Applied Energy Conference

Optimizing energy supply chains with renewable integration: A systems

approach

Li, X., Wang, Y., & Zhang, H.

Abstract

Purpose – The literature that is presently available on sustainable supply chain management
(SSCM) combining Optimization and Industry 4.0 techniques falls short in its depictions of
the recent developments, budding pertinent areas, and the importance of SSCM in the growth
of industrial economies around the world. This article's main objective is to analyze current
trends, highlight the latest initiatives, and perform a meta-analysis of the literature that is
currently accessible in the SSCM area with a special focus on optimization and Industry 4.0
techniques. The paper also proposes a conceptual framework that will assist in illuminating
how the ideas of optimization and Industry 4.0 may contribute to realizing sustainability in
supply chains.

Design/methodology/approach – The proposed study systematically reviews 85 research

publications published between 2010 and 2022 in referenced peer-reviewed journals in
diverse fields, including engineering, business and management, services, and healthcare.
Numerous categories are considered throughout the examination of the literature, including
year-wise publications, prominent journals, type of research design, concerned industry, and
research technique used.

Findings – The study demonstrates a deeper comprehension of the literature in the field and
its evolution throughout numerous industry sectors, which is helpful for both practitioners
and academics. The results from the content analysis highlight various future research
opportunities in the domain.

Originality/value – This is one of the first research articles that have attempted to establish,
analyse, and highlight the current trends and initiatives in the SSCM domain from an
optimization and Industry 4.0 techniques viewpoint. The cluster-based future research
propositions also enhance the novelty of the study.

Keywords: Sustainable Supply Chain Management; Optimization; Industry 4.0.

1. Introduction

Supply chain management is concerned with the movement of goods, finances, and
information through all value chain phases, including suppliers, manufacturers, distributors,
retailers, and consumers. Managing demand, product design and development, supplier
management (Wu et al., 2020) , raw material procurement, production planning and control,
warehouse management, inventory planning, supply chain network design, distribution
management, and last-mile delivery management are all essential subsystems that are
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involved in supply chain management. Within the system, elements such as risks
(Munir et al., 2020)
, disruptions, delays, and uncertainties significantly influence the performance of
the supply chain.

Incorporating sustainable practices into a company's operations is necessary to remain

relevant and thriving in today's market (Gracia and Quezada, 2016) . The concept of
sustainability refers to meeting the present generation's demands without jeopardizing future
generations' capacity to meet their demands (Dumitrascu et al., 2020). Social, economic, and
environmental factors are the primary factors when we refer to sustainable supply chain
management (SSCM).

In recent years, emerging technology has enabled organizations to address difficulties

associated with supply chains in a manner that was impossible before. Despite this,
companies are continuing their search for an effective balance of technology-driven solutions
that would assist them in successfully adopting sustainability into their operations. Enabling
factors for sustainable supply chain management include Industry 4.0 (I4) and optimization
methodologies (SSCM). Even though various research
(Hall et al., 2012a; Souza et al., 2019; Tautenhain et al., 2019)
have addressed the topic of sustainable supply chain management
and the significance of the subject, relatively few of these studies have addressed the role that
Industry 4.0 and optimization approaches play in sustainable supply chain management
(SSCM).

Through an analysis of the current body of literature, this study aims to investigate the
underlying trends and important research themes as well as the role that Industry 4.0 and
optimization play in sustainable enterprises. In content analysis, we have presented future
research views in the field by enumerating proposed research questions drawn from research
gaps in the existing body of literature. In addition, one of our goals is to develop a conceptual
framework that will assist in explaining how the ideas of optimization and Industry 4.0 may
contribute to achieving sustainability in the supply chain.

This article is divided into seven sections, including one that serves as an ongoing
introduction. The second part provides an overview of the historical context of the literature.
The methodology used for our systematic review is discussed in the third section. In the next
section (four), an analysis will be used to demonstrate the categorization of the articles that
made the shortlist. In the fifth section, we present the cluster-based content analysis that
outlines future research propositions. The sixth section offers the theoretical framework we
developed based on literature classification and content analysis results. In the concluding
part, we discuss the most important discoveries and our work's limitations.
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2. Background of Literature on SSCM

This section will describe the theoretical backdrop of SSCM research, including optimization
and Industry 4.0. It is crucial to optimize the supply chain since doing so may help businesses
improve their efficiency, lower their expenses, and elevate the level of satisfaction
experienced by their customers. Companies can optimize their supply chain processes, such
as production scheduling, inventory management, and transportation planning, with the help
of mathematical modeling and algorithms (Nagurney and Nagurney, 2010) . This allows
companies to use resources more efficiently and improve their overall performance. In
addition, optimization may assist businesses in recognizing and mitigating risks, such as
interruptions in supply chain operations, and in coming to better-informed choices about
future expenditures and growth (Sitek and Wikarek, 2015). Optimization may be a handy tool
for establishing sustainability in the supply chain. Companies can optimize the processes in
their supply chains via mathematical modeling and algorithms, which helps them have a more
negligible effect on the environment, consume fewer resources and energy, and enhance their
overall sustainability (Kaboli Chalmardi and Camacho-Vallejo, 2019) . Optimization can be
helpful for businesses, helping them become more profitable, competitive, and sustainable.

Table 1. Summary of past review-based studies in the SSCM domain

Authors Study focus Study Type

(Brandenburg and Analyzes past literature that Review

Rebs, 2015)
formalizes issues related to
SSCM in quantitative models
(Borregan- Examines literature in the field Bibliometric
Alvarado et al.,
of SSCM, keeping I4 and Review
2020)
advanced manufacturing
together
(Dutta et al., 2022) Investigates the role of Review
blockchain and the Internet of
Things (IoT) individually on
SSCM
(Faramarzi- Analyzes meta-heuristic Review
Oghani et al., 2022)
algorithms applied in SSCM
(Jayarathna et al., Investigates literature to assist in Review
2021)
selecting suitable multi-objective
optimization in SSC logistic
modeling
(Paul et al., 2021) Performs a systematic literature Systematic
review for identifying various Literature
Multi-Criteria Decision Making Review
(MCDM) techniques applied in
the field of SSCM.
(Cañas et al., 2020) Conducts a review to assess the Review
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current state of knowledge of the

supply chain from a
sustainability perspective
(Chalmeta and Analyzes the existing literature Bibliometric
Santos-deLeón,
on sustainability, Industry 4.0, Review
2020)
and supply chain management

The term "Industry 4.0," which is often referred to as the "fourth industrial revolution,"
describes the incorporation of modern technologies such as the Internet of Things (IoT), big
data, and artificial intelligence into the operations of manufacturing and logistics.
Technologies from the fourth industrial revolution may play an essential part in achieving
sustainability in the supply chain. These techniques enable businesses to improve the
efficiency of their operations and make smarter decisions
(Quariguasi Frota Neto et al., 2010)
. For instance, innovations from Industry 4.0, such as the Internet of Things (IoT), may
be used to track and control the energy used across the entire manufacturing process. This
enables businesses to discover and cut down on waste and inefficiencies. By identifying the
routes and modes of transportation that are the most time- and resource-effective,
improvement of logistics operations and mitigation of the adverse effects of transportation on
the environment are both possible with the help of technologies such as blockchain
(Varriale et al., 2021)
. Inventory management can be improved with artificial intelligence, which can
also help reduce waste by ensuring that the appropriate inventory quantity is held at any
given time.

Previously, several studies based on literature reviews have been carried out to add to the
theoretical understanding of SSCM research. Table 1 contains information from previous
research conducted in this field. To give a complete overview of the status of research in this
field, we have also provided the study's focus. Although the earlier literature review studies
provided an overview of SSCM research across specific parameters, they omitted the
systematic literature evaluation over numerous parameters in a single study, which would be
of great use to academics and practitioners. Additionally, there was no conceptual framework
explaining how optimization and I4 applications fit into SSCM, presented based on a
literature review.

3. Data and Methodology

The literature review is a crucial component of any research pursuit since it aids in assessing
current trends and active areas in a particular field of research. This study employs a
systematic approach to reviewing the existing, first screening the papers to be reviewed and
then classifying and analyzing those articles to better understand the SSCM in the context of
Optimization and Industry 4.0. A stepwise methodology for systematic literature review is
shown in Figure 1.

3.1 Database and screening of articles

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The fundamental objective of this study is to explore the research threads and advancements
in the field of SSCM domain considering optimization and Industry 4.0 (I4) techniques. For
this purpose, the Scopus database was chosen. We targeted all articles published in top peer-
reviewed journals with the following keywords in their title and abstract, namely:
“Sustainable Supply chain Management,” “Optimization,” and “Industry 4.0”. The search
terms for this study produced 147 SSCM articles across 59 journals, which were further
analyzed based on different perspectives through a classification framework. Articles
featured during 2010–2022 were considered for review as the SSCM field started to gain
popularity in the early 2000s. Subsequently, other documents such as conference papers,
book chapters, editorial notes, and short notes were excluded during the screening stage. We
also omitted documents from psychology, the arts, and neuroscience, and as a result, we
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obtained 104 documents. We then reviewed the abstracts and screened the articles based on
the scope of our study. We narrowed it down to 85 articles for our final literature review.
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4.1 Year-wise
The compilationtrend
of screened
of publications
material includes research articles on an SSCM domain with
several applications across diverse disciplines, including management, engineering, social
The variation
sciences, of “Sustainable
health Supply Chain
care, etc. Although Optimization
the authors andwhen
took care Industry 4.0” articles
choosing based
the final set on
of
the year of publication is indicated in Figure 2, spanning over 13 years (2010–2022).
articles for the review, any pertinent article might have been left out. The authors believe theIn the
early phases, there
categorization is very low
framework, or no interest
identified shown
research by researchers
themes, and practitioners
and analytical whenbe
findings would it
comes
helpful to integrating
to both scholars optimization and I4 techniques with SSCM. Later around 2017, both
and practitioners.
developed and developing economies realized the importance of sustainability in supply
chain operations and showed their willingness to integrate optimization and I4 techniques
with sustainable supply chain management practices. Since 2017, the frequency of publishing
articles has considerably increased. Reference to integrating optimization and I4 in SSCM
may be found in the manufacturing (Yadav et al., 2020) , IT & services, food sector,
healthcare (Daú et al., 2019) , and construction industries. Alongside these, it is notable that
the number of SSCM publications has also increased significantly in several other disciplines.

4.2 Top Journals and Publishers

Figure 2 Year-wise trend of publications

When discussing optimization and I4 approaches in the context of SSCM, there are several
journals from diverse areas such as engineering, computer science, management, decision-
making, and services. Among these esteemed indexed journal sources, the Journal of Cleaner
Production has the most significant portion of articles (11.76%). This journal has
continuously published a wide range of articles since 2010 to raise the degree of familiarity
with the area among scholars and other stakeholders. The International Journal of Production
Research has obtained the second largest percentage (9.41%), indicating a reasonably high
consideration of sustainability in manufacturing practices across industries.

Table 2 Classification based on journals and publishers

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Sources Publishers h-Index Article Percentag

s e
Journal of Cleaner Production Elsevier 232 10 11.76
International Journal of Taylor and Francis 153 8 9.41
Production Research
Sustainability (Switzerland) MDPI 109 6 7.06
Computers and Industrial Elsevier 136 6 7.06
Engineering
Resources, Conservation, and Elsevier 150 4 4.71
Recycling
Annals of Operations Research Springer 111 3 3.53
Advances in Intelligent Systems Springer 48 2 2.35
and Computing
Chemical Engineering Italian Association of 39 2 2.35
Transactions Chemical Engineering -
AIDIC
Operations Management Springer 31 2 2.35
Research
Production Planning and Control Taylor and Francis 85 2 2.35
International Journal of Logistics Emerald Insight 80 1 1.18
Management
European Journal of Operational Elsevier 274 1 1.18
Research
Business Strategy and The John Wiley 115 1 1.18
Environment
Computers and Chemical Elsevier 144 1 1.18
Engineering
Journal of Manufacturing Emerald Insight 76 1 1.18
Technology Management
Electronics (Switzerland) MDPI 49 1 1.18
EMJ - Engineering Management Taylor and Francis 39 1 1.18
Journal
Others N/A N/A 33 38.82
Total N/A N/A 85 100.00
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Sustainability (Switzerland) is ranked third on the list with a 7.06% share of articles. In
addition, the supply chains across various businesses have seen substantial investment in
optimizing and digitizing processes, as evidenced by the fourth-highest proportion (7.06%) of
SSCM research publications published in the Computers and Industrial Engineering Journal.

It is followed at places five and six by Resources, Conservation, and Recycling (4.71%) and
Annals of Operations Research (3.53%). In Table 2, we have provided a detailed breakdown
of how reviewed publications are categorized depending on journals and publishers.

However, when we talk about the publisher-wise distribution of articles, Elsevier contributed
the maximum number of articles (31%) in the SSCM field, followed by Taylor & Francis
(11%), SpringerLink (12%), Emerald Insight (8%), MDPI (8%) and Inderscience Publishers
(2%). The involvement of these publishers can be examined using Figure 3. A vast range of
research areas under the SSCM field was discovered through the literature search from these
publishers.

Elsevier (Martín-Gómez et al., 2019; Vivas et al., 2020) included documents in various
fields, primarily engineering, services, management, and manufacturing. The main topics
addressed by different academics in SpringerLink (Rizzoli et al., 2015; Zeng et al., 2022a)
include conceptual foundation-based papers impacting manufacturing sectors and survey-
centric and framework-based publications. Articles from Taylor & Francis (He et al., 2021)
publications are primarily case study-based techniques covering service and safety-related
issues. In contrast, Emerald Insight's publications focused mainly on the manufacturing and
process industries. However, many papers tried to integrate optimization and industry 4.0
methodologies into the knowledge of sustainable supply chains.

4.3 Distribution based on country of publication

The study of the shortlisted articles demonstrates that the SSCM research, optimization, and
Industry 4.0 have extensive publishing coverage in 25 countries worldwide. Table 3 indicates

Figure 3 Pie chart-based distribution of publishers

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a thorough breakdown of research articles based on the country. China, with a total of 13
publications, has produced most of the research in the field among the 85 documents that
were selected. In addition to India, Brazil, Iran, the UK, Korea, Turkey, and the USA are the
prominent nations that contribute to synthesizing and reinforcing the theoretical
underpinnings of SSCM research work incorporating optimization and industry 4.0. At the
same time, the adoption of Optimization and Industry 4.0 in the context of SSCM was found
to be extremely low in several countries, including Greece, Morocco, Netherlands,
Switzerland, and Thailand.

However, there are many scenarios for SSCM research in various developing countries.
These are considered outsourcing destinations because they can provide an affordable
workforce, which is the most crucial component of supply chain planning. China, India, and
Brazil provided 31% of all research publications. China and India
(Hei et al., 2019; Yadav, Kumari, and Kumar, 2021)
made the most extensive contributions to building a theoretical
foundation and case study-based studies in SSCM; on the other hand, Brazil & UK made the
largest contributions to quantitative applications, including mathematical and optimization-
based studies. The groundwork for the implementation of optimization and I4 techniques in
SSCM was developed with the aid of research articles from China and the UK.

Table 3 Country-wise breakdown of articles

Country Articles Percentage SCP MCP

China 13 15.29 9 4

India 8 9.41 5 3

Brazil 5 5.88 2 3

Iran 5 5.88 2 3

United Kingdom 5 5.88 2 3

Korea 4 4.71 2 2

Turkey 4 4.71 2 2

USA 3 3.53 3 0

Canada 2 2.35 1 1

France 2 2.35 0 2
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Germany 2 2.35 1 1

Italy 2 2.35 2 0

Mexico 2 2.35 1 1

Poland 2 2.35 2 0

Portugal 2 2.35 2 0

Spain 2 2.35 2 0

Australia 1 1.18 0 1

Bulgaria 1 1.18 1 0

Colombia 1 1.18 1 0

Denmark 1 1.18 0 1

Greece 1 1.18 1 0

Morocco 1 1.18 1 0

Netherlands 1 1.18 0 1

Switzerland 1 1.18 1 0

Thailand 1 1.18 0 1

Total 85 100.00 50 35

SCP- Single Country Production; MCP- Multiple Country Production

4.4 Research Method Used

We have analyzed and noted the research method used for each shortlisted article. The
research methods used in the literature include case studies, surveys, mathematical models,
conceptual and theoretical models, interviews, other research methods such as systematic
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literature review, and industry focus. The distribution of research articles based on the
research method is shown in Figure 4. From the analysis, it’s evident that case study-based
papers have got the highest contribution. It's intriguing to see that industry practitioners and
academic researchers have expressed a strong concern in thoroughly examining the case
applications within these case studies. As the primary research method, case studies are used
in about 27% of papers, followed by articles based on mathematical models (21%) and
conceptual model studies (17%). Even though conceptual models are found in 17% of the
studies, there is a vast scope for improvement in this area because there is no standard model
for optimization and I4 implementation in SSCM. Researchers may create a model by
combining optimization with I4-based decision-making strategies.

Several situations (Quariguasi Frota Neto et al., 2010b; Tsai et al., 2021) described in the
literature illustrate the advantages of adopting optimization and I4 methods by integrating
decision-making methodologies. Other studies (Barbosa-Povoa et al., 2018) have contributed
to the development of the theoretical underpinning and frameworks that are equally useful for
practitioners and researchers.

CS- Case Study; MM- Mathematical Model; CM-Conceptual Model; SV- Survey; IT-
Interview; OT-Others

4.5 Industry under consideration

Figure 4 Classification based on the research method used

Every study looks incomplete until its applications are evident and it helps the growth of the
national economy. The contribution of industries to a nation's economy is of crucial
importance. Similarly, it becomes critical to find the applications of optimization and I4 in
SSCM across various businesses.

This evaluation will encourage industry practitioners to implement optimization and I4

strategies inside their process system to achieve organizational sustainability. According to
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the categories in the preceding subsections, it is possible to conclude that case study-based
studies are of great relevance to academics. The sector-wise distribution of selected research
material is shown in Table 5.

The manufacturing industry has the most considerable number of published case application
articles, followed by the food industry and the IT & services sector. Practitioners of the
modern supply chain are confronted with obstacles such as shorter product life cycles, rapid
delivery needs, and sustainable product design and disposal. In addition to these industries,
the healthcare, pharmaceutical, process, electronics, and construction sectors have profited
from using optimization, and I4 approaches in SSCM.

Table 5 Industry-wise distribution

S. No. Industry Sector Number of Articles

1 Manufacturing 15

2 Food Industry 13

3 IT & Services 10

4 Healthcare 9

5 Pharmaceutical 8

6 Process 7

7 Electronics 7

8 Construction 6

9 Textile 4

10 Others 6

4.6 Prominent Authors

For every classification study, it is critical to learn about the authors actively adding to the
knowledge through their research. An early-stage researcher might find exploring a specific
domain's most renowned research undertakings and perspectives very challenging. Therefore,
it would be simpler for the new generation of researchers to investigate the most prominent
studies published by the top authors, which could be identified. They can also utilize it to
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uncover new research ideas as well. A total of 293 writers accounted for 85 articles analyzed
in the current literature review. Here by ‘author,’ we mean authors as well as co-authors.

Table 6 highlights the list of top authors contributing to SSCM research considering
optimization and I4. Among these contributors is Barbosa-Povoa AP, with a total of 4 articles
leading the list of researchers who helped create the theoretical basis, application-oriented
studies, and conceptual framework. Zhang Y also sits at the top, contributing four articles in
almost all research dimensions. Further, Bui T-D, Lim MK, Mota B, and Tseng M-L are
among the top contributors with high research credentials.

Table 6: Most Prominent Authors

Authors Articles h-index i10-index

Barbosa-Povoa AP 4 58 157

Zhang Y 4 31 38

Bui T-D 3 13 15

Lim MK 3 17 20

Mota B 3 9 6

Tseng M-L 3 76 255

Carvalho A 2 61 260

Chan FTS 2 5 3

Ehtesham Rasi R 2 7 7

Gao J 2 62 146

Govindan K 2 112 296

Gunasekaran A 2 135 461

Guo Y 2 62 253

Kumar N 2 105 526

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Kumari R 2 14 19

Lewi S 2 80 147

Lin C 2 71 167

Liu W 2 15 15

Mangla SK 2 62 128

Nascimento MCV 2 16 19

Sarkar B 2 58 199

Sitek P 2 16 20

Tautenhain CPS 2 4 2

5. Content analysis

After comprehensively classifying screened articles, we describe the literature through a

cluster-based content analysis. Our content analysis was built around three major clusters-
Optimization, Industry 4.0, and Sustainability aspects. In each cluster, we summarized the
contributions of the most cited documents and proposed future research questions under each
cluster.

5.1 Cluster 1: Optimization

Optimization techniques such as multi-objective optimization, stochastic programming, linear

programming, supply chain network design, and route optimization have helped businesses
cut costs and improve their performance. We identified 45 documents under this cluster.
(Govindan et al., 2014)
, which has received 434 citations, developed a multi-objective
optimization model that integrates sustainability in decision-making for distribution in a
perishable food supply chain network. The proposed technique aims to optimize
environmental and economic objectives in a perishable food supply chain network by
assessing the number and location of facilities and optimum products delivered to
downstream stages and routes at each level. Additionally, the article offers a novel multi-
objective hybrid technique called MHPV that integrates two recognizable multi-objective
algorithms (MOPSO and AMOVNS) to solve the problem. The study results imply that the
proposed MHPV technique achieves better results than other methods. (Mota et al., 2018) ,
with 101 citations, proposed a standard model for planning and designing closed-loop
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sustainable supply chains. It also considers interrelated problems such as facility location,
supplier selection, capacity determination, purchase levels definition, intermodal and
unimodal transportation network options, technology selection and allocation, supply
planning, remanufacturing, and product recovery. The authors have addressed three
dimensions of sustainability through objective functions like Life Cycle Analysis
methodology, Net Present Value, and GDP-based metric. (Liu et al., 2021) , which received
29 citations, presents an integrated model which solves the location-inventory-routing
problem for perishable products considering carbon emissions, economic cost, and product
freshness. The authors have developed a multi-objective planning-based model with
constraints based on the real location-inventory-routing scenario. Table 7 shows the identified
research gaps and corresponding future research propositions.

Table 7 Proposed research questions enumerated from cluster 1.

Research Gap Proposed Research Questions (PRQs)

1. No studies have been conducted to  How does the performance of

compare the performance of different different multi-objective optimization
multi-objective optimization algorithms algorithms vary for sustainable
for sustainable supply chain network supply chain network design?
design.

2. Optimization models should incorporate  How can factors such as labor rights
other aspects of social sustainability for and human capital development, be
the design and planning of a sustainable incorporated into the sustainable
supply chain. supply chain network optimization
model?

3. Consider some additional factors in the  What is the effect of factors such as
optimization model concerning decisions customer demand and product
on location inventory routing in the availability in optimizing the location
supply chain of perishable products. inventory routing process?

 What is the effect of vehicle speed on

carbon emissions and economic cost
in perishable goods supply chain
operations?

5.2 Cluster 2: Industry 4.0

This cluster highlights the application of Industry 4.0 techniques in the context of SSCM. A
total of 40 documents were identified under this cluster. (Manavalan and Jayakrishna, 2019),
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with 376 citations, proposed a framework for reviewing the preparedness of supply chain
organizations from numerous perspectives to fulfill the conditions of Industry 4.0
transformation. The article also emphasizes the significance of sustainability and technology
deployment, such as the Internet of Things, in attaining organizational goals.
(Mastos et al., 2020)
, which attracted 85 citations, demonstrates the impact of an Internet of Things-based
solution on sustainable supply chain management (SSCM) performance. It also investigates
how implementing a state-of-the-art industry 4.0 technique can improve sustainability both at
the organization and supply chain levels. The study attempts to bridge the gap between
theoretical advancements and real-world application cases concerned with Industry 4.0 and
sustainable supply chain management.

With 42 citations, (Jiao et al., 2018) , suggest data-driven techniques to facilitate a robust
closed-loop supply chain design that alleviates greenhouse gas emissions and uncertainties in
the system. The authors developed two models - an adaptive robust model (ARO) and a
distributed robust optimization model (DRO) - for designing waste disposal facilities and
carryings of closed-loop supply chains. (Balaman et al., 2018) , which has obtained 40
citations, developed an innovative bi-level decision support system (DSS) to assist the
optimization and modeling of multi-product, multi-technology supply chains and co-modal
transportation networks for biomass-based production, uniting two multi-objective
optimization models. The authors designed a regional supply chain transportation network
utilizing the UK’s complete West Midlands (WM) region as a testing space to investigate the
feasibility of the proposed solution methodology and solutions. They also conducted
sensitivity and scenario analyses to deliver further comprehension into the optimization and
design of biomass-based supply chains. Table 8 highlights the identified research gaps and
corresponding future research questions.

Table 8 Proposed research questions enumerated from cluster 2.

Research Gap Proposed Research Questions (PRQs)

1. Few studies evaluate the business's  What criteria should be used when
readiness for adopting Industry 4.0 for assessing a company's readiness for
sustainable supply chains. Industry 4.0 transformation, and how
could those criteria vary depending
on different industries or
organizations?

2. The existing literature provides little or no  What challenges or risks are

insights into potential challenges or risks associated with implementing
associated with implementing Industry 4.0 Industry 4.0 techniques in medium-
technologies on an industrial scale. and large-scale industries?
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3. There is a lack of understanding regarding  Which data-driven approaches would

which data-driven approaches would work work best when dealing with multiple
best when dealing with multi-uncertainties uncertainties in a closed-loop supply
such as buyers’ expectations, demands, chain?
and recovery uncertainties in a closed-
loop supply chain.

4. There is a need for an effective decision  How to incorporate sustainability in a

support system (DSS) that can aid in decision support system that aids in
modeling and optimizing multi- modeling and optimizing multi-
technology, multi-product supply chains, technology and multi-product supply
and co-modal transportation networks chain operations?
while considering economic and
environmental considerations within the
same framework.

5.3 Cluster 3: Sustainability aspects

Major themes under this cluster include social, economic, and environmental sustainability,
circular economy, energy conservation, and Greenhouse gas (GHGs) monitoring and control.
85 documents in the literature discussed these themes of sustainable supply chain
management. (Jabbarzadeh et al., 2018) , with a maximum of 155 citations in this cluster,
provided intuition into the interplay between sustainability and resilience in supply chains.
The article distinguishes tactics that businesses can utilize to enhance their performance in
both aspects. The study underlines the importance of information sharing and collaboration
among supply chain stakeholders. With 111 citations, the article by (Kumar et al., 2017)
demonstrated how to optimize orders among suppliers while factoring in all three dimensions
of sustainable performance. They devised a methodology for assessing various aspects such
as lead time, quality, cost, waste minimization, energy usage, social contribution, and
emission utilizing fuzzy AHP. The authors proposed a fuzzy multi-objective linear
programming technique allocating orders among suppliers.

(Zhang et al., 2014) , with 91 citations, recommended a framework that aids decisions
concerning supply chain planning, design, and expansion problems, and visibly computes the
trade-offs between environmental, economic, and responsiveness performance. The
recommended framework facilitates us in deciding trade-off interactions between different
objectives such as GHG emissions, total cost, and lead time. The framework was verified in
an industrial test case via real-world data extracted from a Dow Chemical organization. The
results revealed visible trade-offs between the three distinct objectives.

(Dubey et al., 2015) , which accounted for 74 citations, developed a responsive, sustainable
supply chain network that can react to a handful of degrees of uncertainty due to unavoidable
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forces. The study shows a comparative analysis that is based on three formulations
performing different parameter-sensitive analyses and test scenarios in terms of CPU time,
final output, degree of closeness towards the ideal solution, level of conservatism, degree of
satisfaction, and degree of balance involved in developing a compromise solution.
(Yadav, Kumari, Kumar, et al., 2021)
, with 57 citations, developed an SSCM model that considers
carbon emissions and wastage reduction. The article proposes an optimization technique to
attain the optimal values of batch size, production rate, preservation investment, and the
number of shipments to maximize the total profit of the system. The study also identifies
potential product combinations that can lead to profitability based on the cross-price elasticity
of demand. Table 9 underlines this cluster's identified research gaps and respective future
research questions.

Table 9 Proposed research questions enumerated from cluster 3.

Research Gap Proposed Research Questions (PRQs)

1. There is a lack of understanding about  How do resilience and sustainability

how resilience and sustainability interact interact in a supply chain
with each other in the context of the environment? What is the industry-
supply chain. wise comparison between these two
dimensions?

2. There is no discussion on how different  What strategies should companies

businesses and countries have adopted employ to ensure sustainability across
sustainable supply chain management their value chain?
practices.

3. Future studies should focus on developing  What are the potential indicators to
new indicators to help quantify quantify sustainability performance in
sustainability performance more a supply chain?
accurately in a supply chain.

4. There is limited exploration of  How to simultaneously integrate

environmental and social dimensions sustainability's environmental and
regarding large commercial corporations' social dimensions into large-scale
sustainability initiatives. commercial businesses?

5. More data must be collected regarding the  What is the effect of preservation
effects of preservation technology technology investments on reducing
investments on reducing waste in large- waste in large-scale businesses?
scale businesses.
 Applied Energy Conference

6. Discussion

6.1 Conceptual Framework

After presenting a comprehensive classification of the research material, and cluster-wise

content analysis, we propose a conceptual framework, as shown in Figure 5. This framework
helps in understanding the outcomes of the use of Industry 4.0 and optimization techniques
which later directs toward achieving sustainability in supply chain management.

Frequently occurring themes coming under Industry 4.0 cluster include Artificial Intelligence
(Ethirajan et al., 2020) & Big Data Analytics (Jain et al., 2022) , Internet of things (IoT)
(Manavalan and Jayakrishna, 2019) , Blockchain (Mangla et al., 2022) , Digitalization
(Belhadi et al., 2022) and Data-driven decision-making (Zhang et al., 2017).

Similarly, the other ‘optimization’ cluster includes articles discussing the application of
techniques such as Linear Programming (Lee and Chung, 2022) , Stochastic Modeling
(Fattahi et al., 2021) , Fuzzy Mathematics (Tseng et al., 2021) , Multi-objective optimization
(Abbassi et al., 2022; Tautenhain et al., 2021) , Supply Chain Network Design
(Sundarakani et al., 2021)
, Spatially Explicit Modeling, and Route optimization (Peng et al., 2022).

The adoption of optimization and Industry 4.0 techniques have offered numerous advantages
to businesses, some of which include- monitoring and control of the use of resources in
production processes, smart and efficient logistic operations (Esmaeilian et al., 2020) ,
transportation planning to reduce carbon footprint, and efficient use of raw materials and
energy in production processes (Sarkar et al., 2021) , smart inventory management. Our
analysis also suggests that the use of optimization and I4.0 techniques has contributed to the
areas such as environmental, social & economic sustainability (Hall et al., 2012b) , circular
economy (Kazancoglu et al., 2021; Mahroof et al., 2021; Mishra et al., 2023) , closed-loop
supply chain (Bisheh et al., 2018), energy conservation, life cycle assessment
(Gao and You, 2017)
, and Greenhouse gas (GHGs) emission monitoring & control (Zeng et al., 2022b). This
is a testament that optimization and Industry 4.0 are potential enablers for achieving
sustainability in supply chain operations.

6.2 Theoretical and Managerial Implications

This study extensively reviews the existing sustainable supply chain management literature at
the interface between optimization and Industry 4.0 techniques. The trend analysis revealed
that it is a growing research area and continues to be a major field of work for researchers.
The further country-specific analysis highlighted that Asian countries are leading the research
in this domain while European and some African countries are underrepresented. The cluster-
wise content analysis results revealed ample opportunities for researchers to explore in the
future. The proposed research questions identified under each cluster can potentially become
a starting point for future studies in the field.
 Applied Energy Conference

Furthermore, a conceptual framework that explains the types and optimization and industry
4.0 techniques that have helped businesses incorporate aspects of sustainability into their
businesses was proposed. This framework will guide policymakers and stakeholders in
decision-making and resource allocation. Intending to review the existing literature
thoroughly, this piece of work derives valuable insights into financial literacy. Overall, this
study serves as a reference excerpt of major trends, key themes, directions for future research,
and a theoretical guideline for exploring research on integrating optimization and Industry 4.0
for sustainable supply chain management (SSCM).
Applied Energy Conference

Figure 5 Framework for the use of optimization and Industry 4.0 techniques in SSC
 Applied Energy Conference

7. Conclusion

7.1 Key Outcomes

One of the key goals of this study was to conduct a systemic literature review of existing
literature that examines the use of optimization and I4 techniques in sustainable supply chain
management. We investigated research articles from 2010 to 2022 in the Scopus database.
Then we performed a screening of available literature based on numerous parameters, and
finally, we were left with 85 documents which we analyzed for further literature evaluation.
These 85 articles were then put under scrutiny based on several classification categories. The
prominent highlights of the classification include the following- there has been a remarkable
growth of research in the area since the year 2017; the Journal of Cleaner Production, and
International Journal of Production Research and Sustainability are among the top sources of
publications in the area, similarly if we talk about the countries, developing economies such
as China, India, and Brazil are leading the chart and case study based articles are highest
when we speak about classification based on research method. After presenting this
classification, we have developed a conceptual framework explaining how optimization and
I4 techniques enable businesses to incorporate sustainability into their supply chain
operations. The proposed classification study and the developed framework will be helpful
for practitioners and researchers belonging to all areas since it underlines the best practices in
the context of applying optimization and I4 in SSCM. Given the massive growth of research
in the area, we also believe there are numerous research opportunities that researchers can
explore in the future.

7.2 Limitations

Future research pursuits require the studies to add to the body of knowledge. Still, at the same
time, they are expected to align with existing studies while contributing to identified research
gaps. We believe the three sustainability dimensions- social, economic, and environmental-
are not new to researchers or practitioners. Still, it’s necessary to remember that we need to
incorporate the balance between these three dimensions while pursuing our future research.

This study attempts to explain the recent advances in sustainable supply chain management
research with particular attention to optimization and industry 4.0 techniques. We have
classified our shortlisted research articles based on year of publication, top journals and
publisher, country of 1st author, the research method used concerned industry, and most
contributing authors to develop a better understanding of the area. This study will facilitate
the practitioners to undertake and redirect their future research better. Despite our best
efforts, this study comes with its own set of limitations which we have described below -

 We have obtained the articles for our study using search keywords such as
‘optimization,’ ‘industry 4.0’, and ‘sustainable supply chain.’ This is quite possible
 Applied Energy Conference

that we missed some of the articles which do not have these keywords in the title or
abstract but still talk about these themes.

 We have not used several categories of classification, such as top affiliations,

classification based on the research design, etc. These can be included in future
studies.

 The presented framework is based on the classification results in findings and our
understanding of the research material. This framework can be subjected to testing
and validation in future research endeavors.

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