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Engineering Management Journal
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/uemj20
Industry 4.0 in Logistics and Supply Chain
Management: A Systematic Literature Review
Maryam Abdirad & Krishna Krishnan
To cite this article: Maryam Abdirad & Krishna Krishnan (2020): Industry 4.0 in Logistics and
Supply Chain Management: A Systematic Literature Review, Engineering Management Journal
To link to this article: https://doi.org/10.1080/10429247.2020.1783935
Published online: 13 Jul 2020.
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Industry 4.0 in Logistics and Supply Chain Management:
A Systematic Literature Review
Maryam Abdirad, Wichita State University
Krishna Krishnan, Wichita State University
Abstract: “Industry 4.0” is a concept that focuses on automa-
tion of system and process, digitalization, and data exchange in
industries. Its goal is to achieve a smart factory to reduce lead
time to respond to the customers’ demand or to unforeseen
events and improve productivity in the system. Using this
concept can lead to improvements in manufacturing, supply
chain (SC), and logistics. The adoption of Industry 4.0 in
supply chain management (SCM) is a new and critical subject
with a need for more research. A few studies have started
reviewing the existing works on Industry 4.0; however, they
do not focus on its role in SCM. This paper presents
a systematic review and synthesis of the current literature on
Industry 4.0 in SCM that brings out some interesting findings,
which will be helpful for the academic and industry, especially
top managers. This work identifies three categories from the
content of the papers as exploratory vs. confirmatory, qualita-
tive vs. quantitative, management level vs. process/technology
level. Additionally, based on the Topic Modeling (TM) techni-
que, three different clusters of Supply Chain, Logistics and
Manufacturing topics were extracted. Current shortcomings,
challenges, and future research directions are discussed in the
conclusion.
Keywords: Industry 4.0, Smart factory, Supply chain, Logistic,
Internet of Things
EMJ Focus Areas: Supply Chain Management, Supply Chain
& Logistics - Industry 4.0, Technology Management
H
aving a modern and agile supply chain (SC) is cur-
rently the goal of every company, because a modern
supply chain (MSC) is fast, automatic in the process
(accept orders, preparing orders and distribute to customers),
more flexible, and transparent. Moreover, an MSC can work in
dynamic systems and with a high volume of data (Barata et al.,
2017; Butner, 2010; Yin et al., 2018).
As an example of MSC, Amazon has millions of orders
every day. Amazon robots fetch and pick up those orders and
bring them to the employees to fill them at the right time. An
interesting example of the use of Big Data technologies in the
area of MSC is DHL. Big Data makes it possible to analyze the
data at a more advanced level than traditional tools allowed.
By collecting and evaluating big data from customers, DHL
can provide customers with information on potential inter-
ference of their respective supply chains. It is possible to
protect and also to improve the efficiency of the supply
chain and no operation interruption in the system. It is pro-
mising to permanently achieve customer satisfaction (Wit-
kowski, 2017).
To have an MSC system to address dynamic conditions, it
is necessary to embrace a concept that facilitates transitioning
from the traditional SC to an MSC. Industry 4.0 is a concept
that focuses on automation, digitalization, and networking in
companies. It helps companies develop a flexible supply chain
system when they are faced with dynamic systems, especially in
enabling integration among all elements of the SC, including
suppliers, manufacturers, and customers. Because Industry 4.0
focuses on mobility and real-time integration, it can be a good
framework in MSCs (Barata et al., 2017).
The first industrial revolution began with the development of
water power and steam power and the mechanization of the
production system in 1784. The second industrial revolution
changed the production system to a mass production system
and advanced assembly lines using electricity in the 1870s. The
third industrial revolution was the automation of production
processes by using computers in 1970. The fourth industrial
revolution leads to the integration of systems through digitaliza-
tion among devices by using IoT and cyber-physical systems
(CPSs) (Lu, 2017), termed as Industry 4.0 (Da Xu, Xu, & Li,
2018; Tang & Veelenturf, 2019). Armengaud et al. defined Indus-
try 4.0 as “the comprehensive introduction of information and
communication technology (ICT) as well as their connection to an
internet of things, services and data, which enables a real-time
production. Industry 4.0 means a higher degree of digitalization
for products, value creation chain, and business models. Industry
4.0 supports digitalization by IT solutions and connections to
improve productivity and reduce costs” (Armengaud et al.,
2017). Exhibit 1 demonstrates the four industrial revolutions.
All companies can use Industry 4.0 for their project. In
Industry 4.0, project managers are the key leaders of projects
with significant strategic importance for the future of companies.
Companies require to have well-informed managers about Indus-
try 4.0 to apply it in their company. Managers who are interested
in digitalization will play a significant role as companies move
forward. They lead companies to digitalization and use innova-
tion, for example, connect sensors to vehicles, attach radio fre-
quency identification (RFID) to delivery packages and/or utilize
cloud technology to restore data. These technologies can help
managers to make timely decisions, reduce risk and increase
productivity (Saucedo-Martínez et al., 2018). This research can
be a decent start for managers to understand Industry 4.0 and
identify the major classification of supply chain in Industry 4.0.
This research provides a review of the existing literature on
Industry 4.0 and MSC and highlights advances, gaps, and future
directions for further research. Although there has been an
increasing interest in applying Industry 4.0 in manufacturing
and logistics systems in recent years, a large gap still exists in the
knowledge regarding the concepts of this topic, in industries and
academic venues (Qin et al., 2016). The goal of this research is to
Refereed Research Manuscript. Accepted by Associate Editor LaScola Needy.
Engineering Management Journal Vol. 00 No. 00 2020 1
systematically review the relevant studies: (1) to find and report on
various articles about the existing knowledge on the topic, (2) to
perform content analysis of the current papers by authors’ opinion
and Topic Modeling (TM) and find research key topics and areas,
and (3) to synthesize research outcomes to frame conclusions and
possibilities for future research.
This paper is organized as follows: Section 2 presents the
fundamental concept of Industry 4.0 and its role in the supply
chain. Section 3 explains the research method applied in this
paper. Section 4 provides a review of the research trends.
Section 5 reviews and analyzes the content of the selected
papers with two different approaches, authors’ opinion and
TM of the papers. Section 6 discusses avenues for research in
the supply chain with the advent of Industry 4.0. Section 7
presents conclusions and future work.
Concept and Role of Industry 4.0
Concept of Industry 4.0
The concept of Industry 4.0 was presented in 2011 by Henning
Kagermann (former top manager of the SAP software corpora-
tion in Germany) (Paprocki, 2016). Industry 4.0, referred to as
the “Fourth Industrial Revolution,” is also known as “smart
manufacturing,” “industrial internet” or “integrated industry”
(Hofmann & Rüsch, 2017). This concept is increasingly becom-
ing more popular and has been receiving attention all over the
world (Liao et al., 2017; Rennung et al., 2016). According to
Google Trend, the number of google searches that contained
the term “Industry 4.0” and “Fourth Industrial Revolution”
began in 2012 and 2015 and there has been an upward trend
as of December 2018, which shows the popularity of this topic
(Exhibit 2). This graph shows that industry 4.0 is an emerging
topic and needs more research. This is a wonderful opportunity
for researchers to develop their research expanse and managers
to discover more about this topic and figure out how they can
implement Industry 4.0 in their companies. However, an exact
definition of Industry 4.0 has not been determined yet. As said
by Lopes de Sousa Jabbour et al. (Lopes de Sousa Jabbour et al.,
2018), “the core feature of Industry 4.0 is connectivity between
machines, orders, employees, suppliers, and customers due to
the Internet of Things (IoT), and electronic devices; as
a consequence, firms are able to produce products using decen-
tralized decisions and autonomous systems.”
The primary focus of Industry 4.0 is to have a smart
manufacturing network based on digitalization and automati-
zation where machines and products interact with each other
with no human involvement (Gilchrist, 2016; Vladimirovich
Sokolov et al., 2017). Moreover, the outcome of Industry 4.0
is the development of factory smart systems that included smart
machines, smart devices, smart manufacturing processes, smart
engineering, smart logistics, smart suppliers and smart pro-
ducts, etc. (Kamble et al., 2018; Li, 2018; Schmidt et al., 2015;
Shrouf et al., 2014).
Industry 4.0 promotes the use of cyber-physical systems, Inter-
net of Things, Internet of Services (IoS), robotics, big data, and
cloud manufacturing, thus including devices, machines, production
modules, and products and applying them to various fields such as
the supply chain, manufacturing, and management, especially to
respond in real time (Pereira & Romero, 2017; Kang et al., 2016;
Moeuf et al., 2018; (Haddud et al., 2017). Machine learning (ML)
algorithms, artificial intelligence (AI), business analysis (BA), and
optimization, especially dynamic optimization (DM), are applicable
techniques for implementing Industry 4.0 in a system, to maximize
automation. Readers interested in these topics can refer to several
references: (Kolberg & Zühlke, 2015; Neugebauer et al., 2016;
Saucedo-Martínez et al., 2018; Wank et al., 2016; Zheng et al., 2018).
Role of Industry 4.0 in the Supply Chain
Industry 4.0 is expected to have a significant impact on supply
chains, business models, and processes in order to achieve an
MSC. Researchers use different names for Industry 4.0 in the
supply chain management context: digital supply network
(DSN), Internet of Things, E-Supply Chain, Supply Chain 4.0,
E-logistics, or Logistics 4.0. As explained in the previous sub-
section, Industry 4.0 increases digitalization and automation in
manufacturing, and creates a digital process to facilitate inter-
action among all parts of a company. By implementing Indus-
try 4.0 in the supply chain systems, four main SC elements—
integration, operations, purchasing, and distribution—are
affected and can increase the productivity of companies as
well (Kayikci, 2018). The main benefits of Industry 4.0 in the
SC are to reduce the lead time for delivery of products to
customers, reduce the time to respond to an unforeseen
event, and to prompt a significant increase in decision-
making quality (Barreto et al., 2017). Industry 4.0 can help
companies afford complicated and dynamic processes in their
SC and to handle large-scale production and integration of
customers (Rennung et al., 2016). Industry 4.0 can bring posi-
tive benefits in current sales and operations planning and also
in the logistics process (Santos et al., 2017). After implementing
Exhibit 1. The Four Industrial Revolutions
First Industrial Revolution
•Mechanization, Water and Steam Power Engine (1784)
Second Industrial Revolution
•Mass Production, Assembly Line using Electriacal Energy (1870)
Third Industrial Revolution
•Use of PLC and IT systems for Automation (1970)
Fourth Industrial Revolution
•Use of IoT and Cyber Physical System (Today)
2 Engineering Management Journal Vol. 00 No. 00 2020
Industry 4.0, real-time information can be shared across this
digitalized process to drive useful decisions.
Research Method
Based on the topic and existing research in this area,
a systematic review on Industry 4.0 in logistics and SCM was
implemented. For this paper, a structured review methodology
was adopted based on the five steps (Denyer & Tranfield, 2009)
suggested for conducting systematic reviews: (1) question for-
mulation, (2) locating studies, (3) study selection and evalua-
tion, (4) analysis and synthesis, and (5) reporting and using
results (Exhibit 3) (Abdirad & Dossick, 2016).
Step 1: Question Formulation
First, the authors analyzed the general research trends in the
literature from the standpoint of the number of studies on Indus-
try 4.0 in the supply chain, Industry 4.0 in logistics, and related
subjects, evaluating the context of studies and different methods.
Second, the authors analyzed findings from the existing research,
the state of research on this subject, and the pros and cons of
previous studies. Then, two questions were formulated to guide
the data collection and analysis, as shown in Exhibit 4.
Step 2: Locating Studies
The relevant research related to the particular review questions
were located, selected, and appraised (Denyer & Tranfield,
2009). Five search keyword phrases: “Industry 4.0 and Supply
Chain,” “Industry 4.0 and Logistics,” “Smart Supply Chain,”
“E-Logistics,” and “E-Supply Chain,” were used to access Goo-
gle Scholar first, because this search engine shows most of the
results from all databases. To identify relevant papers, the title,
abstract, or keywords contained were analyzed. Furthermore, it
was decided to look at other major research databases, includ-
ing Taylor & Francis, Emerald, Elsevier, IEEE, and Springer, to
determine whether relevant papers could be found.
Keyword selection and database lists caused some limita-
tions on finding papers in this research. Because this subject
topic is still new, all review papers in this research were pub-
lished after 2014. Furthermore, only those papers written in
English were selected.
Step 3: Study Selection and Evaluation
To evaluate relevant studies on this topic, the authors reviewed
the content of each paper. They selected related papers, which
discuss supply chain in smart factories with Industry 4.0
Exhibit 2. Number of Google Searches Conducted between January 2012 to December 2018 by Contains the Keywords “Fourth Industrial
Revolution” and “Industry 4.0”
0
20
40
60
80
100
120
Jan
2012
Jan
2013
Jan
2014
Jan
2015
Jan
2016
Jan
2017
Jan
2018
Fourth Industrial Revolution: (Worldwide) Industry 4.0: (Worldwide)
Exhibit 3. Steps of the Systematic Review in Current Research
Question
Formulation
Locating
Studies
Study Selection
and Evaluation
Analysis and
Synthesis
Reporting and
Using Results
Exhibit 4. Question Formula and Analysis Criteria
Question 1: What are the Trends in Industry 4.0 Based on the SC?
Analysis
Criteria
Number of studiesPublication datesResearch
methodsDatabaseGeographical location
Question 2: What is the existing knowledge addressed in research?
Analysis
Criteria
Content analysis based on selected categoriesContent
analysis based on TMOutcome, advantages, and
disadvantages of adopted categoriesResearch gaps and
future research
Engineering Management Journal Vol. 00 No. 00 2020 3
approach. Because there were not enough published papers in
this area and more papers were needed for this research, the
authors looked to other databases like Wiley and Semantic
Scholar. They selected relevant published papers with potential
content about this research, which did not appear during the
search of the selected papers.
Exhibit 5 presents the number of reviewed and selected
papers in each database. A total of 56 out of 507 papers were
selected and included in the analysis.
Step 4: Analysis and Synthesis
In this step, each individual study was analyzed based on the
two questions mentioned above in Exhibit 4. The first question
involved a search for the trends of existing research in Industry
4.0 and the supply chain, for example, the number of studies,
publication date, geographic location (country), and research
method, which classified selected papers into six categories:
survey, interview, case study, content analysis, literature review,
and modeling.
To answer the second question about current research
efforts, collected papers were analyzed and categorized as
exploratory vs. confirmatory, qualitative vs. quantitative, and
management level vs. process/technology level. For example,
a paper could be categorized as exploratory, quantitative, and
process level. Additionally, TM was used to cluster papers
based on their content, and terms that appropriately character-
ize each cluster were derived using the non-negative matrix
factorization (NMF) method. The following results and analysis
are based on these two aforementioned approaches: human
expert and machine based.
Step 5: Reporting and Using Results
According to the methodology, in this step, the research results
are presented based on the evaluation of selected papers by
defined categories explained in the last subsection and also TM.
on the report of the results, research gaps are determined and
recommendations for future research are made. At the end of
this paper, a summary and conclusions are presented.
Review of Research Trends
Exhibit 6 shows the chronological distribution of publications
from 2014 to 2018. The first paper related to Industry 4.0 based
on the supply chain was published in 2014, and there is
a significant growth in the number of studies on Industry 4.0
in the supply chain until 2017. Because the last selected papers
were published in July 2018, this year’s data does not follow the
trend, because it represents only about half of the year.
In Exhibit 5, Elsevier had more contributions with the
highest number of publications of 16 papers, followed by Tay-
lor and Francis with 9 papers. This indicates that Industry 4.0
in the supply chain is a priority topic and covered by the major
publishers and repositories.
In this study, 14 out of 56 papers on the topic of Industry
4.0 in the SC were published in Germany, as shown in Exhibit 7.
As is known, Germany is a pioneer in this research area, which
this study confirmed. As shown previously in Exhibit 6, the
number of studies in this area is increasing, and more scholarly
research on Industry 4.0 in SC from other countries is expected
in the near future.
In terms of research methods implemented in prior
research, most papers (31 out of 56) involve a content analysis
of Industry 4.0 in the SC (Exhibit 8), which indicates a paucity
of other empirical research methods such as surveys, interviews,
case studies, modeling, etc.
Content Analysis
To answer the second goal of current research, to find research
key topics and areas, the authors performed content analysis
based on two approaches, the first one is based on human
Exhibit 5. Number of Reviewed and Selected Studies in Each
Database
Database (Number of Reviewed Papers) Number of Selected Papers
Taylor & Francis (123) 9
Emerald (56) 6
Elsevier (37) 16
IEEE (27) 2
Springer (211) 7
Wiley (38) 1
Other (15) 16
Total 507 56
Exhibit 6. Number of Publications Distributed by Year
0
5
10
15
20
25
30
2014 2015 2016 2017 2018
Number of Publications
Year
Exhibit 7. Geographical Locations of Studies
0
5
10
15
Number of Publications
Country
4 Engineering Management Journal Vol. 00 No. 00 2020
expert analysis and the other one is TM. In the next two
subsections, the results are presented. Furthermore, the most
important trends, issues and findings are discussed.
Human Expert Content Analysis
In line with the literature, whether or not conceptual Industry
4.0 is more important than technical Industry 4.0 in the supply
chain is an open question. The authors did not find any com-
prehensive literature classification for the supply chain based on
Industry 4.0. Therefore, the authors reviewed selected papers to
analyze the content. They classified them into three dimensions:
exploratory vs. confirmatory, qualitative vs. quantitative, man-
agement level vs. process/technology level.
In the exploratory cluster, selected papers focus on answering
a question about how Industry 4.0 can be implemented within the
company’s supply chain. On the other hand, the confirmatory
cluster uses quantitative methods to analyze or provide an imple-
mentation model (Pfohl et al., 2015). Quantitative studies measure
variables with some precision using numeric scales and analysis.
Qualitative studies are based on direct observation of behavior, or
on transcripts of unstructured interviews with experts. Manage-
ment-level papers propose an approach to support companies in
understanding the needed organizational changes to reach
a digitized supply chain system. In contrast, process/technology-
level papers focus on improving the process and implementation of
concepts and frameworks of Industry 4.0 within company systems
(Pfohl et al., 2015). These classifications are being used in literature
analysis.
Some of the 56 selected papers for this research are review
papers with the same nature as the current research but with
a different focus, method, and content, so they cannot fit into
the management level vs. process/technology level categories.
Therefore, it was decided not to consider them in this part of
their analysis. For example, Barata et al. reviewed the mobile
supply chain management (mSCM) and integration (Barata
et al., 2017). Another paper reviewed the role of IoT and its
impact on IoT on the supply chain (Ben-Daya et al., 2017).
One paper offered a framework to identify Industry 4.0 in the
construction supply chain (CSC) (Dallasega et al., 2018).
Strozzi et al. did a literature review on the “smart factory”
(Strozzi et al., 2017). Y. Zhong et al. prepared another review
paper about Intelligent Manufacturing in the context of
Industry 4.0 (Zhong et al., 2017). A review of Industry 4.0
implications in logistics is dealt with in the work of Barreto
et al. (Barreto et al., 2017). A summary of the classified papers
is presented in Exhibit 9.
Exhibit 9. Analysis Level
Management
level
Process/
technology
level
Qualitative 22 11
Quantitative 1 15
0
5
10
15
20
25
30
Number of Publication
Quantitative Qualitative
Management
level
Process/
technology
level
Exploratory 22 16
Confirmatory 1 10
0
5
10
15
20
25
30
Number of Publication
Confirmatory Exploratory
Exhibit 8. Research Method Applied
0
5
10
15
20
25
30
35
Survey Interview Case
Study
Content
Analysis
Literature
Review
Modeling
Number of Publication
Research Method Applied
Engineering Management Journal Vol. 00 No. 00 2020 5
Management level. Some of the papers classified as
management level papers discussed the concept of Industry
4.0 and the supply chain as well as proposing a framework of
implementing of Industry 4.0 and supply chain. In the
conceptual realm, Industry 4.0 was selected as an innovation
in logistics and the SC (Witkowski, 2017). Researchers believe
that this represents an end to the traditional supply chain and
that they should use the main elements of Industry 4.0, such as
cyber-physical systems, IoT, IOS, smart factory, and big data, in
the development of logistics (Almada-Lobo, 2016; Altendorfer-
Kaiser, 2017; Douaioui et al., 2018; Ivanov, Das et al., 2018;
Kache & Seuring, 2017; Tuptuk & Hailes, 2018) Also, some
research were done about the impact of the new digital
technologies (Strange & Zucchella, 2017), process
improvement (Yin et al., 2018) and servitization (service-
based relationships between SC elements) in Industry 4.0 was
discussed (Ennis et al., 2018).
Other papers in the Management Level concentrate more
on the theoretical framework or propose a conceptual frame-
work which classified them in the qualitative category. For
instance, a categorical framework for manufacturing systems
and Industry 4.0 was identified (Qin et al., 2016). In some
research, a theoretical framework was proposed to evaluate
the concepts and the implementation and use of logistics and
supply chains by Industry 4.0 with respect to pros and cons in
the supply chain systems (Bukova et al., 2018; Hofmann &
Rüsch, 2017; Pfohl et al., 2015). Brettel et al. illustrated
a framework depending on related research streams and topics
in Industry 4.0 (Brettel et al., 2014). Another framework pro-
vides the description of a complex management approach with
the digitalization of the system in the supply chain and logistics
(Diedrich, 2017; Kayikci, 2018; Le Tan & Thi Dai Trang, 2017).
Jayaram proposed an enterprise model for a global supply chain
with support for IoT and Industry 4.0 (Jayaram, 2016).
Moreover, Strandhagen et al. designed a framework related to
logistics and business operations and business models
(J. O. Strandhagen et al., 2017). Additionally, Ivanov et al. looked
into the business, information, engineering, and analytics perspec-
tives on digitalization and SC risk (Ivanov, Dolgui et al., 2018).
Furthermore, a study by Man and Strandhagen proposed an
agenda for research into how Industry 4.0 can be used to create
sustainable business models (De Man & Strandhagen, 2017).
Process/technical level. Based on the literature, Industry 4.0
works with real-time data in dynamic systems. Some papers
worked on a dynamic system of SC. For example, Ivanov et al.
designed an algorithm for short-term supply chain scheduling
in smart factories (Ivanov et al., 2016). Similarly, Sokolov et al.
developed a model for dynamic scheduling of services for
Industry 4.0 supply networks (Vladimirovich Sokolov et al.,
2017). In another research, Dunke et al. looked at the impact
of digitalization on Industry 4.0 and SC planning, and how
online optimization copes with real-time challenges (Dunke
et al., 2018).
In some papers, different technical frameworks for Indus-
try 4.0 and the supply chain have been proposed and imple-
mented. For example, Dweekat et al. presented a framework for
an SC performance measurement approach by using IoT, and
they evaluated their work using real examples (Dweekat et al.,
2017). Avventuroso et al. offered a digital factory framework by
focusing on data management (Avventuroso et al., 2017).
Another paper proposed a framework that identified
a correlation between supply chain risks and suitable Industry
4.0 technologies with an example in a steel SC (Schlüter &
Sprenger, 2016). Dossou and Nachidi chose the graphs with
results and actions inter-related (GRAI) methodology to model
supply chain performance (Dossou & Nachidi, 2017).
Premm and Kim used a model involving multi-agent systems
to achieve logistics modeling approaches based on Industry 4.0
(Premm & Kirn, 2015). In a similar paper, a framework was built
for a digital supply chain (DSC) integration in multi-stakeholder
environments based on the blockchain and Industry 4.0 principles
(Korpela et al., 2017). Chhetri et al. discussed combinations of
different components of a manufacturing SC and Industry 4.0
(Chhetri et al., 2018). Armengaud et al. investigated the impacts
of Industry 4.0 along the entire automotive supply chain on the
production life cycle (Armengaud et al., 2017).
The focus of some papers is on the process of SC and
methods for improving it. For instance, Trstenjak and Cosic
(Trstenjak & Cosic, 2017) discussed process planning in an
Industry 4.0 environment. The benefits of Industry 4.0 and
digitalization were evaluated and analyzed by several research-
ers (Bienhaus & Haddud, 2017; Melnyk et al., 2018; Tjahjono
et al., 2017), and the impacts of Industry 4.0 on the procure-
ment function were explored by Glas and Kleemann (Glas &
Kleemann, 2016). Branislav et al. mentioned the concept of
intelligent logistics in the automotive industry (Branislav &
Jozef, 2016), and the application of a production environment
using manufacturing logistics toward Industry 4.0 was reviewed
by Strandhagen et al. (J. W. Strandhagen et al., 2017). Valverde
and Saadé examined the impact of an e-supply chain (Valverde
& Saadé, 2015), and Szozda explained the challenges to reach
an MSC (Szozda, 2017).
The application of a supply chain model based on Industry
4.0 was shown in a paper by Ignacio et al. that validated their
model in a mobile application (Ignacio et al., 2017). Another
way to evaluate the role of Industry 4.0 is by using RFID
(Bienhaus & Haddud, 2017). Interested readers for empirical
research are requested to refer to several references (Fruth &
Teuteberg, 2017; Majeed & Rupasinghe, 2017; Manoel Queiroz
& Telles, 2018). The results of the Content Analysis are sum-
marized in Exhibit 10.
Further discussion on content analysis. The distribution of
categories based on the stack charts shown previously in
Exhibit 9 indicates that the number of papers in management
level and process/technical level is not equal. Therefore, they
were classified to determine more information about their
content. By delving more deeply into them, it was found that
just 4% of the management-level papers are quantitative and
confirmatory, which means that most of them work on the
conceptual part of management.
From Exhibit 9, it can be seen that 57% of the technical
papers (process/technical level) are quantitative and try to
explain their findings based on an analysis or to show a case-
study. The other papers in this category are quantitative. There-
fore, it appears that there is a general lack of research in the
technical and analytical areas to show the possible ways of
implementing Industry 4.0 and to present some case studies.
This study found that the number of exploratory papers is
more than the number of confirmatory level papers at the
technical level. This means that research is in the early stages,
6 Engineering Management Journal Vol. 00 No. 00 2020
Exhibit 10. Content Analysis Summery Based on Human Expert Approach
Title Exploratory Conrmatory Qualitative Quantitative Management
Process and/
or Technology
A categorical framework of manufacturing for Industry 4.0 and
beyond (Qin et al., 2016)
* * *
A conceptual framework for servitization in Industry 4.0: Distilling
directions for future research (Ennis et al., 2018)
* * *
A dynamic model and an algorithm for short-term supply chain
scheduling in the smart factory Industry 4.0 (Ivanov et al., 2016)
* * *
A Multi-agent Systems Perspective on Industry 4.0 Supply
Networks (Premm & Kirn, 2015)
* * *
A networked production system to implement virtual enterprise
and product lifecycle information loops (Avventuroso et al., 2017)
* * *
A supply chain performance measurement approach using the
internet of things (Dweekat et al., 2017)
* * *
An Industry 4.0 research agenda for sustainable business models
(Man & Strandhagen, 2017)
* * *
Big data analytics in supply chain and logistics: an empirical
approach (Manoel Queiroz & Telles, 2018)
* * *
Challenges and opportunities of digital information at the
intersection of Big Data Analytics and supply chain management
(Kache & Seuring, 2017)
* * *
Concept of intelligent logistic for automotive industry (Branislav
& Jozef, 2016)
* * *
Digital supply chain transformation toward Blockchain
integration (Korpela et al., 2017)
* * *
How virtualization, decentralization and network building
change the manufacturing landscape: An Industry
4.0 perspective (Brettel et al., 2014)
* * *
How transport and logistics operators can implement the
solutions of “Industry 4.0.” (Paprocki, 2016)
* * *
Industry 4.0 and its impact on the functioning of supply chains
(Szozda, 2017)
* * *
Industry 4.0 and the current status as well as future prospects on
logistics (Hofmann & Rüsch, 2017)
* * *
Industry 4.0 as digitalization over the entire product lifecycle:
opportunities in the automotive domain (Armengaud et al.,
2017)
* * *
Industry 4.0, global value chains and international business
(Strange & Zucchella, 2017)
* * *
Integrated scheduling of material flows and information services
in Industry 4.0 supply networks (Vladimirovich Sokolov et al.,
2017)
* * *
Internet of Things (IoT) embedded future supply chains for
Industry 4.0: An assessment from an ERP-based fashion apparel
and footwear industry (Majeed & Rupasinghe, 2017)
* * *
Internet of Things, Big Data, Industry 4.0 – Innovative solutions in
logistics and supply chains management (Witkowski, 2017)
* * *
Issues of implementing electronic supply chain management
(E-SCM) in enterprise (Le Tan & Thi Dai Trang, 2017)
* * *
Logistics 4.0 and emerging sustainable business models
(J. O. Strandhagen et al., 2017)
* * *
Engineering Management Journal Vol. 00 No. 00 2020 7
Exhibit 10. Content Analysis Summery Based on Human Expert Approach (continued)
Title Exploratory Conrmatory Qualitative Quantitative Management
Process and/
or Technology
Lean automation enabled by Industry 4.0 technologies (Kolberg
& Zühlke, 2015)
* * *
Lean Six Sigma approach for global supply chain management
using Industry 4.0 and IIoT (Jayaram, 2016)
* * *
Manufacturing supply chain and product lifecycle security in the
era of Industry 4.0 (Chhetri et al., 2018)
* * *
Framework for digitalized proactive supply chain risk
management (Diedrich, 2017)
* * *
Migration framework for decentralized and proactive risk
identification in a steel supply chain via Industry 4.0 technologies
(Schlüter & Sprenger, 2016)
* * *
Modeling supply chain performance (Dossou & Nachidi, 2017) * * *
New flexibility drivers for manufacturing, supply chain and
service operations (Ivanov, Das et al., 2018)
* * *
Process planning in Industry 4.0 environment (Trstenjak & Cosic,
2017)
* * *
Procurement 4.0: factors influencing the digitization of
procurement and supply chains (Bienhaus & Haddud, 2017)
* * *
Security of smart manufacturing systems (Tuptuk & Hailes, 2018) * * *
Simulation based validation of supply chain effects through ICT
enabled real-time-capability in ETO production planning
(Dallasega et al., 2017)
* * *
Supply chain architecture model based in the Industry 4.0,
validated through a mobile application (Ignacio et al., 2017)
* * *
Sustainability impact of digitization in logistics (Kayikci, 2018) * * *
The best of times and the worst of times: empirical operations
and supply chain management research (Melnyk et al., 2018)
* * *
The effect of E-supply chain management systems in the north
american electronic manufacturing services industry (Valverde &
Saadé, 2015)
* * *
The evolution of production systems from Industry 2.0 through
Industry 4.0 (Yin et al., 2018)
* * *
The fit of Industry 4.0 applications in manufacturing logistics:
a multiple case study (J. W. Strandhagen et al., 2017)
* * *
The impact of digital technology and Industry 4.0 on the ripple
effect and supply chain risk analytics (Ivanov, Dolgui et al., 2018)
* * *
The Impact of Industry 4.0 on procurement and supply
management: A conceptual and qualitative analysis (Glas &
Kleemann, 2016)
* * *
The impact of Industry 4.0 on the supply chain (Pfohl et al., 2015) * * *
The industrial management of SMEs in the era of Industry 4.0
(Moeuf et al., 2018)
* * *
The Industry 4.0 revolution and the future of Manufacturing
Execution Systems (MES) (Almada-Lobo, 2016)
* * *
The influence of big data on production and logistics
(Altendorfer-Kaiser, 2017)
* * *
The interaction between Industry 4.0 and smart logistics:
concepts and perspectives (Douaioui et al., 2018)
* * *
8 Engineering Management Journal Vol. 00 No. 00 2020
and investigators are still exploring this subject more than
confirming that their hypotheses are correct.
To answer raised the question of this section, the authors
claim that most of the articles have more focus on conceptual
Industry 4.0 in the supply chain than technical Industry 4.0 in
the supply chain. Therefor more papers are needed to explain
more about the technical part of Industry 4.0 in the supply
chain.
Content Analysis Based on Topic Modeling
TM is a type of unsupervised machine learning that uses
clustering to derive latent variables or hidden structures of
words in documents (O’Callaghan et al., 2015). The goal of
applying TM is to find possible hidden topics in selected papers
and offer them to researchers for future researches. Therefore,
TM was selected to cluster abstracts of 56 collected papers.
To apply TM, the data were cleaned and preprocessed to
remove non-informative stop words, capitalization, suffixes,
prefixes, digits, and punctuation to make it ready to use in
Python programming language.
Term weighting. In this technique, important terms give
higher weights in the document term matrix. The common
approach for term-weighting is term frequency-inverse
document frequency (TF-IDF). The following formula is used
to calculate term-weighting by TF-IDF,
w t;Dð Þ ¼ tf t;dð Þ� log n
df tð Þ
þ1
which tf(t,d) is the number of times that term t occurs in
document d. n is the total number of documents. df(t) is the
number of terms that appear in documents.
The (TF-IDF) were calculated for selected papers. As
a result, a ranking of the top 20 terms was determined, which
shows a very rough sense of the content of the document
collection (Exhibit 11).
Non-negative matrix factorization topic modeling. One
approach for TM to discover the hidden thematic structure in
a large dataset is to apply matrix factorization methods, such as
independent component analysis, singular value
decomposition, and non-negative matrix factorization (NMF).
One advantage of NMF is to generate a matrix with the positive
component that causes a better result for TM than the other
methods. Therefore, it was decided to apply the NMF method
(Rakesh et al., 2009). The output of the NMF method contains
the term weight for each of the k topics and documents
membership weights.
Parameter selection. It is applied to evaluate the different NMF
topics and to select a useful value for the number of topics.
A common approach for parameter selection is the topic
coherence approach, to measure and compare the coherence
of models generated for different values of k clusters
(Coherence is called TC-W2 V as a measure to evaluate topic
models).
In this research, the minimum K = 2 and the maximum
K = 10 were chosen as the number of topics for parameter
selection. Additionally, to build a word-embedding model for
coherence measure, which is used for the calculation of coher-
ence, the Stanford Natural Language Processing (NLP) dataset
was selected. As shown in Exhibit 12, the highest mean coher-
ence (0.5030) by the top five terms, three topics (Topic 1:
manufacturing, Topic 2: supply chain, and Topic 3: logistics)
were selected for this database, which is explained below.
Exhibit 10. Content Analysis Summery Based on Human Expert Approach (continued)
Title Exploratory Conrmatory Qualitative Quantitative Management
Process and/
or Technology
The position of Industry 4.0 in the worldwide logistics chains
(Bukova et al., 2018)
* * *
Time traps in supply chains: Is optimal still good enough? (Dunke
et al., 2018)
* * *
What does Industry 4.0 mean to supply chain? (Tjahjono et al.,
2017)
* * *
Review Papers
Digitization in maritime logistics—What is there and what is
missing?
*
Industry 4.0 as an enabler of proximity for construction supply
chains: A systematic literature review
*
Industry 4.0 implications in logistics: an overview *
Intelligent Manufacturing in the Context of Industry 4.0:
A Review
*
Internet of things and supply chain management: a literature
review
*
Literature review on the ‘Smart Factory’ concept using
bibliometric tools
*
Mobile supply chain management in the Industry 4.0 era *
Engineering Management Journal Vol. 00 No. 00 2020 9
Topic 1: manufacturing, production, industry, product,
systems
Papers assigned to this topic generally discuss the integra-
tion of smart manufacturing systems and production systems,
how to implement Industry 4.0 in these systems, and the effects
of Industry 4.0 on them.
Topic 2: supply, chain, SCM, risk, management
This topic includes papers that explain Industry 4.0 and its
impact on SCM as well as the effect of integrated SCM on the
IoT and digitalization technology. There is also an attempt to
determine if there is any risk to control it.
Topic 3: logistics, industry, information, business, solutions
The selected papers on this topic show that Industry 4.0 is
changing in industries, logistics, and their business. Some
Exhibit 12. Number of Topics Based on Coherence
Exhibit 11. Term Frequency-Inverse Document Frequency
Number Top Word Weight
1 Industry 5.98
2 Supply 5.58
3 Chain 4.34
4 Production 3.70
5 Manufacturing 3.56
6 Logistics 3.50
7 Research 3.16
8 Systems 2.89
9 Data 2.76
10 Technologies 2.75
11 Business 2.62
12 Industrial 2.52
13 Supply Chain Management 2.50
14 Paper 2.49
15 Information 2.45
16 Product 2.36
17 Management 2.33
18 Chains 2.30
19 Companies 2.15
20 Literature 2.14
10 Engineering Management Journal Vol. 00 No. 00 2020
researchers introduced Industry 4.0 as solutions for process
improvement in their system.
Because all paper subjects are about Industry 4.0, the word
“industry” is repeated in the selected papers. This word has
appeared in topic 1 and topic 3. It means that this word is in
the top five terms of selected papers for topic 1 and topic 3.
Other words of supply chain or manufacturing and logistics
make each topic very especial.
The extracted research topics indicate that, to have MSC,
Industry 4.0 affects three major aspects of smart factories
including; supply chain, logistics and manufacturing. To benefit
from these results, these three key topics proposed for future
research of researchers who are interested in Industry 4.0 in
supply chain topic.
Discussion of Avenues for Research in Supply Chain with
Advent of Industry 4.0
In the current research, the content analysis of the selected
papers is explained in two ways: the first one based on the
authors’ systematic review, and the second one based on the
TM that leads this research to find research gaps and future
research opportunities.
●Most of the papers in the technical part of this literature
review point out an aspect of a conceptual or technical
framework. However, there remains a large gap, which
needs more technical papers to explain the possible process
and technical implementations.
●The small number of studies show the impact of Industry
4.0 on the supply chain and to evaluate it by showing
different case studies before and after implementation.
Furthermore, it is necessary to know what the achieve-
ments would be after implementing Industry 4.0 in com-
pany systems or if there is any impact on company
productivity.
●Almost half of the technical papers identified a type of
framework to integrate Industry 4.0 into a specific supply
chain. However, these studies could not come up with
a generalizable framework for an Industry 4.0-based supply
chain that could be used as an implementation guideline by
companies (firms).
●Small number of studies worked in the quantitative area,
suggesting that there is not enough research based on
analytical results and proves the lack of technical know-
how in this area.
●Each of the proposed topics can be searched and negotiated
in three different classification approaches (exploratory vs.
confirmatory, qualitative vs. quantitative, management
level vs. process/technology level) as well which are applic-
able for engineering managers and researchers.
Contributions and Implication
The present research provides research opportunities for
researchers. First, this research reflects the current state of
research on this topic. Second, developing a classification of
reviewed papers is insightful and could be further used by
researchers for similar studies in Industry 4.0 and supply
chain. Third, the clustering of selected papers based on topics
and methodologies represent the main aspects of this topic for
future research. Finally, the fourth contribution of this research
is proposed new research topics by incorporating TM and
classification. Researchers can modify their research line by
incorporating the provided insight gained from this paper
(Daneshvar Kakhki & Gargeya, 2019).
Challenges
There is a set of challenges for implementing Industry 4.0. For
instance, the lack of technology infrastructure makes its imple-
mentation hard. Furthermore, there is a shortage of experts and
knowledgeable employees in this area to start a new system or
remodel the current system to obtain the maximum outcome.
Additionally, for most of the managers, it is not yet clear if
there is any benefit or return on the investment and the payoff
period is also not known. Consequently, there is insufficient
support and commitment from managers for implementing
Industry 4.0. According to Industry 4.0, implementing this
standard would minimize human involvement and interactions
in the system. A question remains as to what would happen to
employees who lose their job. Industry 4.0 needs to address
system integration and ways to increase reliability in this envir-
onment. Further research in this subject would clarify these
vague points, potential benefits and the effects on companies’
productivity.
Managerial Implications
Currently, most major companies are employing engineering
managers for various leadership roles. Engineering managers
coordinate and direct projects, create detailed plans to accom-
plish goals and lead the integration of technical activities.
Besides analyzing pertinent technologies and assessing the fea-
sibility of projects, an engineering manager’s responsibilities
also include planning and directing the installation, testing,
operation, maintenance, and repair of better technologies for
facilities and equipment. Therefore, implementing Industry 4.0
is a tremendous undertaking as a part of their responsibilities.
Hence, an exposure to the concept is important for them to
facilitate executing their responsibilities more effectively. If the
engineering manager reads this research, they become more
familiar with Industry 4.0 and different perspectives and char-
acteristics about it. Additionally, they become familiar with
different infrastructures in smart factories for Industry 4.0 in
supply chain. In its contribution to the engineering managers,
this paper shows Industry 4.0 can provide a framework for
addressing productivity, traceability, transparency, and effi-
ciency in the production system which are known as grand
challenges in Engineering management. This review helps engi-
neering managers understand the pros and cons of Industry 4.0
and where the current state of Industry 4.0 in supply chain that
focuses more on theoretical Industry 4.0, thus providing better
decision criteria for the application of Industry 4.0 and digita-
lization. They can be familiar with how companies approach
Industry 4.0 and implement digitalization. As observed, the
number of companies willing to experiment and implement
Industry 4.0 is very low for various reasons, such as payback
period and initial capital requirement. Thus, a better under-
standing of the technology helps managers make better-
informed decisions toward the implementation process.
Limitation
The authors explored the major limitations of this study. First,
for this research like other systematic papers specific keywords
Engineering Management Journal Vol. 00 No. 00 2020 11
such as “Industry 4.0,” “supply chain,” or “logistics” was used
(Abdirad & Dossick, 2016). The second limitation of this
research is the English language that removes non-English
papers. Some other existing papers, especially in German,
were ignored in this research. The third limitation of this
work is the scarcity of articles on this subject that limit TM
results in three clusters, and it was impossible to create sub-
clusters to develop this research. The final limitation of this
work is the potential flaws in the search process. Authors spent
a considerable amount of time in the search process finding
papers. Despite their effort, and due to the scope of work, some
papers might be neglected accidentally or because of the errors
in the search process (Daneshvar Kakhki & Gargeya, 2019).
Suggestions
The small number of papers on this topic indicates that
a comprehensive solid document is lacking. It is suggested
that a set of guidelines be written by experts in this area to
serve as a dependable reference for factories when starting and
developing this new concept. More than suggested research
topics, there is an opportunity for further research with
a narrower concentration on identifying factory roles in Indus-
try 4.0 in the supply chain. As mentioned in the introduction,
this subject is very applicable to real-time problems. For future
work, topics such as “The Role of Industry 4.0 in the Supply
Chain Using a Dynamic Problem Approach” is suggested.
Implementing Industry 4.0 is complex and challenging. One
of the major topics of research in this field as future works is
defining where, when and how companies should implement
Industry 4.0 in their SC approach. Another topic would be to
determine the advantages and disadvantages of Industry 4.0
implementation and the total cost and benefits of implementa-
tion to companies.
Conclusion
In this paper, the authors conducted and reported a systematic
literature review to highlight the trends, advances, and gaps in
research on the application of Industry 4.0 in supply chain
management. The findings from the literature review of this
study show that Industry 4.0 is seen as a concept that has an
important role in the MSC. By applying this concept, human
interaction would be minimized, and productivity would be
increased in companies (Kayikci, 2018).
This work provides both researchers and managers with an
insightful description of the current state of research in Indus-
try 4.0 in supply chain and related future trends in research and
practice. Moreover, through the analysis performed, the results
showed that supply chain, logistics, and manufacturing are
affected areas through Industry 4.0 in supply chain. Adding
three dimensions; exploratory vs. confirmatory, qualitative vs.
quantitative, management level vs. process/technology level to
three mentioned clustered, open new research topics for
researchers, as well. These fields can be a baseline for engineer-
ing managers to start Industry 4.0 implementation and for
researchers to develop their research in these areas. These can
be the important topics for future research and need further
investigation. However, the limited number of professionals
with expertise in Industry 4.0 is a factor that limits advance
research and discussion about this subject.
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About the Authors
Maryam Abdirad earned her BS in Industrial and System
Engineering and her MS in System Engineering at Florida
Institue of technology. She is currently a PHD candidate of
Industrial and Manufacturing Engineering at Wichita State
University. Her primary research interests are in supply chain,
facilities planning, operation research analyst, and heuristic
algorithms.
Krishna Krishnan is a Professor of Industrial and Manu-
facturing Engineering at Wichita State University. He teaches
courses on Facilities Planning and Material Handling, produc-
tion systems and system design. His primary research interests
are in supply chain, facilities planning, material handling, and
integrated manufacturing systems. He has been involved with
several funded projects from NSF and local industries such as,
Boeing, Cessna, and Learjet.
Contact: Maryam Abdirad, Industrial, Systems and Manu-
facturing Engineering Department, Wichita State University,
1845 Fairmount St., Wichita, KS 67260, USA; mxabdirad@wi-
chita.edu
Engineering Management Journal Vol. 00 No. 00 2020 15
