MSc IN LOGISTICS AND SUPPLY CHAIN
MANAGEMENT
SCHOOL OF ECONOMICS
Dissertation Project
Assessing the Impacts of Supply Chain 4.0
Solutions on Supply Chain Operations
Ntanatsas Christos
Supervisor: Dr. Michael Madas
ABSTRACT
The digitalization - digital transformation of the firms does not focus only
on the creation and adoption of the new technologies. It can also consist an
important tool for the redesign of a company in the way by which it can
connect its data, its people, and its processes both in the internal and the
external environment of the company. As its point of reference, the fourth
industrial revolution has the combination of knowledge and training of
machines, data science (Data Science) and technical intelligence (Artificial
Intelligent), which both can and do create an innovative, useful, and
competitive environment for companies (MIT Forbes 2019). Most companies
focus on the digital formation of their supply chains (digital supply chain).
In order for the companies to become competitors and increase their profits,
they must invest in the digitization of supply chains. Thus, their main
priority is the training of staff in the latest technological innovations.
Nowadays, the rate of digitization has improved considerably due to the
possibilities that companies now have. The creation of new applications
resulted in many improvements in areas such as transactional activities,
the planning and organization of the entire supply chain, the management
and coordination of the warehouses, the forecast of the demand and, finally,
the acceleration of analysis in decision - making. By applying digitization
and new technologies, the new innovations that offered various benefits to
supply chains and logistics services were Augmented Reality (AR), Big Data
(BD), Cloud Computing (CC), Robotics (R), Internet of Things (IoT), SelfDriving Vehicles (SDV), and 3D Printing (3DP). Another part of the
digitalization are the customers. When the customers are at the center of
the digital ground, the influence of digitalization is more effective. In short,
digitization focuses on new technologies for the purposes of improving
service levels, flexibility, costs, leadership, forecasting, inventories, as well
as for the purposes of properly implementing the processes and
organizational changes that will occur, in order to achieve the goals that
businesses aim for. Although innovations and digital transformations have
had a serious impact on supply chains, especially on the digital supply
chain, they were presented with many challenges. The present thesis
examines the impact supply chain 4.0 technologies have on supply chain
operations. It utilizes the Scor model, including functions, metrics and Kpis.
The purpose of the present thesis is to analytically present each individual
technology and critically evaluate their effects. The method that is used to
evaluate the impact supply chain 4.0 technologies have on supply chain
operations is the House of Quality. The key findings of the thesis are, all
operations in the supply chain are interconnected with no clear borders
among them. Thus, some technologies can result in either positive or
negative effects. Despite the challenges, it is concluded that the supply
chain will become smarter, more transparent and efficient at every stage,
through digital transformation and the use of intelligent technologies.
1
Table of Contents
ABSTRACT ..................................................................................................... 1
CHAPTER 1 .................................................................................................... 7
Introduction .................................................................................................... 7
1.1 Overview ................................................................................................ 8
1.2 Supply Chain 4.0 ................................................................................. 9
1.3 The reasons to evaluate SC 4.0 technologies on SC operations. ....... 10
1.4 Aim and objectives .............................................................................. 12
1.5 Outline of the thesis ............................................................................ 13
CHAPTER 2: METHODOLOGY.................................................................. 15
SYSTEMATIC LETERATURE REVIEW .................................................... 15
2.1 SYSTEMATIC LETERATURE REVIEW .......................................... 16
2.2 Systematic literature review justification. ........................................ 17
2.3 Systematic literature review steps ..................................................... 18
2.3.1. Mapping score research ............................................................... 18
2.3.2. Define Research Scope ................................................................. 19
2.3.3. Identify the keywords and the literature sources. ..................... 20
2.3.4. Search strings ............................................................................. 20
2.3.5. Define the search criteria. ........................................................... 21
2.3.6. Data extraction ........................................................................... 22
2.3.7. Analyze and synthesize the data. ............................................... 23
2.3.8. Writing Up ................................................................................... 23
CHAPTER 3 .................................................................................................. 24
DESCRIPTIVE ANALYSIS ......................................................................... 24
3.1 Chronological Analysis ....................................................................... 25
3.2 Geographical Analysis ........................................................................ 25
3.3 Quantitative and qualitative approach .............................................. 27
3.4 Overview of existing SC 4.0 Technologies. ......................................... 28
CHAPTER 4 .................................................................................................. 37
Supply chain 4.0 Technologies ..................................................................... 37
Internet of Things: IoT.............................................................................. 38
Big Data..................................................................................................... 39
Cyber-physical System.............................................................................. 40
Intelligent Transportation System ........................................................... 42
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3D-printing ................................................................................................ 43
Cloud Computing ...................................................................................... 44
Drones........................................................................................................ 46
Artificial Intelligence ................................................................................ 46
Blockchain ................................................................................................. 48
Internet of Services ................................................................................... 50
Deep Learning ........................................................................................... 52
RFID (Radio Frequency Identification).................................................... 53
Data mining............................................................................................... 55
Augmented Reality ................................................................................... 57
Robotics ..................................................................................................... 59
Smart Sensors ........................................................................................... 61
Automatic guide vehicles (AGV) ............................................................... 62
Real Time Locating System ...................................................................... 64
Autonomous mobile robots (AMRs) - Warehouse Robots ........................ 65
Machine 2 Machine ................................................................................... 68
Self-driving Vehicles ................................................................................. 70
CHAPTER 5 .................................................................................................. 72
SUPPLY CHAIN OPERATIONS REFERENCE (SCOR) ........................... 72
MODEL ......................................................................................................... 72
5.1 Introduction ......................................................................................... 73
5.2 SCOR Model ........................................................................................ 74
5.2.1 Process ........................................................................................... 75
5.2.2 Metrics and performance attributes ............................................ 78
Performance attribute ........................................................................... 79
Metrics .................................................................................................... 80
CHAPTER 6 .................................................................................................. 83
Impact Assessment ....................................................................................... 83
6.1 Introduction ......................................................................................... 84
6.2 The combination of SCOR model and Technologies with HoQ ......... 85
6.3 The reasons to use QFD-HOQ. ........................................................... 86
6.4 Quality Function Deployment (QFD) ................................................. 87
6.5 House of Quality Chart (HOQ) ........................................................... 88
6.6 Previous Relevant Research. .............................................................. 90
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6.7 Assessment of Supply Chain 4.0 Solutions on supply chain
operations. ................................................................................................. 91
6.8 Results ................................................................................................. 95
6.8.1 The profile of the experts .............................................................. 97
6.8.2. Discussion of results .................................................................... 97
CHAPTER 7 ................................................................................................ 101
Challenges and Barriers ............................................................................ 101
7.1 CHALLENGES AND BARRIERS ....................................................... 102
7.1.1. Political challenges .................................................................... 102
7.2.2. Environmental Challenges and Organizational Challenges .... 103
7.3.3. Technical Challenges ................................................................. 104
7.2 CONCLUSIONS- FUTURE RESEARCH ........................................... 105
REFERENCE ............................................................................................. 107
4
List Of Figures
FIGURE 1.1: TRADITIONAL SUPPLY CHAIN ..................................................................... 11
FIGURE 1.2: SUPPLY CHAIN ECOSYSTEM ......................................................................... 11
FIGURE 1.3: OUTLINE OF THE THESIS ............................................................................. 13
FIGURE 2.1:SYSTEMATIC LITERATURE REVIEW ............................................................. 17
FIGURE 2.2: RESEARCH SCOPE ........................................................................................ 19
FIGURE 2.3: PROCESS OF DATA EXTRACTION ................................................................. 22
FIGURE 3.1: YEAR OF PUBLICATIONS .............................................................................. 25
FIGURE 3.2: CONTINENT DISTRIBUTION OF ARTICLES .................................................. 26
FIGURE 3.3: COUNTRY DISTRIBUTION OF ARTICLES ..................................................... 27
FIGURE 3.4: STUDY METHOD DISTRIBUTION ................................................................. 28
FIGURE 4.1: REPRESENTS THE ARCHITECTURE OF IOT ................................................. 39
FIGURE 4.2: REPRESENTS THE ARCHITECTURE OF BIG DATA ....................................... 40
FIGURE 4.3: REPRESENTS THE ARCHITECTURE OF CPS ................................................. 42
FIGURE 4.4: REPRESENTS THE ARCHITECTURE OF ITS .................................................. 43
FIGURE 4.5: THE ROLE OF 3D-PRINTING TO SUPPLY CHAIN .......................................... 44
FIGURE 4.6: THE OVERVIEW STRUCTURE OF CLOUD COMPUTING .............................. 45
FIGURE 4.7: PRESENTS THE ROLE OF DRONE IN A LARGE WAREHOUSE ........................ 46
FIGURE 4.8: THE ARTIFICIAL INTELLIGENCE IN THE WAREHOUSE .............................. 48
FIGURE 4.9: THE BLOCKCHAIN TECHNOLOGY .............................................................. 50
FIGURE 4.10:THE INTERNET OF SERVICES ..................................................................... 51
FIGURE 4.11: PRESENTS THE ARCHITECTURE OF DEEP LEARNING ................................ 53
FIGURE 4.12:THE ARCHITECTURE OF RFID SYSTEM ..................................................... 55
FIGURE 4.13:PRESENTS THE ARCHITECTURE OF DATA MINING .................................... 57
FIGURE 4.14:THE AUGMENT REALITY SYSTEM .............................................................. 59
FIGURE 4.15:THE ROBOTS AND DRONES IN THE WAREHOUSE...................................... 60
FIGURE 4.16:PRINCIPAL COMPONENTS OF THE ARCHITECTURE OF SMART SENSORS .. 62
FIGURE 4.17:EXAMPLES OF AVG IN THE WAREHOUSE ................................................. 64
FIGURE 4.18:ARCHITECTURE OF RTLS .......................................................................... 65
FIGURE 4.19:EXAMPLE OF THE AMR IN THE WAREHOUSES ......................................... 66
FIGURE 4.20:EXAMPLE OF WAREHOUSE ROBOTS........................................................... 68
FIGURE 4.21:PRESENTS THE ARCHITECTURE OF MACHINE 2 MACHINE ...................... 69
FIGURE 4.22:THE SELF-DRIVING VEHICLE, ROBOT AND THE DRONE ............................ 71
FIGURE 5.1:SCOR MODEL .............................................................................................. 74
FIGURE 5.2:SCOR MODEL'S LEVELS ............................................................................... 76
FIGURE 6.1: THE STEPS FOR THE OVERALL RESEARCH METHODOLOGY ...................... 86
FIGURE 6.2: HOQ CHART ................................................................................................ 89
5
List Of Tables
TABLE 1: THE DATABASES USED FOR THE RESEARCH .................................................... 20
TABLE 2: SEARCH STRINGS .............................................................................................. 21
TABLE 3: FINAL ACCOUNT OF DATA ............................................................................... 23
TABLE 4: KEYWORDS FREQUENCY ................................................................................. 30
TABLE 5: LITERATURE REVIEW TABLE ........................................................................... 31
TABLE 6: SCOR MODEL PROCESS ................................................................................... 76
TABLE 7: SCOR MODEL ATTRIBUTES ............................................................................. 80
EXPERT PANEL SURVEY ........................................................................................... 94
MASTER EXPERT PANEL .......................................................................................... 96
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CHAPTER 1
Introduction
The first chapter is the introductory chapter of the present thesis. The
structure of this chapter is explained by the following figure:
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1.1 Overview
According to the Council of Supply Chain Management (CSCM), supply
chain includes many activities and methods for the best coordination
between the suppliers and the final customers. In other words, supply chain
is the key for the original suppliers, partners, companies, and stakeholders
to share, use and produce information, services, and products with others
or among them (Schrauf & Berttram, 2016). The rapid changes and growth
that occur in the technological and financial aspects have an impact on the
supply chains development (MacCarthy et al., 2016). In order for the
companies to achieve their main goals, to “stay” competitive and capable of
maximizing their revenue and minimizing their risks, they must
incorporate the new technologies in their operating environment (Kaprova,
2017). Nowadays, technology is a necessary factor in the supply chain of all
firms, which try to adapt to these changes as rapidly as possiblein order to
operate better (Sehgal, 2011). In this new digital era, the influence of the
fourth industrial revolution (Industry 4.0), such as the Information and
Communication, Internet of Things (IoT) etc., based on the cyber-physical
system (CPS) for the logistics services and the supply chain applications,
leads to digitalization (digital transformation). The digitalization can be
considered as a digital transformation in logistics, which improves the ways
of communication and the exchange of information through the supply
chain. For example, the communication between the supply chain
managers, logistics service providers and final customers can be performed
under an online network. Because of the applications and technologies of
Industry 4.0, the digitalization in the supply chain has made the companies
and many industries to direct their attention in these transformations.
(Büyüközkan & Göçer, 2017). With the influence of industry 4.0, terms such
as ''Supply Chain 4.0'', ''Digital Supply Chain'' DSC, ''Digital Logistics'', we
can now be seen in the field of industries and companies. These terms
illustrate the application of the digital technologies in the process of
manufacturing, trading and logistics, which are connected by a network in
order for the final products or services to arrive at the final consumers.
Specifically, in the modern era, supply chain 4.0, or digital supply chain
(DSC), can be defined as a sum of interconnected processes that associate
with the coordination, planning and controlling of services or products
between the suppliers and customers. (Büyüközkan & Göçer, 2017).
Moreover, the manufacturing process is constantly evolving by a large scale
and therefore affects all the different parts of the supply chain. The aim is
to educate the big firms on how to improve their revenues, by effectively
using the new digital technologies in their organization.
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1.2 Supply Chain 4.0
As was referred before, the term “supply chain 4.0” can be considered as the
sum of activities of the supply chain, such as design, planning, consumption,
production, distribution and reverse logistics with the use of the digital
technologies, that were developed in the fourth industrial revolution,
“Industry 4.0'”. Particularly, in the modern day, Supply chain 4.0 can be
defined as a number of interconnected processes that associate with the
coordination, planning and controlling of services or products between the
suppliers and customers. (Büyüközkan & Göçer, 2017). Moreover, the
manufacturing process is constantly evolving by a large scale and therefore
affects all the different parts of the supply chain. The firms should adopt
the new technologies rapidly and become aware of how supply chain 4.0 will
help them coordinate their business more effectively. The objective is the
creation of new methods in order to improve communication and
collaboration for consumers and suppliers, manufacturing, forecasting,
acquisition, distribution, sales and marketing, as well as other activities
that happen in the process of supply chain (Chan, 2003). Finally, the most
important aim is for firms to improve their revenue and become more
competitive against other firms (Büyüközkan & Göçer, 2017, Tjahjono et al.,
2017). Supply Chain 4.0 has six characteristics (Wu et al. 2016).
1. Instrumented: The systems with sensors, RFID tags and other meters
that are useful to gather all of the information - data for better decision making.
2. Interconnected: All the supply chain's members are connected, for
example the IT systems (Information Technology), the products, their
assets, and many other objects.
3. Intelligent: Smart systems that are collecting and analyzing a high
number of data, in order to improve decision - making and maximize the
performance.
4. Automated: Automated activities that have the objective of replacing the
resources with the lowest efficiency.
5. Integrated: Integrated activities that are useful among the members for
decision - making, collaboration, communications and using the common
systems.
6. Innovative: The capacity for developing new values after receiving the
most efficient solution for a problem.
In summary, the digital supply chain is a very useful process to gain new
forms of revenue and to develop new technological approaches for the firms.
Supply chain 4.0 is about the way processes are managed under a new
variety of innovative technologies, not about digital goods or services.
9
1.3 The reasons to evaluate SC 4.0 technologies on SC operations.
At first, supply chains link the suppliers with the customers in a linear
manner, in order to deliver the products. In fact, every firm has sourcing
inputs from suppliers and, in turn, delivers the products or services to
consumers. The firms in “simple supply chains” (figure 1.1 provides the
steps of traditional supply chains), or “traditional supply chains”, use
regular methods for the communication between their suppliers and
customers, such as telephone, fax, e-mail or sometimes the negotiations
were performed face to face. These tools were used many years ago and,
even in our days, they form the main means of communication for a lot of
supply chains. However, such means result are neither time, nor cost
effective. Another problem that firms used to have is the difference between
the supply and the demand. This happened, because the process of every
supply chain was to link the customers to the suppliers, with each firm
delivering the products or services to consumers. The aim was for every firm
to successfully deliver the products in the best time and with least
percentage of failure. The planning process was designed to guarantee that
the deliveries and the supplier’s delivery activities were organized in
accordance with the customers’ activities. With the passage of time and the
evolution of technology, various new technologies were applied in the
functions of the supply chain. The purpose was to solve all the above
problems, but also to upgrade the functions of the supply chain in all of its
parts. However, with the application of new technologies, there were
various effects on the logistics. For example, some companies made use of a
new technology, but without knowing the effects it would have on other
functions of the chain. The use of a technology will certainly have different
implications for certain logistics functions. Obviously, the effects for some
functions will be positive and for some negative. Another possibility is that
some of the functions are not affected at all. Moreover, some technologies
did not offer what was necessary in logistics and had a high cost, as they
created disadvantages in several cases. Finally, after considering all of
these problems, SC 4.0 technologies can be applied to supply chain
operations to improve an integrated supply chain ecosystem. The flow of
information is directed in every part of the firm correctly and efficiently and
the response takes place in real time. In order to rapidly respond to the
changes in customer demand and to track and trace them effectively, supply
chains have enabled new sensing technologies, that are called internet of
things (IoT), including radio frequency identification (RFID), Bluetooth,
and GSM (global system for mobile communication), as well as many others.
With these digital transformations, the firms can watch the changes in
customers’ demand and respond rapidly and effectively to them. Thus, with
a good supply chain coordination, the firm can ultimately achieve a
competitive advantage. In conclusion, the need to evaluate all SC 4.0
technologies in logistics functions is very important.
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Figure 1.1: Traditional supply chain
Figure 1.2: Supply chain ecosystem
Source: Michael J. Ferrantino, Emine Elcin Koten
11
1.4 Aim and objectives
Nowadays, the digitalization and the digital transformation of the supply
chain is a topic that one needs to pay attention to. With the rapid
improvement of the industry revolution, the firms adopt all these new
technologies and innovations, in order to become more competitive in the
economic world of the market. The present thesis focused on published
academic journals, articles and books. The present literature review focuses
on the property and characteristics of each technology 4.0 separately, as
well as on the impact they have and the way they are used in the operations
of the supply chain. Additionally, the present literature review tackles the
challenges and barriers posed by digitization. In conclusion, the present
thesis will focus on the review of published academic work on the impact of
SC 4.0 technologies on supply chain operations and, more specifically, on
the analysis of the digital integration of the levels of the SCOR model. The
current thesis examines two main objectives:
Objective 1. To review the impacts of Supply Chain 4.0 technologies on
supply chain operations.
Research Question: What is the impact of SC 4.0 technologies on
supply chain operations?
Objective 2. To present the challenges and barriers.
Research Question: What are the challenges and barriers of digital
transformation?
Keywords: Supply Chain 4.0, Supply Chain operations
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1.5 Outline of the thesis
Figure1.3: Outline of the thesis
Chapter 1
Introduction
Introduce the topic.
Identification of
thesis objectives and
research scope.
Chapter 4
Supply Chain 4.0
Techologies
Chapter 2
Methodology
Define the SLR
methodology
Defines the SLR
process of the
thesis
Chapter 3
Descriptive analysis
Presentetion of the
descriptive results
Analyze and discuss
the findings
Chapter 5
Chapter 6
SCOR model
Impact Assessment
Presentation of SC
4.0
Define the SCOR
model
Define the HOQ
analysis
Define the use of
tecnhologies and
their characteristics
Discussion about the
supply chain
operations and
metrics.
Presentation and
discuss the
thematical results
13
Chapter 7
Challenges-Barriers,
Conclusions and Future
research
Presentations and
define the challenges
and barriers
Summarize the findings.
Identification of future
research opportunities
The systematic literature review technique will be outlined in further detail
in Chapter 2. The SLR model's procedures will be outlined and,
furthermore, a comparison will be made between the traditional research
technique used and the unique research method that the present thesis
utilizes. Additionally, the extent of the research will be analyzed, as well as
the steps that will be taken. More information on how to extract transparent
findings will be provided. The descriptive findings of the investigation,
which will be documented using tables, schemes, and diagrams, will be
presented and discussed in chapter 3. In chapter 4, the SC 4.0 technologies
will be presented and analyzed, along with their use characteristics. In
chapter 5, the SCOR model, metrics, and operations will be presented in
detail. The present thesis’ findings will then be provided and debated in
chapter 6. Barriers and challenges are evaluated and discussed in chapter
7. Finally, in chapter 8, the major findings and conclusions will be
summarized, as well as future research potential will be explored.
14
CHAPTER 2: METHODOLOGY
SYSTEMATIC LETERATURE REVIEW
Overview
The systematic literature review (SLR) is the method that is being used for
the present thesis. The SLR is a method that guarantees safe results
through an analysis of published academic journals and articles. The
process is to gather information (articles or journals) that is related to the
research questions. The results must cover all of the topics and questions
and must be characterized by transparency. There are many steps that
must be followed in order to complete this method. The objective of
systematic literature review is to find, evaluate and concentrate the results
in order to provide ''sincere'' findings. The second chapter mostly presents
the method used in the current thesis, specifically, the SLR (systematic
literature review) method and its basic procedures. The following figure
represents the structure of chapter two:
15
2.1 SYSTEMATIC LETERATURE REVIEW
The method that will be used in the current analysis is not something “new”.
The technique called systematic literature review started to develop in the
field of health sciences, mainly in the United Kingdom, as well as in other
fields of research. Transparency, replicability, and objectivity define the
SLR process. The focus of the systematic literature review is to extract the
best outcomes in each study area. The idea of best outcomes implies safer
assumptions and specific results for the subject of each analysis. In
addition, the aim of this approach is to ensure that there are no cases of
errors or prejudices by processes over personal study. Moreover, with the
tactic of Ray Moynihan and Melissa Sweet, an analysis was used in
healthcare issues in order to achieve as few gaps in the research subject as
possible, as well as a clear image - conclusion for the thesis on the existing
problem (Sweet and Moynihan, et. al. 2007). Repetitive literature tackles
and presents a particular challenge - problem, fills literature gaps and
heads to a conclusion, providing a solution for potential problems
(Baumeister & Leary, et. al. 1997). Therefore, this literature review method
includes a general picture of the facts. It is not an ongoing method that
encompasses all the information - knowledge of a specific the specific
category that it deals with. However, the subjectivity that scholars use for
sampling these papers and journals should be taken into notice, as well as
the fact that conclusions extracted in such a way may not be an object of
reliance in supporting future studies (Weyinmi Demeyin, et. al. 2016). The
following figure (2.1) shows the process of the systematic literature review.
Also, a detailed methodological approach is useful as a part of a systematic
literature review. There are three kinds of literature reviews. The first one
is the theoretical background, which is the most common. It focuses on the
theoretical foundations and on the context of the research and it helps to
bring the questions of the topic into focus. The second one is called literature
review as a chapter of a graduate thesis, also called as thesis literature
review. The last one is the stand-alone literature review. The purpose of
this review is to check a topic in a field, without having collected or analyzed
any primary data. Finally, the SLR method consists of many steps, that
researchers must follow, in order to completely extract the final results.
16
Figure 2.1: Systematic Literature Review
Source: Kitchenham, Brereton,2013
2.2 Systematic literature review justification.
The method that is used is the systematic literature review (SLR). There
are many advantages for conducting this method. First of all, this method
is distinguished by its scope, purpose of the research and by its strictness.
For the researchers, it constitutes a referenced work tool that they can use,
when conducting new research, as it helps them to create a first clear
outline of the literature review. This method has a main purpose and
extracts specific conclusions, that are related with the research. The
existing bibliography is analyzed and can be considered as objective as
possible. Finally, after summarizing the conclusions, it can also be used to
highlight gaps in the current research and provides a framework for future
researchers. On the other hand, there are many disadvantages in
systematic literature review method. The first one is, that there are many
situations in which limited studies are available, because they might not
represent the best knowledge available. Also, a systematic literature review
method is inappropriate, when the questions of the research are too vague
17
or too wide. In such a case, it would yield a lot of different studies. On the
other hand, if the question is too narrow, it would yield very few studies.
Finally, another consideration is whether the researcher has enough time
and energy. For example, in the case that the volume of the bibliography,
which must be analyzed is very large, the question arises whether the
researcher can consume all the amount of his time and energy for
conducting the systematic literature review. After considering all of the
above, the systematic literature review (SLR) method is selected for the
present thesis.
2.3 Systematic literature review steps
2.3.1. Mapping score research (identify the purpose of the research)
This step can also be called the purpose of the research. It is the first step
of the process of the systematic literature review method and it is a very
important part, because it is used to guide the author at aiming and
creating specific questions at the specific field. The choice of questions that
the author develops, moves the entire literature review process
(Kitchenham and Charters 2007). Thus, the choice of academic studies,
books, journals etc. to be included in the review, the method for data
extraction, the criteria the author uses for the selection of studies and
synthesis, should all be orientated towards answering the research
questions. Α very common mistake is to select a question which will guide
to a big amount of data, resulting in the literature review becoming hard to
limit and control. For this reason, it is suggested that researchers use a
prereview mapping to help the subtopics within a research question
(Brereton et al. 2007). After reviewing the literature on the research
question, the researchers have the opportunity to develop a fast-mapping
procedure to identify the methods of the research, which relate to the
research question. Finally, the mapping helps the researchers to decide if
the range of materials should be limited to a specific research question or
to check most of it.
The present thesis focuses on two research questions:
1. To review the impacts of SC 4.0 technologies on SC operations.
2. To present the challenges, barriers, and drivers.
18
2.3.2. Define Research Scope
Before reviewing the ways by which the research will take place, it is
important to define the field of the research. The concept of digital supply
chain is something new for the supply chain managers and for the
researchers (Gülçin Büyüközkan & Fethullah Göçer, 2017). Moreover, it
proved difficult for all companies to adopt the digitization of all processes.
Some of them responded quickly and the results were efficiency and
improvement in all of their areas. On the other hand, there were companies
that found it quite difficult to move forward in this new era. Thus, in this
research, the entire existing bibliography will be reviewed, with its focus
being on digital conversions in the supply chain 4.0. Additionally, the effects
digital transformations have on the supply chain operations will be
examined, as well as which are these specific effects. Finally, the challenges
created for all companies to implement the digitization will be presented.
The next figure (2.2) shows the main areas and the scope of the research.
The main areas of the research are Supply Chain 4.0 and Supply Chain
Operations, the common point of which is the field of the research.
The following figure (2.2) describes the relationship between the two
keywords and the research area between them:
Figure 2.2: Research Scope
Supply
Chain
operations
Supply
Chain 4.0
The research area of the thesis
19
Supply Chain operations: Warehousing, Transportation, Order Fulfillment, Manufacturing,
Procurement, Demand planning, Forecasting, Distribution process, Outsourcing process,
Safety stock planning, Supply network planning, Customer collaboration, Supplier
collaboration, Purchase order, Receipt confirmation, Invoice verification, Production
planning, Manufacturing execution, Inbound and outbound process, Cross docking,
Warehousing and Storage, Physical inventory, Sales order, Billing process, Freight cost
process, Lifecycle planning, Promotion planning.
2.3.3. Identify the keywords and the literature sources.
The second step is to identify the keywords, which will be used in the
present thesis, as well as the literature sources (Table 1) for developing the
entire systematic literature review. The second step, in which the author
needs to identify the keywords is crucial for the process of the systematic
literature review. First, they relate with the objectives of the research, so
the author must identify the keywords with close attention, because they
must relate with the topic and its aims. Moreover, the keywords must
“connect” with the scope of the research, which is very important and one of
the first parts of the research that requires careful attention. Thus, the
objective of the present thesis is reviewing the published research on the
digital transformation on the supply chain 4.0. The keywords for the present
thesis are the following:
Keywords: Supply Chain 4.0, Supply Chain Operations.
Table 1: The databases used for the research
Databases
Description
Scopus
Scopus is a top library database that includes summaries and citations
for academic journal articles. After all these years, it is one of the
databases of first choice, as it covers nearly 21,000 titles from 5,000
publishers, of which 20,000 are peer-reviewed journals in many areas.
The owner of Scopus is Elsevier.
Web of Science is a library of many databases. It is a database itself that
provides many articles and journals for academic use. It is also
considered as one of the top open databases.
IEEE Xplore is one of many research databases. It provides access to
many journals and articles, that relate to different study areas, like
computer science, electrical engineering etc. IEEE Xplore provides 5
million documents, related to the different research areas.
Web of
Science
IEEE Xplore
2.3.4. Search strings
In the systematic literature review, the search method is called Boolean
search (“AND”, “NOT”, “OR”). The combinations of the terms “AND”, “NOT”
and “OR” are used for literature search. The literature search focuses on
different academic articles, books, and journals from different countries of
the world, which were published mainly between 2015 and 2020. The terms
20
''AND'', ''OR'' and “NOT” are used in the literature search with the following
ways:
The term ''OR'' is used in the present thesis and will be included before, or
after, of one or of the two keywords. The term ''NOT'' is not used in the
research. It is used when there is a chance that one of the keywords will
change the results. Finally, the term ''AND'' is used in the research and
explains that the terms before and after the term ''AND'', are both used at
the same time for the search.
Table 2: Search strings
Combination Search strings
(“Supply Chain 4.0”) AND (“Logistics” OR “Warehousing” OR
“Transportations” OR “Order Fulfillment” OR” Manufacturing” OR
“Procurement” OR “Demand planning” OR “Forecasting” OR “Distribution
process” OR “Outsourcing process” OR “Safety stock planning” OR “Supply
String 1+2 network planning” OR “Customer collaboration” OR “Supplier collaboration”
OR “Purchase order” OR “Receipt confirmation” OR “Invoice verification”
OR “Production planning” OR “Manufacturing execution” OR “Inbound and
outbound process” OR “Cross docking” OR “Warehousing and Storage” OR
“Physical inventory” OR “Sales order” OR “Billing process” OR “Freight cost
process” OR “Lifecycle planning” OR “Promotion planning”)
2.3.5. Define the search criteria.
After completing all the previous parts and research in the databases,
identification and selection of the criteria must be performed. As was
referred before, the aim of the present thesis is to provide a complete
overview in the field, that focuses on issues that concern digital
transformations. In order to improve the search results, the next part of the
systematic literature review method is to define the exclusion and inclusion
criteria. Exclusion criteria are the criteria that are used by the author to
limit the academic studies, journals, articles etc. Inclusion criteria are the
opposite, as they are used for the last part of the process.
Inclusion criteria:
1. The review related to the Field (digital transformation) and on the
question of the research.
2. The publication year of the studies are between 2010 and 2020.
3. Must be written in the English.
4. The articles and journals must come from trusted sources.
21
Exclusion criteria
1. Studies that are not related to the field are rejected.
2. Papers with publication year before 2010 are rejected.
3. Not written in English.
4. Not academic types of studies.
The following figure 2.3 represents the process of the data extraction of the
articles, by using the combination search string (1+2) to the three
databases. These articles were filtered by the exclusion and inclusion
criteria according to following table. After being filtered through the
exclusion and inclusion criteria, the overall number of articles that will be
evaluated are 381.
Figure 2.3: Process of data extraction
©
Search string
combination (1+2)
Total articles
18.018
Total articles
Not written in
English
27.653
270
Not academic
types of studies
Total articles
27.383
9365
Before 2010
Total articles
7.735
10.283
Studies not related
to the area
9.902
Total articles
381
2.3.6. Data extraction
Using the above string, an amount of articles was found from the three
databases. After taking into account the inclusion and exclusion criteria,
according with the previous table, the total initial count of the remaining
articles was 381. Thus, the final total sum of articles that will be reviewed
22
after this process is 63. The following table 3 represents the process of the
data extraction in detail.
Table 3: Final account of data
Databases
Without criteria
Initial count
(after criteria)
Final count
Scopus
15.498
Web of Science IEEE Xplore
11.542
613
Count
27.653
205
123
53
381
31
20
12
63
2.3.7. Analyze and synthesize the data.
After the data extraction, the review of the remaining papers starts at the
present stage. There are three critical parts. The first part is the projection
of abstracts and names, the second part is the projection of the text as a
whole and the third part is the calculation of the paper’s quality
(qualitative, quantitative and mixed). In this step, all the articles were
synthesized and organized according with their quality.
2.3.8. Writing Up
It is the last step of the process. After all of the previous steps are over, the
last level is to write up the findings from the research.
23
CHAPTER 3
DESCRIPTIVE ANALYSIS
Overview
In the present chapter the amount of selected articles is being evaluated,
based on the characteristics (keywords, year of publication etc.) and
organized on tables, schemes, and figures. Specifically, the papers will be
classified based on the year of publication, the geographical distribution,
the keywords, and the research methods. Moreover, in the present chapter
the keywords’ frequency, the technologies and the operations will be
presented, according with the literature review. The following figure
presents the structure of chapter three.
24
3.1 Chronological Analysis
The chronological analysis is the presentation of the publication year of the
articles that were selected from the databases (Chapter 2, SLR), after
filtering them though the criteria limitation (Chapter 2), that were used for
the selection of the literature review. As it was mentioned before, one of the
criteria was the publication year of the articles. Specifically, the articles
that were published before the year 2010 were rejected. On the other hand,
the selected articles were published between the years 2010 and 2020. The
interest on the digitalization of the supply chain operation is growing, due
to the development of the technologies over the last years. For that reason,
most of the selected articles were published between 2016 and 2020, so that
all the new technologies and impacts to the supply chain can be presented.
The following Figure (3.1) provides the distribution of the 63 articles:
Figure 3.1: Year Of Publication
number of articles
16
14
12
10
8
6
4
2
0
2010
2011
2012
2013
2014
2015
2016
number of articles
2017
2018
2019
2020
As it was presented in the above figure, the number of the most articles
were published in the year 2019. From the year 2016 to the year 2020, the
digitalization of the technologies and innovations has changed and
improved dramatically. Finally, over the last 4 years, new technologies have
appeared, that also play an important role in the supply chain.
3.2 Geographical Analysis
The geographical analysis confirms that the selected articles were
published in different countries, specifically in 12 different countries. Most
of the selected articles were published in European countries (46%), with
articles published in North/South American countries being the second
highest in quantity (36%). The remaining selected articles were published
in Asian (16%) and African countries (2%). The following figure (3.2)
describes the percentage of the geographical distribution of published
articles, by continent.
25
Figure 3.2: Continent distribution of articles
Distribution Of articles
2%
Europe
36%
46%
Asia
North/South America
Africa
16%
From the above figure (3.2), it can be observed that the biggest percentage
of published articles come from Europe (46%), North/South America (36 %)
and Asia (16%). In Europe, many international companies pay attention in
the new technologies. The same holds true for companies in North/South
America, with the United States of America being the main point of
developing the digitalization. In Asia, countries like China, South Korea,
and Taiwan, play an important role in the field of global digitalization.
Those are the reasons why most of the selected articles were published in
the above three continents.
The following figure (3.3) describes analytically the origins of the literature
review from different countries of the world.
26
Figure 3.3: Country Distribution of articles
Articles
LITHUANIA
GREECE
FRANCE
UKRAINE
CHINA
CALIFORNIA
THAILAND
ENGLAND
SOUTH AFRICA
AUSTRIA
SRI LANKA (COLOMBO)
UNITED ARAB EMIRATES
GERMANY
HAWAI
CANADA-(TORONDO)
INDIA
IRELAND
SPAIN
USA
TURKEY
LATVIA
BRAZIL
NORWAY
RUSSIA
POLAND
SWITZERLAND
1
1
1
1
3
2
1
3
1
2
1
1
5
1
2
5
1
2
14
3
1
5
1
2
2
1
0
2
4
6
Articles
8
10
12
14
3.3 Quantitative and qualitative approach
In the literature review, it is clear that two research methods were used.
These research methods are the quantitative analysis and the qualitative
analysis. The articles, that were selected, followed the qualitative analysis,
as they explain the significance of digitalization, the Supply Chain analytics
and the definition Supply Chain 4.0, which must be understood. For
example, Gülçin Büyüközkan’s and Fethullah Göçer’s article on the digital
supply chain and the impact of digital technologies like blockchain, IoT, Big
data, automation, and robotics, follow a qualitative literature review
analysis (Büyüközkan, Göçer,2017). On the other hand, the rest of the
articles followed the quantitative approach and explain the main points
through mathematical and statistical approaches. For example, the article
by Mariusz Kostrzewski, Monika Kosacka-Olejnik, Karolina WernerLewandowska 2019 (Assessment of innovativeness level for chosen
solutions related to Logistics 4.0), used a statistical and mathematical
approach, in order to analyze which solutions, that are related to Logistics
4.0, are really innovative and which are not. The above article followed the
27
16
quantitative analysis. The following table (3.4) presents the percentage of
articles that followed the quantitative, mixed, and qualitative approach.
Figure 3.4: Study method distribution
Study Distribution
23%
Qualitative
59%
18%
Quatintative
Mixed
3.4 Overview of existing SC 4.0 Technologies.
In the selected articles, many topics are analyzed and a lot of them are
focused on Supply Chain 4.0 technologies. In order to analyze the
digitalization of the supply chain, it was necessary to present all the
technologies that were developed these last 10 years. The main topics that
are analyzed in the selected articles are Augmented reality, Big Data, Cloud
computing, Internet of Things, Robotics, Sensor technology / smart sensors,
Self-driving vehicles, Drone, Cyber-physical systems, RFID, Intelligent
Transportation Systems, Machine 2 Machine sensors, Blockchain, Artificial
Intelligence, Deep learning, Automated Guided Vehicles (AGVs),
Autonomous Mobile Robot (AMR), Real-time locating systems (RTLS), Data
mining, and finally the Internet of Services. The following table 5 (literature
review Table) presents all of the 21 technologies that were found due to the
literature review and the selected articles that referenced them. The
following technologies will be analytically defined in the next chapter. The
importance of supply chain 4.0 technologies has been highlighted in recent
literature and numerous scholars have discussed their uses and
characteristics. The present thesis examines the current literature review
of supply chain 4.0 technologies and supply chain operations from an
28
academic, as well as from an industrial perspective. It is examined how
supply chain 4.0 technologies affect supply chain operations and thus
compile a list of new technologies, as well as which are the challenges barriers of these digital transformations. The research was conducted in
two phases. The first phase consisted of examining the supply chain
operation and metrics. Thus, a part of the literature review focused on the
terms of the operations, on the metrics and on the role they have on the
supply chain. For the description and presentation of the supply chain
operation and level metrics, the Scor model constitutes an important and
useful tool for the current research. For the evaluation of the impacts, the
level 1 metrics of the Scor model has been used. In the following chapters a
detailed description of the Scor model, of the functions and of the metricsKpis is presented. The Scor model was the main pillar of the present
research, because every function and every metric had to be examined and
presented to explain their role and the way the technologies could be used
in combination with them. However, the main part of the literature review
was based on the new Supply Chain 4.0 technologies, that were developed
and are still developing to this day. The roles and the characteristics of each
one of these technologies will be introduced on the next chapter “Supply
Chain 4.0 technologies (Thematical Analysis)”. In the next part of the
present thesis, the metrics (level 1 Scor model) and the new technologies
are necessary to develop the QFD analysis. The QFD (Quality Function
Deployment) or House of Quality (HOQ) is a method used in this research
to clarify and evaluate the impacts that the supply chain 4.0 technologies
have in supply chain operations. Concerning the HOQ, it is the central
nervous system that regulates the entire QFD operation. For new product
design, the HOQ is a well-known and commonly used method. It converts
customer expectations into a sufficient number of engineering goals to be
met by a new product design, based on marketing research and
benchmarking results. The review of academic journals, articles and books
follows a classification process, that explains how the literature is used as
a foundation for the conceptual and theoretical framework. The
classification - methodology used in the present literature, as well as the
introduction of the literature review was explained above. A comprehensive
search is used to acquire relevant academic publications with the goal of
collecting, organizing, and synthesizing existing knowledge. However,
because supply chain 4.0 is a new phenomenon that only developed recently,
the corresponding publication channels are still scattered. The research of
the present thesis focuses on major online databases such as Scopus, IEE
Xplore and Web of Science. Books, peer and non-peer reviewed articles,
industry reports and white papers, are among the literature sources
explored through scientific databases and conventional search engines. As
a result, the current study examines published academic publications and
books to determine the implications and trends of SC technologies in supply
29
chain operations. The research is performed based on the year of
publication, subject, and objective of the published articles.
From the databases (Chapter 2), the keywords for the selection of the
articles were defined. The following table presents the frequency of the
keywords that were used.
Table 4: Keywords Frequency
Keywords
Frequency
Supply Chain 4.0
8
Supply Chain Analytics
14
Digital Transformation
6
Digitalization
5
Technologies
8
Industry 4.0
15
Logistics
9
Challenges
4
Big Data
3
Robotics
1
Artificial Intelligent
5
Internet of Things
7
Cyber Physical System
1
Blockchain
4
Radio Frequency Identification
4
Automated Guided Vehicles
1
Autonomous Mobile Robot
1
Data mining
1
Deep Learning
3
Drones
1
Internet of services
2
Cloud Computing
2
Real Timing Location System
2
Smart Sensors
4
Self-Driving Cars
4
30
Table 5: Literature review Table
Description of articles
Objective
Abbreviations References
This article provides the limitations and benefits
from digital supply chain. The paper provides the
digital supply chain trends, and the knowledge gaps
are consolidated.
The paper analyzes the continuous development
and evolution of supply chains and their impacts in
the economic development. Also, the terms of
supply chain analytics (SC 4.0) and digital
transformation are provided.
The aim of this article is to present the influence
and the impacts of digital technologies in the supply
chain operations and the degree of these effects.
Also, it describes the threats and opportunities of
the digital transformation and industry 4.0.
The paper describes the characteristics and the use
of smart technologies- smarts sensors in the digital
supply chain. Finally, it presents the logistics
operations and the benefits from digital
transformation.
The paper presents the impacts and the
technologies like Big Data, Cloud Computing and
3Dprinting and explains the term of supply chain
4.0.
This paper provides the terms of the supply chain
4.0 and the influence of industry 4.0 in the global
supply chains, and presents the challenges from
digitalization.
Digital Supply Chain
DSC
Supply Chain 4.0
SC 4.0
Industry 4.0/
Digital Supply Chain
IN4.0/DSC
Sensor technology smart sensors
ST/SC
Big Data /
Cloud Computing /
3D Printing
BD/CC/
3DP
Industry 4.0
IN 4.0
The paper explores the opportunities of the
technologies Big Data, Artificial Intelligence and
Robotics, that change the conditions on the
efficiency and customer service levels.
Robotics / Big Data /
Artificial Intelligence
/
Augmented reality
R/BD/AI/
AR
This article presents the benefits and impacts of an
effective digital transformation to the supply chains
operations.
Digital Supply Chain
DSC
This article provides the term of industry 4.0 and
what influence it has on supply chains. Also, it
presents the impacts on the supply chains from
digitalization and the characteristics of internet of
things.
This paper provides the term of digital
transformation and digital strategy. Also, it
presents the determinants of digital transformation.
Internet of Things
IoT
Digital Supply Chain
DSC
The paper presents an overview of the terms
Logistics 4.0. It presents solutions and influences
concerning Industry 4.0 by developing a
mathematical model which evaluates the solutions
in the level of their innovations.
Industry 4.0
IN 4.0
31
Et. al. Gülçin
Büyüközkan,
Fethullah Göçer
[1]
Et. al. Michael J.
Ferrantino,
Emine Elcin Koten
[2]
Et. al. Yevhen
Krykavskyy,
Olena Pokhylchenko,
Nataliya Hayvanovych
[3]
Et. al. Vladimir
Scherbakov,
Galina Silkina,
Vladimir Scherbakov
[4]
Et. al. Zaza Nadja,
Dimitrios Makris,
Lee Hansen, Omera
Khan [5]
Et. al. Felipe Campos
Martins, Alexandre
Tadeu Simon, Renan
Stenico Campos
[6]
Et. al. Massimo
Merlino,
Ilze Sproģe
[7]
Et. al. Enis Gezgin,
Xin Huang, Prakash
Samal, lldefonso Silva
[8]
Et. al. B. Tjahjono, C.
Esplugues, E. Ares, G
Pelaez
[9]
Et. al. Bohnsack, R.
Hanelt, Marz D
Marante
[10]
Et. al. Mariusz
Kostrzewski, Monika
Kosacka-Olejnik,
Karolina WernerLewandowska[11]
This paper explains the growing of the new
technologies, through digital transformation and
clarifies the effect that industry 4.0 has on supply
chain.
The paper provides the influence of digitalization in
the global economy and the benefits and
characteristics of smart technologies.
Digital Supply Chain
DSC
Sensor technology smart sensors
ST
This paper presents the technology Internet of
things, the challenges in Logistics from the digital
transformations and the requirements to have an
effective transformation.
The aim of this paper is to present the solutions
which were recognized in the areas of organization
and technology innovations. These solutions are the
technologies Internet of things and Big Data. Also,
the impacts of these technologies on supply chains
operations are provided.
The aim of this paper is to present the criteria for
logistics center to adopt the effects of Industry 4.0.
Internet of Things
IoT
Internet of Things /
Big Data
IoT/
BD
Digital Supply Chain
/
Industry 4.0
DSC/
IN 4.0
The paper provides the benefits of technologies like
Cyber-Physical Systems (CPS), Internet of Things
(IoT), Internet of Services (IoS). One of Industry
4.0 key technologies is the Cyber-Physical Systems,
because it can be applied in many areas.
The article provides a literature review of Big Data
technology. Also, it describes the role of the
technology in the supply chain transformation.
Cyber Physical
System /
Internet of Services /
Internet of Things
CPS/
IoS/
IoT
Big Data
BD
In this paper the terms of digitalization, digitation
and digital transformation are explained. The
benefits and consequences to supply chain
operations by these three terms are also provided.
Digital Supply Chain
DSC
The main objective of the paper is to provide the
characteristics and benefits of the technologies
Artificial Intelligence, Real Time locating System
and Internet of things.
The paper evaluates the impacts on the supply
chain operations through the performance metrics.
Also, it presents how the application of artificial
intelligence combines with these metrics.
Internet of Things /
Artificial Intelligence
/ Real-time locating
systems
Artificial Intelligence
IoT/AI/
RTLS
The purpose of this article is discovering new
metrics through the SCOR model, that relate to
Industry 4.0, in order to understand and clarify the
digitalization on the operations of supply chain.
This article measures the supply chain operations
(SCOR model) and, with the performance
indicators, it analyzes the impacts of the new
technologies to supply chain.
Industry 4.0
IN 4.0
Digital Supply Chain
DSC
32
AI
Et. al. Senthil
Muthusami, Mohandas
Srinivsan
[12]
Et. al. MohamedIliasse Mahraz,
Loubna Benabbou,
Abdelaziz Berrado
[13]
Et. al. L. Barreto,
A. Amarala,
T. Pereira
[14]
Et. al. Krzysztof
Witkowski
[15]
Et. al. Volkan Yavasa,
Yesim Deniz OzkanOzen
[16]
Et. al. Gleison Matana,
Alexandre Simon,
Moacir Godinho Filho,
Andre Helleno
[17]
Et. al. Mondher Feki,
Imed Boughzala,
Samuel Fosso
Wamba[18]
Et. al. Alina
Bockshecker, Sarah
Hackstein, Ulrike
Baumöl
[19]
Et. al. Kersten,
Wolfgang, Blecker,
Thorsten, Ringle,
Christian M. [20]
Et. al. Francisco
Rodrigues LimaJunior, Luiz Cesar
Ribeiro Carpinetti
[21]
Et. al. Ertugrul
Ayyildiz, Alev Taskin
Gumus
[22]
Et. al. Sri Yogi
Kottala, Kotzab Herb
[23]
The paper presents the advantages and
characteristics of using Blockchain technology in
the supply chain. Also, it provides the
transformation on supply chain operation through
the block chain approach.
Blockchain
BC
The modeling and evaluating of the performance of
logistics processes is the main objective of this
article. Also, the influences of new technologies on
the warehouse and transportation activities are
provided.
The operations of supply chains are presented in
this article. Also, the challenges of digitalization to
the supply chain operations are referred.
Digital Supply Chain
DSC
Digital Supply Chain
DSC
The understanding of relationship between supplychain processes and supply chain performance are
important. In this article, the supply chain
performance and the influence of Industry 4.0 on
them is being presented and evaluated.
The impacts and challenges of digital supply chain
are presented. The new technologies and the
influence of industry 4.0 to every part of the supply
chain are provided.
Industry 4.0
IN 4.0
Digital Supply Chain
/ Industry 4.0
DSC/
IN 4.0
This paper refers to the development of the RFID
(Radio Frequency Identification) and the
Blockchain technology. The advantages and the
disadvantages of these two technologies are also
presented.
The main objective of this article is to determine the
new technologies that are used in logistics and
supply chain operations, as well as their impact on
them. Also, this article focuses on specific
technologies like AGVS, IDS and RFID.
The potential of Blockchain and the efficiency
influence that has on supply chain are presented in
this article.
RFID/ Blockchain
RFID /BC
Automated Guided
Vehicles / RFID
AGVS/ RFID
Et. al. Rajiv Bhandari,
Mumbai
[30]
Blockchain
BC
The paper presents issues related to design and
organization of warehouse processes. Also, it pays
attention on the modelling logistics process and the
way the technologies can improve the processes.
Intelligent
Transportation
Systems / Digital
Supply Chain
ITS/DSC
This paper introduces radio frequency
identification (RFID) technology and presents the
advantages and disadvantages of RFID in supply
chain operations.
This paper analyzes the way of the use of
autonomous mobile robots and the capabilitiesbenefits the supply chain gains from the application
of AMR.
RFID
RFID
Autonomous Mobile
Robot
AMR
Et. al. Pankaj Duttaa,
Tsan-Ming Choib,
Surabhi
Somanic,Richa Butalac
[31]
Et. al. Michał
Kłodawski,
Konrad Lewczuk,
Ilona Jacyna Gołda,
Jolanta Żak [32]
Et. al. Gary M.
Gaukler Ralf W.
Seifert
[33]
Et. al. Gerhard P.
Hancke
[34]
33
Et. al. Ilhaam A.
Omar, Raia
Jayaraman, Khaled
Salah, Mazin Debe,
Mohammed Omar
[24]
Et. al. E. Lepori.
D.Damand
B. Barth
[25]
Et. al. R.H.
Thilakarathna, M.N.
Dharmawardana,
Thashika Rupasinghe
[26]
Et. al. Archie
Lockamy,
Kevin McCormack
[27]
Et. al. Margarita
Stohler,
Tobias Rebs,
Marcus Brandenburg
[28]
Et. al. Feng Tian,
Vienna
[29]
In this paper the definition of the data mining
technology, its process, and characteristics are
presented.
This paper describes the use of artificial intelligence
and the deep learning technology. Also, it clarifies
the impacts and the influence on the supply chain.
The article provides an overview of deep learning
technology and its capabilities. The criteria for
using this technology are also referred.
The aim of this paper is to show the applications of
deep learning and artificial intelligent on supply
chain and on other areas.
In this study technologies like Robotics, Artificial
Intelligence, Autonomous Vehicles, Augment
reality, Blockchain, Drones are defined
(Characteristics, Capabilities, Disadvantages).
Data Mining
DM
Artificial Intelligence
/ Deep learning
AI/DL
Deep learning
DL
Deep learning /
Artificial Intelligence
DL/AI
Blockchain / Drone /
Artificial Intelligence
/
Cyber-physical
systems /
3D Printing /
Augmented reality
Internet of Things /
Internet of Services
BC/DR/AI/CPS
/ 3DP/AR
Et. al. Christopher S.
Tanga, Lucas P.
Veelenturf
[39]
IoT/IoS
This article presents the key challenges of cloud
computing. Also, this study describes the
relationships and the effects between cloud
computing and internet of services.
Cloud Computing /
Internet of Services
CC/IoS
This article explains the types of machine learning
systems- deep learning that are used in the supply
chain and analyzes the processes and tasks that
they aim to complete.
The definition of the real time locating system is
presented in this study. Also, its capabilities and
disadvantages are analytically presented.
Deep learning
DL
Et. al. Robert
Kleinfeld, Dr. Stephan
Steglich, Lukasz
Radziwonowicz,
Charalampos Doukas
[40]
Et. al. Rafael MorenoVozmediano, Rubén S.
Montero, Ignacio M.
Llorente
[41]
Et. al. Michael Veale
Irina Brass
[42]
Real-time locating
systems
RTLS
This article describes the results of the
implementation of the technologies real timing
located system and the internet of things.
Internet of Things /
Real-time locating
systems
IoT/RTLS
This paper presents the advantages and the
characteristics of the automated driving vehicles.
Automated Guided
Vehicles
AGVs
The influence and the benefits from self-driving
vehicles in transportations problems are described
in this article. Also, the characteristics and the way
of using them are defined.
This article explains the understanding of selfdriving vehicles (concepts, the way of using and
benefits).
Self-driving vehicles
SDV
Self-driving vehicles
SDV
In this study, concepts and the connection of
technologies that are called internet of thing and
internet of services are provided.
34
Et. al. N. Lakshmi,
G.H. Raghunandhan
[35]
Et. al. Tien Yin Wong,
Neil M. Bressler
[36]
Et. al. Li Deng, Dong
Yu
[37]
Et. al. Mario Coccia
[38]
Et. al. Witsarawat
Chantaweesomboon,
Charuwalee
Suwatthikul,
[43]
Et. al. You-Wei Lin.
Chi-Yi Lin
[44]
Et. al. David M.
Woisetschläger
[45]
Et. al. Danielle Dai,
Daniel Howard
[46]
Et. al. Brandon
Schoettle, Michael
Sivak.
[47]
The history of the self-driving vehicles and
automated vehicles system is described in this
article. Also, it explains the relationships of these
technologies with the supply chain operations.
Self-driving vehicles /
Automated Guided
Vehicles
SDV/AGVs
This paper analyzes the key technology of selfdriving cars and illuminates the state-of-art of the
self-driving cars.
Self-driving vehicles
SDV
This paper analyzes and describes all the types of
the warehouse robots, the way they work and also
the benefits that warehouses have from their
development.
In this article the roles of the robotics to
warehouses are described, as well as the robots’
architecture and characteristics.
This article explains how useful the robots are to
the warehouses, the role of every type of warehouse
robot, especially all the types of Autonomous guide
vehicles.
The key technology of a self-driving car is discussed
in this study. The four major technologies of selfdriving cars are discussed and surveyed in this
paper: car navigation system, path planning,
environment perception and car control.
This paper presents the internet of things and many
others technologies, as well as how they co-work
under the same circumstances in the supply chain.
Robotics
R
Robotics
R
Et. al. Ruthie Bowles
[51]
Automated Guided
Vehicles
AGVs
Automated Guided
Vehicles
AGVs
Internet of Services
IoS
The connection between the industry 4.0, the
internet of things and smart sensors are presented
in this study. The impact these technologies have on
the supply chain operations and the way they
improve the process of supply chain are also
presented.
This article presents the challenges and the concept
of digital transformations. It analyzes the new
technologies and describes the challenges of digital
transformations.
This study provides an overview of technologies,
industry 4.0 and digital transformation in the new
area.
This paper analyzes how to use the smart sensors
and when they are appropriate to use. Also, it
provides the advantages and benefits of their
application in other areas.
Sensor technology smart sensors /
Industry 4.0
ST/IN 4.0
Et. al. Lothar Schulze,
Sebastian Behling,
Stefan Buhrs
[52]
Et. al. Dimitrios
Bechtsis, Naoum
Tsolakis, Dimitrios
Vlachos, Eleftherios
Iakovou. [53]
Et. al. Jacqueline
Zonichenn Reis,
Rodrigo Franco
Gonçalves. [54]
Et. al. Martin Neuhold
[55]
Digital Supply Chain
DSC
Et. al. Westerman
[56]
Digital Supply Chain
/
Industry 4.0
Sensor technology smart sensors /
Industry 4.0
DSC/
IN 4.0
This study introduces the technologies internet of
things (IoT), Artificial Intelligence and RFID, and
the impact that was created with the development
of these three technologies on the warehouse
operation.
Internet of Things /
Artificial Intelligence
/
RFID
IoT/AI/ RFID
Et. al. Yogita Malik.
Arora
[57]
Et. al. Joseph
Fitzgerald. Adam
Mussomeli. Andrew
Daecher. Mahesh
Chandramouli
[58]
Et. al. Li Juntao, Ma
Yinbo
[59]
35
ST/IN4.0
Et. al. Mike Daily,
Swarup Medasani,
Reinhold Behringer,
Mohan Trived
[48]
Et. al. Jianfeng Zhao,
Bodong Liang Qiuxia
Chen
[49]
Et. al. Will Allen [50]
The paper describes an overview of smart sensors
and sensor fusion, targeted at biomedical
applications and sports areas.
Sensor technology smart sensors
ST
This paper presents the machine 2 machine
technology, the way it is used in the supply chain,
the architecture and the characteristics of the
technology.
The fundamental conceptual and implementation
features of a practical strategy for the development
and application of model based, customized M2M
transformations are presented in this paper.
This study examined the characteristics,
architecture and the benefits from the deep
learning technology.
Machine 2 Machine
sensors
Μ2Μ
Machine 2 Machine
sensors
M2M
Deep learning
DL
36
Et. al. Alves Mendes,
Marinho Vieira,
Marcelo Bissi Pires,
Sergio Luiz Stevan
[60]
Et. al. Meriam
Bouzouita, César Viho.
[61]
Et. al. Tomas Skersys,
Paulius Danenas,
Rimantas Butleris
[62]
Et. al. Tahani
Aljohani, Alexandra
Cristea. [63]
CHAPTER 4
Supply chain 4.0 Technologies
Overview
The chapter four presents the SC 4.0. Moreover, chapter four defines the
use of technologies and their characteristics at the supply chain operations.
The following figure present the structure of this chapter.
37
Internet of Things: IoT
The British Kevin Ashton is the one that created the technology that is
called internet of things. The concept was formulated in 1999 and at first it
was a system to describe the communication between the physical world
with the computers (B. Tjahjono, C. Esplugues, E. Ares, G Pelaez et. al. 2829 June. 2017). It is a system for describing communication between the
physical world and computers, that have sensors, software, and other
technologies integrated in them for the purpose of communicating and
exchanging data with other devices and systems over the Internet. The
convergence of many technologies, such as real-time analytics, machine
learning, ubiquitous computing, commodity sensors, and embedded
systems, has resulted in the evolution of things (L. Barreto, A. Amarala, T.
Pereira et. al. 28-30 June 2017). The Internet of Things is enabled by
traditional domains such as embedded systems, wireless sensor networks,
control systems, automation (including home and building automation), and
others. It contains platforms with smart and data devices in order for the
firms to have a plan about their operations (Gleison Matana, Alexandre
Simon, Moacir Godinho Filho, Andre Helleno et. al. 2020). All these years,
the technology developed and changed at a very high degree and was
considered as a system that was created not only for objects but also for
processes, data, etc. The main characteristics of Internet of things are
context, omni-presence, interconnectedness, intelligence, innovation,
integration, instrumental and optimization. The first characteristic, the
context, connects the digital with the physical world, by providing
information such as location, physical condition etc. (Krzysztof Witkowski
et. al. 2017) Interconnectedness describes the use of smart devices’
technology and information systems. Intelligence is used to optimize the
performance. Innovation presents the development of the devices.
Integration describes the relationships in all the parts of the supply chain.
Instrumental is about the machines that are used for information and
decisions. (Krzysztof Witkowski. 2017, Kersten, Wolfgang, Blecker,
Thorsten, Ringle, Christian M. et. al. 11 June.2019). Omnipresence presents
the fact that objects communicate with the human operators in a large
degree over the recent years. The last one, optimization, describes the
expression of every function, which every object possesses. Internet of
Things can offer many opportunities in the area of performance (You-Wei
Lin. Chi-Yi Lin, et. al. 21 May 2018, Et. al. Li Juntao, Ma Yinbo. July 112016). For example, the transport cars can be controlled automatically and
the hosts can operate them with a standard speed, as to maximize the fuel’s
economy. In case of delays, the customer can check his/her order and be
informed of the reasons for any complications. Moreover, for the process of
storage in warehouses, the internet of things improves the inventory
management, by making it faster, more predictable and safer. Finally, the
internet of things is important for logistics and the transport sector. In this
38
field, the solutions can provide data about the locations and monitoring on
the condition of things. Through this information, the customer service
improves by shortening the cycle of the logistics process and by optimizing
the cost (Robert Kleinfeld, Dr. Stephan Steglich, Lukasz Radziwonowicz,
Charalampos Doukas et. al. October 08, 2014).
Figure 4.1: Represents the architecture of IoT
Source: et. al. Rocky Osborn. 2020
Big Data
Big Data is a technology that gives the opportunity to analyze and evaluate
all the amounts of information that are produced by the Internet. Big data
is a field that deals with methods for analyzing, methodically extracting
information from, or otherwise dealing with data volumes that are too large
or complicated for typical data-processing application software to handle
(Massimo Merlino, Ilze Sproģe, et. al. 19-22 October 2016). Data with a lot
of fields have more statistical power, however data with a lot of attributes
or columns have a higher false discovery rate. Data capture, storage,
analysis, search, sharing, transfer, visualization, querying, updating,
information privacy, and data source are all issues in big data analysis.
(Mondher Feki, Imed Boughzala, Samuel Fosso Wamba, et. al. 2016) The
three major notions of big data were initially related with three important
concepts: volume, diversity, and velocity. (Zaza Nadja, Dimitrios Makris,
Lee Hansen, Omera Khan et. al. 12 Feb 2019). Because massive data
analysis poses sampling issues, only observations and sampling were
previously allowed. As a result, big data frequently comprises data in
amounts that typical software cannot process in a reasonable amount of
time or for a reasonable price. Big Data are very important and consist a
very useful tool for the separation of information. This technology is used
for the analysis and separation of the useful-important (from the less useful)
conclusions and increases the ability of businesses to achieve their
39
objectives, by supporting them with effective transfer of knowledge.
(Krzysztof Witkowski, et. al. 2017) The characteristics of Big data according
to Forrester are value, velocity, variety, and volume. (Massimo Merlino, Ilze
Sproģe, et. al 19-22 October 2016. Latvia, et. al. Zaza Nadja, Dimitrios
Makris, Lee Hansen, Omera Khan 12 Feb 2019). Volume is the amount of
data, that refers to the datasets, whose size is larger than the capacity of
tools for storage, management, and analysis. (Massimo Merlino, Ilze
Sproģe, et. al. 19-22 October 2016) The variety of data describes the variety
of the sources. For example, the sources can be social networking,
transactional systems, internet etc. Velocity of data describes the
production speed of new data and analysis. Value of the data's goal is to
isolate the amount of information that is useful for the business, and which
information is not (Zaza Nadja, Dimitrios Makris, Lee Hansen, Omera
Khan, et. al. 12 Feb 2019). This is a very important step, because it relates
directly with business objectives. An example of Big Data on logistics is the
DHL. Big data presents the opportunity for service providers to optimize
the logistics operations. It also improves the customer service and provides
a new developing business model for the small firms in the area of
marketing (Massimo Merlino, Ilze Sproģe, et. al. 19-22 October 2016).
Figure 4.2: Represents the architecture of Big Data
Source: et. al. April Reeve. Managing Data Motion. 2013
Cyber-physical System
A cyber-physical system (CPS) is a computer system in which computerbased algorithms operate or monitor a mechanism. Physical and software
components are closely linked in cyber-physical systems, allowing them to
operate on diverse spatial and temporal scales, display diverse and distinct
behavioral modalities, and interact with one another in context-dependent
ways (Matana, Alexandre Simon, Moacir Godinho Filho, Andre Helleno, et.
al. 2020). CPS combines cybernetics, mechatronics, design, and process
science in a transdisciplinary approach. Embedded systems are a term used
40
to describe process control. The emphasis of embedded systems tends to be
on the computational parts, rather than a strong relationship between the
computational and physical parts. The physical and computational parts
are combined and coordinated in CPS. (Christopher S. Tanga, Lucas P.
Veelenturf, et. al. 6 June 2019). In summary, CPS are physical systems,
that were made to monitor, coordinate and control their operations, using
computing and communication systems. A cyber physical system consists of
a set of network factors and involves the connections with the physical
world. These factors are sensors, control processing systems and
communications devices (Christopher S. Tanga, Lucas P. Veelenturf, et. al.
6 June 2019). Through the continuous and efficient using of these
technological applications (sensors, control processing and communication
systems), the result is the development of new applications and, also, the
improvement of the technological applications that already exist. The
development of new applications is contributing to the parts of the
production process, to the transportation system, to logistics services, to
autonomous vehicles, to machine 2 learning and to smart sensors. The main
characteristics of CPS are related with the characteristics of IoT, and they
are visibility, transparency, innovation, instrumental, interconnectedness
(Enis Gezgin, Xin Huang, Prakash Samal, lldefonso Silva, et. al. November
2017). Visibility and transparency are used in order to achieve fewer faults
in the supply chain parts and in order to achieve adequate forecasting of the
resources. Innovation presents the development of the devices.
Instrumental is about the machines that are used for information and
decisions. Interconnectedness describes the use of smart devices technology
and information systems. Thus, the implementation of CPS will increase
the productivity, flexibility, and agility on the different parts of the supply
chain. Finally, the efficient connections between the integration and the
main factors will increase the visibility and transparency in order to achieve
better forecasting of the sources. (Enis Gezgin, Xin Huang, Prakash Samal,
lldefonso Silva, et. al. November 2017).
41
Figure 4.3: Represents the architecture of CPS
Source: Tomas Robles, Diego Martin, Ramon Alcarria, Borja Bordel, 2017
Intelligent Transportation System
Intelligent Transportation System (ITS) is a new technology that operates
in the different areas of the transportation systems. Intelligent
transportation system is an advanced application that strives to provide
novel services related to various modes of transportation and traffic
management, allowing users to be better informed and make safer, more
coordinated, and “smarter” use of transportation networks (Barreto et al.,
2017, Yavas & Ozkan-Ozen, et. al. 2020). ITS refers to systems that use
information and communication technology in the realm of road
transportation, encompassing infrastructure, vehicles, and users, as well as
traffic and mobility management and interactions with other modes of
transportation. In a variety of settings, such as road transport, traffic
management, mobility, and so on, ITS may increase the efficiency and
safety of transportation. ITS technology is being used all around the world
to boost road capacity and cut travel times. The main transportations
systems that ITS is involving are transportation management, control
methods, infrastructure, operations and driving policies (Michał Kłodawski,
Konrad Lewczuk, Ilona Jacyna Gołda, Jolanta Żak.,et. al. 2017). Like all the
new technologies, the ITS involves innovations, such as computing
hardware, sensor technologies, location systems, telecommunications, data
process, virtual operation and planning strategies. The main characteristics
are integration, safety, and reliability (Michał Kłodawski, Konrad Lewczuk,
Ilona Jacyna Gołda, Jolanta Żak, et. al. 2017). Integration is a basic issue
in the transport field and plays an important role as a solution to a lot of
problems. ITS provides safety for the reduction of accidents and for better
conditions of distribution. Finally, reliability plays a similar role to safety,
as it is also important for the same reasons. However, reliability
42
additionally controls the travel speed and the traffic flow, it provides
services that are related with navigations systems and it does not
exclusively refer to the vehicular traffic. Reliability is very important,
because it contributes to air transportation systems, to water
transportation system and to rail systems. Thus, the adoption of sensors
technology, drones and intelligence systems, will improve the process of
decision-making quality in management and will allow the improvement of
logistics operation through the convergence of machine-to-machine
communication and communication systems. It is a very important
technology for logistics operation (in our days it is used for truck parking
and delivery management) and it is very useful for saving fuel consumption,
reducing the presence of heavy vehicles and supporting the truck drivers
(Westerman, et. al. 2011).
Figure 4.4: Represents the architecture of ITS
Source: et. al. Sakib M. Khan, Mizanur Rahman, Amy Apon, Mashrur Chowdhury.
2017
3D-printing
This is a technique that takes a digital 3D model of an object and turns it
into a real object by layering materials (plastic, resin, stainless steel,
ceramics, etc.). Companies may use additive manufacturing to produce
prototypes (product prototypes) and customized products more quickly,
cheaply, and effectively (Sodhi and Tang. 2017). In the case of spare parts,
it was found that as the number of parts grows, additive manufacturing
adds more value to the business. For part design, it was discovered that the
higher design expense of additive manufacturing can be offset by lower
logistical costs and shorter lead times. It was shown that a business that
adopts additive manufacturing would be able to compete by offering more
product choices, a company that produces self-replicating 3D printers,
investigate the tradeoff between development and sales of these selfreplicable 3D printers (Zaza Nadja, Dimitrios Makris, Lee Hansen, Omera
Khan, et. al.12 Feb 2019). In conclusion, 3D-printing has many roles in the
43
supply chain 4.0, but its main role is in the area of spare parts, as was
referred above. For example, in the situations that the demand has
fluctuations, with the help of 3D-printing any part can be printed by
request. However, despite the fact that 3D-printing is having an important
impact on the development of the supply chain, the adoption of this
technology is very limited. The characteristics (Decentralization,
complexity, rationalization) and the areas that it impacts, are the more
efficient use of resources, decentralization on manufacture, reduction of
customization and complexity, rationalization of logistics and inventory and
finally the process of product design (Christopher S. Tanga, Lucas P.
Veelenturf, et. al. 6 June 2019).
Figure 4.5: The role of 3D-printing to supply chain
Source: et. al. Carsten Engel. 2014
Cloud Computing
The on-demand availability of computer system resources, particularly data
storage (cloud storage) and computational power, without direct active
management by the user, is known as cloud computing. The word refers to
data centers that are accessible via the Internet to a large number of people.
Functions from central servers are commonly dispersed across numerous
locations in today's large clouds. It may be characterized as an edge server
if the connection to the user is quite close. Clouds can be restricted to a
particular organization or made available to a large number of them (public
cloud). To achieve coherence and economies of scale, cloud computing relies
on resource sharing. Cloud computing (CC) is a very useful technology for
the successful completion of the supply chain (Gleison Matana, Alexandre
Simon, Moacir Godinho Filho, Andre Helleno, et. al. 2020). Cloud
Computing is developing and improving rapidly and it impacts many
44
different sectors significantly, especially the supply chain management.
Cloud Computing provides new opportunities and improves the flow of
information to the different parts of the supply chain, starting from the
suppliers all the way to the final customers. Cloud Computing is also related
to the Internet of Things. For example, the Internet of Things provides the
opportunity to access applications and services that are provided by the
cloud. On the other hand, Cloud Computing give access to the services
constantly and thought out the whole world (Enis Gezgin, Xin Huang,
Prakash Samal, lldefonso Silva, et. al. November 2017). One of the
characteristics and benefits that the cloud computing provides is the
improvement of visibility. For example, Cloud Computing provides the
firms with the chance to forecast the demand, by capturing the remarkable
information. Secondly, cloud computing provides the opportunity for cost
reduction on sourcing and procurement. Additionally, cloud computing
provides statistical tools, that are useful on the reduction of of the inventory
levels and manufacturing units, which is important in order for the
production to meet the demand. Finally, in the sales management area,
cloud computing presents a system, which helps to keep the customers
satisfied. In conclusion, cloud computing is the tool, which can help the
business to integrate the information flow efficiently and to control the
operation environment effectively. Thus, it is very important for supply
chain managers to constantly be informed on the new technological
innovations, in order to optimize the logistics. (Christopher S. Tanga, Lucas
P. Veelenturf, et. al. 6 June 2019).
Figure 4.6: The overview structure of Cloud Computing
Source: et. al. Natalie Walker. 2015
45
Drones
The growth of technological innovations created opportunities for the
emergence of new technologies, devices, and applications. One of these
devices that was created is called drone. Drones are unmanned vehicles,
which fly and are controlled remotely. Most of the drones have opportunities
to record data with the help of sensors, which the drones carry in order to
monitor and inspect operations on the delivery of certain devices (Michael
J. Ferrantino, Emine Elcin Koten, et. al. 2019). The most frequent use of
drones is found on the field of research and rescue operations. The main
characteristics of drones are speed, safety and the improvement of
productivity. With drones, businesses can deliver services faster and more
safely. In summary, drones provide speed and safer response and recovery.
Finally, the firms can improve their productivity through the use of drones
(Christopher S. Tanga, Lucas P. Veelenturf, et. al. 6 June 2019).
Figure 4.7: Presents the role of drone in a large warehouse
Source: et. al
Jason
Reagan.2020
Artificial Intelligence
With the dramatic evolution of technology over the years, the world has
changed greatly. Nowadays, robotics has a great influence and its existence
is considered necessary in industrial applications, as well as on warehouse
operations. Thus, artificial intelligence is one of the new technologies that
was developed over the last years and it continues to improve even today.
Is a very important tool for all industries and companies and can be used in
a variety of fields. Unlike physical intelligence, artificial intelligence uses
computers to evaluate external data, it then learns from these data and
performs descriptive, predictive, and prescriptive analysis. Examples of
Artificial intelligence include the IBM systems (a system for answering
questions), the GE system (a system to collect data from the turbines), etc.
(Kersten, Wolfgang, Blecker, Thorsten, Ringle, Christian M, et. al. 11-June
46
2019). On the other hand, many believe that artificial intelligence will
surpass physical intelligence. In many situations and in many parts of the
supply chain, artificial intelligence has replaced the workforce. For
example, the influence of artificial intelligence on business, and especially
on warehouses, can be discerned through Autonomous Vehicles. In
conclusion, the rapid growth of Industry 4.0 and Smart Factories (connected
with the Internet of Things) will make the supply chain more effective in all
of its areas (Tien Yin Wong, Neil M. Bressler, et. al. November 29, 2016).
The characteristics- roles of Artificial Intelligence that can be observed on
the supply chain include load cost, optimizing inventory, powered customer
experience and autonomous delivery fleet. The load cost refers to the variety
of the shipping cost which can depend both from the season, as well as from
the time and conditions of the day (for example sunny or rainy weather),
making it difficult to predict the price of a load. AI can assist in monitoring
such conditions and in determining the best price, based on delivery time
and on the shipment's "lane" (route and destination). These algorithms
monitor a variety of factors such as traffic, weather and socioeconomic
problems, in order to assist businesses in determining a fair price, that all
parties will agree on (Mario Coccia, et. al. 17 October 2019). The term
optimizing inventory refers to the democratization and accessibility of
information through the use of AI, by providing a fair price quote to ensure
a fair deal for all parties, and by tracking inventory and load ability, in order
to ensure that trucks do not miss deliveries. The AI technology can also
secure and track supplier inventory, as well as the number of delivery
trucks available. Smart algorithms provide this information in advance,
allowing clients to know the exact price and availability of specific inventory
and trucks for potential deliveries. AI may also perform data analysis to
determine which carriers have moved which freight in the past and at what
price and service level (Christopher S. Tanga, Lucas P. Veelenturf, et. al. 6
June 2019). The term power customer experience refers to the fact that
artificial intelligence (AI) technologies like robotic process automation,
natural language processing and machine learning, can help logistic
companies analyze customer feedback with accuracy and speed, that human
operators cannot match. For that reason, building AI-powered custom
software that manages customer service is critical for any logistics business.
Finally, conventional delivery fleets will be replaced with more advanced
autonomous delivery fleets in order to entirely remove the reliance on
human drivers (Christopher S. Tanga, Lucas P. Veelenturf, et. al. 6 June
2019).
47
Figure 4.8: The Artificial Intelligence in the warehouse
Source: et. al. Leeway Hertz.2020
Blockchain
Blockchain is a technology that has tremendous potential to turn supply
chain functions, from Supply Chain provenance to business process
reengineering to security enhancement. Blockchain is a technology that
ensures network security, transparency, and visibility through a specific
combination of features such as decentralized layout, distributed notes and
storage system, consensus algorithm, smart contracting, and asymmetric
encryption (Pankaj Duttaa, Tsan-Ming Choib, Surabhi Somanic, Richa
Butalac, et. al.18 August 2020). Blockchain is a tamper-proof, immutable
distributed ledger technology (DLT), that is used in a decentralized and
synchronized environment, where all transactions are authenticated and
are traceable by users. It allows for a decentralized environment, in which
all network users can safely communicate without the need for a trusted
authority. Consequently, by validating and storing all transactions by
distributed consensus, it removes the need for a central entity (Ilhaam A.
Omar, Raia Jayaraman, Khaled Salah, Mazin Debe, Mohammed Omar, et.
al. 2019). Moreover, Blockchain is made up of a series of interconnected
blocks that can be easily traced back to previous blocks, making the
technology transparent and secure. Users in that network verify and record
all transactions, which are also time stamped, ordered in sequential order,
linked to the previous block and irreversible, once connected to the network.
Blockchain's entire framework qualifies it as a trusted technology.
48
Moreover, one of the most essential features of blockchain, that makes it
trustworthy, stable and transparent, is called “consensus process”. Records
are stored in blocks that are connected by hash values and the decision to
add a new block to the list is being made by a consensus mechanism. Any
adjustment to an existing block allows the opponent to compete with all
users in order to create a longer branch, which aids DLT in preserving
historical data security through coordination mechanisms (Ilhaam A.
Omar, Raia Jayaraman, Khaled Salah, Mazin Debe, Mohammed Omar, et.
al. 2019). The blockchain characteristic are the following:
•
•
•
•
•
•
•
•
•
•
Decentralized: Multiple systems can access, track, archive, and
upgrade the data on the device (Feng Tian, et. al. 2016).
Transparent: Data is registered and maintained on the network
with the network's approval and it is accessible and traceable
throughout its life cycle (Feng Tian, 2016, Pankaj Duttaa, TsanMing Choib, Surabhi Somanic, Richa Butalac, et. al. 18 August
2020).
Immutable: To ensure immutability, the blockchain offers
timestamps and controls (Feng Tian, Austria. 2016, Pankaj
Duttaa, Tsan-Ming Choib, Surabhi Somanic, Richa Butalac, et.
al.18 August 2020).
Irreversible: The technology of blockchain holds a clear and
verifiable record of any transaction ever produced (Feng Tian,
Austria. 2016, Et. al. Pankaj Duttaa, Tsan-Ming Choib, Surabhi
Somanic, Richa Butalac, et. al.18 August 2020).
Autonomy: Each node on the blockchain can securely access,
transfer, archive, and upgrade data without the need for a third
party (Feng Tian, et. al.2016).
Open source: Everyone in the network has open-source access to
the blockchain and there is a sense of hierarchy (Feng Tian, et.
al.2016).
Anonymity: The identity of the user remains anonymous during
data transfer between nodes (Feng Tian. 2016, Pankaj Duttaa,
Tsan-Ming Choib, Surabhi Somanic, Richa Butalac, et. al.18
August 2020).
Ownership and uniqueness: Every document exchanged on the
blockchain has a unique hash code that records who owns it
(Pankaj Duttaa, Tsan-Ming Choib, Surabhi Somanic, Richa
Butalac, et. al.18 August 2020).
Provenance: Every product has a blockchain-based digital record
document that verifies its authenticity and origin (Pankaj Duttaa,
Tsan-Ming Choib, Surabhi Somanic, Richa Butalac, et. al. 18
August 2020).
Contract automation: It's a tiny, computerized program that
assists with contract execution. It removes the need for a
49
conventional contract, while enhancing security and lowering
transaction costs. Smart contracts are normally coded with rules,
fines, and actions that will be extended to all parties involved in
the transaction. Smart contracting enables supply chain
operations to respond quickly (Li et al. 2019).
Figure 4.9: The Blockchain technology
Source: et. al. Greg Carter. 2018
Internet of Services
The first three industrial revolutions were triggered by mechanization,
electricity, and information technology, in that order. The fourth industrial
revolution is now underway, thanks to the introduction of the Internet of
Things and the Internet of Services into the manufacturing world.
Nowadays, the Internet of Services (IoS) is one of the central pillars of the
future Internet, according to the Cross-European Technology Platforms (XETPs) (Gleison Matana, Alexandre Simon, Moacir Godinho Filho, Andre
Helleno, et. al.2020). IoS's main goal is to make everything on the Internet
available as a service, including software applications, the framework for
developing and delivering these applications, and the underlying
infrastructure. Moreover, the Internet of Services (IoS) reference
architecture views the Internet as a global network for retrieving,
combining and using interoperable tools. The next-generation Internet
would allow private users and businesses to collaborate seamlessly through
the use of interoperable, electronic services, accelerating the
industrialization of information-intensive services (Robert Kleinfeld, Dr.
Stephan Steglich, Lukasz Radziwonowicz, Charalampos Doukas October,
et. al. 08 2014). The term "Internet of Services" was coined when two other
ideologies, Web 2.0, and SOA (service-oriented architecture), collided. The
concept of reusing and composing existing resources and facilities sits at the
50
crossroads of these two areas (the Service Oriented Architecture (SOA) is a
logical model for reorganizing software systems and infrastructure into a
series of interconnected services). On the other hand, the Web Service is
defined as a software system designed to allow interoperable machine-tomachine interaction over a network. IoS has many advantages, such as cost
savings because infrastructure and network sizes can be tailored to service
demands, service elasticity by automated scaling of services and
infrastructures, reduction in the development and infrastructure
implementation times that improve service time-to-market, as well as
duplication of service components and rapid deployment of new service
instances, which enhanced service availability and reliability (Rafael
Moreno-Vozmediano, Rubén S. Montero, Ignacio M. Llorente, et. al. 2012).
IoS is described by three characteristics: availability, reliability, resiliency.
•
•
•
Availability: It measures the average service availability over a
specific period of time. (Jacqueline Zonichenn Reis, Rodrigo Franco
Gonçalves, et. al. 9 July 2019)
Reliability: It refers to the probability of the system failing in a
certain amount of time. (Jacqueline Zonichenn Reis, Rodrigo Franco
Gonçalves, et. al. 9 July 2019)
Resiliency: It refers to the ability of a system to achieve and sustain
a satisfactory standard of service in the face of a variety of faults and
obstacles in regular operation. (Jacqueline Zonichenn Reis, Rodrigo
Franco Gonçalves, et. al. 9 July 2019)
Figure 4.10: The Internet of Services
Source: et. al. Daniel Oberhaus.2015
51
Deep Learning
Under the term of artificial intelligence, deep learning is a modern branch
of machine learning technology. For years, Google and other technology
companies (such as Facebook and Apple) have used deep learning for big
data analysis in order to predict how people navigate the internet, where
they want to go, what they want to buy, what their favorite food is and who
they could become friends with (Tien Yin Wong, Neil M. Bressler, et. al.
November 29, 2016). Deep learning has a lot of promise in health care,
including predicting which patients are more likely to have a disease, as
well as which patients are more likely to have a specific illness. However,
the use of deep learning to the supply chain is a collection of computational
methods that allow an algorithm to self-program by learning from a large
number of examples that demonstrate the desired behavior (Michael Veale,
Irina Brass, et. al. 2019). Most machine learning and signal processing
techniques used shallow-structured architectures until recently. Nonlinear
feature transformations are usually found in either one or two layers in
these architectures. When the kernel trick is used or not, SVMs use a
shallow linear pattern separation model of one or zero feature
transformation layers (Tahani Aljohani, Alexandra Cristea, et. al. July
2019). While shallow architectures have been shown to be successful in
solving a variety of simple or well-constrained problems, their limited
modeling and representational capacity can trigger problems when dealing
with more complex real-world applications involving natural signals, such
as human voice, natural sound and language, as well as natural image and
visual scenes. Finally, the characteristics of deep learning are supervised, a
huge amount of resources, a large amount of layers, cost function and
optimizing parameters. Supervised: Supervised learning occurs when
category labels are present when the data is being trained. Linear
regression algorithms are examples of supervised algorithms (Li Deng,
Dong Yu, et. al. 2013). Volume of data: Big data, which can be either
organized or unstructured, requires the processing of a large volume of data.
Depending on the amount of data fed in, more time is often needed to
process the data. Large number of layers: A large number of layers, such as
input, activation and output, may be required. Most of the time, the output
of one layer can be applied to another layer by making a few small
discoveries, which are then summed up in the SoftMax layer to determine
a wider classification for the final output. Cost function: The aim of each
iteration in a Deep Learning Model is to reduce the cost in comparison to
previous iterations. Different algorithms use different forms of mean
absolute error, mean squared error, hinge loss and cross entropy.
Optimizing parameters: Since Deep Learning provides a correlation
between layer predictions and final output predictions, the learning rate
must be fine-tuned for good Deep Learning Model accuracy (Mario Coccia,
et. al. 17 October 2019).
52
Figure 4.11: Presents the architecture of deep learning
Source: et. al. Louis Columbus.2018
RFID (Radio Frequency Identification)
Radio Frequency Identification (RFID) is a contactless interrogation system
for identifying items. RFID is a technology that employs electromagnetic
fields to identify and track tags attached to items. A radio transponder, a
radio receiver and a transmitter make up an RFID system. The tag
transmits digital data, usually an identifying inventory number, back to the
reader when triggered by an electromagnetic interrogation pulse from a
nearby RFID reader device. This number can be used to keep track of
inventory (Li Juntao, Ma Yinbo, et. al. July 11-2016). RFID tags are widely
employed in a variety of industries. An RFID tag placed to a car during
manufacture, for example, can be used to follow its progress through the
assembly line. With the aid of existing technologies, businesses started to
implement radio frequency identification (RFID) for better organization and
survival in a competitive world. The majority of inventory management is
achieved with the use of Radio Frequency Identification (RFID) technology,
which automates the process. This is particularly critical since a product's
cost accounts for 75% of its total cost. The company saves a lot of money by
automating this process. RFID-enabled companies will know exactly where
a product is, at any given time. The supplier is aware of the retailer's realtime stock using this technology (Feng Tian, et. al. 2016). The advantages
of RFID in logistics, transportation and warehousing can be divided into
53
two categories: (1) labor and time savings and (2) improved visibility
benefits. The concept of "uninterrupted supply chain" has also been used to
define the first gain group. In more detail, a significant portion of a product's
journey through a supply chain or logistics system is spent waiting for
identification or the completion of a manual procedure, such as counting
cases related to identification and documentation. As a result, stopping
points disrupt the movement of goods. A system that uses automatic
identification via RFID will potentially eliminate many of these stumbling
blocks, allowing the product to pass through the system more quickly and
at a lower cost. If the product in the supply chain is a serialized product,
these strictly operational cost savings would be a particularly significant
commodity that requires case-by-case identification rather than bulk palletby-pallet identification. Computers, digital printers, electronics equipment
and other serialized items are such examples. Since each machine can have
a different configuration, these are serialized goods. Improved inventory
accuracy is another field where RFID can be beneficial (Gary M. Gaukler
Ralf W, et. al. 2015). The distinction between conceptual and actual
inventory is referred to as inventory accuracy. The sum of inventory on
record in the computer system is referred to as logical inventory. Physical
inventory refers to what is already on hand. Ideally, the conceptual and
physical inventory amounts should be equivalent, but they can be very
different for a variety of reasons (shrinkage, input errors, loss of product,
misplacement, etc.). The conceptual inventory displayed in computer
systems is usually greater than the actual inventory. Due to the automation
of the scanning process, RFID can aid in the improvement of logical
inventory records. For example, although these relatively minor gains in
efficiency and accuracy might be sufficient to warrant an RFID
implementation, the majority of the benefits are usually expected to come
from increased visibility. Supply chain decision-makers would be able to
operate a far more effective supply chain if they have precise real-time
knowledge of the amount of inventory and its position in the supply chain.
Knowing what is in the replenishment pipeline and when it will arrive could
enable safety stocks to be reduced while customer service levels are
maintained or improved. Essentially, RFID visibility shifts the
inventory/service-level exchange curve in a supply chain. Many more
sophisticated policies that manage supply chain activities in an automated
way, based on RFID visibility, are expected to emerge as RFID installations
become more widespread (Li Juntao, Ma Yinbo, et. al. July 11-2016).
54
Figure 4.12: The architecture of RFID System
Source: et. al. Zetes.2018
Data mining
Data mining, or the non-trivial extraction of novel, tacit, and actionable
knowledge from large data sets, is a developing technology that is a direct
consequence of the growing use of computer databases to efficiently store
and retrieve information. It is also known as Knowledge Discovery in
Databases (KDD) and enables data exploration, data analysis, and data
visualization of huge databases at a high level of abstraction, without a
specific hypothesis in mind. The working of data mining is understood by
using a method called Modeling-With-It to make predictions. Artificial
neural networks, decision trees, and genetic algorithms are examples of
data mining techniques that are the result of a long period of analysis and
product creation (Michael J. Ferrantino, Emine Elcin Koten, et. al. 2019).
With the introduction of computers and the ability to store large amounts
of data digitally, people began gathering and storing a wide range of data,
relying on the power of computers to sort through this jumble of data. The
vast amounts of data stored on fragmented structures quickly became
overwhelming, prompting the creation of hierarchical databases and
database management systems (DBMS). Database management systems
efficiently manage large corpora of data and enable effective and efficient
retrieval of specific information from a large collection when required, as
well as contributing to the recent massive gathering of all kinds of data. The
technology of data mining contributes to this extraction of data as and when
necessary (Gülçin Büyüközkan, Fethullah Göçer, et. al. 2 March. 2018).
Data mining may be thought of as a natural progression of information
technology. This technology allows for the widespread availability of
massive quantities of data, as well as the urgent need to convert the data
into usable information and knowledge. The extraction of interesting
patterns or information from large amounts of data is known as data
mining. The term "data mining" refers to the process of analyzing data in a
database with software that looks for patterns or anomalies without
55
knowing what the data means. It is mainly used by statisticians, database
analysts, and business groups. Data mining software not only changes the
way data is presented, but it also uncovers previously unknown
relationships between the data. A historical archive of previous experiences
contains the data from which the data mining process operates. Data
mining is not limited to a single form of media or data in principle. Every
kind of knowledge repository should be suitable for data mining (Michael J.
Ferrantino, Emine Elcin Koten, et. al. 2019). Some of the major components
of data mining are mentioned below:
•
•
•
•
•
•
Database, data warehouse or other information repository: One or
more databases, data centers, spreadsheets, or other types of
information repositories make up this part. On the files, data
cleaning and data integration techniques can be used (N. Lakshmi,
G.H. Raghunandhan, et. al. 17 -18 February. 2011).
Data warehouse server: The part is in charge of retrieving relevant
data based on the user's data mining request (Gülçin Büyüközkan,
Fethullah Göçer, et. al. 2 March 2018).
Knowledge base: This is the domain information that is used to direct
the quest or to assess the usefulness of the patterns that are found.
It contains definition hierarchies, which are used to group attributes
or attribute values into various levels of abstraction. (Gülçin
Büyüközkan, Fethullah Göçer, et. al. 2 March 2018).
Data mining engine: This is an important part of a data mining
framework and it should preferably include a collection of functional
modules for tasks like characterization, association analysis,
classification, evolution and deviation analysis. (Gülçin Büyüközkan,
Fethullah Göçer, et. al. 2 March 2018)
Pattern evaluation module: This component usually uses
interestingness measures and works with data mining modules to
narrow down the quest to interesting patterns. Depending on how
the data mining system is implemented, the pattern evaluation
module can be combined with the mining module. Data mining can
be made more efficient by incorporating the assessment of pattern
interestingness early in the process, allowing the quest to be limited
to only the most interesting patterns. (Gülçin Büyüközkan, Fethullah
Göçer, et. al. 2 March 2018)
Graphical user interface: This module facilitates interaction between
users and the data mining system by allowing users to define a data
mining query or mission, provide information to help focus the search
and perform exploratory data mining based on intermediate data
mining results. This component also allows users to browse database
and data warehouse schemas or data structures, analyze mined
patterns, and visualize them in various ways (N. Lakshmi, G.H.
Raghunandhan, et. al. 17 -18 February. 2011).
56
Figure 4.13: Presents the architecture of data mining
Source: et al. Oleh Nicholas. 2019
Augmented Reality
Coating computer simulation models over the physical layout of the real
environment is known as Augmented Reality. AR is defined as a system
that combines real and virtual worlds, allows for real-time interaction, as
well as for accurate 3D registration of virtual and real items. The
superimposed sensory input might be beneficial (i.e., beneficial to the
natural environment) or harmful (i.e., harmful to the natural environment
or masking of the natural environment). The experience is so well
integrated with the physical world that it is viewed as a fully immersive
part of the real world. Augmented reality modifies one's continuing view of
a real-world environment, whereas virtual reality totally replaces the user's
real-world environment with a simulated one. Mixed reality and computermediated reality are two phrases that are nearly synonymous with
augmented reality (Yogita Malik. Arora, et. al. 17 August 2016). The
layering of computer simulation models over the physical layout of actual
surroundings is known as Augmented Reality. In several ways, this is the
trademark of virtual reality, but AR refers to the use of this data to increase
the performance of today's supply chain processes. The most common types
of Augmented Reality involve a wearer using some kind of glass-based
visual display to boost efficiency and performance. Smart glasses in the
factory are an example of Augmented Reality in the supply chain. One of
the fastest-growing sectors is the "Industrial," which refers to the use of
virtual reality in the supply chain, which includes manufacturing,
distribution, and logistics (Martin, 2015). In order picking processes,
57
augmented reality is currently being used to provide a sense of scene
recognition. The majority of conventional order picking processes include
using a pen and paper or using voice-activated systems. However,
inefficiencies continue to exist as a result of this. Employees in a warehouse
must normally perform several acts at any given time in order to effectively
select an order. The picker, for example, must find the correct product, scan
it and deliver it to the loading dock. Scene recognition and argumentative
fact, on the other hand, allowed a camera-operated device to autonomously
determine where a product is located, if it is the right product and how to
switch to the next product at a faster rate (Christopher S. Tanga, Lucas P.
Veelenturf, et. al. 6 June 2019). AR is also being used to help with
transportation planning. For example, Volkswagen has developed a vehicle
that can show the current speed, status alerts and other information on the
windshield of the vehicle to improve the driver's safety. AR may be used to
assist a driver in quickly identifying the precise location of the shipment
inside the vehicle, significantly reducing the amount of time spent not
driving. AR could drastically alter how customers and companies perceive
traditional supply chain operations, resulting in a virtual reality supply
chain. AR may be used to assist an entry-level technician in quickly
identifying incorrect circuits and issues inside a product. On the other hand,
augmented reality may be used to display a video stream from a customer
about the current state of a product. This video could be used on the AR side
of the augmented reality supply chain's company or customer service end to
quickly identify what's wrong with the product. As a result, the buyer does
not waste time getting the product into the shop, the supply chain partner
does not waste time evaluating the product's issues and the consumer can
get a repair or replacement more quickly (Massimo Merlino, Ilze Sproģe, et.
al. 19-22 October 2016). Consumer quality is improved, which aids in the
advancement of the entire supply chain. Essentially, the applications of
augmented reality continue to develop. Consumers are expecting more from
the modern supply chain and rivalry among supply chain service providers
is increasing. Using a radio frequency-driven headset seemed to be the
perfect option for supply chain management and technology use at one time.
However, virtual reality is bringing technology to a new stage, and this
trend can only continue as society becomes more adept at and dependent on
emerging technologies (Cirulis, Ginters, et. al. 2013)
58
Figure 4.14: The Augment Reality system
Source: et al. Jesus Gimeno.2011
Robotics
Manufacturers and supply chain management companies must find a way
to satisfy customer needs as the need for fast order fulfillment and precision
in supply chain processes grows. Many businesses have had to come up with
new ways to complete the same amount of work in a shorter period of time,
if not considerably more. The solution to this problem is robotics. Robotics
in our days develop in a high speed to satisfy the needs of the supply chain.
Autonomous Mobile Robots (AMRs) - warehouse robots, automated guided
vehicles (AGVs) – autonomous forklifts and self-driving vehicles are
examples of the development of robotics. An organization must achieve
dynamic scalability and it must have a workforce that can adapt to changing
conditions (Ruthie Bowles, et. al. November 18, 2020). Furthermore,
manufacturers must persuade the public that the industry requires more
employees, particularly because many other occupations seem to be
attracting more workers than manufacturing. However, as robotics becomes
more commonly used, affordable, and usable, it may be able to remove many
of these manufacturer concerns. The customer, in the end, is the driving
force behind the expansion of robotics in logistics and manufacturing
processes. Consumers want the goods they've paid for quicker. More reliable
processes from production to distribution would enable repeat purchases,
resulting in a company’s growth and success. Robotics would not work if
there was no market demand for them. Robots have the ability to build an
unlimited workforce without adding to a company's costs (Massimo Merlino,
Ilze Sproģe, et. al. 19-22 October 2016). When robots are used in supply
59
chain systems, for example, health insurance, paid time off, overtime pay,
adherence to regular work schedules and other characteristics of modern
jobs are fully removed (Will Allen, et. al. October 2019). Robotics also has
an effect on supply chain productivity and research. Robots will sort
incoming and outgoing packages more quickly, position them on the
required shelves or shipping containers and ensure that the packages are
free of defects that might result in unwanted returns or delays in the order
fulfillment process. Robots may also be able to identify problems that arise
in their environment. For example, Robotics may be used to stop a truckload
of goods from fleeing the factory after a collision several miles away (R.H.
Thilakarathna, M.N. Dharmawardana, Thashika Rupasinghe, et. al. 8
December 2015). Alternatively, robotics may have a different route for the
drivers to follow before they leave. Robotics, on the other hand, will be
equivalent to the physical activity that occurs when an inefficiency is
identified. As a result, robotics can be used to automate the software aspects
of supply chain operations, even though human input is still needed.
Inhuman feats, such as raising large items or touching small spaces, are
possible for robots. This has an effect on how things are produced. Since our
tools only allow us to perform certain acts, humans, for example, must
construct an object from the inside out. Alternatively, a robot may penetrate
a tighter space with a tool with a much longer reach and a smaller grasp to
perform a specific action. As a result, finding simpler, more effective ways
to create a product becomes possible (Mariusz Kostrzewski, Monika
Kosacka-Olejnik, Karolina Werner-Lewandowska, et. al. June 24-28, 2019).
Furthermore, since newer robots can be repurposed to meet the needs of the
manufacturing and logistics industries, they have more applications.
Modern robots are usually lighter and simpler to move around a
manufacturing plant or an order fulfillment center than their predecessors.
Figure 4.15: The robots and drones in the warehouse
Source: et. al. Kayla Matthews. 2020
60
Smart Sensors
Industry 4.0 and the Internet of Things (IoT) in factories and workplaces
are powered by smart sensors. The combination of sophisticated sensors and
increased computational capacity, once applied at scale, will allow new ways
to analyze data and gain actionable insights to improve a wide range of
operations. As a result, manufacturing processes can be more responsive
and agile, ensuring and improving efficiency in a variety of industries
(Joseph Fitzgerald. Adam Mussomeli. Andrew Daecher. Mahesh
Chandramouli, et. al. 17 July 2016). A sensor is a system that provides
predictable, reliable, and observable input on a physical process or material.
Smart sensors are advanced platforms with onboard technologies including
microprocessors, storage, diagnostics and networking tools that turn
conventional feedback signals into true digital insights (Yevhen
Krykavskyy, Olena Pokhylchenko, Nataliya Hayvanovych, et. al. June
2019). These smart sensors can provide timely and useful data to power
analytical insights, which can lead to cost, efficiency, or customer
experience improvements. The smart sensor's position in the broader
knowledge and analytics ecosystem is a differentiator. The increased range
of opportunities for increased efficiency, higher capacity, greater reliability,
and advanced innovation can be exponentially increased by the accelerated
exchange of physical-to-digital knowledge (Vladimir Scherbakov, Galina
Silkina, Vladimir Scherbakov, et. al. September 2019). Beyond the
implementation of specific tasks, sensors can help a company respond faster
to customer reviews and order changes, making it more economical to
manufacture smaller quantities of personalized goods. Furthermore, the
combination of smart sensors and artificial intelligence, which is a key
component of Industry 4.0, allows sensors to self-test, track, and improve
their own efficiency, minimizing the likelihood of data corruption. Sensors
that warn of harm before a failure will become increasingly common in
optimal industrial processes. The early application of smart sensor
technology was based on improving existing product manufacturing
processes. Observed inputs are converted into digital form using five
primary interface methods: digital, logic, voltage, current, frequency, and
phase (Mohamed-Iliasse Mahraz, Loubna Benabbou, Abdelaziz Berrado, et.
al. October 23-25, 2019). For data aggregation and analysis, transmission
standards like Wi-Fi, Bluetooth, NFC, RFID, and others are used to
communicate this data to other sensors, controller devices, centralized
management systems, or distributed computing platforms. As a result, data
processing and analysis may be done at or near the source, minimizing the
amount of data that must be transferred between the system and the
platform. Furthermore, the advent of microelectromechanical systems
(MEMS) technology has enabled more lightweight, higher-functioning
smart sensors by effectively integrating microelectronic functions in a small
amount of space (José Jair Alves Mendes, Marinho Vieira, Marcelo Bissi
61
Pires, Sergio Luiz Stevan, et. al. 23 September 2016). Finally, smart sensor
integration across the supply chain can lower operational costs, boost asset
performance, improve demand forecasting, and provide crucial insight into
consumer conduct. Companies should understand the variety of smart
sensors available and decide how to best sensor-enable their supply chains
from end to end as unified channels and communication networks for IoT
devices continue to evolve (Martin Neuhold, et. al. 2018).
Figure 4.16: Principal components of the architecture of smart sensors
Source: et.
al.
Antsiperov
V.E,
Gennady K.
Mansurov.
2016
Automatic guide vehicles (AGV)
Automatic guide vehicles (AGV) are load carriers that ride along the floor
of a facility without an onboard operator or driver. They are computercontrolled and wheel-based. A mixture of software and sensor-based
guidance systems directs their movement. AGVs provide safe load
movement because they travel on a predictable path with precisely
regulated acceleration and deceleration and provide automatic obstacle
detection bumpers (David M. Woisetschläger, et. al. 8 June 2014).
Transportation of raw materials, work-in-process and finished products in
support of manufacturing production lines, as well as storage/retrieval or
other movements in support of picking in warehousing and distribution
applications are examples of typical AGV applications being incorporated
into existing manufacturing processes, because they provide a variety of
benefits in terms of economic, environmental, and social sustainability.
AGVs are typically associated with high fixed capital expenditure costs
(Peterson, Michalek, et. al. 2013). AGVs have a higher economic potential
than traditional vehicles, because of their lower operating costs and
capacity to operate. The term "autonomous ground vehicles" (AGVs) refers
to the social effect and enhancement of human protection. In
62
transportation, the use of manual forklifts is one of the most common causes
of injuries. AGVs come in a variety of shapes and sizes. This includes the
following: Automated carts, Unit load AGVs, Automated forklift AGVs,
Tugger AGVs (Mike Daily, Swarup Medasani, Reinhold Behringer, Mohan
Trive, et. al. December 2017). Computer-based software collects data about
and unit's current location using wireless networks, then interfaces with
software for destination and routing logic to provide real-time control and
monitoring of multiple AGVs. By wirelessly communicating specific tasks
to the AGVs via radio frequency (RF), the program guides the vehicles'
movement. Stops, starts, speed changes, raising, lowering, multi-point
turns, reverses, diverging from the guide route and interfacing with other
automatic and static material handling equipment and systems are all part
of the instructions (Lothar Schulze, Sebastian Behling, Stefan Buhrs, et. al.
19-21 March 2018). AGVs are used, with the specific characteristics they
have, in a range of areas in a plant to facilitate processing and handling:
•
•
•
•
•
•
•
•
Assembly: Moving goods into the manufacturing process is known as
assembly (Dimitrios Bechtsis, Naoum Tsolakis, Dimitrios Vlachos,
Eleftherios Iakovou, et. al. 14 October 2016).
Kitting: Kitting is the process of gathering parts for assembly (Rajiv
Bhandari, et. al. 2012).
Transportation: Pallets and loose pieces are loaded into trucks
(Dimitrios Bechtsis, Naoum Tsolakis, Dimitrios Vlachos, Eleftherios
Iakovou, et. al. 14 October 2016).
Staging: Delivering pallets for manufacturing processes is known as
staging (Dimitrios Bechtsis, Naoum Tsolakis, Dimitrios Vlachos,
Eleftherios Iakovou, et. al. 14 October 2016).
Warehousing: Moving goods from stretch wrappers to docks or
storage is referred to as warehousing (Dimitrios Bechtsis, Naoum
Tsolakis, Dimitrios Vlachos, Eleftherios Iakovou, et. al. 14 October
2016).
Order picking: Transporting a platform for a picker to position
selected items upon, as well as moving ordered goods to a trailerloading area for delivery (Dimitrios Bechtsis, Naoum Tsolakis,
Dimitrios Vlachos, Eleftherios Iakovou, et. al. 14 October 2016).
Just in time delivery: Towing parts/materials trucks to consumption
points (Dimitrios Bechtsis, Naoum Tsolakis, Dimitrios Vlachos,
Eleftherios Iakovou, et. al. 14 October 2016).
Transfer: Moving loads across high-traffic areas (Rajiv Bhandari, et.
al. 2012).
63
Figure 4.17: Examples of AVG in warehouse
Source: et. al. Alexandra Leonards.2016
Source: et al. David Edwards.2020
Real Time Locating System
A real-time locating system is one that uses the least amount of time to
detect the location of an object within a coverage range. The detected object
typically has an embedded electronic system that can connect with or send
a signal to other nearby embedded devices. Various methods that calculate
different qualities of RF signal between the transmitter and the receiver,
such as received signal intensity (RSS), angle-of-arrival (AoA) method and
time-of-arrival (ToA)/time-of-flight (ToF) method, can be used to
approximate the object's position. Anchors, tags, and a position engine are
common components of an RTLS (Witsarawat Chantaweesomboon,
Charuwalee Suwatthikul, et. al. 2016). The anchor is typically an embedded
device that serves as a point of reference within the structure. For the
geometry-based position calculation to work, at least three anchors are
needed. The tag is a handheld embedded system that is connected to the
tracked object and can wander the RTLS coverage area. The position engine
is responsible for calculating or estimating the tag's position. The location
engine, which may be in a server computer, typically uses one of the
locations estimating algorithms such as trilateration, triangulation, and
multilateration. Using ultra-wideband (UWB) technology, real-time
locating systems (RTLS) with centimeter precision have recently become
commercially viable. A trilateration algorithm can be used to approximate
the tag's position on the same two-dimensional (2D) plane as the anchors,
using the distances between the tag and at least three anchors.
Furthermore, if four or more anchors are visible, the tag's threedimensional position can be determined (Kersten, Wolfgang, Blecker,
Thorsten, Ringle, Christian M, et. al. 11 June. 2019). The difficulties faced
in calculating the tag's position at various locations were discussed. The
difference in efficiency of the system's 2D localization when set up with
three and four anchors was also found to be negligible. Furthermore, the
third dimension, or the height of the tag above the earth, was discovered to
be less precise. So, real time locating system is useful to supply chain, to
64
check the location of an order. Also, with the help of RTLS managers can
watch where the shipment is and calculate the time that requires to arrive
to the customer. With the growth of Industry 4.0, the RTLS has adopted
and added new technologies in its environment, such as Bluetooth, that
makes it easier to control and watch the location of orders (You-Wei Lin.
Chi-Yi Lin, et. al. 21 May 2018).
Figure 4.18: Architecture of RTLS
Source: et
al. Ahmet
Aktas. 2019
Autonomous mobile robots (AMRs) - Warehouse Robots
Because of their usefulness and implementations in today's world,
autonomous mobile robots have become more common in recent years. They
have become more promising and useful due to their ability to maneuver in
an atmosphere without the use of physical or electro-mechanical guidance
systems. Autonomous mobile robots are being used in a variety of settings,
including businesses, factories, hospitals, institutions, agriculture, and
households, to optimize facilities and everyday activities (Gerhard P.
Hancke. March, et. al. 4, 2020). Because of the tasks and resources they
provide, such as lifting large items, surveillance, and rescue operations, the
need for mobile robots has grown as technology has advanced.
Devices/sensors, as well as popular sensor fusion techniques that have been
developed to address issues such as localization, estimation, and navigation
in mobile robots, are being introduced and they are organized by
importance, strengths and weaknesses. An autonomous mobile robot is a
device that functions in an environment that is both uncertain and
unknown. This means the robot must be able to travel without being
disrupted and must be able to clear any obstacles set in its path of
movement (Ertugrul Ayyildiz, Alev Taskin Gumus, et. al. 16 October 2020).
65
The movement of an autonomous mobile robot (AMR) requires little to no
human interaction and is programmed to follow a predetermined direction,
whether in an indoor or an outdoor environment. Regarding the use of
automated mobile robots in the internal environment of the supply chain,
the mobile robot uses a floor plan, sonar sensing, and an Inertial
Measurement Unit (IMU) to navigate indoors. A variety of environmental
sensors are needed for an autonomous mobile robot to complete its mission.
These sensors are either built into the robot or used as an external sensor
that is placed in the environment. The design of the overall device is difficult
due to the numerous types of sensors installed on the mobile robot to
perform complex tasks, such as estimation and localization. Locomotion,
vision and navigation are the foundations - characteristics of mobile robotics
(E. Lepori. D.Damand B. Barth, et. al. June 19-21, 2013).
Figure 4.19: Example of the AMR in the warehouses
Source: Keith
Shaw. 2018
Warehouse Robots
The use of automated systems, robots and advanced software to move goods,
perform different tasks, and streamline/automate warehouse processes is
referred to as warehouse robotics. Robotics has risen to prominence in the
supply chain, fulfillment center, and warehouse management circles in
recent years and it continues to play an important role in warehouse
automation. Modern warehouses are being forced to seriously consider the
use of robotics as a result of technological advances in an increasingly
competitive market environment. Warehouse robots are no longer nice-tohave gadgets for successful warehouse operations, thanks to their ability to
maximize productivity, precision and operational performance. All
warehouse automations add value to warehousing operations by
automating menial, routine tasks, allowing human employees to
concentrate on more complex tasks (Ertugrul Ayyildiz, Alev Taskin Gumus,
et. al. 16 October 2020). Several types of warehouse robots are used in the
66
warehouse robotics industry and they serve a range of purposes and
functions, such as order picking and product movement. The different forms
of warehouse robots include, first of all, Automated Guide Vehicles (AGVs).
More specifically, inside warehouse facilities automated guided vehicles
assist in the transportation of products, equipment, and inventory. AGVs
are used in operations to replace forklifts and pick carts that are operated
manually. Automated storage and retrieval systems (AS/RS) are another
type of warehouse robot, which refers to a set of computer-controlled
systems that aid in inventory management and on-demand storage and
retrieval of products. These systems, which are typically combined with
warehouse execution software, are designed to help with fast product
retrieval and placement (E. Lepori. D.Damand B. Barth, et. al. June 19-21,
2013). Thirdly, warehouse robots types include collaborative robots, which
are semi-autonomous mobile robots that assist human workers in
performing a variety of tasks in a warehouse environment. Some
collaborative robots accompany human pickers around the factory floor and
serve as mobile storage bins for orders that have been selected. The final
type of warehouse robots is articulated robotic arms, which are used to
manipulate goods within distribution centers and warehouses and consists
a form of pick-and-place robot. These articulated robotic arms can be used
in warehouse operations such as palletizing, receiving-storage and pickingpacking, because of their ability to can rotate, switch, raise and manipulate
items (Sri Yogi Kottala, Kotzab Herb, et. al. 26 March 2019). In general,
warehouses use robotics for a number of applications thanks to
advancements in navigation technologies and practical capabilities. Some
of the warehouse tasks that robots are used for include loading and
unloading, palletizing and un-palletizing, sorting, picking (picking robots
reduce order delivery times and related costs by cutting down on warehouse
travel time), packaging, transportation (AGVs are configured to handle a
variety of loads), storage, delivery and replenishment (drones scan barcode
labels up to 50% faster than manual scanning and send inventory counts
back to the warehouse management system using RFID to track inventory
levels). Moreover, payload capability is another way to classify warehouse
robots. Warehouse robots with a certain payload capacity are often used
more commonly in some industries than others, depending on the nature of
the industry and the warehouse's needs (Ertugrul Ayyildiz, Alev Taskin
Gumus, et. al. 16 October 2020).
67
Figure 4.20: Examples of warehouse robots
Machine 2 Machine
The data flow between individuals, computers and systems is known as
Machine-to-Machine communication (M2M). The information gathered will
be used to manage and track the system remotely. M2M is made up of three
essential elements: Embedded processor for data storage, management
applications for monitoring and control, communication technology for data
transfer. Machine-to-machine, mobile-to-machine, or man-to-machine
contact is referred to as M2M. M2M is described as the link and
communication between all aspects of a physical enterprise, including
information personnel, business applications, and assets and equipment
that are located in different locations (Volkan Yavasa, Yesim Deniz OzkanOzen, et. al. 2020). M2M refers to the combination of machine connectivity,
communications, and information technology that is required to link
machines–or assets–to applications. As a result, M2M is a platform for
streamlining business processes, monitoring assets, and/or generating new
revenue. Anything with mechanical, electrical, electronic, or environmental
properties can be classified as a computer. Manufacturing equipment,
refrigeration systems, telecommunications equipment, server cabinets,
data centers, HVAC systems, storage tanks, and security equipment are
only a few examples. Utility equipment, natural gas compressors and
pipelines, traffic control systems, restaurant and convenience store
equipment are examples of industry-specific assets that can be called
machines in the sense of M2M. Computers that store information are
becoming more popular in modern machines. They may also include builtin radios for transmitting and retrieving data that aid in the efficient and
68
safe operation of home, industrial, industry, and medical processes. Such
knowledge is crucial for the operator to behave ''appropriately''. Even if
machines do not have computers, sensor radio devices can be connected to
provide physical information (Tomas Skersys, Paulius Danenas, Rimantas
Butleris, et. al. December 2016). Furthermore, the devices may be out of
range of one another, necessitating information routing, i.e., M2M requires
both point to point and point to multi-point communication. With its
innovative and intelligent routing algorithm incorporated into the radio
equipment, Bluetronix swarm technology offers a full M2M solution. In
general, M2M technology is used to supply chain, because it implements
wireless networking technologies, such as sensing and MANET, into a
broader range of applications that often make use of the internet. Even
though machine networking is still in its infancy, it has the potential to
have a significant effect. With the widespread availability of low-cost
computer chips combined with the ease with which wireless phone networks
can be accessed, no system, no matter how insignificant, will ever be lonely
(Meriam Bouzouita, César Viho, et. al. December 2015). One of the
characteristics of machine to machine is that M2M communication is
notable for its low energy consumption, which boosts device performance
during data exchanges. The network operator is in charge of service
bundles, which also include monitoring capabilities so that users can keep
track of important events. If higher priority data is sent at the same time,
data transfers can be delayed throughout the network. Users may also use
a timer to plan data transfers, or small quantities of data can be transferred
indefinitely. Machines in logistics may also be configured by position to send
out alerts or switch on automatically when they enter a specific region
(Volkan Yavasa, Yesim Deniz Ozkan-Ozen, et. al. 2020).
Figure 4.21: Presents the architecture of Machine 2 Machine
Source: et al.
Bode IdowuBismark.
Francis
Idachaba.
Aderemi
Atayero.2017
69
Self-driving Vehicles
Because of rapid developments in information technology, the autonomous,
self-driving car is one of the automotive industry's major projects in the
twenty-first century. Automobile manufacturers' financial bottom lines are
expected to benefit from technological advancements in the area of
autonomous driving. The self-driving car, also known as a wheeled mobile
robot, is a type of intelligent vehicle that arrives at a destination using data
collected from automotive sensors, such as perception of the path
environment, route information and vehicle control (Senthil Muthusami,
Mohandas Srinivsan, et. al. 2017). The key feature of a self-driving car is
that it can move people or items to a predetermined destination without the
need for human intervention. The concept of a self-driving car is similar to
that of an automobile. With the advancement of sensor technology,
computer technology, mobile internet and the self-driving car reached the
laboratory applications stage until recently (Danielle Dai, Daniel Howard,
et. al. 2014). For the duration of the ride, the vehicle performs all safetycritical driving functions and tracks road conditions, relieving the driver of
all responsibilities. In terms of market opportunities, safety, and traffic
management, the vision of fully automated driving in its highest form is
seen as promising. Nowadays, self-driving vehicles’ existence is a given in
the environment of the warehouse and they are very useful for
transportation of the products (Brandon Schoettle, Michael Sivak, et. al.
July 2014.) The characteristics of self-driving vehicles are the following:
Improved safety: Self-driving cars can constantly track and respond to
changing traffic and weather conditions, as well as avoid roadblocks.
Reduced environmental impact: Autonomous vehicles are programmed to
reduce environmental impact by using less cars and more efficient fuel
consumption. Emissions from self-driving cars can be reduced. Of course,
this is good for the atmosphere and reduces traffic congestion on the roads
(Mike Daily, Swarup Medasani, Reinhold Behringer, Mohan Trived, et. al.
December 2017). Higher efficiency: Autonomous driving improves
performance by allowing traffic to flow more quickly and reducing
congestion. Autonomous vehicles can set high speeds and intelligently avoid
congested routes thanks to vehicle-to-vehicle communication. Owners of
driverless vehicles can reduce their carbon footprint and motoring costs by
around 15% by maximizing fuel efficiency and by optimizing driving and
convoying. Greater comfort: The pilot of an autonomous vehicle becomes a
passenger. He or she may not need to keep an eye on the road ahead of them
and can instead relax and engage in other activities. Self-driving cars are
also a very appealing mode of transportation for the elderly, minors, people
with physical disabilities and even the inebriated. All of the above are
performed with greater vigilance, pace and protection than what human
drivers are capable of (Jianfeng Zhao, Bodong Liang Qiuxia, Chen, et. al. 19
October 2017).
70
Figure 4.22: The self-driving vehicle, robot and the drone
Source: et al.
Jim
Butschli.2017
71
CHAPTER 5
SUPPLY CHAIN OPERATIONS REFERENCE (SCOR)
MODEL
Overview
SCOR is a management tool for communicating, addressing, and improving
SCM choices within a company as well as with customers and suppliers.
Many businesses utilize it as a beneficial tool for implementation. It
connects the company's business processes with the goal of meeting
consumers' needs and improving their experience in the supply chain
(Hwang et al. 2016) The chapter five is the SCOR model. In this chapter
will define the role of SCOR model, the supply chain operations and metrics.
The following figure explains the structure of chapter five.
72
5.1 Introduction
The Supply Chain Council (SCC) established and endorsed the Supply
Chain Operations Reference Model (SCOR) as the cross-industry standard
for supply chain management. Pittiglio Rabin Todd and McGrath (PRTM)
and Advanced Manufacturing Research (AMR) founded the Supply Chain
Council in 1996, with 69 voluntary member companies at the time. The SCC
is a national, non-profit organization that welcomes all businesses and
organizations involved in implementing and promoting cutting-edge supply
chain management systems and practices. All users of the SCOR-model are
asked to credit the SCC in all documents that describe or depict the SCORmodel and its application. The SCOR model, even in our days, is developing
continuously. In other words, the SCOR model is considered a tool for
management. It is a supply-chain management process reference model
that stretches from the supplier's supplier to the customer's customer. The
SCOR model was created to explain the business activities involved in
meeting a customer's demand at all stages. The model can be used to define
supply chains that are very simple or very complex using a similar collection
of concepts, since it describes supply chains using process building blocks.
As a result, diverse sectors can be connected together to define the width
and breadth of almost every supply chain. The model was able to accurately
define and provide a foundation for supply chain transformation for both
global and site-specific projects. Furthermore, adopting the SCOR model
has a few organizational advantages. Some of these benefits are:
• Enhanced operational control from common core processes
• Streamlined management reporting and organizational structure
• Rapid evaluation of supply chain results
• Clear recognition of performance discrepancies
• Efficient supply chain network overhaul and optimization
• A thorough game plan for introducing new companies and goods
• Systematic supply chain mergers that capture expected savings
• Alignment of supply chain team capabilities with strategic goals
Finally, the SCC's first move was to create a common vocabulary and
notation that could be applied to any supply chain. As a result, the first step
for a team that wants to use the SCOR model is to build a common language
so that everyone is referring to the same things in the same way. All supply
chain methods, according to the SCOR approach, can be classified into one
of five general subtypes: Plan, Source, Make, Deliver, Return, Enable. The
following table represents the Scor model structure.
73
Figure 5.1: SCOR model
Source: et. al. Sri Yogi Kottala, Kotzab Herbert. (adapted from the scor model Version 7)
5.2 SCOR Model
In summary, the SCOR model explains the operations of business. Linked
with meeting the demand of a client, plan, source, make, delivery and
return are included. Use of the model involves evaluating the current state
of an individual. Procedures and priorities of the company, quantifying
operational output and contrasting the performance of the company with
data for benchmarks. A set of metrics was developed by the SCOR model
that organizations should use, as well as best practices for assessing the
efficiency of their supply chain. In detail, many variations of these simple
processes make up complex supply chains. The SCOR model also
establishes three levels of description (top, configuration and process
element). The scope and content of the supply chain are defined at the top,
the company's supply chain is designed in compliance with company
strategy at the configuration level and the company's operations strategy is
"fine-tuned" at the process element level, which includes process element
concepts, inputs – outputs, process efficiency indicators, and best practices.
SCOR analyses the supply chain's efficiency and evolution using historical
data. It describes five generic performance attributes and three levels of
metrics that analysts may use. Once an organization has a strong
understanding of the As-Is process's strengths and disadvantages, they will
consider how they want to compete and what they would need to do to
execute such actions, regardless of which supply chain approach they
prefer. In essence, the SCOR approach assists businesses in developing new
designs and then expects that businesses can decide how to execute the
74
changes. In conclusion, the SCOR reference model is made up of four basic
components, which are the following:
• Performance and strategic objectives: Common metrics are used to
identify process performance and strategic objectives.
• Processes: For example, common descriptions of process interactions and
management processes.
• Practices: Management software that improves process efficiency.
• People: This component establishes a standard for the skills required to
improve supply chain processes.
(Source: Ertugrul Ayyildiz, Alev Taskin Gumus et. al.)
5.2.1 Process
Since the SCOR model is the primary organizing structure for the current
thesis, a brief description is required on the different levels of processes.
SCOR model processes are modelled in four levels (Figure 5.2). The SCOR
model was built on five distinct management processes, known as Level 1
processes, namely Plan, Source, Make, Deliver, Return and Enable. The top
level, which deals with process forms, is Level 1. Level 2 is the configuration
level, which is where method categories are dealt with. Level 3 refers to the
process function and is the lowest level in the SCOR model's scope. Level 4
delineates the specific tasks that each of the Level 3 activities entails. In
more detail, the scope and content of the core management processes for the
above-mentioned decision areas are specified at Level 1. For example, the
SCOR Plan process is characterized as processes that balance aggregate
demand and supply in order to develop actions that best meet sourcing,
output and delivery requirements. Level 2 defines the characteristics of the
preparation, implementation, and allows process types that are used in the
core processes. For example, supply chain partners include processes for
planning the entire supply chain as well as processes to help source, make,
distribute and return decisions. A balance between demand and supply, as
well as a stable planning horizon, are characteristics associated with
successful planning processes. Level 2 process categories are characterized
by the relationship between a core management process and the process
type in the SCOR model. In summary, at Level 2, the aim is to streamline
the supply chain and increase its overall versatility. For each Level 2
process category, Level 3 offers comprehensive process element information.
At this stage of the SCOR model, inputs, outputs, definitions and the basic
flow of process elements are captured. Finally, at level 4, each company has
its own set of tasks and interactions. Daily, this level of detail is necessary
to execute and maintain the supply chain. In most organizations, level 4
process concept equates to quality process definition. Level 4 is where
75
supply chain processes are implemented. At this stage, immediate
objectives are set, intra- and inter-company supply chain changes are made,
targets are established and quick results are expected and studied.
Figure 5.2: SCOR Model's level
Source: Samuel H. Huanga, Sunil K. Sheoranb, Harshal Keskar, Stephens
The following table presents the areas which are based on the model and
also the level 1 process.
Table 6: SCOR model process
Process
•
PLAN
Description
Reference
The processes of the Plan identify the tasks involved with the
creation of supply chain operation strategies. The procedures of
the Plan include the compilation of specifications, the collection
of information on required resources, the balance of requirements
and resources in order to identify expected capability and the
demand or resource holes to identify steps to correct these gaps.
Et. Al. Samuel
H. Huanga,
Sunil K.
Sheoranb,
Harshal
Keskara
76
•
SOURCE
Ordering (or arranging of deliveries) and reception of products
and services are defined by the Source processes. The Source
approach involves issuing buying orders or arranging deliveries,
obtaining, validating, and storing products and approving the
supplier's invoice. Apart from products or services from Sourcing
Engineer-to-Order, all supplier recognition, certification,
components, contract negotiating procedures are not defined by
using Source process.
Et. Al. Samuel
H. Huanga,
Sunil K.
Sheoranb,
Harshal
Keskara
•
MAKE
The Make processes define the tasks related to the processing or
processing of materials contents for services. Instead of 'making'
or 'manufacturing', material conversion is used to reflect all sorts
of material conversions: fabrication, chemical refining, cleaning,
restoration, overhaul, recycle, refurbishment, remanufacturing and
common names for the methods of content transfer. As a general
guideline, one or more item numbers go in and one or more
distinct item numbers come out by these processes.
Et. Al. Samuel
H. Huanga,
Sunil K.
Sheoranb,
Harshal
Keskara
•
DELIVERY The Delivery procedures identify the tasks involved with
customer order formation, management and fulfillment. The
distribution process includes collecting, validating, and producing
customer requests, arranging delivery of orders, choosing,
packing and delivering and invoicing the customer. A simpler
view of source and deliver processes run in a make-to-stock-only
retail operation is given by the deliver retail process.
Et. Al. Samuel
H. Huanga,
Sunil K.
Sheoranb,
Harshal
Keskara
•
RETURN
The return methods define the operations related to the reverse
movement of products. The process of return involves the
recognition of the need to return, the decision-making process for
disposal, the preparation of the return and the shipping and receipt
of the items returned. Using Return process materials,
maintenance, recycle, refurbishment and remanufacturing
processes are not defined.
Et. Al. Samuel
H. Huanga,
Sunil K.
Sheoranb,
Harshal
Keskara
•
ENABLE
The Enable processes define the processes associated with the
supply chain management. Enable process includes management
of company legislation, performance management, data
management, inventory management, facility management,
contract management, supply chain network management,
regulatory enforcement management and risk management.
Et. Al. Samuel
H. Huanga,
Sunil K.
Sheoranb,
Harshal
Keskara
77
5.2.2 Metrics and performance attributes
Managing supply chain efficiency entails assessing the discrepancies
between real and desired results in order to recognize and flag important
performance gaps (Melnyk et al. 2013). Understanding the root causes, as
well as implementing and tracking change action plans, are also needed.
Adoption of performance evaluation systems is often recommended to make
the execution of supply chain improvement strategies easier (Melnyk et al.,
2013, Qi et al., 2017). Choosing appropriate performance measures will help
managers devote resources to the most applicable improvement behavior
(Elgazzar et al. 2019). It is important to use metrics that allow for a
balanced assessment of supply chain efficiency as well as the setting of
performance goals that represent a company's strategy and objectives.
However, most companies have struggled to build a framework that offers
a consistent image of supply chain success and encourages the
implementation of action plans (Lakri et al., 2007). The metrics used to
evaluate supply chain efficiency have an effect on strategic, tactical, and
operational decision-making (Gunasekaran et al., 2001, Melnyk et al.,
2004). Strategic level assessments are linked to top-level management
decisions and they often represent "an examination of broad-based
strategies, corporate financial plans, competitiveness, and level of
commitment to organizational objectives" (Gunasekaran et al., 2004).
Metrics for efficiency, order lead time, overall cash flow time, cost-cutting
programs and lead time towards industry expectations are all included in
strategic level steps. Decisions on resource distribution and performance
assessment against objectives are taken at the tactical level in order to
achieve the strategic level's goals. Order processing time, cash flow, and
capability flexibility are all tactical level indicators. Performance
assessment at the organizational level necessitates detailed data to
determine the results of low-level manager decisions. Measures of human
resource productivity, percentage of defects, consistency of delivered
products, on-time delivery of goods, and delivery reliability performance are
all included in the organizational level performance assessment. Managers
and employees must set organizational targets that, if accomplished, would
contribute to the achievement of tactical goals (Gunasekaran et al., 2001,
Gunasekaran et al., 2004).
An attribute of results is a classification or categorization of metrics used to
express a particular technique. It is not possible to quantify an attribute
itself, as it is used for setting strategic course. Metrics assess the potential
for these strategic directions to be reached. Five output qualities are known
by SCOR: Reliability, Responsiveness, Agility, Cost, Assets. The first three
attributes are customer-focused, addressing the efficacy of supply chain
systems, while the remaining attributes are internal-focused, addressing
performance (Chorfi et al., 2018). To develop a strategic direction, these
characteristics should be adopted (SCC, 2012). The SCOR model suggests
78
the use of certain performance metrics associated with each attribute,
which are structured in three hierarchical tiers, in order to assess the
effectiveness of these strategies' implementation. Such as the part of the
process, the SCOR model recognizes 3 level of metrics. The SCOR model
emphasizes the importance of this performance diagnosis practice in
defining processes that need further study and change.
Performance attribute
Reliability: The Reliability attribute addresses the ability to execute tasks
as needed. Reliability is based on the predictability of a process's outcome.
The standard parameters for the attribute of reliability include: On-time,
the correct quantity and the correct quality. The main output predictor of
SCOR (level-1 metric) Perfect Order Fulfillment. Reliability is an attribute
based on the consumer. (Francisco Rodrigues Lima-Junior, Luiz Cesar Ribeiro
Carpinetti, et al. 2019)
Responsiveness: The trait of Responsiveness represents the speed at
which tasks are completed. Responsivity addresses the pace of doing
business repeatedly. Example metrics for responsiveness are cycle time
with metrics. Order lead time is the main SCOR efficiency metric for
responsiveness. Responsiveness is a trait based on the client. (Francisco
Rodrigues Lima-Junior, Luiz Cesar Ribeiro Carpinetti, et al. 2019)
Agility: The Agility trait determines the ability to respond to external
influences, the ability and speed to respond to transition. External effects
include non-predictable demand rises or declines, suppliers or associates
quitting the business, natural disasters, (cyber) terrorist activities,
availability capital tools (the economy), labor problems. The main
performance metrics for SCOR include Adaptability, Stability and Valueat-Risk. Agility is an attribute based on customers. (Francisco Rodrigues
Lima-Junior, Luiz Cesar Ribeiro Carpinetti, et al. 2019)
Cost: The Cost attribute defines the cost of the process running. Typical
costs include the expense of labor, the cost of supplies, the cost of transport.
The main key performance metric for SCOR is the total cost to serve. Cost
is an attribute that is internally oriented. (Francisco Rodrigues Lima-Junior,
Luiz Cesar Ribeiro Carpinetti, et al. 2019)
Assets: The Asset Management Performance value ('Assets') defines the
ability to use assets effectively. In the supply chain, stock management
techniques include inventory reduction and in-source against outsource.
The SCOR primary performance measures include: Asset returns,
Inventory days of supply, Return on Fixed Assets. Also, example metrics
are product days of delivery and capacity usage. Performance in Asset
Management is an individual, oriented characteristic. (Francisco Rodrigues
Lima-Junior, Luiz Cesar Ribeiro Carpinetti, et al. 2019)
79
Metrics
A metric is a standard for a supply chain or process output assessment. The
SCOR metrics considered as diagnostic metrics (compare to how diagnosis
is used in a medical office). Three levels of pre-defined metrics are known
by SCOR: Level 1 metrics are diagnostic indicators for the general health of
the supply chain. These metrics are also known as primary performance
measures and competitive metrics (KPI). Metrics for level-1 help to set
achievable objectives to sustain the strategic direction. For the level-1
metrics, level-2 metrics act as diagnostics. The partnership between
diagnostics helps for a level-1 measure, defines the root cause or causes of
a performance difference. Level-3 metrics act as diagnostics for level 2
metrics. In the current thesis, only the level 1 metrics will be presented and
analyzed, because they will be used for the House of Quality analysis
(Chapter 4).
SCOR recognizes the following strategic metrics (KPIs):
Table 7: SCOR model attributes
ATTRIBUTE METRICS
LEVEL 1
1.Reliability
Definition
Reference
Perfect order
fulfillment
The percentage of orders that arrive on time, with
full and correct documentation and no shipping
damage. All goods and quantities shipped on time
(as specified by the customer) and documentation
for packing slips, bills of lading and invoices
(accuracy of order fill) are all part of perfect order
fulfillment. It is estimated as follows: [total orders
shipped on time and in full-orders without faulty
documentation—orders with shipping damage]/
[total orders].
Qing Lu, Mark Goh,
Robert De Souza.2018
Percentage of
orders
delivered in
full
It is considered as a component of perfect deliver
performance. It is the percentage of orders that
fulfill the customer's quantity and accuracy criteria.
It assesses the quantity and quality of the
distribution, i.e., the efficiency with which the
correct item and quantity are delivered.
Qing Lu, Mark Goh,
Robert De Souza.2018
Deliver
Performance
In comparison to the total number of orders
shipped, the proportion of orders delivered on
schedule. The total number of orders issued, the
number of orders scheduled to the customer's
request date, the total number of orders shipped, the
percentage of orders delivered on time (to the
request date), the number of orders delivered ontime to the commit date and the percentage of
Qing Lu, Mark Goh,
Robert De Souza.2018
80
orders delivered on-time to the customer commit
date, are all components of delivery success. It
influences the accounts receivable balance sheet.
2.Responsive
ness
3.Agility
Returns and
customer
complaints
The total number of consumer complaints and the
returns of their orders.
et. al. R.H.
Thilakarathna, M.N.
Dharmawardana,
Thashika Rupasingh
2015
Order lead time
The average time it takes for an order to reach its
intended recipient after it has been placed. For
warehouses and distribution centers, this is one of the
most important KPIs.
et. al. Hector Sunol
2020,
Manufacturing
lead time
The total amount of time it takes to make a specific
item or batch.
et. al. R.H.
Thilakarathna, M.N.
Dharmawardana,
Thashika Rupasingh
2015
Stock out
probability
Probability that a requested item is out of stock
right now.
et. al. R.H.
Thilakarathna, M.N.
Dharmawardana,
Thashika Rupasingh
2015
Back Order
Rate
The rate at which orders for out-of-stock products
are received. This can happen in circumstances,
where demand spikes suddenly. However, if this
rate is consistently high, it suggests that planning
and forecasting are missing.
e.t Hector Sunol. 2020
Upside supply
chain
adaptability
The highest percentage increase in quantity
delivered that can be sustained over the course of
30 days.
Qing Lu, Mark Goh,
Robert De Souza.2018
Production
flexibility
(Upside
flexibility)
There are two forms of output flexibility: upside
flexibility and downside flexibility. The number of
days needed to achieve an unplanned, sustainable
20% increase in production is known as upside
flexibility. The downside flexibility is that it is only
possible to reduce the percentage of orders placed
30 days prior to delivery without incurring
inventory or cost penalties. Internal manufacturing
capability, direct labor and material availability, all
influence production flexibility, which has an effect
on inventory on the balance sheet.
Samuel H. Huanga,
Sunil K. Sheoranb,
Harshal Keskar. 2015
81
4.Cost
5.Asset
Total supply
chain
Management
cost
Order management, stock procurement, inventory
carrying, finance and preparing, and MIS costs are
all included in supply chain costs. It is measured as
the sum of all costs. In general, it is a term that
refers to the costs of running a supply chain.
et. al. Qing Lu, Mark
Goh, Robert De
Souza.2018.
Cost of goods
sold
The cost of purchasing raw materials and
manufacturing finished products. This expense
covers both direct and indirect costs (labor and
materials).
Samuel H. Huanga,
Sunil K. Sheoranb,
Harshal Keskar. 2015
Distribution
cost
Transportation and storage costs are included in the
overall distribution cost.
et. al. R.H.
Thilakarathna, M.N.
Dharmawardana,
Thashika Rupasingh
2015
Manufacturing
cost
The cost associated with creating a product. Labor,
maintenance and rework costs are all included in
the overall production cost.
et. al. R.H.
Thilakarathna, M.N.
Dharmawardana,
Thashika Rupasingh
2015
Inventory cost
Includes the Investment inventory: the value of held
inventory as an investment. Inventory obsolescence:
Costs associated with outdated inventory, which
may involve spoilage in some cases. Work in
process: Expenses incurred as a result of work-inprogress the inventories things that have been
completed. Finished cost: Costs associated with
finished goods that have been kept for a long time
in the inventories.
et. al. R.H.
Thilakarathna, M.N.
Dharmawardana,
Thashika Rupasingh
2015
Return on
fixed assets
Return on Fixed Assets (ROFA) is the amount of
money a corporation makes in exchange for its
assets.
et.al. E. Lepori.
D.Damand. B. Barth.
2013
Inventory days
of supply
The Inventory Days of Supply metric, also known
as Days in Inventory, is an efficiency ratio that is
used to calculate how long it takes for a company to
sell all of its inventory in days. It represents the
average time between the procurement of raw
materials and the sale of the final product to a
distributor in the case of a manufacture.
et. al. Samuel H.
Huanga, Sunil K.
Sheoranb, Harshal
Keskar. 2015
Asset turns
The asset turns ratio compares the value of a
company's assets to the value of its sales or
revenues. The asset turns ratio is a metric that
measures how effectively a corporation uses its
assets to produce income.
et. al. Samuel H.
Huanga, Sunil K.
Sheoranb, Harshal
Keskar. 2015
82
CHAPTER 6
Impact Assessment
Overview
The House of Quality (HOQ) is one of the matrices in the Quality Function
Deployment iterative method (QFD). It is the central nervous system that
regulates the entire QFD operation. For new product design, the House of
Quality Matrix is the most well-known and commonly used method. It
converts customer expectations into a sufficient number of engineering
goals to be met by a new product design, based on marketing research and
benchmarking results. It is carried out by a multidisciplinary team that
includes
representatives
from
marketing,
design
engineering,
manufacturing engineering, and all other company-critical functions.
Industries in Japan and America have commonly used HOQ in various
fields such as Automobiles, Electronics, Integrated Circuits, Apartment
Layout Planning, Home Appliances, Clothing and other renowned
industries. However, in the current thesis, the house of Quality will be used
for other situations. The purpose of impact assessment is to analyze and
evaluate in details the impacts-influence of SC 4.0 technologies to supply
chain operations, with the help of HoQ method. Moreover, the purpose is to
examine the impact that each technology has to every SC operations. In
contrast with the general format of the analysis, which has six components
(customer requirements, technical requirements, a planning matrix, an
interrelationship matrix, a technical correlation matrix and a technical
priorities or benchmarks and targets section), there will be four major
components in the current thesis. The next figure presents the structure of
chapter six.
83
6.1 Introduction
Manufacturing companies or corporations used to introduce their products
into the marketplace using a product-driven approach, which ignored the
opinions and desires of customers. However, knowing the desires and
requirements of consumers is regarded as a crucial factor in effective
product growth (Engelbrektsson, 2002). In order to fulfill consumer
demands and achieve long-term success in the dynamic business climate,
manufacturing companies are looking to change their business operations
from a product-oriented approach to a marketing-oriented approach (Lai,
2003). A matrix format is used in Quality Feature Deployment (QFD) to
capture a range of issues that are important to the planning process. The
construction of a series of matrices, known as the "House of Quality",
underpins the QFD product/service development process or methodology
(Bernal et al., 2009). To integrate customers' informational needs, QFD
employs four phases of HOQ (Clausing & Hauser, 1988, Hauser, 1993). The
House of Quality Matrix is the most well-known and commonly used method
for increasing customer satisfaction. It is "a kind of conceptual map that
offers the means for tetrafunctional preparation and communication,"
according to Hauser and Clausing. According to Bailom et al. (1996),
analyzing service customer preferences or needs can aid company managers
in identifying requirements that the user or client is aware of, but that have
yet to be met by the current services available. As a result, the house of
quality acts as a valuable resource for both future developments and new
investments (Bailom et al., 1996). It can be used to measure benchmarking
indexes, prioritization indexes and quality management indexes, according
to Bernal et al. (2009). (Johnson, Muller, Sieck & Tapke,). The quality
assurance index has intense principles that are crucial when making
quality decisions. The severe values reflect the importance of the service
requirements. According to Bailom et al. (1996), the higher the positive
range rating, the greater the relative importance in the service user's
perception of service quality. Conversely, the lower the relative importance
of service quality, the higher the negative value of the quality management
measure (Bailom et al.1996). Similarly, according to Johnson et al. (n.d.),
high priority quality characteristics suggest that engaging in defined
service consumer needs would provide substantial value to the individuals
or society they represent. Benchmarking can also be done using the house
of efficiency measurements by managers. As a result, Keegan and O’Kelly
(2004) described benchmarking as "a continuous, systematic process for
comparing the outputs of organizations, functions, or processes against the
"best in the world," with the aim of not only matching, but exceeding those
performance levels." The house of quality method enables company
executives to evaluate competitors from the viewpoint of their clients
(Bernal et al. 2009). This is referred to as reverse engineering by Lankford
(2001). Benchmarking is the method of evaluating "the entire consumer
84
path of a competitor's organization. Managers should benchmark using the
house of quality's two stages of assessment. The first phase is known as
customer (service user) comparative evaluation, in which service users
evaluate the relative performance of the organization's services against its
key rivals in the private sector or "best in the world" organizations based on
the needs defined by the service users (Chan & Wu, 2005). The evaluators
conduct a technological competitor comparison in the second stage, in which
the design requirement fulfillment is compared. The second phase,
according to Bernal (2009), should be carried out by the staff in charge of
the product and/or service design. Customers' (or service users') perceptions
are critical in deciding what to sell and how to deliver it to them. Managers
of public institutions should use the "House of Quality" tools for quality
management, benchmarking, and prioritizing.
6.2 The combination of SCOR model and Technologies with HoQ
The HOQ method is a well-known and widely utilized method for new
product design. Based on marketing research and benchmarking results, it
converts customer expectations into a sufficient number of engineering
goals that may be accomplished by a new product design. In the current
thesis, this method will be employed to evaluate the impact that
technologies have on the supply chain function. However, it is important to
mention how SC 4.0 technology and SCOR metrics/Kpis are combined with
the HoQ/QFD method. The first "step" was the search for the technologies
through the systematic literature review. The second "step" was to examine
and present the supply chain operations. As was mentioned in the previous
chapters, the SCOR model is the major pillar of the current research,
because every operation and metric/Kpis had to be examined and presented
to explain their role and purpose. In summary, the SCOR model was
employed to identify the supply chain operations and their metrics. For the
research, in the HoQ, only the level one SCOR metrics were used. The
reason why the HoQ was chosen, is that the assessment of the results of
this method provides the relationships between the customer's needs and
the characteristics of the product. For the current thesis, the results that
the HoQ provides are the relationship and impacts that new technologies
have on SC operations. The HoQ consists of several "rooms." However, only
the four rooms were used (customer requirements, degree of importance,
engineering characteristics, and relationship matrix). To combine the
technologies and the SCOR metrics in HoQ, it was necessary to evaluate
the characteristics of the elements of each room, so that they could be
replaced by the technologies and SCOR metrics. The room with the
requirements of the customers was replaced by the metrics, and the room
with the engineering characteristics was replaced by the technologies. The
replacement was developed with this process, because the degree of
85
importance of each function had to be evaluated by experts, and the purpose
of the dissertation is to measure the impact of technologies on functions. If
the opposite were to happen, for example, if one were to replace the
technologies in the customer requirements room and the metrics in the
engineering characteristics room, then the results would provide the effects
that the metrics cause on the technologies. Obviously, this was not the
purpose of the current research, thus the reason why the replacement
process was done in the above way. The HoQ is the link between technology
and SCOR. All three of these elements combine to give the ultimate value
of the effects and relationships between technologies and logistics functions.
The following figure explains the steps for the overall methodology.
Figure 6.1: The steps for the overall research methodology
Step 1
Search for the
technologies
• Systematic
literature
review
Step 2
• SCOR
model
Present SC
operations and
metrics
Step 3
Ιmplementation
HoQ/QFD
• The method
used to
evaluate
the effects
6.3 The reasons to use QFD-HOQ.
One of the most critical and impactful elements of any organization's
performance is effective communication. The QFD approach efficiently
communicates customer demands through various business processes,
including design, quality, manufacturing, distribution, marketing and sales
within the company. The voice of the consumer is effectively communicated,
allowing the entire company to collaborate and deliver goods with high
levels of customer perceived value (Bernal et al., 2009). There are a number
of other advantages in using Quality Function Deployment:
86
1.Consumer-Centered (Customer Focused): The QFD approach focuses on
the customer's desires and needs, rather than what the organization thinks
the customer wants. Technical design criteria are translated from the
customer's voice. Design specifications are driven down from machine level
to device, sub-system and part level requirements during the QFD phase.
Finally, during the manufacturing and assembly processes, the design
requirements are monitored to ensure that the customer's needs are met.
(Chatree Homkhiew, Thanate Ratanawilai, Klangduen Pochana. 2012)
2.Voice of consumer competitor analysis: Using the QFD "House of Quality"
method, one can see how one’s concept or product compares to the
competition in terms of meeting the voice of consumer. This simple review
will help one to make design decisions that will put them ahead of the
competition. (Bernal et al. 2009)
3.QFD decreases the risk of late design changes by concentrating on product
features and enhancements based on consumer needs, resulting in a shorter
development time and lower cost. Effective QFD approach avoids
squandering project time and money on the implementation of non-valueadded functionality and functions. (Bernal et al. 2009).
4.Structure and Documentation: Throughout the product development
process, QFD offers a formal method and tools for documenting decisions
taken and lessons learned. This information base can be used as a historical
document to help with potential ventures (Chatree Homkhiew, Thanate
Ratanawilai, Klangduen Pochana, et al. 2012). Companies must bring to
the market new and enhanced goods that satisfy the real wants and needs
of customers, while reducing production time. The QFD approach is for
companies who are dedicated to hearing the customer's voice and fulfilling
their needs (Bernal et al. 2009).
6.4 Quality Function Deployment (QFD)
Today's average customer has a lot of choices when it comes to similar
products and services. The majority of customers make purchases based on
a general understanding of quality or value. Customers tend to get the best
value-for-money product. To stay competitive, businesses must figure out
what influences a customer's view of value or quality in a product or service
(Lai et al, 2003). They must identify which product features, such as
dependability, styling, or performance, influence the customer's perception
of quality and value. The Voice of the Customer (VOC) is gathered and
integrated into the design and manufacture of many popular organizations'
products. Quality and consumer perceived value are deliberately designed
into their goods and services. These businesses use a systematic method to
identify their customers' desires and needs, then translate them into
concrete product designs and manufacturing processes that meet those
87
needs. Quality Function Deployment is the method or tool they're using
(Bernal et al., 2009).
Quality Function Deployment (QFD) is a method and collection of tools for
defining customer requirements and converting them into comprehensive
engineering specifications and plans for producing products that meet those
requirements. QFD is used to convert customer specifications (or VOC) into
measurable design goals and move them down through the assembly, subassembly, part and manufacturing process stages (Engelbrektsson et.al.
2002, Berna et al. l. 2009). The QFD technique includes a collection of
matrices that can be used to help with this process. Yoji Akao, who worked
for Mitsubishi's shipyard in the late 1960s, was the first to build QFD in
Japan. Other businesses, including Toyota and its supply chain, later
introduced it. QFD was first launched in the United States in the early
1980s, mostly by the big three automakers and a few electronics firms. The
adoption and development of QFD in the United States was slow at first,
but it has since gained traction and is now used in manufacturing,
healthcare and service organizations.
6.5 House of Quality Chart (HOQ)
The quality feature implementation includes the house of quality process
(QFD). When developing products and providing services, the QFD
approach will help a business to ensure quality. For this, QFD employs a
variety of matrices. The house of quality is the first and most significant
matrix in QFD because it is shaped like a house with a roof and a body. It
starts by separating consumer needs from technical requirements in this
matrix and by assessing these two factors separately (Wael SH. Basri et. al.
2015). The House of Quality (HoQ) is a matrix of spaces, roofs and
basements, that organizes product design knowledge in a systematic
graphic representation. The Quality House is a helpful and illustrative
review of product data. The diagram does not reflect the true value of the
HoQ (K.G. Durga Prasad, K. Venkata Subbaiah, K. Narayana Rao et. al.
2014). Rather, the true meaning is in group decision-making, which
necessitates that the team address the design issue and eventually comes
to a shared understanding of it. It is noted that the team aims to get a
detailed understanding of the design problem during problem formulation.
It collects and reviews data on consumer and company requirements, their
weighted value, engineering characteristics, and competitive products used
as benchmarks (Chan & Wu et. al. 2005)
The following knowledge is organized in a structured way by the HoQ (Wael
SH. Basri et. al. 2015).
88
1.
2.
3.
4.
5.
6.
customer requirements (Room 1)
customer importance weights (Room 2)
engineering characteristics (Room 3)
correlation ratings of requirements and characteristics (Room 4)
benchmark satisfaction ratings (Room 5)
coupling between engineering characteristics (Room 6)
The following structure (6.1) represent the House of Quality chart for
product planning:
Figure 6.2: HoQ chart
Source: et. al Wael Sh. Basri
89
6.6 Previous Relevant Research.
The HoQ will be used to evaluate the impact of SC 4.0 technologies on SC
operations. This method has been used to measure in detail the contribution
each individual technology has (weak or strong) on SC operations. There
exists related work that has used the HoQ for similar purposes, but in a
different branch. For example, the Turkey's biggest mobile communication
provider employed "House of Quality" to produce a smart phone in line with
customer expectations as a new product development. This study was
chosen from the ISEAIA 2013 (Girne American University) International
Symposium on Engineering Artificial Intelligent and Applications. In 2010,
the company began selling its own smart phones in Turkey, while also
marketing world-famous smart phones such as the iPhone and Samsung
Galaxy series. First, the “House of Quality” was constructed and analyzed
and development areas and necessary technical specifications had been
determined. Later on, the Kano Model had been included in order to
prioritize the significance of customer necessities, and the new House of
Quality had been built. Moreover, the “House of Quality” was employed by
a four-star hotel in Zanjan to increase service quality. Customer happiness
is a critical goal to achieve at a reasonable cost in today's competitive
market. In this study, a HOQ matrix was created to meet the needs of the
customers. Furthermore, this study explored customers' satisfaction with
services and the importance of each requirement, using a survey method.
The "Thai furniture industry's" manufacturing activities are today quite
competitive. The study vehicle for this investigation was a plywood
wardrobe. "House of Quality" was hired to design and manufacture new
types of prototype plywood wardrobes in order to test product shape,
pattern, color, functioning and material quality. The absolute and relative
technical criteria' importance levels were computed, and the House of
Quality was born. The findings demonstrated that overall satisfaction levels
for all new product categories increased over those for existing items. A real
case study uses the "House of Quality" in manufacturing to generate
ecologically sustainable products. The "Directorate of Higher Education
Indonesia" (DIKTI) and the University of Muhammadiyah Gresik in East
Java, Indonesia, financed this research with Grant Beasiswa Luar Negeri
(BLN) DIKTI. Every industry is being pushed by the green manufacturing
challenge to raise environmental consciousness by designing and producing
environmentally friendly products. Several experts from various industries
are involved in the research mentioned above (marketing, design,
manufacturing, environmental field etc.). The team design identifies 14
customer and environmental criteria based on literature analysis, historical
data, brainstorming, and a comprehensive discussion with several experts
at the manufacturing company (CER). The priority order of environmental
indicators was determined using CER weights in combination with the
"House of Quality." In addition, "House of Quality" is used in the case study
90
of designing in the field of the refrigerator family. A product development
team can use the customer demands priority structure acquired using the
proposed methodology to design a product family that meets the consumers'
expectations. Marketing research techniques, such as factor analysis,
cluster analysis, and conjoint analysis were used to create "HOQ." A
questionnaire was created and distributed to 200 people from varied
backgrounds. Factor analysis is carried out by completing a questionnaire
survey and analyzing the data using the SPSS software. For each client
segment, a cluster analysis and a conjoint analysis were conducted.
According to the findings, energy usage is assigned the greatest priority for
both the entire customer group and the customer segment. For the creation
of car dashboards, Toyota and Honda used "House of Quality." The
automobile sector is the backbone of any country's economy. The dashboard
is an important section of the car's interior that is utilized to regulate many
operations. Two significant advancements in the car dashboard, the forced
exhaust system and the multipurpose cup holder, were introduced in
response to customer feedback. A market survey identifies client demands.
The characteristic of "size of the blower" obtained the highest score on the
HOQ for car dashboard. The "speed of the blower" is the second most
important feature. The "Voice of the Customer" was obtained using a
questionnaire (VOC). Customer needs were translated into technical
specifications using the "Voice of the Customer" method. Finally, the output
from the House of Quality (HOQ) was used to generate concepts. KIMEP
(Kazakhstan Institute of Management, Economic and Strategic Research)
employed "House of Quality" to improve management education standards.
The needs and requirements of students were recognized in order to lay the
groundwork for providing high-quality curriculum and services in higher
education. The transformation process from student needs to instructional
development is depicted in the House of Quality (HOQ). The results of the
data analysis indicate that the curriculum should be restructured. The
number of tutoring sessions should be raised, the exam should be
restructured, and the quiz weight should be enhanced.
6.7 Assessment of Supply Chain 4.0 Solutions on supply chain operations.
However, in the current thesis, HoQ will be used in order to measure the
importance of each supply chain performance metric and, more importantly,
it will be examined what effects the technologies have in the supply chain
operation. For example, how the Augmented Reality technology will affect
the order lead time or the manufacturing lead time etc. In order to measure
the impacts of new technologies on the supply chain operations, only four
rooms will be needed. In room 1 there will be the metrics that measure the
supply chain operations. In room 2 the degree of importance will be
evaluated. In room 3, the room of engineering characteristics will be
91
replaced by the 21 SC 4.0 technologies. Finally, room 4 will be correlation
ratings of requirements and characteristics. The degree of importance and
the relationship matrix between the metrics and the technologies will be
evaluated by logistics experts. The types of companies that evaluated the
degree of importance are related with logistics. The first type of companies
that evaluate the degree of importance of metrics are 3PL logistics centers,
In-house logistics warehouses, marketing and processing logistic companies
and the final type is the IT logistics center. The aim of House of quality is
to accomplish three things. Firstly, it enables businesses to bring higherquality goods to the market more efficiently and at a lower cost. Secondly,
the business will achieve customer-driven product creation. Thirdly, the
business will have a framework in place to monitor potential design or
process changes. The following are some of the outcomes that can be
expected from conducting House of quality studies:
• A better understanding of the needs of consumers.
• Better project management on construction projects.
• A stronger start to the development.
• Less configuration changes at the end of the process.
• Fewer production start-up concerns.
• A reputation for being quality-conscious.
• Increased revenue.
• Reported customer-driven product concept.
However, the purpose of using the House of Quality analysis in the current
thesis is to understand the impact that the new technologies have on the
supply chain operation. Thus, the chart of analysis will include the metrics,
which measure the supply chain operations and the new technologies, in
order to calculate the impact and relationships between them.
As was mentioned above, in order to analyze the impacts of the new
technologies on the supply chain operations by using the House of Quality
analysis, only four rooms will be used and will be replaced by the metrics
(which measure the supply chain operations) and the 21 technologies that
are used in the supply chain. The four initial rooms are customer
requirements, degree of importance, engineering characteristics and
Relationship Rating Matrix.
Customer Requirements: In the first column, customer requirements are
summarized as rows. A concise list of roles and sub-functions focuses on the
customer's most critical needs. Customer language or wording is often used
to express the customer's speech. However, for the current research, all the
92
level 1 metrics from the Scor model will be presented in this room. Thus,
the metrics will replace the customer requirements in this column (room 1).
Degree of importance: The degree of importance column is placed next to
the metrics column. The weights value, which vary from 1 to 5, decide how
important the companies consider each metric in comparison to the others.
Engineering characteristics: A list of quantitative output parameters and
their corresponding units is arranged in a row vector along the top row
underneath the roof triangle. Such as on the customer requirements
column, in this row the 21 technologies that were identified through the
SLR method will replace the engineering characteristics room. The sum of
fulfillment of each metric can be quantified using a technology.
Relationship Rating Matrix: A cell is located at the intersection of a row and
a column and is used to show the degree of association - impact between a
metric and a technology. Positive correlation is rated 1 (low), 3 (medium),
or 9 (high), whereas negative correlation is rated -1, -3, -9. There is no
discernible correlation, so the cell is left blank. While other number systems
have been used, the numbers 1,3, and 9 are commonly used in practice.
Degree of importance scale: The weight value of the importance will be
evaluated from 1 to 5 (et. al. Wael SH. Basri, 2015).
1
Not Important degree.
2
Slightly important degree.
3
Moderately important degree.
4
Important degree.
5
Very important degree.
Relationship rating Matrix:
•
Positive correlation
1
Indicates a weak contribution.
3
Indicates a medium contribution.
9
Indicates a strong contribution.
The expert panel survey that will be evaluated by the experts is the
following:
93
Suppl y cha i n
per for ma nce
metr i cs
R el i a bi l i ty
Perfect order
fulfillment
Percentage of orders
delivered in full
Delivery Performance
Returns and customer
complaints
R esponsi veness
Order lead time
Manufacturing lead
time
Stock out probability
Back Order Rate
Ag i l i ty
Upside supply chain
adaptability
Production flexibility
(Upside flexibility)
Total supply chain
Management cost
C ost
Cost of goods sold
Distribution cost
Manufacturing cost
Inventory cost
Asset
Return on fixed assets
Inventory days of
supply
Asset turns
94
IoS: Internet of
Services
Data mining
Real-time locating
systems (RTLS)
Autonomous Mobile
Robot (AMR) –
Warehouse robots
Automated Guided
Vehicles (AGVs) –
Autonomous forklifts
Machine learning –
Deep learning
Artificial Intelligence
Smart contracts –
blockchain
Machine 2 Machine
sensors
Intelligent
Transportation
Systems
RFID
Cyber-physical
systems
3D printing
Unmanned Aerial
Vehicle (drone)
Self-driving vehicles
Sensor technology /
smart sensors
Robotics
Internet of Things
Cloud computing
Big Data
Augmented reality
Supply Chain 4.0
Technologies
Degree of
importance
EXPERT PANEL SURVEY
The final step after the evaluation of the degree of importance and the
relationship rating matrix, is to calculate the percentage of impact and
influence that each individual technology has on the operations, in
relationship with metrics. The scores are determined by adding the metrics
(customer importance rating) and the value assigned to the correlation
relationship matrix in each column. For example, the absolute value will be
calculated as follows: (degree of importance*relationship rating matrix) +
[(degree of importance (2) *relationship rating matrix (2)] +…= absolute
value of the impact.
Example:
Absolute value: (5*9) +(4*3) +(5*1) +(5*1) +(4*3) +(3*3) +(3*1) +(4*1) +(4*1)
+(4*3) +(5*9) +(4*3) +(5*1) +(3*3) +(4*1) +(3*3) +(4*1) +(3*1) =202
The absolute value expressed as a percentage of the sum is the relative value.
Supply Chain Performance Metrics
Reliability
Degree of importance
Perfect order fulfillment
Percentage of orders delivered in full
Delivery Performance
Returns and customer complaints
Augmented Reality
5
4
5
5
9
3
1
1
4
3
3
4
3
3
1
1
4
4
1
3
5
4
5
3
4
9
3
1
3
1
3
4
3
3
1
1
202
Responsiveness
Order lead time
Manufacturing lead time
Stock out probability
Back Order Rate
Agility
Upside supply chain adaptability
Production flexibility (Upside flexibility)
Cost
Total supply chain Management cost
Cost of goods sold
Distribution cost
Manufacturing cost
Inventory cost
Asset
Return on fixed assets
Inventory days of supply
Asset turns
ABSOLUTE VALUE
6.8 Results
95
Autonomous Mobile
Robot (AMR) –
Warehouse robots
Automated Guided
Vehicles (AGVs) –
Autonomous forklifts
Artificial Intelligence
1
1
3
9
1
3
1
3
3
3
3
3
3
1
Percentage of orders
delivered in full
5
3
3
1
3
9
3
3
3
1
3
3
1
3
1
9
3
3
9
3
3
1
Delivery Performance
5
3
9
3
9
9
3
9
3
1
3
9
3
3
3
3
9
9
3
9
3
1
Returns and customer
complaints
4
3
9
3
3
3
3
3
3
1
3
3
1
1
3
3
3
1
3
1
3
3
Order lead time
5
3
3
1
9
9
3
9
3
3
3
3
9
3
3
3
3
3
9
3
3
1
Manufacturing lead
time
4
3
9
1
9
9
3
3
1
9
3
3
1
3
1
3
3
3
1
1
3
1
Stock out probability
4
1
9
3
3
1
3
1
1
3
3
3
1
3
3
9
9
1
1
1
9
1
Back Order Rate
3
1
9
1
9
1
3
1
1
3
3
3
1
3
3
9
9
1
1
3
9
1
Upside supply chain
adaptability
4
3
9
3
3
3
1
1
1
3
3
3
1
3
1
9
3
1
3
3
3
1
Production flexibility
(Upside flexibility)
4
3
9
3
9
9
3
1
1
9
3
3
1
9
1
9
9
3
3
3
3
1
Total supply chain
Management cost
4
9
9
3
9
9
9
3
3
3
3
3
3
3
9
9
9
9
9
3
9
3
Cost of goods sold
4
1
9
9
3
9
3
1
1
3
1
3
1
3
3
3
9
3
3
1
3
3
Distribution cost
4
3
9
3
3
3
3
9
9
3
3
9
9
3
1
9
3
9
3
9
3
3
Manufacturing cost
3
3
3
3
9
9
3
1
1
9
3
3
1
3
3
3
3
1
1
3
3
1
Inventory cost
4
1
9
3
9
9
9
1
1
3
3
9
1
3
3
3
9
1
1
3
3
1
Return on fixed assets
3
3
3
3
3
9
3
3
3
3
1
3
3
3
3
9
3
3
3
3
3
3
Inventory days of
supply
4
1
9
1
1
1
3
3
1
3
1
3
1
3
3
9
9
1
1
1
3
1
Asset turns
3
1
3
3
3
3
3
3
3
3
1
3
3
3
1
3
3
3
9
3
3
3
430
470
256
248
162
244
188
324
174
232
182
426
408
242
274
230
282
116
Data mining
RFID
3D printing
Robotics
Cloud computing
Big Data
IoS: Internet of
Services
3
Real-time locating
systems (RTLS)
3
Machine learning –
Deep learning
Smart contracts –
blockchain
9
Intelligent
Transportation
Systems
9
Cyber-physical
systems
1
Unmanned Aerial
Vehicle (drone)
9
Sensor technology /
smart sensors
9
Internet of Things
5
Augmented reality
Perfect order
fulfillment
Supply chain
performance
metrics
Degree of
importance
Machine 2 Machine
sensors
Self-driving vehicles
Supply Chain 4.0
Technologies
MASTER EXPERT PANEL
Reliability
Responsiveness
Agility
Cost
Asset
ABSOLUTE
VALUE
226
534 188
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6.8.1 The profile of the experts
The issue of digitalization nowadays is an important event and is a point of
reference for companies. Every year the technologies improve and the
companies try to adapt to the technological changes. In reality, digital
transformations in the supply chain are still a difficult topic requiring
considerable and in-depth understanding. As I mentioned above (6.7.1), this
is the reason that the number of samples in this thesis is relatively low. The
number of samples that were gathered is seven. The input of professionals
with extensive logistics expertise and knowledge proved very useful. The
profile of the experts is as follows: Τwo of the experts are academicians/
faculty members with substantial expertise in logistics and supply chain
management. One expert is an academician/ faculty member with
substantial expertise in digital transformation. The rest of the experts are
industry professionals with long experience (more than 10 years) in logistics
operations. In addition to the experts, one of the questionnaires was
evaluated. The profile of the author of the current thesis is as follows: The
author of the current thesis is a graduate of the Financial Department of
Democritus University of Thrace. The author is also a student in the
postgraduate program in logistics and supply chain management at the
Aristotle University of Thessaloniki. The author has real life work
experience, as he has worked in a logistic center, mainly in a 3PL. In
conclusion, the opinion and the judgment of the experts were very useful in
conducting the present research. Their knowledge and experience helped
the author understand the concept of digitization, as well as the impact that
technologies have on logistics.
6.8.2. Discussion of results
The above panel presents the total results from seven house of quality
evaluation samples answered by experts. Samples are relatively few
because digital conversions in the supply chain are still a complex issue that
requires extensive and in-depth knowledge. Obviously, the opinion of
experts with great experience in logistics and with a lot of knowledge was
necessary. The questionnaire is addressed to experts with specialties such
as: manager trade logistic, logistics project manager, digital
entrepreneurship and Technological Innovation manager, professors in
design, operations and production systems, professor’s department of
applied informatics. For this reason, in the current thesis the research on
the effects of new technologies on logistics functions focused on the opinion
of experts on new technologies. At this point, it is important to mention that
in order to unite the evaluation results of the experts in the above table, the
geometric mean was used. Each one of the experts judged differently the
degree of importance of each function and the impact that each technology
has on supply chain operation (one cell has seven different values, after the
judgement of the seven expert). The geometric mean was used to combine
the seven different judgements (of each cell) to one judgement (to one cell).
97
For example, experts have judged differently the importance of a supply
chain metric. The geometric mean was used to unite all the evaluationsjudgements of the experts in a single cell. The same process was performed
for the relationship matrix. In this case, the geometric mean was preferred,
because according to the literature review, in panel researches that the
average is needed, such as in the current panel, geometric is preferred over
the arithmetic mean. Moreover, in order to calculate the average rate of
change over a particular period of time, the appropriate mean is not the
arithmetic mean, but the geometric one, because it is expressed as a
proportional change.
As was presented in the panel, there are several technologies that have a
great influence on various functions. On the contrary, some technologies are
used, but they do not have the same impact. This is the case, because some
technologies nowadays are both necessary to complete a supply chain
operation and they also provide several benefits. Other technologies, on the
other hand, are not as widely used for various reasons, such as their high
cost, the knowledge required for their application, or because they do not
provide the necessities that a business needs. The Big Data technology
seems to have the greatest degree of impact (534) on almost all functions.
This is the case, because it is a very useful technology for any supply chain
and it offers many advantages in many areas. It is a well-known technology
in the field of logistics and it is used by many companies and, in fact, it is
applied in many processes. The second most impactful technology is that of
robotics with 470 degrees of impact, a well-known application that is
applied not only in the field of logistics, but almost everywhere else. As far
as the supply chain is concerned, it has a great influence on the creation of
products (it has an impact on the cost and time for the creation of a specific
product) and on the procedures concerning the orders (delivery
performance, perfect order fulfilment, order lead time etc.). Finally, the
technologies that follow are the Internet of Things with 430 degrees of
impact, Artificial Intelligence with 426 degrees of impact and Machine
learning with 408 degrees of impact. Among these specific technologies,
their degrees of impact are quite close, because each technology completes
the application of the other and they are interconnected. The Internet of
Things is nowadays a very important application for the interior functions
of a company. With its operation, it gives the possibility of developing other
new technologies, such as Artificial Intelligent, Machine learning, IoS, CC
etc. In this way, these technologies operate under a common framework and
have almost the same effect on the functions of a chain. However, it must
be emphasized that although each technology is interconnected, their
function is different and they are used for different purposes. On the other
hand, technologies such as drones, intelligent transportation systems and
IoS do not have as much influence for various reasons. Drones, for example,
are used for very specific purposes and mainly in large warehouses for
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transporting products. The intelligent transportation system is a new
technology, which costs a lot and is mainly used for the transport of orders.
Finally, IoS is a category of Internet of Things and is rarely used in logistics.
Thus, the analysis reveals various findings that will be further discussed.
It is noticed that technologies whose main use is for data storage and
evaluation, have a big impact on almost all logistics functions. For example,
Internet of Things, Big Data and Machine Learning technologies have a
high degree of impact on almost all functions. This shows how important
information and data are for a supply chain. It is very important for a
company to be able to forecast, control and manage its operation, based on
the data it has in its possession. With the help of new technologies, a
company has the ability to predict in the field of manufacturing, in the field
of orders, in the field of product creation, as well as in the field of finance.
Based on their usefulness, the production time is reduced, a complete
'picture' of the orders (percentage of orders delivered, perfect order
fulfillment, delivery performance etc.) is presented and it is possible to
predict the minimum cost (distribution cost, manufacturing cost, inventory
cost etc.). On the other hand, there are technologies, such as 3D printing,
which has almost no impact in other fields, except on the production sector,
where its impact is great. For example, from the panel, it can be observed
that its use plays an important role in manufacturing lead time, production
flexibility and manufacturing cost functions. This is the case, because this
technology helps the company to design, analyze, evaluate and forecast the
production of a product. On the contrary, it is designed in such a way that
it cannot have an impact and benefit on other functions. For this reason, it
is used in this field where it has a significant effect. Moreover, robots are
necessary for any warehouse. They can be used everywhere, because they
offer minimal time in terms of manufacturing, transportation, and ordering.
However, they do not have any impact, when it comes to order percentages,
inventory supply days or the possibility of stock-out. Finally, there are those
technologies, as was mentioned before, that do not have as much of an
impact. This is the case, because many of these technologies are
''overshadowed'' by existing ones (some are used for the same purpose),
because they cost too much and companies are not willing to spend without
being sure of the benefits that the technology will offer them, or because
these technologies are still not so well known in the logistics market. For
example, drones are a well-known technology, but they are suitable for large
warehouses in functions that have to do with recording and transporting
stocks. For this reason, it can be observed in the panel that drones have low
degrees of impact in almost all functions. In addition, technologies such as
IoS, CC, Artificial Intelligent, Machine learning are overshadowed by IoT
(they all operate under a common framework). All these technologies have
a similar functionality, but IoT offers additional possibilities, it is a wellknown technology and its environment is more accessible for use in logistics.
99
The IoT is a technology that can cover everything in terms of logistics and
for this reason it is preferred over the rest. In addition, data mining is a
technology that has a lot of promise for helping businesses identify patterns
in their data, that can be used to forecast customer, product, and process
behavior. Data mining technologies must be directed by users who
understand the firm, the data, and the general nature of the analytical
operations involved. Realistic expectations can have a favorable impact in
a range of situations, ranging from increased income to reduced spending.
Data mining is a technology that has many similar elements to IoT, and as
it observed in the results, data mining is a technology on a high impact
index (282). As it was also observed in the results, another factor for the low
or high rate of the impact technologies have on logistics is the
implementation of certain technologies, which will lead to opportunities or
risks for businesses. For example, AR, 3DP, and simulation are technologies
that are used in specific sectors and have a high percentage of chances to
offer opportunities to companies. On the other hand, big data, cloud
computing, IoT, RFID, robots, drones, and M2M are examples of
technologies that can create both possibilities and threats. Because all of
the different fields are interconnected and there are no obvious boundaries
between them, technology can have a beneficial or negative impact,
depending on where it is applied. Increased flexibility, quality standards,
efficiency, and productivity are the most important advantages. This would
enable mass customization, allowing businesses to satisfy client
expectations while also adding value by delivering new products and
services to the market on a regular basis. Furthermore, machine-human
collaboration could have a societal impact on the lives of future workers,
particularly in terms of decision-making optimization. Natural disasters,
virus outbreaks, and operational risks can create issues and SC
vulnerability. Managing the severe impact and consequences of such events
on SC networks is always a challenge. Blockchain, in combination with IoT
or crowdsourcing-based social media platforms, can improve SC agility and
resilience. For example, combining blockchain with IoT and RFID can
improve information management systems and SC security, as well as
consensus methods for dynamic data storage, transparency, data
protection, and reliability. Following the COVID-19 pandemic, enterprises
should rethink and revamp their SC risk management and resilience
strategies, putting a greater emphasis on digital platforms powered by
blockchain, IoT, and cloud computing. The deployment of robotics under the
direction of a blockchain-based system would also be beneficial in reducing
disruptions caused by viral epidemics. The options for blockchain
implementation are boundless. However, the technology is still in
development and this is the reason for the low impact index on the results.
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CHAPTER 7
Challenges and Barriers
Overview
Chapter seven is the final chapter of the thesis. This chapter summarize
the findings and includes objectives fullfilment, presentation-definition of
challenges-barriers, limitations and recommendation of future research.
101
7.1 CHALLENGES AND BARRIERS
In terms of technological challenges-barriers, one of the first is the need to
build Industry 4.0-enabled technologies, which are still in their early stages
and require significant progress to achieve solidity and generate higher
benefits, as was recognized by the diverse operational models across supply
chain members (Wu et al., 2016. Haddud et al. 2017) and was identified by
the various dynamical and temporal structures of manufacturing processes
and equipment that could jeopardize data collecting, analysis, and
exchange, as well as the programming of the complete production structure
(Ivanov et al. 2016). Moreover, the technological viability of adopting big
data technologies and the shortage of data specialists for the creation of
prospective technology was highlighted by the absence of skills to use this
data efficiently. (Kynast & Marjanovic et. al. 2016. Arya. 2017). In addition,
the lack of awareness of the technology's potential in many countries was
observed. Over the years, many new challenges and barriers were presented
in terms of digital transformations to supply chain operations. In many
situations, the most common challenge was highlighted by the lack of
specialists to leverage the digitization in companies. (Bienhaus & Haddud
et.al. 2018) It was already pointed out that among the barriers to developing
intelligent applications in SC are the lack of initiative, skills and knowledge
in technologies, as well as the immaturity of the Supply Chain 4.0 concept
and its initial stage of development (Wu et al. 2016). Furthermore, several
contributors in the macro group for financial, environmental and legal
concerns, stressed the substantial investment required to execute such
solutions (Büyüközkan & Göçer, 2018). Such investments in obtaining data
and services resulting from these technologies would not always yield
financial rewards (Pishdar et al. 2018).
7.1.1. Political challenges
In terms of legislative policies, regulations are needed to safeguard the
safety and integrity of people in this new environment, where humans and
robots share space and collaborate both indoors (Tjahjono et al. 2017) and
outdoors (Büyüközkan & Göçer. 2018, Casey & Wong et.al. 2017).
Furthermore, international legislation is required to provide security and
privacy (Weber, 2010), to resolve issues linked to personal damage and
product liability in the event of failure (Mohr & Khan, 2015) and to share
responsibility for information (Alotaibi & Mehmood, 2017). In the digital
''universe'', the balance of legal obligations between infrastructure
providers and customers (Urquhart & McAuley et.al. 2018) is especially
vital to examine. Researchers that are discussing big data, RFID, (Isasi et
al. 2015) and the Internet of Things (IoT) (Khanna & Sharma et.al. 2017),
addressed the issue of worldwide standards aimed at system
102
interoperability, where the lack of these standards can cause challenges in
data collecting and utilization. Finally, concerning legislative policies, the
relevance of regulations was emphasized in forming the foundation for
technological development. The lack of law is a problem that can lead to a
slew of others (Pishdar et al. 2018).
7.2.2. Environmental Challenges and Organizational Challenges
Industry 4.0 technologies are posing environmental issues, such as higher
energy usage due to higher usage of electronic equipment and integrated
systems in supply chain (Miao & Zhang et.al. 2014). Pishdar et al. (2018),
expressed concern about the quantity of electronic components that will
become obsolete over time and require replacement.
Moreover, one of the issues in sociocultural problems is the lack of ability to
combine data and obtain quality data, which should be considered by
businesses due to:
1. The numerous communication patterns and formats that are now in use.
2. The various methods for gathering, storing, and merging data.
3. A scarcity of IT specialists who understand how to match and use data
effectively.
4. Data and system synchronization issues, as well as their quality.
These constraints make it difficult to exploit data in its entirety, resulting
in a low return on investment in technology (Arya et al. 2017, Büyüközkan
& Göçeret.al. 2018). Cooperating with supply chain participants, entails
challenges ranging from mutual adoption and investments in Industry 4.0
technologies, to the difficulty of sharing responsibilities for errors in the
digital context (Büyüközkan & Göçer. 2018, Clancy. 2017). Furthermore, in
many situations the lack of connectivity across functions, the lack of a
supply chain governance structure, the lack of a technology strategy and
the inability to adapt and integrate enterprises to this new digital business
model were criticized (Haddud et al., 2017. Pishdar et al. 2018). The
challenges that businesses confront in finding technology solutions that fit
their specific requirements, is also a serious barrier (Khanna & Sharma
et.al. 2017). The failure to implement technology at all levels of the supply
chain results in a loss of integration and visibility (Isasi et al. 2015). Finally,
the fear of using intelligent applications in supply chain is one of the
challenges that many companies faced, as was the reluctance to embrace
and learn to use new technologies, the ethics and safety difficulties of
dividing the workspace with machines, the replacement of the workforce by
technologies, as well as the fear of using intelligent applications in supply
chain (Büyüközkan & Göçer. 2018, Haddud et al. 2017).
103
7.3.3. Technical Challenges
In terms of technical challenges, big data technology and the issues
associated with data generation was highlighted. These difficulties start
with data search, fragmentation and visualization, and progress to the
requirement to evaluate and get high-quality information, as well as the
requirement to access and make this data apparent to other supply chain
members (Kynast & Marjanovic, 2016). Because of the volume, variety, and
heterogeneity of the data, issues such as storing, analyzing and processing
data using standard methods exist. Moreover, developing a system capable
of processing and displaying value information from data is difficult and
necessitates a comprehensive plan, a well-trained staff and infrastructure
capable of supporting all computational requirements. Furthermore, the IT
systems with varying levels of maturity can present issues to the global
network's efficiency and visibility. The system scalability and resilience
barriers are related to data gathering, processing and simultaneous
transmission. At this this point, the stability and vulnerability of data
transmission to systems must be emphasized, particularly when done
through a wireless network. IoT and other digital applications, including
big data, face hurdles in storing, discovering and sharing data, as well as
scalability and interoperability issues. When connecting all systems in the
supply chain, there exist compatibility issues. The difficulties of predicting
breakdowns, as well as an example of a basic electrical failure, are causes
of chain reactions to all related enterprises (Khanna & Sharma et.al. 2017).
Vulnerability, reliability, robustness, complexity and compatibility are the
main issues to IoT and RFID systems. Finally, integration, efficient
information interchange (Chen et al., 2017), and validation of solutions
within the supply chain are all issues that CPS (Cyber Physical System)
and other automation systems face.
104
7.2 CONCLUSIONS- FUTURE RESEARCH
The objective of the current thesis was to evaluate the impacts of supply
chain 4.0 technologies on supply chain operations. The answer was covered
by using the method of house of quality. The HoQ revealed the relationships
between the new technologies and the supply chain operations. Moreover,
the challenges and barriers of digital transformations are presented with
the systematic literature review method. Nowadays, the great majority of
organizations, especially logistics firms, are committed to product,
technical, technological, and organizational innovation. Customers are
becoming more informed and demanding in terms of growing customer
expectations relating to lead time, thus businesses are focusing on
delivering value to them. Delivery services, product availability and
dependability are all important factors. The latest innovations, such as the
Internet of Things and Big Data, provide chances to address client
expectations, while also advancing logistics and supply chain management.
Technologies should be assessed in terms of efficacy and efficiency using
well-known and widely utilized modeling methodologies, including, in
particular, simulation modeling. It is clear that the development of
digitalization and automation creates tremendous problems for logistics, as
well as chances for efficiency gains. Because all of the diverse fields are
interconnected with no clear borders between them, some technologies can
result in both opportunities or threats. Depending on which field the
technology was evaluated, it could have a positive or negative result.
However, digitalization and the creation of new technologies place a
significant demand on education, as smart factories (or other smart
facilities) and smart logistics require highly qualified employees and
management professionals. In the near future, not only specialists but also
operational workers would conduct primarily organizational and conceptual
activities. This is why it's so important to adapt the educational process to
a new environment. Briefly, the supply chain will become smarter, more
transparent and efficient at every stage, as a result of digital
transformation and the usage of intelligent and cooperative technologies. In
the near future, there will be a particular focus on new models that are more
closely tailored to individual client demands, promoting a major rise in
decision-making quality and becoming more flexible and efficient.
With the method of SLR, by combining the descriptive and thematic
analysis of the articles, the current thesis examined the digital
transformation of the supply chain. The fourth industrial revolution, often
known as Industry 4.0, is now evolving rapidly. The business environment
must assimilate and adopt these developments, to achieve goals and to
withstand fierce competition. Monitoring and analyzing the evolution of
supply networks and following the adoption of new technologies, would be
105
quite interesting. Because technology is always evolving in real-time, it
would be very useful, in the coming years, to perform a comparison of how
technologies have been developed within the supply chain and which of
them will still be in use. Some technologies are not developing over the years
and are not used at the same rate as in previous years, resulting in
companies preferring other new technologies over the older ones. Moreover,
it is interesting to analyze and examine how much these 21 technologies
will develop in the following years. In addition, it would be very useful to
perform a comparison study between new technologies, that have not yet
been used as much in logistics (such as nanotechnology and how it will
overshadow the other technologies), and the remaining parts of the chain.
The evolution of companies through the adoption of new technologies over
the years is also a very important element of study. It could provide the
opportunity for further research on how valuable and necessary new
technologies are in logistics. In the following years, it is obvious that new
technologies will be created, in order for the SC operations to further
develop and improve. In addition, a comparison could be drawn between
supply chains that have adopted new technologies and those that have not.
It would be of great interest for an examination to be performed between
two supply chains, that both have implemented new technologies, but
utilize them at different levels of logistics. The comparison could lead to
business excellence by optimizing the utilization of these technologies in
logistics. Last but not least, further research could be performed on the
same study, but with a larger number of samples, or by including firms from
a variety of industries as research material. Finally, future research could
also be performed on the impact technologies have on the supply chain with
other methods or by improving the House of Quality method on
digitalization of supply chains.
106
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