wanming@stu2018.jnu.edu.cn)
Ting Quh,g*
h School of Intelligent Systems Science and Engineering, Jinan University (Zhuhai Campus),
519070, Zhuhai, PR China
g Institute of Physical Internet, Jinan University (Zhuhai Campus), 519070, Zhuhai, PR
China (Corresponding author: quting@jnu.edu.cn)
Abstract
Increasingly, circular economy (CE) has been adopted globally to operationalise
supply chain sustainability. The development of industry 4.0 technologies provides a new
opportunity to improve the effectiveness and efficiency of adoption of CE, in particular, from
the waste management perspective. More recently, scholars acknowledge the need for more
studies on industry 4.0 and CE-driven sustainability aspects in supply chains. This research
aims to fill the literature void and make a contribution from the perspective of smart waste
management in supply chains using industry 4.0-based CE operations. Eleven key drivers
were identified through semi-structured interviews, administered to experienced supply
chain practitioners in China. A fuzzy DEMATEL method was used to analyse the
interrelationships among these key drivers. The results show that the most fundamental
causal drivers of smart waste management are overcoming operational challenges, recovering
value, speeding up operations, saving cost and improving profit. There is a virtuous cycle
between market demand and the improving price-performance ratio of industry 4.0
technologies. Our findings are part of the development of a bottom-up approach to adopting
smart waste management in supply chains. The interrelationships identified in this research
provide valuable insights into driving forces. Organisations, policy makers and technology
providers can apply these insights when utilising industry 4.0 technologies to improve supply
chain waste management in line with the CE principle, and to achieve supply chain
sustainability.
Keywords: Circular economy; Industry 4.0; Internet-of-Things; Smart waste management;
Supply chain sustainability; Sustainable waste management
Article Classification: Research article
1. Introduction
Circular Economy (CE) proposes a paradigm shift and a new vision for firms for
operationalizing supply chain sustainability (Farooque et al., 2019a). CE signifies a circular
material flow in the economy (Su et al., 2013). From a waste management perspective, CE
employs circular thinking about how to regenerate biological materials and to increase the
reuse, remanufacturing and recycling of technical materials through innovative product
design and waste management, thereby producing zero waste (de Sousa Jabbour et al., 2018;
Farooque et al., 2019a; Veleva et al., 2017). The rapid development of industry 4.0 concepts
and technologies equips firms to adapt, moving from the present linear supply chain
operations to the circular model (Batista et al., 2018b; Fatorachian & Kazemi, 2018), enabling
‘smart waste management’ (Chowdhury & Chowdhury, 2007). The interplay between industry
4.0 and CE offers a promising way to achieve supply chain sustainability (Mangla et al.,
2018b). This paper adopts the smart waste management perspective to address industry 4.0-
enabled CE operations in supply chains.
The CE principle is to change the linear production pattern in the traditional business
model (i.e., take, make, use, and dispose) to a circular system, wherein the resources circulate
in the supply chain up- and downstream. This flow is facilitated by innovative logistics and
supply chain ecosystems (Batista et al., 2018a). From the viewpoint of environmental and
social sustainability, CE is desirable for business firms and society. From an environmental
perspective, eco-design and waste management address concerns in the early stage of
product development and reduce the negative impact of final products. Also, from a social
perspective, CE contributes towards reducing poverty and improving living conditions
through positive changes to ecological systems, for example, reduction in the risk of shortages
of natural resources (Mangla et al., 2018a).
The trade-off between environmental/social and economic performance has been long
debated in the supply chain sustainability literature (Tang, 2018). CE seems to offer a
solution to the synergy issue (Genovese et al., 2017). The restorative and regenerative nature
of CE solves the problem of rising procurement costs or shortage of raw
materials/components and extends the product life cycle, thus creating economic value
(Mangla et al., 2018a). Within the present business models, supply chain sustainability is
largely driven by economic motives (Masi et al., 2018). Because of all these advantages of the
CE paradigm, it has become a driving force of sustainability (Hobson, 2016).
CE has been adopted by more and more economies in recent years (Farooque et al.,
2019b). For example, the European Commission embraced it quickly and has continuously
evaluated the relevant policies to maximize environmental and economic value (Govindan &
Hasanagic, 2018). Japan and USA applied CE as a practical tool in the design of
environmental and waste management policies (Ghisellini et al., 2016). CE is of paramount
importance in China. As an emerging economy, it relies on energy-intensive and heavy
manufacturing industries for its rapid economic growth (Govindan & Hasanagic, 2018).
Therefore, the diminution of reserves of energy and natural resources is a real threat to that
economic growth and to sustainability. Additionally, increasing pressure from the global
community to implement sustainable operations is countered by different barriers in the
arena of international competition. It is a challenge for Chinese firms which have not
considered environmental factors such as waste management (Su et al., 2013). These looming
challenges pushed China to embrace CE as part of its national development strategy. The
Circular Economy Promotion Law was passed in 2009, making China one of the three
countries that has legislated CE-related policies (Su et al., 2013), introducing CE through a
top-down approach as a new development model to usher in a more sustainable economic
structure (Yong, 2007).
Waste management is a core area in CE (Su et al., 2013). The circularity which CE is
named for largely relies on effective and efficient waste management to create a closed-loop
flow of materials in the economic system. CE takes the form of a closed material loop in both
technical (e.g., restorative) and biological (e.g., regeneration) cycles (Batista et al., 2018b). In
the technical cycle, waste management means transformation and recycling of waste back
into production systems. In the biological cycle, waste management means utilization of the
waste resources of other companies in the economic system. Thus, the understanding of
waste management is critical in the implementation of CE practices.
Moreover, there are relatively explicit economic values (e.g., cost reduction) and
environmental benefits (e.g., conservation of scarce resources) through appropriate waste
management (Wen et al., 2016). The synergy of economic and environmental bottom lines in
waste management provides firms, especially for-profit ones, with incentives and resources
to implement the practices in the full range of CE areas (e.g., consumption and production).
Therefore, the studies on waste management are vital in CE in order to develop ‘a balanced
interplay of environmental and economic systems’ (Ghisellini et al., 2016).
To be in line with the CE principle, supply chain operations needs to have waste
management integrated into them. In modern business, firms operate and compete all across
the supply chain (Tate et al., 2012). The production and usage of materials – and, thus, waste
generation – are carried out in a highly interdependent manner between supply chain
partners. Waste management should be operationalised at the supply chain level to create a
true circular system. Moreover, supply chain waste management provides an opportunity to
maximise the retained values of waste. Supply chain partners are more likely to share critical
knowledge and resources in operations and, therefore, are more likely to explore and learn
about opportunities of waste regeneration from one another (Govindan & Hasanagic, 2018).
For example, while some by-products have little value for the firms that generate them, they
can be regenerated by partner firms that work in the same supply chains to create value.
The requirement of CE (circular operations in the supply chain waste management)
goes beyond the capacity of the linear model in the traditional supply chain management
(Mangla et al., 2018a). In recent years, researchers and practitioners have tried to apply
industry 4.0 technologies to overcome the challenges in supply chain waste management.
Industry 4.0 provides a conceptual model to facilitate the application of what is termed ‘smart
waste management’. Industry 4.0 is defined as ‘smart manufacturing’, which is an
information technology (IT) driven manufacturing system (de Sousa Jabbour et al., 2018).
Internet of Things (IoT) (e.g., Radio-Frequency Identification [RFID]), cloud manufacturing,
cyber-physical systems, and additive manufacturing (such as 3D printing) are the core
technologies of industry 4.0 (Kang et al., 2016), providing managers with real-time
information on production, machines, and the flow of components/materials in order to
optimise supply chain operations. Industry 4.0 technologies have been increasingly used by
firms to improve operations. For example, Wal-Mart coerced its suppliers to integrate RFID
systems in order to improve the efficiency and effectiveness of Wal-Mart’s supply chain
operations (Deitz et al., 2009). In the service industry, vehicle recovery service providers use
a cloud platform to collect and share real-time operational data (on, for example, loading time)
with the downstream supply chain partners so as to optimise towing service schedules and
resources (Duong et al., 2017).
Smart waste management is the practice of using innovative waste management
systems supported by industry 4.0 technologies. The real-time data collected and shared
through the applications of IoT automates the recognition and categorisation of waste at
different supply chain stages, and thus makes waste management activities more intelligent,
effective and efficient. For example, Bin-e devices have been implemented in municipal waste
management (Bodamer, 2017). The data collected by the sensors in rubbish bins are sent to
remote servers, stored, processed and used to monitor the current waste level; to make
intelligent decisions for collection routes, times, and container size; and, most importantly,
optimise the overall waste management process. Hence, industry 4.0 is likely, in line with
CE, to eliminate the technological barriers to waste management in supply chains. It is
noteworthy that many firms have already been applying industry 4.0 technologies in
manufacturing and logistics operations (Kang et al., 2016).
There is a lack of research from the smart waste management perspective on how
business firms can utilise industry 4.0 concepts to do better at adopting CE and, thus, supply
chain sustainability. The adoption of general CE practices has been widely studied as a new
approach to developing sustainable supply chains. Su et al. (2013) reviewed the long-term
development of CE concepts, practices and assessment in China and found that CE provides
a way to ease the tension between economic development and environmental concerns at the
macro level; yet there are substantial challenges. Ghisellini et al. (2016) explored CE
applications in a broad range of cultural contexts (e.g., China, U.S., and Japan) and at
multiple levels (macro-, meso-, and micro-) and confirmed the promising benefits of CE to
overall society; yet the implementation is still at an early stage. Particularly, the authors
found a significantly positive effect on waste management (e.g., improved waste recycle rates)
when the CE concepts were integrated. The concepts of industry 4.0 have been rapidly
developed and are found to substantially improve firms’ operations (Fatorachian & Kazemi,
2018; Lee et al., 2015). However, the use of industry 4.0 technologies in improving the
effectiveness and efficiency of CE adoptions is still a novel research field.
de Sousa Jabbour et al. (2018) provided a pioneer five-step roadmap connecting
industry 4.0 technologies to CE applications for supply chains. Mangla et al. (2018b) called
for research on the different aspects in the integration of industry 4.0 and CE in developing
sustainable supply chains. In particular, the authors emphasised the importance of studies
which explore drivers and barriers of such integration as major research themes, and provide
practical answers on how business firms address industry 4.0 and CE-driven supply chain
sustainability. In this study, we answered their call. Specifically, we focused on the interplay
between industry 4.0 and CE from the perspective of waste management in supply chains, or
‘smart waste management in supply chains’.
Smart waste management can be adopted at different levels in society (Ghisellini et
al., 2016). The study of it includes add-on attributes that can be incorporated into an existing
process, rather than a heavy change-over, thus providing a transition to other areas of CE
(e.g., production and consumption). In the review of literature, we found that the previous
research on smart waste management has remained at the macro- or meso –level. Scholars
have mainly focused on smart waste management in municipalities (Binder, Quirici,
Domnitcheva, & Stäubli, 2008; Glouche & Couderc, 2013; Omar et al., 2016; Binder et al.,
2008; Catania & Ventura, 2014; Glouche & Couderc, 2013; Omar et al., 2016). Because of
China’s leading role in the adoption of CE at the national level, urban smart waste
management has been widely researched in Chinese cities (Anagnostopoulos et al., 2017; Chi
et al., 2011; Park et al., 2010). Other studies took a mainly technical perspective on smart
waste management (Chowdhury & Chowdhury, 2007; Shyam et al., 2017). To the best of our
knowledge, there is a lack of research from the operations management perspective on how
smart waste management can be adopted at the corporate level and in supply chains. The
previous studies provide a top-down approach to the implementation of smart waste
management, which is more likely to be in line with the broad and macroeconomic concept
of CE. Nonetheless, business firms may find it difficult to determine their individual role and
develop daily operations within the broad principle of CE. Especially, because operations
among supply chain partners are so highly interdependent, the supply chain scope should
be covered for waste generation, which can create a truly circular model of smart waste
management and contribute to true sustainable development. Thus, in this study, we covered
the research gap by exploring the bottom-up approach to the implementation of smart waste
management in supply chains, as a transition to industry-4.0 enabled CE. We opted to focus
on Chinese firms. The long-term development of CE concepts and practices in China provides
a big enough object for a valid and reliable study of the integration of industry 4.0 in waste
management. Also, our study based on Chinese firms is likely to provide practical and
confirmatory results for other emerging markets (e.g., India) that follow a similar CE pattern.
Specifically, we address the following two research objectives.
• To identify the key drivers of industry 4.0 in the supply chain operations of
smart waste management for a transition to a circular economy
• To understand the cause-effect relationships between the key drivers of smart
waste management
The contribution of this research lies in the novelty of exploring the drivers of smart
waste management at the supply chain level, thus complementing the literature from the
waste management perspective, deepening understanding for business firms about
operationalising “industry 4.0 and CE driven sustainability aspects in the supply chains”
(Mangla et al., 2018b). The identified drivers, and particularly cause-effect relationships,
provide a clear road map to successful adoption of smart waste management in line with CE
principles. Moreover, this study focuses on Chinese firms. The Chinese government has
ambitiously embraced CE as part of its national development strategy and is a leading
country among emerging economies using the CE principle to develop sustainably (Su et al.,
2013). While our study provides timely guidance for managers in China, the findings in this
research are likely to be applicable to other emerging markets (e.g., India), owing to the
comparable CE context among emerging economies (e.g., government regulations and top
management commitment to CE) (Yadav et al., 2019).
The rest of this paper is organized as follows. In the following section, we briefly cover the
background literature. Methodology and data collection procedures are explained in Section
3. Section 4 presents the results, analysis, and findings. Section 5 discusses the managerial
and policy implications. Section 6 concludes the research.
2. Background Literature
In the first step of understanding the background literature, we adopted the
systematic literature review (SLR) procedure suggested by Tranfield et al. (2003). Articles with
the combination of keywords smart waste management, technology in waste management,
circular economy waste management, waste technologies, smart technologies in waste
management, waste recycling, Internet of Things (IoT), waste collection and handling, and
waste monitoring systems were retrieved from databases such as Scopus, EBSCO and JSTOR.
We reviewed the collected literature using the forward and backward snowball technique to
finalise a list of more relevant articles to our work (Yadav and Desai, 2016; Yadhav et al.
2018). Furthermore, this review is structured in three sub-sections.
2.1 Waste Management and Circular Economy
Traditionally, waste is generated from household, commercial and institutional
processes, and effective management of it is a challenge in densely-populated cities (Sadaf et
al., 2016; Kumar et al., 2017). Also, a huge amount of value has been lost, and waste
generation poses a serious challenge to sustainability. In the CE literature, managing this
waste is essential to maintaining the circularity of energy and resources and providing
environmental and economic benefits (and the ensuing resource efficiency benefits).
Businesses see it as a mechanism to gain competitive advantage through integrating systems
and cultivating partnerships with other stakeholders (Geng et al., 2009).
In addressing the challenge of waste treatment, the CE philosophy is to think
innovatively about waste management, wherein waste is considered as a resource (Veleva et
al., 2017). Also, only 30 percent of materials are used for recovery at the global level, of which
11 percent goes to material recovery and 19 percent to energy recovery (Singh and Ordonez,
2016). CE mimics the natural ecosystem by transforming the so-called waste into valuable
feedstock through biological decomposition (such as reuse) and technical restoration (such
as remanufacturing, repairing and recycling) (Genovese et al., 2017; The Ellen MacArthur
Foundation, 2013). At present, waste-to-energy techniques are classified into four broad
types: physical, thermal, chemical and biological. Among them, landfill is still a dominant
physical waste disposal pattern worldwide (Ghisellini et al., 2016). The other prominent
techniques are gasification (thermal), combustion (chemical), co-digestion, anaerobic
digestion and fermentation (biological) (Pan et al., 2015). All these processes facilitate CE to
directly or indirectly address the problem of energy demand and greenhouse gas (GHG)
emissions.
Moreover, when waste is generated faster than traditional technology can deal with it,
the negative environmental impact creates an obstacle for the long-term development of
human society (Su et al., 2013). It is to address this that CE envisions always restoring value
from used resources (that is, waste) and creating zero waste. Waste management in line with
CE philosophy requires continuous exploration of the opportunities to decrease waste
generation while increasing the rate of waste reclamation. Firms also embark on waste
management strategy through specific initiatives such as Circular Economy 100 (The Ellen
MacArthur Foundation, 2013).
In theory, waste management under the guidance of the CE principle offers promising
environmental and economic benefits. However, in practice, waste management is always
challenging, especially at the supply chain level, where it requires a considerable
transformation of waste treatment in terms of the flow of procurement, production, logistics
and consumption processes. Supply chain operations need to be extended to utilise by-
products and waste. At the same time, they need to be cost-efficient and socially acceptable.
Many factors, such as political governance, government regulations, taxes and support
incentives also drive waste management strategy (Malinauskaite et al., 2017). To fulfil the CE
vision of waste management, all the supply chain stages should be integrated, including
product design, manufacturing procedures and restoration (Jensen & Remmen, 2017). Yet
supply chain practices that take place beyond firm boundaries are extremely complex and
difficult (Giunipero, Hooker, & Denslow, 2012). Firms do not always have the full information
on products throughout their life cycle due to the multiplicity of production stages in supply
chains, and technological challenges are a major barrier to integrating the information and
managing the restoration of waste (Govindan & Hasanagic, 2018).
Moreover, waste management based on the CE principle at the supply chain level
demands substantial financial investment in internal processes and coordination of supply
chain partners, which discourages many firms from adopting the most effective waste
management practices of a CE system (Sousa Jabbour et al., 2018). Furthermore, scholars
have found, consumers have not always been fully aware of or had high regard for the
restorative value of product waste (Govindan & Hasanagic, 2018; Hazen, Mollenkopf, & Wang,
2017). At the last stage of the traditional product life cycle, when consumers do not accept
products remanufactured from waste, it decreases the potential value of waste management
at the supply chain level. Additionally, Hazen et al. (2017) discussed how consumers’
attitudes are an important factor of environmental and economic benefits from CE (Gaur et
al., 2019). The transparent information flow facilitated by smart waste management is likely
to change their attitudes toward using remanufactured products and their willingness to
participate in waste management.
2.2 Smart Technologies in Industry 4.0 Realm
In the current environment, the challenge is to uphold the principles of sustainability
along with the flexibility of supply chain operations. Industry 4.0 allows the systems to
integrate a cyber-physical network of machines, sensors and facilities to streamline data
management (Luthra et al., 2018a). Such a network involves technologies such as intelligent
production, human-computer interaction, remote operations and data networks. These all
help in real-time monitoring of waste management performance in terms of energy
consumption and other operational parameters (Esmaeilian et al., 2018).
Table 1. Recent developments in smart waste management
Author (s) & year
Major contribution in smart management
Anganostopoulous et al.
(2017)
Dynamic waste management model using sensors, RFID, and
actuators
Saha et al. (2017)
Integrated web-based solution called smartbox, which
optimises waste collection
Lu et al. (2017)
New bin scheduling algorithm using multi-restricted and
multi-compartmental routing problem
Ramya et al. (2017)
Smart bin solutions
Aazam et al. (2016)
Cloud-based smart waste management monitoring system for
all stakeholders
Ramasami et al. (2016)
Location decision algorithm to select suitable land for the
landfill construction
Thakker et al. (2015)
Container screening system using near-infrared spectroscopy
(NIR) to alert about the problems of dumps that are not
cleaned on time.
Folionto et al. (2015)
Intelligent monitoring system
Wahab et al. (2014)
Smart system of trash recycling
From the technological perspective, the CE agenda is to move from the old-fashioned
disposal procedures to the intelligent waste treatment technologies, which mainly involves
linking physical waste with digital information (Glouche and Coudrec, 2013). Towards that
purpose, industry 4.0 smart technologies have been increasingly used by firms to improve
supply chain and operational performance along with the waste treatment process. The
technologies are classified into four main groups: spatial (e.g. GIS), identification (e.g. Radio
Frequency Identification and barcodes), data acquisition (e.g. sensors, imaging), and data
communicating technologies (e.g. Wi-Fi) (Esmaeilian et al., 2018). These technologies act as
intelligent control units to customise service by integrating the concept of Internet of Things
(IoT) (Hong et al., 2014) and are discussed in detail by Pardini et al. (2019) (Table 1 provides
a snapshot of smart waste management technologies). These innovations have led to the
proposal of various frameworks within the realm of smart waste management. For example,
Catania and Ventura (2014) provide a roadmap for applications of smart technologies in waste
management, and Aazam et al. (2016) proposes a cloud-based arrangement. All these
developments aim to reduce the waste management costs and make the process more
transparent, starting from improving the quality of selective sorting of items to recycling them
(Chowdhury and Chowdhury, 2007; Glouche and Coudrec, 2013; Pardini et al., 2019).
Along similar lines, the recent development of smart cities forces city administrators
to have a ubiquitous waste management architecture with real-time information across
various nodes. As a result, innovations like new sensor-based technologies and data
analytics, along with social networking interactions, are integrated to effectively manage
waste. They are mainly used for various applications within the waste treatment domain,
such as waste recognition, collection and route optimisation , reduction of fuel costs and
tracking of the performance of the garbage collectors and workers and of stolen or damaged
containers (Glouche and Coudrec, 2013; Catania and Ventuura, 2014; Kansara et al., 2019).
Recent works propose an IoT framework integrating the Geographical Information System
(GIS) to monitor waste bins daily through a new, optimised algorithm (Shyam et al., 2017).
In addition, they apply add-on technologies and know-how that can be relatively easily
incorporated into existing supply chain operations without investing in extensive changes
(Malinauskaite et al., 2017). Thus, the financial investment required for smart waste
management is reduced when the technologies to be invested in are similar to those already
in use. In a similar context, it is interesting to investigate other managerial factors that impact
the deployment of smart technologies.
2.3 Waste Management in China
China is now experiencing rapid urbanisation and industrialisation, which leads to
challenges in managing both household and industrial waste (Gu et al., 2015). According to
the World Bank estimate, the total amount of waste in China will be over 480 million tons in
2030 (Chen et al., 2014). In order to address this challenge, the Chinese government has
been promoting CE initiatives through legislation since 2009. It is a growing concern for
China, as it produces 30 percent of the world’s solid waste (Gu et al., 2017). There is a special
focus on recycling, treatment technologies and infrastructure in China’s current 13th five-
year plan, which was released in 2016. As a result, China has proposed high-level CE
frameworks; however, their enforcement may vary due to regional practices (Ranta et al.,
2018). The government has commenced CE initiatives in 27 provinces, coupled with smart
technologies in key sectors such as metallurgy, textiles, transportation and pharmaceuticals
(Li and Lin, 2016). The government also shows interest in developing eco-cities and industrial
parks (Li qiang, 2019). In spite of the authorities steering sustained efforts and multiple
initiatives to implement CE practices assisted by smart tools, the progress so far has been
modest, and it is important to understand the factors that drive the adoption of technology
in the current CE realm.
The above-structured review summarises the importance of smart technologies in the
current CE environment. While studies such as that of Ranta et al. (2018) qualitatively
explore the institutional CE drivers in the Chinese context, they point to and leave a scope
for understanding those drivers and barriers – specifically, in regard to smart waste
management applications in the Chinese CE context. Such understanding of drivers and
barriers would accelerate CE implementation and inform the design of policy for further
improvement. The present study adopts a mixed-methods approach to analyse, in two stages,
the drivers and their interrelationships. A qualitative method was used in the first stage to
identify key drivers: interviewing practitioners who were experienced and knowledgeable
about the supply chain operations of smart waste management in China. We elaborate on
various drivers of industry 4.0-enabled smart waste management in Section 4.1. At the
second stage, fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) was
applied to examine the cause-effect relationships among the identified drivers. The DEMATEL
technique is a rigorous tool for disentangling the interrelationships between factors (Wu &
Lee, 2007).
3. Methodology and Data Collection
3.1 A Mixed-methods Approach
A combination of qualitative and quantitative methods has recently been advocated
for investigating business-related issues (Gölcük & Baykasoğlu, 2016; Govindan &
Chaudhuri, 2016; Shao et al., 2016). Figure 1 depicts the framework of the research
procedures. Mixture of methods fits the nature of the research and its two objectives as
outlined in Section 1. As a pioneering and exploratory work in smart waste management, this
research requires a qualitative method first to identify the key drivers of industry 4.0 in the
supply chain operations of waste management for a circular economy. After that, it employed
a quantitative method, the fuzzy DEMATEL technique, to classify barriers as causal and
effect. Additionally, a threshold analysis was conducted to identify the significant
interrelationships among drivers, bringing to light the most impactful ones.
Figure 1. Research Framework
Semi-structured interviews were conducted in the qualitative phase. A semi-
structured interview method provides enough structure to keep conversations focused. At the
same time, it allows enough flexibility to take new directions and dig deeper into unexpected
findings (Bell et al., 2018). An information sheet was provided to interview participants to
explain the concepts of CE and smart waste management, and the possible application of
industry 4.0 technologies (including internet-of-things) in the supply chain operations of
waste management. The interview protocol that was used to guide the process is included as
Annexure 1.
The information sheet also provided an initial list of factors. The list was compiled
based on a survey of news reports and academic literature (Walker et al., 2008; Hsu et al.,
2013; Giunipero et al., 2012; Govindan et al., 2015; Govindan & Hasanagic, 2018). These
factors were the influences of market demand, regulatory pressure and organisational vision;
the need good marketing image, cost saving, speed of operations and value recovery from
waste; and concern about the amount of waste going to landfills, the environment and
operational challenges encountered in waste management. The interviewees were advised
that this list of factors was not exhaustive and was meant to prompt their thinking to identify
more factors.
The quantitative phase of the research employed the fuzzy DEMATEL technique. This
technique (Gabus & Fontela, 1972) supports multi-criteria decision-making through the
creation and analysis of structural models of causal relationships between system
components. It has been increasingly used in managerial studies, especially in the
sustainable supply chain domain (Zhu et al., 2014; Seleem et al., 2016; Shao et al., 2016;
Bai et al., 2017; Luthra et al., 2018b; Farooque et al., 2019b). Venkatesh et al. (2017) and
Farooque et al. (2019b) made comprehensive comparisons of DEMATEL and other multi-
criteria decision-making techniques. They suggested that DEMATEL is better for barrier
studies than Interpretive Structural Modelling (ISM), Analytic Hierarchy Process (AHP),
Analytic Network Process (ANP) and Structural Equation Modelling (SEM). This study
employed a fuzzy set extension to the standard DEMATEL technique to handle the inherent
subjectivity and vagueness in human judgments (Wu & Lee, 2007; Lin, 2013). Another
variation is grey-based DEMATEL, which uses very similar methodological procedures but
with grey numbers to handle subjectivity and vagueness in input data (Si et al., 2018). The
researchers chose fuzzy DEMATEL because it is slightly more sophisticated, using triangular
fuzzy numbers which have three dimensions (e.g., 0, 0, 0.25), while grey numbers have only
two dimensions (e.g., 0, 0.25). The technical details of the fuzzy DEMATEL method can be
found in Venkatesh et al. (2017) and Farooque et al. (2019b).
3.2 Data Collection
Research data were collected from the Pearl River Delta of China, which has a reputation
as the factory of the world. The Chinese language was used in both data collection stages. In
the first stage, an invitation to participate in research was emailed to 20 potential participants
along with the interview information sheet. A purposeful sampling approach (Gentles et al.,
2015) was taken, selecting organisations and their experienced staff members who had most
to do with the practice of smart waste management. After follow-up communications with the
potential participants, we were able to secure 14 interviews in August and September 2018,
either face-to-face at a participant’s organisation or over the phone. Each interview lasted
about 30-50 minutes. The interviews involved organisations in both the public and private
sectors and a variety of ownership types. Their industry types included government,
healthcare, property development, logistics and manufacturing. Annexure 2 presents the
profile of research participants.
In the second stage, we obtained quantitative data for DEMATEL analysis. We asked the
participants to judge the cause-effect relationships among shortlisted driver factors by
making pairwise comparisons. We surveyed three participants (Annexure 3 provides their
profiles) from different organisations, each having a different perspective on the supply chain
operations of waste management. We asked each to fill out a survey form on smart waste
management in supply chain operations. Such a research design is more robust than one
which obtains data from a single type of organisation. It helps to avoid the bias of a single
type of supply chain stakeholder, rather providing a more holistic understanding of the
research topic. These three organisations were as follows:
• A technology provider: a manufacturer which designs and produces smart waste
management equipment/systems;
• A technology user in the private sector: a property development and construction
business which has been using smart waste management equipment/systems;
• A technology user in the public sector: a local government agency which oversees
waste collection and management activities.
4. Results, Analysis and Findings
4.1 Key Drivers of Smart Waste Management
The qualitative phase of the research arrived at a final list of 11 important drivers of the
implementation of smart waste management in supply chain operations. The researchers
shortlisted these 11 mainly because they were most frequently identified by the 14
interviewees. According to the research framework as illustrated in Figure 1, the researchers
also took into consideration the important driver factors identified in the literature. Based on
the input from both the literature and interviewed experts, the researchers had two meetings
to discuss and shortlist the 11 drivers. The detailed description of individual drivers follows.
D1 – Profit maximisation: This driver is the overall financial benefit associated with the use
of industry 4.0 technologies in waste management for improving the sustainability of supply
chain operations. Smart waste management helps an organisation to increase its profit when
its implementation cost is outweighed by monetary returns.
D2 – Cost saving: Industry 4.0 technologies can save organisations cost in waste
management. For example, IoT sensors can be used to provide location intelligence on how
full rubbish bins are, informing efficient use of waste collection vehicles (Gutierrez et al.,
2015).
D3 – Value recovery from waste: Industry 4.0 technologies enable more effective value
recovery. For example, an interviewee introduced his organisation’s newly-invented smart bin
that separates glass from other waste. The recycled glass is then used for making handicraft
products.
D4 – Operational challenges in waste management that require smart solutions: Some
challenges in the supply chain operations of waste management are insurmountable until a
new industry 4.0 technology becomes commercially viable. For example, source separation is
a best practice in sustainable household waste management. However, it has not been
widespread in many developing countries due to a variety of infrastructural, cultural and
behavioral obstacles. In China, a pilot project used two-dimensional (2D) barcodes to identify
and trace each rubbish bag and hold residents accountable for not sorting rubbish. The
implementation was proved to be very effective in enforcing source separation in a residential
community (Xu, 2017).
D5 – Improved speed of operations in waste management: Many waste management
activities are labor-intensive and time-consuming. Some industry 4.0 technologies can speed
operations up through automation. For example, Apple Inc. uses robots to disassemble end-
of-life iPhones to recover technical materials. It is much faster and more cost-efficient than
manual operations.
D6 – Alignment with organisational vision/marketing image: An organisation is more
likely to embrace industry 4.0 technologies for sustainable waste management if such an
implementation is aligned with its vision and marketing image. For example, manufacturers
including Cadbury, Mars Nestlé, Heinz, Premier Foods and Kerry Noon are committed to both
CE and industry 4.0 (Mangla et al., 2018). Therefore, they have a great incentive to apply
industry 4.0 technologies for a CE transition in their waste management functions.
D7 – Market demand: Customers and consumers are important stakeholders of any
organisation. As the public (and therefore the market) has become more environmentally-
conscious in the past decade, there has been increasing demand for all supply chain stages,
including waste management, to be more sustainable (Mangan & Lalwani, 2016). This trend
drives the utilisation of the latest industry 4.0 technologies for more effective and sustainable
waste management.
D8 – Regulatory pressure: Regulatory pressure is one of the key drivers of greening supply
chain operations (Mangan & Lalwani, 2016). Increasingly, enterprises are influenced by
regulatory norms to adopt industry 4.0 technologies for reducing harmful waste and meeting
the environmental requirements.
D9 – Corporate social responsibilities (CSR) expectations of the public: The public and
the media are now paying increased attention to the social responsibilities of enterprises. This
change drives businesses to better protect the environment and to reduce the amount of
waste going to landfills. Recent studies (Eccles, Ioannou, & Serafeim, 2014; Flammer, 2013)
found that shareholders reward businesses which do better in CSR and penalise those that
ignore it.
D10 – Top management’s environmental values: Top management sets organisational
directions, so its environmental values influence how the organisation manages waste.
Giunipero et al. (2012) identified top management initiatives as the top-ranked driver in the
context of broad sustainability management. Sroufe’s (2003) work proved that top
management’s support for the environmental management system is positively linked to
waste management practices.
D11 – Increasing price-performance ratio of industry 4.0 technologies: As technologies
advance, they usually become more capable and cheaper. This translates into an improving
price-performance ratio of industry 4.0 technologies, which has been a driving force behind
their adoption worldwide. The same is true for their implementation in waste management.
4.2 Fuzzy DEMATEL Analysis Results
Fuzzy DEMATEL analysis yields a total relation matrix. From this matrix, it is easy to
calculate the sum of rows (R) and of columns (C) for each driver factor, and their (R+C) and
(R-C) values. The (R+C) value depicts the prominence (importance) of a driver factor for smart
waste management in supply chain operations. It indicates the total effect, including both
influenced and influential driver strength. The relation or influence (R-C) value represents
the cause-and-effect relationship. If the (R-C) value is positive, the driver factor is in the
causal category; otherwise, it is in the effect category (Wu & Lee, 2007; Lin, 2013). Based on
the quantitative results, a prominence-causal relationship diagram is generated to visually
classify driver factors. The diagram also maps significant relationships above a threshold
value, which is calculated by adding 1.5 standard deviations to the mean of the total relation
matrix (Fu et al., 2012; Zhu et al., 2014).
4.3 Results from the Technology Provider’s Perspective
Table 2 shows the total relation matrix from the perspective of the technology provider. The
threshold value is 0.229. The values greater than this are highlighted in bold in the table.
They are also mapped in Figure 2 to indicate significant cause-effect relationships.
Table 2. Total relation matrix from the technology provider’s perspective
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
D11
D1
0.104
0.094
0.053
0.075
0.134
0.146
0.148
0.050
0.126
0.096
0.062
D2
0.218
0.050
0.038
0.077
0.056
0.089
0.134
0.032
0.049
0.050
0.043
D3
0.278
0.155
0.067
0.157
0.117
0.172
0.180
0.055
0.104
0.109
0.100
D4
0.260
0.167
0.173
0.100
0.161
0.176
0.189
0.058
0.081
0.110
0.146
D5
0.200
0.182
0.127
0.148
0.077
0.113
0.127
0.070
0.064
0.069
0.133
D6
0.228
0.122
0.106
0.176
0.109
0.122
0.254
0.135
0.161
0.176
0.098
D7
0.298
0.171
0.109
0.176
0.175
0.188
0.131
0.093
0.120
0.164
0.158
D8
0.249
0.120
0.127
0.200
0.197
0.171
0.206
0.083
0.209
0.256
0.216
D9
0.244
0.139
0.124
0.196
0.188
0.237
0.171
0.149
0.110
0.252
0.177
D10
0.204
0.137
0.158
0.193
0.181
0.230
0.167
0.145
0.200
0.118
0.172
D11
0.206
0.154
0.094
0.156
0.154
0.123
0.170
0.078
0.097
0.105
0.076
Significant relationships: D3-D1, D4-D1, D7-D1, D8-D1, D8-D10, D9-D1, D9-D10, D10-D6
Figure 2. DEMATEL prominence-causal relationship diagram from the technology provider’s
perspective
Results from the technology provider’s perspective show that the two most fundamental
causal drivers are D8 (Regulatory pressure) and D9 (CSR expectations of the public), which
both arise from external stakeholders. The third most important causal driver is D10 (Top
management’s environmental values), which is highly dependent on the two aforementioned.
The other causal driver is D3 (Value recovery from waste), which has a significant effect on
D1 (Profit maximisation). These results suggest that external causal drivers are of greater
importance than internal ones. This finding is consistent with a recent study by Farooque et
al. (2019b) that identified the higher influence of external factors over internal ones in
sustainable circular food supply chains in China.
The most prominent drivers (those which have the greatest R+C values) are D7
(Market demand) and D1 (Profit maximisation). It is reasonable to see D7 (Market demand)
being rated as the most prominent by the technology provider, given that their survival and
growth depends on market demand. However, it is a surprise to find D1 (Profit maximisation)
to be an effect driver, despite a high prominence score. Nevertheless, as can be seen in Figure
2, this is because D1 (Profit maximisation) is dependent on multiple drivers, including D3
(Value recovery from waste), D8 (Regulatory pressure), D4 (Operational challenges in waste
management that require smart solutions), D9 (CSR expectations of the public) and D7
(Market demand). Based on an interview with the technology provider, D8 (Regulatory
pressure) and D9 (CSR expectations of the public) have a good influence on D7 (Market
demand), which in turn stimulates technological advancements to improve D1 (Profit
maximisation). D3 (Value recovery from waste) and D4 (Operational challenges in waste
management that require smart solutions) have a direct impact on D1 (Profit maximisation).
4.4 Results from the Private Sector Technology User’s Perspective
Table 3 and Figure 3 show the results from the perspective of the technology user in the
private sector. The threshold value is 0.316 for determining a significant relationship.
Table 3. Total relation matrix from the private sector technology user’s perspective
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
D11
D1
0.239
0.327
0.270
0.187
0.304
0.254
0.208
0.121
0.152
0.203
0.236
D2
0.296
0.188
0.234
0.195
0.240
0.253
0.148
0.108
0.126
0.138
0.143
D3
0.245
0.267
0.152
0.185
0.223
0.209
0.136
0.074
0.117
0.126
0.130
D4
0.276
0.269
0.277
0.138
0.280
0.230
0.193
0.110
0.133
0.143
0.148
D5
0.377
0.369
0.340
0.229
0.218
0.263
0.214
0.124
0.151
0.166
0.172
D6
0.279
0.203
0.181
0.144
0.224
0.137
0.174
0.140
0.120
0.132
0.137
D7
0.256
0.250
0.191
0.156
0.233
0.177
0.114
0.103
0.170
0.178
0.180
D8
0.383
0.377
0.317
0.237
0.289
0.230
0.151
0.097
0.155
0.209
0.215
D9
0.140
0.136
0.124
0.103
0.126
0.115
0.095
0.074
0.061
0.093
0.095
D10
0.325
0.312
0.287
0.270
0.324
0.300
0.224
0.132
0.234
0.145
0.216
D11
0.349
0.340
0.314
0.237
0.289
0.269
0.180
0.127
0.199
0.241
0.147
Significant relationships: D1-D2, D5-D1, D5-D2, D5-D3, D8-D1, D8-D2, D8-D3, D10-D1,
D10-D5, D11-D1, D11-D2
Figure 3. DEMATEL prominence-causal relationship diagram from the private sector
technology user’s perspective
Results from the private sector user’s perspective show three important causal drivers: D8
(Regulatory pressure), D10 (Top management’s environmental values), and D11 (Increasing
price-performance ratio of industry 4.0 technologies). It should be noted that this user firm
is a medium-sized enterprise with about 300 employees. It is privately owned and not publicly
listed. This explains why D9 (CSR expectations of the public) was not rated as a causal driver
for this user, although it was rated as a key causal driver by the technology provider. This is
coherent with the reality in China: the public has been paying more attention to CSR issues,
but the focus has been mainly on publicly-listed large enterprises. This firm therefore does
not face much CSR pressure on its supply chain sustainability. This finding affirms the
importance of regulatory pressure, which was identified by Zhu et al. (2014) and Mangla et
al. (2018) in their studies of sustainability barriers in two different developing countries.
D1 (Profit increase), D5 (Improved speed of operations in waste management) and D2
(Cost saving) are the most prominent drivers, although all are effect drivers. This reflects a
set of business priorities typical of firms in the Pearl River Delta of China. Due to rapidly
rising operating costs, many businesses in the region rely on speed to be competitive and
profitable (Zhang & Huang, 2012; Zhang et al., 2012; Huang et al., 2013), although cost
control is still important.
4.5 Results from the Public Sector Technology User’s Perspective
Table 4 and Figure 4 present the results from the perspective of the technology user
in the public sector. By adding 1.5 standard deviations to the mean of the total relation
matrix, the threshold value is calculated as 0.392 for determining a significant relationship.
Table 4. Total relation matrix from the public sector technology user’s perspective
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
D11
D1
0.243
0.259
0.263
0.154
0.135
0.221
0.381
0.260
0.295
0.365
0.350
D2
0.348
0.174
0.243
0.179
0.094
0.160
0.251
0.224
0.226
0.332
0.287
D3
0.333
0.216
0.193
0.151
0.107
0.193
0.345
0.294
0.289
0.357
0.376
D4
0.347
0.320
0.292
0.147
0.190
0.250
0.387
0.332
0.329
0.407
0.390
D5
0.270
0.222
0.225
0.157
0.107
0.301
0.349
0.304
0.301
0.366
0.352
D6
0.378
0.290
0.293
0.212
0.121
0.214
0.353
0.329
0.358
0.401
0.386
D7
0.178
0.144
0.146
0.097
0.146
0.252
0.182
0.242
0.240
0.261
0.252
D8
0.150
0.120
0.123
0.083
0.094
0.228
0.220
0.148
0.211
0.192
0.224
D9
0.288
0.239
0.245
0.119
0.122
0.245
0.296
0.309
0.210
0.310
0.300
D10
0.422
0.357
0.330
0.175
0.148
0.289
0.366
0.367
0.402
0.317
0.334
D11
0.378
0.290
0.324
0.245
0.149
0.284
0.360
0.361
0.294
0.406
0.295
Significant relationships: D4-D10, D6-D10, D10-D1, D10-D9, D11-D10
Figure 4. DEMATEL prominence-causal relationship diagram from the public sector
technology user’s perspective
Figure 4 show three important causal drivers: D4 (Operational challenges in waste
management that require smart solutions), D5 (Improved speed of operations in waste
management), and D6 (Alignment with organisational vision/marketing image). The results
are consistent with the understanding acquired from the government agency in an earlier
interview: it would consider a smart waste management technology if such technology could
benefit its operations, for example, by overcoming challenges and improving speed. The
alignment with policy directions from the higher level is also important. However, cost/profit
considerations are not at the top of its priority list, which is understandable, given that it is
not profit-oriented. The two most prominent drivers are D10 (Top management’s
environmental values) and D11 (Increasing price-performance ratio of industry 4.0
technologies). This suggests that an immediate implementation is largely dependent on the
leadership team’s attitude and whether a relevant technology is justifiable from a price-
performance viewpoint. Apparently, economic factors are important, and they influence the
attitude of the management on sustainability initiatives (Mangla et al., 2018; Farooque et al.,
2019b).
D10 (Top management’s environmental values) shows significant dependence on D4
(Operational challenges in waste management that require smart solutions), D6 (Alignment
with organisational vision/marketing image), and D11 (Increasing price-performance ratio of
industry 4.0 technologies). Initially, we were surprised by these results because we thought
that one’s environmental values were relatively independent of other factors. After taking the
results back to the respondent, we realised that our belief was not valid for the current
situation in China. Environmental values have just started evolving there and are not yet
deeply rooted in people’s minds. Consequently, D10 (Top management’s environmental
values) is often contingent on the practical benefits of implementing smart waste management
technologies, and on the organisational vision and government policy directions.
For the same reason, D7 (Market demand) and D8 (Regulatory pressure) are the most
obvious effect drivers from the perspective of the government agency. The market for smart
waste management technologies is still at a nascent stage. Its growth is highly dependent on
the operational benefits that the evolving industry 4.0 technologies can deliver for the supply
chain operations of waste management. The Chinese government has a rather pragmatic
approach to exerting regulatory pressure. It is more likely to push for the use of the latest
technologies for improving waste management when (a) the industry is concerned about
environmental protection and (b) the technologies have reached a good price-performance
ratio.
4.6 Summary of Findings
The DEMATEL analysis results presented above offer insights from three different
representative stakeholders: a technology provider, a private sector user and a public sector
user. Comparing and contrasting the results from a holistic perspective, we summarise the
key findings as follows.
1. The most fundamental causal drivers of smart waste management lie in what it can
do for the supply chain operations of waste management in terms of overcoming
operational challenges, recovering value, speeding up operations, saving cost and
improving profit.
2. There is a virtuous cycle between market demand and the improving price-
performance ratio of industry 4.0 technologies. Market demand stimulates research
and development investment in industry 4.0 technologies to improve their price-
performance ratio. Conversely, better price-performance ratio stimulates greater
market demand for smart waste management solutions.
3. Regulatory pressure has a great impact on the uptake of smart waste management
solutions. However, the actions of the relevant government agencies in China are
dependent on the effectiveness and price-performance ratio of industry 4.0
technologies and on the attitude within the industry about environmental protection.
4. The CSR expectations of the public can influence organisational behaviors. However,
the effect is mainly felt by publicly-listed large enterprises in China due to their
visibility. Privately-owned small- and medium-sized enterprises are yet to be
influenced much at present.
5. Top management’s environmental values drive the adoption of the latest industry 4.0
technologies for more sustainable waste management. However, environmental values
are not yet deep-rooted in the Chinese business culture, and are often contingent on
organisational vision and government policy directions.
All the key findings summarised above are original and contribute to the development of
literature on sustainable waste management. The generic aspects of key findings 3-5 were
also reported by earlier studies (Zhu et al., 2014; Mangla et al., 2018; Farooque et al., 2019b),
confirming the validity of this research. However, our study findings provide additional
insights that are unique and contextual to Chinese industries, so they might serve as a useful
guide for managers and policy makers.
5. Discussion
We can advance several general propositions based on the findings presented above. First,
the most fundamental driver of smart waste management is the effectiveness of industry 4.0
technologies for improving the supply chain operations of waste management. Second, the
market demand for smart waste management solutions and their price-performance ratio are
both improving over time and enforce each other in a virtuous cycle. Third, regulatory
pressure has a deep impact, but the Chinese government is rather pragmatic about exerting
it for the implementation of industry 4.0 technologies in waste management. The fourth is
that the business leaders in China have some commitment to environmental values, but it is
fairly superficial; pressure from the government, the public, and higher-level management
are necessary if business leaders are to take action to invest in the latest industry 4.0
technologies for improving the sustainable operations of waste management. Based on these
general propositions, we derive policy and managerial implications in the following two
subsections.
5.1 Policy Implications
To expedite a transition to CE as part of its national development strategy, the Chinese
government should make it a priority to support the research, development and
commercialisation of industry 4.0 technologies for improving the supply chain operations of
waste management. Improvements in smart waste management technologies have a direct
and significant impact on their adoption, engendering the virtuous cycle between the market
demand and the price-performance ratio. The government can provide financial support in
the form of research and development funds, subsidies and tax benefits available to providers
of the technology for smart waste management solutions. Given how the drivers interact,
such support is likely to snowball the uptake of smart waste management solutions, which
will, in turn, advance the government’s CE agenda. The government should also consider
supporting, promoting and benchmarking of enterprises that take the lead in the use of smart
waste management technologies. The government can involve industry associations to
organise site visits and tours to help the industries to learn from the leading enterprises about
smart waste management. In this way, more enterprises and managers will become aware of
the potential benefits of smart waste management technologies, and their implementation in
their own businesses.
Although CE has been legislated in China as part of its national development strategy,
only modest progress has been made in implementing it over the past ten years (Mathews &
Tan, 2016). There is a need for the Chinese government to exert regulatory pressure to bring
CE from legislative paper further into the realm of concrete actions. The National
Development and Reform Commission (NDRC) has been responsible for the promotion of CE
in China. It needs to strengthen its enforcement mechanism to implement CE at the micro
(supply chain operations) level. It should be noted that the NDRC has only published CE
indicators for the macro (regional economy) and meso (industrial park) levels, but not for the
micro level (Geng et al., 2012). Developing industry-specific micro-level indicators will be
useful for measuring the progress toward more sustainable supply chain operations of waste
management. Thus, it will galvanise the adoption of the latest industry 4.0 technologies for
making waste management more effective. Although it will be a challenging task to develop a
diverse range of specific, micro-level indicators for a large variety of industries, the NDRC
should gear up its efforts to do so. Publication of such indicators will make it feasible to better
measure the performance of supply chain operations of waste management for a transition
to a circular economy.
The Chinese government needs to embark on a journey to transform its culture into
one that seriously values environmental sustainability. The Chinese government started its
economic reform in 1978. In the first three decades, there was a negligence of environmental
protection as economic growth was given an absolute priority. In the most recent decade, the
resulting environmental degradation issues drove the Chinese government to turn away from
the traditional measure of GDP to that of green GDP in order to make development
sustainable. However, environmental values are still far from being deeply embedded in the
Chinese culture and in the decision-making of the government and of enterprises. The
Chinese government should continue to fine-tune its green GDP measurements and
monitoring system, so as to transform its governance culture and to guide the business
culture to commit more to environmental sustainability. There is also a need to exert greater
regulatory pressure on businesses and citizens to protect the environment, and a need to
hold people accountable for irresponsible behaviour toward the environment. The Chinese
government should also improve its environmental education in schools to instill
environmentally-friendly values in the younger generations and deepen the public’s
commitment to environmental protection.
5.2 Managerial Implications
Smart waste management presents a good business opportunity for technology providers, as
the market has a promising future; however, it is still at a nascent stage. The first movers are
likely to gain an advantage by establishing their brands and customer base. However, they
must continuously invest in research and development to improve the effectiveness and cost-
efficiency of the technologies. This is not just to stimulate market growth, but also to defend
market share, as competitors are likely to race for innovation in the rapidly-evolving industry
4.0 landscape. Among the wide variety of industry 4.0 technologies available, there is a need
to focus on those which are relatively mature and low-risk, having a favourable price-
performance ratio when commercialised into smart waste management systems. To this end,
technology providers should conduct a thorough investigation and comparison of relevant
industry 4.0 technologies before deciding which one to invest in for the supply chain
operations of waste management.
Potential users of smart waste management technologies should be aware of and
consider products on the market for improving the supply chain operations of their waste
management activities. On one hand, they can evaluate whether some of the existing products
suit their operational needs, enabling them to manage waste more sustainably and at the
same time be better off financially. On the other hand, they may partner with industry 4.0
technology providers to develop smart waste management solutions that are not available in
the market and to overcome their own operational challenges in this area. The resulting
solutions may make the user a sustainability leader in the industry, enhancing their brand
image and marketing position. Each potential user should analyse the unique trade-offs that
they face and the options available, to decide on technology providers’ expertise and solutions,
or to invest in resources to jointly develop solutions with technology providers.
Non-government organisations (NGOs) may play an important role in driving the
implementation of more sustainable and smarter waste management solutions. Given that
the business leaders in China are very pragmatic about environmental sustainability, a push
from NGOs is likely to win the commitment of some business leaders who otherwise would
not be supportive. At present, the public and the media mainly pay attention to the publicly-
listed large enterprises. However, NGOs may be able to exert pressure on some small- and
medium-sized enterprises as well. An example of a potentially helpful NGO is the Institute of
Public and Environmental Affairs (IPE), a non-profit environmental research organisation
based in Beijing, China. Since 2006, the IPE has been collecting, collating and analysing
government and corporate information to build a database on the environment. By publishing
the data free online, the IPE has empowered the public to hold the government and
businesses accountable for their environmental performance. The researchers advocate
establishment of more NGOs to promote environmental protection, to monitor the
government’s and businesses’environmental management, and to hold them accountable for
their irresponsible actions toward the environment.
6. Conclusions
CE has been increasingly explored as an effective approach to supply chain sustainability.
The development of industry 4.0 technologies provides business firms with an opportunity to
upgrade their supply chain operations in line with CE, especially the waste management
operations. Our study focuses on the drivers of industry 4.0-enabled smart waste
management in supply chain, providing an initial insight from the perspective of waste
management on the interplay between CE and industry 4.0.
We used a mixed-methods approach in this study, including semi-structured
interviews and the fuzzy DEMATEL technique. We found 11 key drivers of the implementation
of smart waste management in supply chain operations. We analysed the causal effects of
these 11 key drivers based on data from different supply chain actors. We found that the
fundamental causal driver is the effectiveness of industry 4.0 technologies for improving
operational performance in supply chain waste management (D4 and D5). Interestingly, we
found a virtuous cycle between market demand (D7) and increasing price-performance ratio
of industry 4.0 technologies (D11), indicating interrelationships between the drivers. Other
important causal drivers include regulatory pressure (D8) and top management’s
environmental values (D10).
Our study makes three contributions. First, we complement the existing literature on
smart waste management by exploring the drivers at the supply chain level, thereby adding
insights on the integration of industry 4.0 in the context of CE. Waste management is an
original area in CE and, most likely, the initial step in any CE implementation (Govindan &
Hasanagic, 2018; Su et al., 2013). The previous studies on smart waste management mostly
focused on the meso- and macro- levels, which is likely to be in line with the widely-used top-
down approach to CE adoption (Geng & Doberstein, 2008; Geng et al., 2012). In contrast, our
study explored a bottom-up approach to industry 4.0 technologies-driven CE
implementation. Business firms are a major force for waste generation, innovation and use
of industry 4.0 technologies (Fatorachian & Kazemi, 2018; Ghisellini et al., 2016). The “micro-
level” drivers of smart waste management found in our study draw from the business firms’
perspective to add an increment of understanding about the interplay between industry 4.0
and CE. Also, our focus on smart waste management in supply chain operations covers a
more complete flow of waste generation. Because our findings are from the supply chain
perspective, they are more likely to maximise the adoption and value of smart waste
management. Second, we found causal effects among the drivers, showing a clear roadmap
to adopting smart waste management. These causal effects illuminate the prioritisation of the
fundamental driving forces and the adoption process. For example, technology providers and
policy makers should primarily focus on presenting the explicit improvement in supply chain
operational performance to be gained through smart waste management. Understanding
these causal effects shows ways to improve effectiveness and efficiency in the adoption and
propagation of industry 4.0-driven CE practices. Third, our study focuses on Chinese firms.
China is a leading country in CE adoption (Masi et al., 2018). Our findings, therefore, provide
timely guidance for Chinese firms as they consider and compare business risks and
government policies and explore new business opportunities. In addition, our analyses is
relevant to the CE context in China at the macro- and micro-level (e.g., constraints of
government regulations and top management commitment) and, furthermore, to other
emerging markets (e.g., India [Mangla et al., 2018a] and United Arab Emirates [Thornton et
al., 2013]). Emerging economies are more likely to share sustainability practices with each
other than with non-emerging economies (Yadav et al., 2019). The similar development
patterns of CE imply the applicability of our findings in the broad range of emerging markets.
In particular, China is a leading emerging country that has implemented CE at the national
level for over ten years (Geng et al., 2012). Our study based on Chinese firms is more likely
to provide practical and confirmatory results for other emerging economies which are waiting
to follow CE adoption.
Although this study was based on careful and rigorous analysis, there are inevitable
trade-offs and limitations. Also, some avenues of future research may be derived. The novelty
of applying smart waste management in supply chains constrained the sample size in
DEMATEL analyses and meant that the findings had to be of an exploratory nature. Future
research can build on this study to include a broader scope in the supply chain operations
of smart waste management (e.g., logistics service providers). This research focuses on waste
management, while production, consumption and other areas are also important in the
structure of CE practices in China (Su et al., 2013). It would be interesting to study how these
drivers of smart waste management could contribute to implementations in other areas in
CE practice (e.g., production and consumption). The interaction of drivers across different
areas of industry 4.0-enabled CE would provide a holistic framework for building sustainable
supply chains. Also, future research can explore the drivers of smart waste management in
the context of developed countries (e.g., Germany) and provide comparative analyses with
those reported in our research. The CE infrastructure of developed countries is substantially
different from that of emerging economies (Yadav et al., 2019). Study of common and
contrasting drivers in emerging and industrialised countries could inform a dynamic
structure which is applicable for business firms operating across different market
environments (e.g., multi-national firms).
Acknowledgements
The authors would like to acknowledge partial financial support from the National Natural
Science Foundation of China (51875251), 2018 Guangzhou Innovation Leading Talent
Program(China)(201909010006), Blue Fire Project (Huizhou) Industry-University-Research
Joint Innovation Fund of the Ministry of Education (China) (CXZJHZ201722), and the
Fundamental Research Funds for the Central Universities (11618401).
Annexure 1 – Interview Protocol
1. Is your organisation involved in the practice of smart waste management?
2. If yes, what type of equipment/systems are available? List out the Industry 4.0
technologies employed and describe them.
3. Can you please give some examples of how the technologies are used?
4. If applicable, what were the important factors that drove the implementation of
smart waste management in your supply chain operations?
5. If applicable, what are the factors that push your organisation to continuously
improve a smart waste management system?
Annexure 2 – Profile of research participants in the first stage
Participant
Number Industry sector Designation
Years of
experiences
n1
Manufacturing (smart
waste
equipment/systems)
Vice-general manager 15
n2
Manufacturing (smart
waste
equipment/systems)
Administrative specialist 4
n3
Logistics General manager 18
n4
Government
Secretary of the community
Party committee 31
n5
Healthcare secretary 6
n6
Property development
and construction Administrative director 5
n7
Manufacturing
Chief human resource
officer 12
n8
Manufacturing
Chairman of the Workers’
Union 20
n9
Manufacturing Engineer 10
n10
Manufacturing
Security and Environmental
Management Director 11
n11
Manufacturing General manager 15
n12
Manufacturing
Sales director 30
n13
Manufacturing
Executive 30
n14
Manufacturing
Secretary 5
Annexure 3 – Profile of research participants in the second stage
Participant
Number Industry sector Designation
Years of
experiences
p1
Manufacturing (smart waste
equipment/systems) Vice-general manager 15
p2
Property development and
construction Buyer 5
p3
Government
Government
administrator 10
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