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A DEGREE OF INDUSTRY 4.0 STRATEGIES IMPLEMENTATION AND PRACTICES IN AMONG AUTOMOTIVE MANUFACTURERS IN THAILAND NUCHON MEECHAMNA A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DOCTOR DEGREE OF BUSINESS ADMINISTRATION GRADUATE SCHOOL OF COMMERCE BURAPHA UNIVERSITY JUNE 2017 COPYRIGHT BURAPHA UNIVERSITY
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A DEGREE OF INDUSTRY 4.0 STRATEGIES IMPLEMENTATION AND
PRACTICES IN AMONG AUTOMOTIVE MANUFACTURERS
IN THAILAND
NUCHON MEECHAMNA
A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DOCTOR DEGREE OF
BUSINESS ADMINISTRATION
GRADUATE SCHOOL OF COMMERCE
BURAPHA UNIVERSITY
JUNE 2017
COPYRIGHT BURAPHA UNIVERSITY
ACKNOWLEDGEMENT
Firstly, I would like to express my sincere gratitude to my thesis advisor
Dr. Teetut Tresirichod for the continuous support of my study and related research,
for his patience, motivation, and immense knowledge, including his guidance that
helped me in correcting this thesis. I am respectfully thankful for your guidance and
could not have imagined having a better advisor and mentor for my study.
Besides my advisor, I would like to thank the Chairman of my thesis
committee, Assistant Professor Dr. Winit Chinsuwan for, his guidance in this thesis’
completion, Dr. Supasit Lertbuasin and Assistant Professor Dr. Rapeeporn Srijumpa,
for their insightful comments and guidelines about compiling the data to complete this
thesis, and also the professors from the Graduate School of Commerce in Burapha
University for their teaching which motivated my knowledge and experiences in this
thesis.
My sincere thanks also go to the President of the Automotive Parts
Manufacturers Association and the group of automotive industry administrators, who
provided me an opportunity to join their team as an intern, and who gave access to
conduct research and interviews at their facilities. Without their precious support it
would not have been possible to conduct this research. Also researcher thanks the
department of which the researcher is a member for their irreplaceable support and
understanding during all the time of this research.
Last but not the least, I would like to thank my parents, the greatest
benefactors since my early days, for their wisdoms, loves and caring, and also for
their compassion which is behind the success of this thesis. Also to my family, friends
and everyone, for supporting and assisting me throughout the writing of this thesis.
As for this research’s value and benefits, I shall present this to my parents, to
my professors who bestowed the knowledge on me, and everyone who was involved
in this study.
Nuchon Meechamna
ii
52870076: MAJOR: BUSINESS ADMINISTRATION; D.B.A. (BUSINESS
ADMINISTRATION)
KEYWORDS: INDUSTRY 4.0/ DIGITAL NOVICE/ VERTICAL INTERGRATOR/
HORIZONTAL COLLABORATOR/ DIGITAL CHAMPION
NUCHON MEECHAMNA: A DEGREE OF INDUSTRY 4.0 STRATEGIES
IMPLEMENTATION AND PRACTICES IN AMONG AUTOMOTIVE
MANUFACTURERS IN THAILAND. ADVISORY COMMITTEE: TEETUT
TRESIRICHOD, Ph.D., SUPASIT LERTBUASIN, Ph.D., RAPEEPORN SRIJUMPA,
Ph.D. 135 P. 2016.
This research aimed to examine the Industry 4.0 manufacturing paradigm as it
applies to automotive parts manufacturers in Thailand. There were several research
questions; 1. What are the basic principles of Industry 4.0 and how do they apply within
the automotive industry? 2. What is the current state of implementation of Industry 4.0 in
Thai automotive parts manufacturing firms? 3. What are the potential impacts (positive
and negative) of applying Industry 4.0 principles in the Thai automotive industry?
4. What is the degree of industry 4.0 strategies implementation among automotive
manufacturers in Thailand compared to the best practice? and 5. What do manufacturers
need to do to implement Industry 4.0? The study was conducted using a mixed methods
approach. The qualitative stream collected data with semi-structured interviews and
analyzed the data with content and thematic analysis. Respondents were selected
purposely, and snowball samplings were used to gain access to a wider sample pool. The
target respondents were representatives of automotive parts manufacturers implementing
Industry 4.0 strategies (n = 20). The quantitative stream used survey data from a self-
assessment questionnaire, collected from 10 firms selected using convenience sampling
and analyzed using descriptive statistics. The result indicated that the current state of
implementation of Industry 4.0 in Thailand’s automobile industry is relatively low. As the
interviews revealed, this may be because firms have faced little external pressure or
support from global supply chain partners for implementation, although investment costs,
human resources and lack of existing automation also influence its implementation. Thus,
Industry 4.0 mainly lies in the future for the Thai automobile industry, although large
firms may be more advanced.
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CONTENTS
Page
ABSTRACT ............................................................................................................ iv
CONTENTS ............................................................................................................ v
LIST OF TABLES .................................................................................................. vii
LIST OF FIGURES ................................................................................................ viii
CHAPTER
1 INTRODUCTION ......................................................................................... 1
Background of the study ....................................................................... 1
Problem statement ................................................................................. 2
Research questions ................................................................................ 3
Research objectives ............................................................................... 4
Scope of the research ............................................................................ 4
Research framework ............................................................................. 5
Research contributions .......................................................................... 7
Limitations of the study ........................................................................ 8
Definition of key terms ......................................................................... 9
2 LITERATURE REVIEWS ............................................................................ 11
Thailand's automotive industry ............................................................. 11
The history and evolution of industry strategies ................................... 13
The evolution and technological foundations of industry 4.0 ............... 16
Self-assessment of industry 4.0............................................................. 30
Industry 4.0 implementations in automotive and related industries ..... 32
Best practices of industry 4.0 implementation ...................................... 37
Summary of literature ........................................................................... 39
3 RESEARCH METHODOLOGY................................................................... 42
Research process ................................................................................... 42
Research philosophy ............................................................................. 43
Research approach ................................................................................ 46
Sampling and sample size ..................................................................... 47
Data collection ...................................................................................... 48
iv
CONTENTS (CONTINUED)
CHAPTER Page
Validity and reliability .......................................................................... 48
Data analysis ......................................................................................... 49
Limitations of methods used ................................................................. 50
Ethical considerations ........................................................................... 51
4 RESULTS AND DISCUSSION .................................................................... 52
Results ................................................................................................... 52
5 DISCUSSION AND IMPLICATION ........................................................... 105
Discussion ............................................................................................. 105
Conclusion ............................................................................................ 110
Knowledge contribution........................................................................ 113
Research implications and contributions .............................................. 114
Research limitations .............................................................................. 117
Recommendations for future research .................................................. 118
REFERENCES ....................................................................................................... 120
APPENDICES ........................................................................................................ 128
BIOGRAPHY ......................................................................................................... 135
v
LIST OF TABLES
Tables Page
1 Differences between smart and traditional factories ...................................... 21
2 Benefits and challenges of industry 4.0 ......................................................... 40
3 The industry sector participation ................................................................... 53
4 Summary of industry sector participation ...................................................... 55
5 Understanding of the basic principles of industry 4.0 ................................... 56
6 Summary of the basic principles of industry 4.0 ........................................... 59
7 Perception of application of industry 4.0 ....................................................... 61
8 Summary of perspective on application of industry 4.0 in the automotive
industry .......................................................................................................... 64
9 Firms’ history of Industry 4.0 implementation .............................................. 65
10 Summary of firm history of Industry 4.0 implementation ............................. 68
11 Reasons for implementing Industry 4.0 ......................................................... 69
12 Summary of reasons for the firm implementing Industry 4.0 ........................ 72
13 Implementation process of Industry 4.0......................................................... 73
14 Summary of implementation process of Industry 4.0 .................................... 76
15 Benefits of implementing Industry 4.0 .......................................................... 77
16 Summary of benefits of implementing Industry 4.0 ...................................... 79
17 Drawbacks of implementing Industry 4.0 ...................................................... 80
18 Summary of drawbacks of implementing Industry 4.0 .................................. 83
19 Rating of the firm’s Industry 4.0 implementation ......................................... 84
20 Overall rating of the firm’s Industry 4.0 implementation .............................. 87
21 Recommendations for other firms implementing Industry 4.0 ...................... 88
22 Summary of recommendations for other firms implementing Industry 4.0 .. 91
23 Firm information: Number of employees ...................................................... 93
24 Firm information: Annual revenue ................................................................ 94
25 Descriptive statistics: Business models, products and services .................... 97
26 Descriptive statistics: Market and customer access ....................................... 99
27 Descriptive statistics: Value chains and processes ........................................ 101
28 Descriptive statistics: IT architecture ............................................................ 103
vi
LIST OF FIGURES
Figures Page
1 Research framework ..................................................................................... 5
2 Research process framework ........................................................................ 7
3 Automotive industry production and sales, 1996-2012 ................................ 12
4 Automotive production and domestic sales, 2013-2015 .............................. 13
5 The smart factory .......................................................................................... 19
6 Three types of industry 4.0 smart factory integration .................................. 23
7 Information transparency with smart objects ............................................... 27
8 The research process .................................................................................... 43
1
CHAPTER 1
INTRODUCTION
Background of the study
The proposed research examines the implementation of Industry 4.0
strategies and practices in the automotive industry in Thailand. The idea of Industry
4.0, often termed the fourth industrial revolution, emerged from the German
manufacturing industry following government-supported industrial development
(Kagermann, Lukas, & Wahlster, 2011). The original idea of Industry 4.0 was to
implement advanced automation technologies by leveraging the Internet of Things
(IoT) and by improvements in production processes (Kagermann et al., 2011).
Industry 4.0 has emerged as a paradigm that incorporates social, technological and
industrial change stemming from rapid advancement of ubiquitous computing
technology, increasingly cheap and ubiquitous online communication, and growing
social demand for products that can be improved through the use of big data and
analytics (Schwab, 2016).
The heart of Industry 4.0 as it is emerging is that manufacturing is a cyber-
physical system (CPS), or one in which computing technology and controls and the
physical environment and machinery are seamlessly integrated (Lee, Bagheri, & Kao,
2015). Within the CPS, sensors collect and monitor data in the manufacturing
environment, including machine data and general environmental data (such as
temperature, etc.). The data is relayed to analytical systems that control and
synchronize the manufacturing process in order to manufacture to precise
specifications. These specifications are determined by further integration with systems
such as order fulfillment (OF), enterprise resource management (ERP), and other
business management systems (Lee et al., 2015). This strategy represents a significant
step forward in manufacturing automation systems, since it enables more efficient and
collaborative function of the information and production systems of the
manufacturing plant (Lee et al., 2015).
Although Industry 4.0 is a relatively new idea, it has some similarity to the
automation and integration of information systems already widely in use in the global
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automotive industry (Brettel, Friederichsen, Keller, & Rosenberg, 2014). For
example, automotive supply chain firms were some of the pioneers in the
implementation of Electronic Data Interchange (EDI), and have long had integrated
information systems that allowed information and manufacturing systems of
customers and suppliers to communicate (Brettel et al., 2014). Looking forward,
Industry 4.0 can be considered to be a best practice for the industry, as it facilitates
rapid and efficient communication, process refinement, and zero-waste or low-waste
production (Gruber, 2014).
The importance of Industry 4.0 as a manufacturing paradigm has not
escaped the notice of the Thai government and industry, although firm policies for its
support are not yet in place. One report indicates that Industry 4.0 has potentially
significant implications for manufacturing industries, with the connection of the entire
plant and its support systems increasing efficiency and improving production
processes and ultimately products (Asia Pacific Plant Management Magazine
[APPM], 2016). However, there are factors that affect acceptance of the concept,
including “readiness for automated tools and equipment, capital resources, availability
of resources and people, and the mentality of how manufacturers manage and fully
leverage the concept of industry 4.0 (APPM, 2016),” according to an interview with
industry expert Dr. Tatchapol Poshyanonda, who works with the Cisco ASEAN
Partner Business Group.
Problem statement
The automotive industry is one of the industries that may benefit most from
the implementation of Industry 4.0, as it is already well prepared for further advances
in automation and systems integration (Gruber, 2014). However, there are a number
of potential barriers to implementation of Industry 4.0 in Thai manufacturing firms
(APPM, 2016). These include, for example, lack of available capital resources to
enact full-scale automation and analytic systems, and poor availability of people with
the appropriate knowledge and skills for implementation (APPM, 2016). Furthermore,
there is little evidence that the Thai government has undertaken an organized Industry
4.0 initiative. Although the automotive sector is recognized as a distinct industrial
sector and provided with government support (APPM, 2016), Industry 4.0
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development is not yet a matter of official policy. There is little evidence in the
academic literature for how Industry 4.0 is being implemented in developing countries
like Thailand, despite their overall importance to the global automotive industry as
sites of low-cost manufacturing. It is clear that Industry 4.0 is one of the strategies in
use by Western automotive industry multinationals, especially German firms, when
managing its supplier relationships in countries like China (Kinkel, Lichtner,
Hochdörffer, & Rurhmann, 2015). However, there is limited evidence for how the
approach is being used in Thailand. There is also limited evidence for how supply
firms and original equipment manufacturers (OEMs) may implement Industry 4.0
strategies on their own.
The problem that this research addresses is how Industry 4.0 can be
implemented effectively in the Thai automotive industry. This research will help to
resolve literature gaps and practice gaps and provide information for the application
of Industry 4.0 strategies in the industry. First, it is clear that there is a gap between
the promotion of Industry 4.0 as a manufacturing strategy and its actual
implementation. In Thailand, firms may struggle with implementation because of high
capital costs and demands and lack of appropriate human resource support, among
other reasons. Second, the academic literature on industry 4.0 in the automotive
industry is limited and does not address the context of developing countries.
Research questions
The aim of this research is to examine the Industry 4.0 manufacturing
paradigm as it applies to automotive parts manufacturers in Thailand. There are five
questions in this study as follows:
1. What are the basic principles of Industry 4.0 and how do they apply
within the automotive industry?
2. What is the current state of implementation of Industry 4.0 in Thai
automotive parts manufacturing firms?
3. What are the potential impacts (positive and negative) of applying
Industry 4.0 principles in the Thai automotive industry?
4. What is the degree of industry 4.0 strategies implementation in
automotive manufacturers in Thailand compared to the best practice?
4
5. What do manufacturers need to do to implement Industry 4.0?
Research objectives
There are several research objectives that support this aim. These include:
1. To study the principles of Industry 4.0 for the automotive industry
2. To investigate the current state of implementation of Industry 4.0 in Thai
automotive parts manufacturing firms
3. To identify the potential impacts (positive and negative) of applying
Industry 4.0 principles in the Thai automotive industry
4. To compare the degree of industry 4.0 strategy implementation in
automotive manufacturers in Thailand compared with the best practice
5. To identify manufacturer needs for Industry 4.0 implementation.
Scope of the research
This study addresses Industry 4.0 strategy and practice implementation in
automotive firms operating in Thailand as well as compares it with the best practice.
The study was conducted at the firm level. The firms included both domestic supply
firms and international subsidiaries, though the precise mix of firms depended on the
number of firms found to be implementing Industry 4.0 strategies.
The study was conducted using a mixed methods approach. The qualitative
stream collected data with semi-structured interviews and analyzed the data with
content and thematic analysis. Respondents were selected purposely, and snowball
sampling was used to gain access to a wider sample pool. The target respondents
were representatives of automotive parts manufacturers implementing Industry 4.0
strategies (n = 20). Respondents include a mix of technical experts, managers, and
executives who have played a role in the implementation process. Interviews were
conducted face-to-face or via Skype depending on location and availability of
respondents. The quantitative stream used survey data acquired by a self-assessment
questionnaire, collected from 10 firms selected using convenience sampling and
analyzed using descriptive statistics. The questionnaire is based on an existing
assessment instrument (the PWC Industry 4.0/ Digital Operations Self-Assessment).
5
Research framework
Figure 1 shows the research framework which includes research methods,
research processes and results.
Figure 1 Research framework
The research process framework of the study is shown in figure 2. This
framework is a tool that was used to align the research along the aims of the study and
the theoretical relationships suggested in the research. However, it is important to note
that because the Industry 4.0 model is such a new concept, it is difficult to state
specific hypotheses based on this theory or on the existing research. Thus, this
framework is a general guide, rather than a strict model.
The framework incorporates three essential elements that create the
conditions for Industry 4.0 production. These elements were selected based on
Schwab’s (2016) identification of the integration of information technologies into the
production process. The first aspect is information technologies. According to the
literature on Industry 4.0, integration of information technologies and the physical
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environment (cyber-physical systems), Internet of Things or IoT, and big data
collection, analysis and use are all typical of the Industry 4.0 production environment
(Baheti & Gill, 2011; Wang, Wan, Li, & Zhang, 2016). These aspects of IT connect
the firm not just to itself, but also to its production partners and customers, enabling
direct communicationg and leveraging of available data (Schwab, 2016). The second
element is the smart factory, which details how cyber-physical systems are employed
in the manufacturing process (Wang et al., 2016). The smart factory incorporates its
physical resources (production equipment and people), with an industrial network in
order to communicate (Radziwon, Bilberg, Bogers, & Madsen, 2014; Wang et al.,
2016). It uses cloud computing and a supervision and control terminal in order to
maximize efficiency and integration. The final element is the design principles of
Industry 4.0, which are enacted through the system and which are expressed in the
firm’s products and production methods (Hermann, Pentek, & Otto, 2016). The self-
assessment is performed under 4 principles; 1) Business model/ products and service
plan, 2) Market and customer accessibility, 3) Supply chain and manufacturing
process and 4) IT architecture developed by Pricewaterhouse Coopers [PWC] (2016).
This self-assessment is targeted to evaluate the degree of Industry 4.0 strategy
implementation among Thai automotive manufacturers in Thailand. The interview
also assesses 4 principles; 1) The basic principles of Industry 4.0 and how they are
applied within the automotive industry, 2) The current state of implementation of
Industry 4.0 in Thai automotive parts manufacturing firms, 3) The potential impacts
(positive and negative) of applying Industry 4.0 principles in the Thai automotive
industry and 4) Whether implementation industry 4.0 is needed. It aims to explore
how the Industry 4.0 manufacturing paradigm can be used as it applies to automotive
parts manufacturing in Thailand.
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Figure 2 Research process framework
Research contributions
The main contribution of the proposed research was to the academic
literature on Industry 4.0 implementation. The Industry 4.0 paradigm is an emergent
concept, and has not been fully developed in the literature although there is a lot of
existing research on it. The presence of developing countries in the manufacturing
chain and the implications of global manufacturing chains, as occur in the automotive
industry, have not yet received full attention. The Industry 4.0 concept is also an
Industry 4.0 Self-assessment
Business Model/ Products/
Service Plan
Market and Customer
Accessibility
Supply Chain and
Manufacturing Process
IT Architecture
Source: PWC (2016)
A Degree of Industry 4.0 Strategies
Implementation Thai Manufacturers
Automotive in Thailand
Industry 4.0 Interview
The basic principles of Industry 4.0 and how
apply within the automotive industry
The current state of implementation of Industry
4.0 in Thai automotive parts manufacturing firm
The potential impacts (positive and negative) of
applying Industry 4.0 principles in the Thai
automotive industry
Implementation industry 4.0 needed
Industry 4.0 manufacturing paradigm as it applies to
automotive parts manufacturing in Thailand
8
academic formulation of an evolutionary practice on the shop floor, and as such does
need to be connected to its origins. This research explores the practice of firms,
industries, and the Thai government with regard to Industry 4.0 implementation,
which provide information about the current state of implementation and how firms
and industries can support implementation. This will be useful for future researchers
examining the implementation of Industry 4.0 manufacturing strategies, practices and
technologies in a global context of a global industry in a developing nation.
The research also has a secondary importance to firms in Thailand applying
Industry 4.0 strategies and business practices. The study provides insights about
development priorities and implementation processes and best practices from the
perspective of domestic experts in firms that are already applying or beginning to
apply Industry 4.0 principles. This offers valuable insights for firms that are beginning
to implement these principles or are considering the strategic movement toward
Industry 4.0. For example, firms that are considering strategy implementation could
gain a better understanding of the challenges, benefits, and limitations of Industry 4.0
approaches. They could also gain an understanding of what the impact on the firm
was. The main benefit was for automotive industry firms, but firms in other industries
could also benefit from the general overview.
Limitations of the study
There are several limitations of the study. The first limitation is that results
may only apply to Thailand, which has a unique industrial context and environment
and relationship to the auto industry. Firms in other countries may face different
structural issues that could change implementation practices and policies. Second, the
study is limited to the automotive industry. The Industry 4.0 paradigm could be
applied outside this industry. However, since industries have different structural
issues, organization and regulation, other industries would need to be considered
separately. These limitations should not interfere with the general usefulness of the
study.
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Definition of key terms
A Degree of Industry 4.0. The current state of implementation of Industry
4.0 is in firms (Dai et al., 2012).
Analytics. Analytics is the process of extracting relevant information and
knowledge from data in order to make decisions (Minelli et al., 2012).
Automation. Automation refers to the use of mechanical and electronic
devices as a replacement or supplement for human labor in the stages of the
manufacturing process (Gupta & Arora, 2013).
Automotive industry. The automotive industry is the global, vertically
integrated industry tasked with design, production and sale of automotive vehicles,
including large nameplate firms, suppliers, and sales firms (Maxwell & Drummond,
2010).
Big data. Big data refers to the large-scale collection of untargeted, unsorted
and unorganized data that result from routine processes such as the automation
process (Minelli et al., 2012).
Cyber-physical systems. Cyber-physical systems (CPS) are systems that
integrate computational and physical capabilities and provide for multiple modes of
human-machine and machine-machine interaction (Baheti & Gill, 2011). CPS have
the potential for machine learning and adjustment, which can improve their
performance and their ability to maintain and improve their own operations, for
example self-repair capabilities (Lee, Kao & Yang, 2014). CPS consist of a
combination of standardized architecture and modular, reconfigurable hardware and
software, as well as feedback loops that allow for system operation and optimization
(Baheti & Gill, 2011).
Industry 4.0. Industry 4.0 (Industries 4.0) is a production paradigm
focusing on automated production, machine intelligence and big data and analytics as
a route to manufacturing effectiveness (Hermann et al., 2016).
Internet of things. The Internet of Things (IoT) refers to automated,
Internet-connected ubiquitous devices that share data and communicate while
performing routine tasks (Greengard, 2015). IoT devices are typically designed to
collect and act on environmental sensor data or receive commands remotely in order
to improve their performance in some way.
10
Principles of industry 4.0 design. The principles of Industry 4.0 design are
the basis for designing and incorporating CPS and smart factories (Hermann et al,
2016). Core principles include: integration of physical and information resources
(CPS); interoperability of physical and computing assets; virtualization of the
production environment; information transparency at all levels of production; real-
time capabilities, for example adjustment of production and optimization and self-
repair of systems; modularity of physical and virtual systems; and decentralization of
physical and virtual control (Hermann et al., 2016).
Smart factory. A smart factory (also known as a real-time factory,
ubiquitous factory, or intelligent factory) uses CPS to monitor and manage production
and related processes (Marr, 2016; Radziwon et al., 2014). The smart factory is based
on information and context capture and machine and object communication through a
trusted cloud, enabling cross-linked products, assets, and computational systems
(Wahlster, 2014). Layers of the smart factory include physical resources, industrial
networks, cloud-based computing systems, and supervision and control terminals
(Wang et al., 2016).
Strategies implementation. The activities within a workplace or
organization manage the execution of a strategic plan (Business dictionary, 2017).
11
CHAPTER 2
LITERATURE REVIEWS
This chapter presents the findings of the literature review. Sources include
journal articles featuring Industry 4.0 research and news, government, and industry
reports that were used to gather up-to-date statistics regarding Thailand’s automotive
industry and various Industry 4.0 implementations. The chapter includes;
1. Thailand’s automotive industry
2. The history and evolution of industry strategies
3. The evolution and technological foundations of industry 4.0
4. Industry 4.0 design principles
5. Self-assessment of industry 4.0
6. Industry 4.0 implementations in automotive and related Industries
7. Best practices of industry 4.0 implementation
Thailand’s automotive industry
Thailand’s automotive industry makes a significant contribution to the
nation’s economy. With 2,400 establishments and 750,000 unit sales in 2015,
Thailand is Southeast Asia’s leading vehicle manufacturer (International Trade
Administration, 2016). As of 2012, the industry supplied 10% of the nation’s gross
domestic product and more than 500,000 skilled labor jobs, as well as bringing
significant additional value through indirect impacts on Thailand’s service industries
(Thailand Automotive Institute, Ministry of Industry, 2012). Industry strengths
include a solid supply chain that was built up over the course of more than half a
century (Chu & Chaichalearmmongkol, 2015). The majority of parts for the industry
are imported from Japan, with China as the second-largest supplier (International
Trade Administration, 2016). Overall, the industry sources approximately half of its
parts locally and imports the other half (Thailand Automotive Institute, Ministry of
Industry, 2012).
Thailand’s automotive industry enjoyed nearly steady growth from 1998
onward, with declines occurring only in the wake of the 2008 economic downturn and
12
again in 2011 due to floods in Thailand and the Japanese tsunami causing a parts
shortage. The dramatic growth of Thailand’s automotive industry is evident in the fact
that light vehicle production increased by 347.8% and sales by 116.2% between 1994
and 2014 (Automotive Supply Chain Competitiveness Initiative [ASCCI], 2015).
Figure 3 Automotive industry production and sales, 1996-2012 (Thailand
Automotive Institute, Ministry of Industry, 2012)
Thailand’s automotive industry produced 1,920,000 vehicles in 2014, and
aims to manufacture 3,000,000 in 2017 (Chu & Chaichalearmmongkol, 2015), but
this goal may be thwarted by declining domestic sales due to rising household debt,
exacerbated by political instability. According to Chu and Chaichalearmmongkol
(2015), domestic car sales fell by almost 11% between February 2014 and February
2015 as part of an overall decline that had been going on for nearly two years. The
authors note that the industry may be able to bolster its declining sales by increasing
exports, as vehicle shipments saw a corresponding increase of 11% in the same period
due to increased European demand for low-emission eco-cars and rising Australian
demand for Thai cars in general. However, despite some promising developments in
the export market, the domestic downturn and political uncertainty may cause
automotive manufacturers to locate their operations in nations such as Indonesia,
which has become increasingly attractive due to the rupiah’s depreciation and rising
domestic demand. Figure 3 shows the decline in Thailand’s automotive production
and domestic sales between 2013 and 2015.
13
Figure 4 Automotive production and domestic sales, 2013-2015 (Chu &
Chaichalearmmongkol, 2015)
Despite the problem of declining domestic sales, there are some optimistic
indicators. The International Trade Administration (2016) reports that although some
automobile manufacturers and suppliers are choosing to do nothing until they have a
better sense of how the industry is likely to fare in the future, other major companies,
including Nissan and Mercedes, have recently begun production in Thailand in
response to a 23% increase in passenger car exports from January to September 2015.
Moreover, during the same period, the value of Thailand’s auto component exports
increased by 11% to $12,900,000. Also, Thailand’s government has made an
investment of approximately $144,000,000 to promote the production of eco-cars,
with an eight-year corporate income tax exemption for producers and duty-free
machinery imports. A number of global automotive manufacturers have applied to
participate in Phase 2 of the scheme, including Ford, General Motors Company,
Honda, Mazda, MG, Mitsubishi, Nissan, Suzuki, Toyota, and Volkswagen. In
addition, the industry is poised to benefit from the global production shift from west
to east if it can increase its competitiveness by improving productivity (Thailand
Automotive Institute, Ministry of Industry, 2012), which could be achieved with the
implementation of Industry 4.0.
The history and evolution of industry strategies
The Industry 4.0 model is based on a historic perspective of evolution of
industry strategies, the phases of each of which include an intertwined set of
14
innovations and paradigmatic changes in power, control, and production systems and
accompanying changes in social, technological, and cultural systems (Schwab, 2016).
This evolutionary process has not been monotonic; instead, three of the four
evolutions identified by Schwab (2016) have occurred since the beginning of the 20th
century. This model of industrial strategy development can be equated to the
paradigm model of scientific evolution as explained by Kuhn (2012). In the scientific
paradigm model, day-to-day scientific research results in incremental discovery, but
the practice and assumptions of science itself are occasionally interrupted by
paradigm changes, or philosophical changes resulting from radical or disjoint
discoveries that force scientists to reject their prior worldviews (Kuhn, 2012). As
explained by Schwab (2016), evolutionary breaks in industrial process also relate to
radical innovations that cause incommensurable changes in the manufacturing and
production process. Thus, the evolutionary development of Industry 4.0 can be
likened to Kuhn’s model (Kuhn, 2012) of scientific paradigm change.
The first Industrial Revolution, which can be traced to the mid-18th century
in Europe (particularly England), represents the first stages of industrial
mechanization (Schwab, 2016). Industry 1.0 is associated with the development of
steam and water power, originally used in grain mills (Schwab, 2016). The first
Industrial Revolution began in the mid-1700s with the gradual centralization of fabric
production in England (Stearns, 2012). Previously, this industry had been diffused
throughout the countryside, with cottage industry production of different stages of
work (such as spinning, dying, weaving, and finishing). As Stearns (2012) relates,
development of steam power and water power enabled the development of
mechanized machinery, such as spinning jennies (which produced thread) and large
looms. It also required the centralization of production into one place, which resulted
in large mills and mill towns (Stearns, 2012). While other industries followed the
fabric industry and the industrial revolution technology spread around the world, the
next major shift in the industrial strategy did not occur until the early 20th century
(Schwab, 2016).
The second industrial revolution, or Industry 2.0, drew on the power of
electricity in order to develop assembly-line production and mass production
(Schwab, 2016). This development began in the American automotive industry, with
15
automotive pioneer Henry Ford developing both technologies and philosophies of
mass production (Marsh, 2012). The main technology of mass production was the
assembly line (Marsh, 2012; Schwab, 2016). The assembly line built on the
mechanization of the industrial revolution, increasing efficiency by performing
production stages in a controlled sequence. This stage of industrial development was
also informed by Taylorism, or scientific management, which is a philosophy of
worker supervision and control that aimed to maximize efficiency (Littler, 1978). The
assembly line was a significant step forward in manufacturing efficiency, significantly
reducing waste and the cost of manufactured goods, and is associated with an increase
in living standards for countries that benefited from it (Marsh, 2012).
The third industrial revolution, or Industry 3.0, followed much more quickly
than the second, emerging in the mid-20th century (Schwab, 2016). This stage in
industrial evolution was enabled by the development of computers, which could be
combined with electric power to enable automation of mass production (Schwab,
2016). The foundational philosophy of the third industrial revolution was the
philosophy of cybernetics, or systems thinking (Wiener, 1961). Cybernetics as a
philosophy was concerned with the interaction of man and machine (Wiener, 1961).
The manufacturing production system associated with this stage is a system of total
control of production, incorporating computerized control, production robots and flow
production as well as management models such as the Toyota Production System
(TPS) and its successors, including lean management and Six Sigma (Slack & Lewis,
2011). These practices are still associated with Taylorism, often termed post-Fordism
due to internalization of control (Marsh, 2012). This stage is also associated with
flexible production, which is a step beyond assembly line production (Schwab, 2016).
Flexible production, in which the production line can be modified to produce different
products, enables just-in-time production and mass customization (Slack & Lewis,
2011).
Recently, advances in computerization and control of production have led to
further incorporation of computerization and control beyond that in the Industry 3.0
stage (Schwab, 2016). This stage, Industry 4.0, is the stage of concern in this research.
16
The evolution and technological foundations of industry 4.0
There have been three industrial revolutions in the past: mechanical
production starting in the mid 1700s, the application of electricity and division of
labor starting in the late 1800s, and the digital revolution that began in the 1970s with
the introduction of information technology (IT) and increased automation. Industry
4.0, also known as the fourth industrial revolution, began in recent years with the
establishment of global networks for warehousing, production, logistics, and
information exchange, controlled by CPS (Hermann et al., 2016).
Industry 4.0 is a paradigm shift whereby products become active
communicators that can tell virtual systems what they require to reach their desired
end states, providing details about the production steps needed to achieve this, as well
as monitoring processes and alerting the CPS to problems (Kagermann et al., 2011).
It will have profound effects on how business is conducted in the future, with
organizational structures adapting, hierarchies becoming more flexible, outcome-
based accountability giving way to process-based accountability, and companies
adopting collaborative networked models with decentralized decision-making and
distributed work teams (Schwab, 2016).
Industry 4.0 can solve a number of technical problems and make factories
more efficient. However, it also has the potential to address global problems caused
by the negative impacts of production and the shrinking workforce (due to population
aging) by reducing energy requirements and waste and increasing automation (Wang
et al., 2016). Industry 4.0 is built upon emerging information technologies such as
cloud computing and artificial intelligence, as well as theoretical foundations and
technological trends such as the Internet of things (IoT), big data (Wang et al., 2016),
CPS (Baheti & Gill, 2011), and smart factories (Radziwon et al., 2014), which are
discussed in the sections that follow.
1. Information technologies
Industry 4.0 is built upon emerging information technologies such as cloud
computing and artificial intelligence, as well as theoretical foundations and
technological trends such as the internet of things (IoT), big data (Wang et al., 2016),
CPS (Baheti & Gill, 2011), and smart factories (Radziwon et al., 2014), which are
discussed in the sections that follow.
17
1.1 Cyber-physical systems
CPS are new-generation systems in which physical and computational
capabilities are integrated and there are multiple modes of human-machine interaction
(Baheti & Gill, 2011). CPS can also interact with other systems, which gives them the
potential to become self-aware and learn, thus improving their performance and
ability to self-maintain (Lee, Kao & Yang, 2014). Within these systems, embedded
networked computers control and monitor physical processes, typically with the use
of feedback loops (Lee, 2008). CPS require standardized architectures and
reconfigurable hardware and software that can support modular design (Baheti & Gill,
2011). More advanced CPS are largely theoretical at this time, and while they
represent the future of industry, they are not currently established in many
manufacturing firms (Lee et al., 2014).
CPS are potentially useful not only for the manufacturing of vehicles in
general, but also the development of particular design features. For example, they will
likely be integrated directly within new vehicles to create energy-efficient and self-
driving cars and trucks (Baheti & Gill, 2011).
Despite the many benefits they can provide, CPS have some drawbacks,
which include potential problems in the areas of reliability and security (Baheti &
Gill, 2011). However, many nations have been making significant investments in
these systems to increase the likelihood that they will be safe and reliable (Lee, 2008).
1.2 The internet of things
The IoT can be defined as “the networked interconnection of everyday
objects, which are often equipped with ubiquitous intelligence” (Xia, Yang, Wang, &
Vinel, 2012, p. 1,101). This has been made possible by recent innovations in wireless
sensor network technologies that not only measure environmental indicators, but also
understand and make inferences from them (Gubbi, Buyya, Marusic, & Palaniswami,
2013). The building blocks of this new system are smart objects, which are physical
objects with embedded technologies (such as RFID tags) that connect them to the
internet (Kopetz, 2011). The IoT supports the development of distributed device
networks that communicate with one another and with human beings (Xia et al.,
2012), and enable humans or machines to control physical systems from distant
locations (Kopetz, 2011). With the evolution of the IoT, physical assets have become
18
components of information systems, able to gather information, make computations,
communicate with one another as well as with human operators, and facilitate
collaboration using embedded sensors. In the future, with the advancement of
Industry 4.0, these assets will likely expand their capabilities to react to environmental
changes and adapt as needed. Smart objects will therefore increase process efficiency
and support new business models (Bughin, Chui, & Manyika, 2010).
Bughin et al. (2010) provide two examples of the ways in which
technologies that have been developed as part of the IoT are changing existing
business models within the automobile industries of Europe and the U.S. First,
insurers are providing the option to install sensors so that insurance pricing can be
based on actual driving behavior rather than demographics, providing a more accurate
accounting of risk. Second, vehicles are being equipped with networked sensors that
can take evasive actions to avoid accidents. A third example that has particular
relevance to the automobile manufacturing industry is logistics management with
sensors and other wireless network technologies (Gubbi et al., 2013).
1.3 Big data
Boyd and Crawford (2012) argue that big data is not so much about
quantity, but rather the usefulness of data based on the ability to search large data sets
and aggregate and cross-reference them to gain helpful insights. They define big data
as not only a technological and scholarly phenomenon, but also a cultural one. New
technologies support real-time collection, computation, comparison, and analysis, and
that analysis can be used to identify patterns that have technical, economic, legal, and
social relevance.
Lee et al. (2014) note that companies wishing to remain competitive
within the modern information-driven business environment must seize the
opportunities provided by big data, which include the ability to make rapid decisions
that improve overall productivity. However, they argue that many manufacturers may
be unable to do so because they lack the advanced analytical tools and skills required
to make use of big data. While nations such as Germany have become leaders in
embedded software for the creation of intelligent products within advanced CPS,
other nations may be left behind during the fourth industrial revolution because they
fail to adopt these new technologies.
19
According to Lee et al. (2014), in the context of manufacturing, big data
might include current aggregated information regarding pressure, vibration, and other
signals, combined with historical data. This big data, used by a self-aware machine
capable of assessing its state of health and maintaining itself, would be able to
respond appropriately as needed to prevent costly failures. However, the technologies
required to use big data for smart analytics conducted by self-aware machines is still
in development. Current factory machines are passive; they obey commands made by
human operators even when those commands will lead to undesirable outcomes.
Intelligent machines were able to make appropriate suggestions or conduct their own
adjustments to improve performance and product quality based on their analysis of the
data to which they have access.
2. The smart factory
Smart factories have been variously defined as ubiquitous factories
(U-factories), real-time factories, and intelligent factories, and characterized as either
technologies or paradigms (Radziwon et al., 2014). These factories make use of CPS
to monitor and manage physical processes (Marr, 2016). A model of the smart factory
is provided below.
Figure 5 The smart factory (Wahlster, 2014, p. 3)
The internet of things in the smart factory: A network of intelligent objects
20
According to Wang et al. (2016), smart factories are made up of multiple
layers: physical resources, industrial network, cloud, and supervision and control
terminal. The physical resources layer comprises a self-organizing and autonomous
collection of smart objects (products, machines, conveyors, etc.) that communicate
with one another and with human operators via the industrial network. The industrial
network layer provides the infrastructure that facilitates communication and connects
physical resources with the cloud. The cloud layer, which is based on cloud
computing technology, provides easily scalable computing and storage space. Data
produced by smart objects in the physical layer is transmitted to the cloud via the
industrial network for storage and processing by information systems, and the
resulting analytics can be used to manage and optimize the system at the supervision
and control layer, which links human operators with the smart factory. Using tablets,
PCs, or even mobile phones, human workers can access the data stored in the cloud
and perform various activities as needed (such as reconfiguration, maintenance, or
diagnostics) remotely via the Internet.
Many factories already use existing RFID technologies, but there is the
potential to make RFID sensors smarter. Although currently used RFID technologies
are useful for tracking, embedding greater intelligence to create smarter smart objects
would significantly enhance the capabilities of a factory system, allowing machines
and other objects to not only sense, but also interpret and react to events within their
physical environment and communicate with human users or other machines as
needed (Kortuem, Kawsar, Sundramoorthy, & Fitton, 2010). Such smart objects are
the building blocks not only of the IoT, but also of the smart factory, a key component
of Industry 4.0.
According to Lee et al. (2014), big data, modern sensors, and advanced
networks will give Industry 4.0 factories the capacity for self-awareness, prediction,
comparison, reconfiguration, and maintenance. Smart factory components and
machines contain sensors that are not only able to detect faults, as is the case in
current factory systems, but also have the self-awareness and prediction capabilities to
monitor degradation and determine how much useful life remains, and to assess
overall health and take action as needed. Production systems are networked to keep
21
operations lean, reduce waste, self-organize and configure, and conduct their own
maintenance as needed.
Radziwon et al. (2014) note that the smart factory of the future will be
independent and self-sufficient, yet interconnected with suppliers and others on which
it relies, obtaining its supplies from local sources, which will enable swift adjustment
to new market demands and business models. Proximity to suppliers will allow for
responsiveness and customization to better meet customers’ requirements regarding
product quality, type, and delivery time. Moreover, proximity and self-sufficiency
have the potential to increase efficiencies and reduce costs and environmental impacts
while supporting local economies. Differences between the smart factory and the
traditional factory are summarized in table 1.
Table 1 Differences between smart and traditional factories (Adapted from Wang
et al., 2016, p. 6)
Traditional factory Smart factory
1. Limited, predetermined resources
2. Fixed product routing
3. Shop floor control network with no
connections or communication
among machines
4. Separated field devices and upper
information systems
5. Independent control for each
machine
6. Isolated information, often within
single, local machines
1. Diverse resources
2. Adaptable routing
3. Comprehensive connections, with
machines interacting via a high-speed
network
4. Virtualization connecting devices and
information systems
5. Self-organization and integration
6. Big data stored and processed in the
cloud, accessible from remote
locations
Wang et al. (2016) list the smart factory’s many benefits. The first is
flexibility, a descriptor that is frequently cited in the Industry 4.0 literature due to the
potential for quick and easy reconfiguration as needed to tailor products to customer
specifications. Flexibility allows smart factories to respond quickly to rapidly
22
changing market demands. The second is productivity. By minimizing the time
required to switch from one type of product manufacturing to another, smart factories
are far more efficient at producing different product types in smaller batches, and
efficiency is further increased by regular feedback and self-coordination. The third is
energy efficiency, a critical concern, given industrial impacts on the environment. By
providing better information about the production process and producing better
quality (and therefore more durable) products that do not need to be replaced as often,
raw material requirements are reduced. Moreover, applying new technologies (such as
speed-controlled motors) saves energy. The fourth is information transparency. Smart
factories provide comprehensive information in real time on all aspects of production,
which supports faster and better decision making and production planning. The fifth is
integration, which enables better collaboration among companies at each stage, from
design to manufacturing to logistics. The sixth is greater profitability. Compared to
traditional production methods, smart factory production is more cost-effective,
particularly for small-batch or customized production. The seventh is user
friendliness. Machines within a smart factory operate autonomously, freeing staff
from mundane tasks, granting access to big data analytics via powerful new software
tools, and providing better user interfaces, all of which make maintenance and
diagnostics easier to perform. The smart factory also facilitates collaboration among
human workers so that people with the required skills can work together remotely to
perform repairs as needed.
Although the smart factory brings many advantages, there are also a few
significant disadvantages. Setting up a smart factory requires a substantial initial
investment in new technologies, mass automation can lead to lost jobs for human
operators, and making information more widely available can present data security
risks (Marr, 2016).
3. Industry 4.0 design principles
In the coming years, industry will be revolutionized by the introduction of
new technologies, creating smart factories that are run on distributed systems. These
CPS will enable communication between people and machines within large networks,
transforming manufacturing industries in the areas of integration, virtualization, and
self-optimization (Brettel et al., 2014). Design principles of Industry 4.0 include
23
integration and interoperability, virtualization, information transparency, real-time
capability, modularity, and decentralization (Hermann et al., 2016), which are
discussed in the sections that follow.
3.1 Interoperability and integration
Within Industry 4.0 smart factories, engineering, manufacturing,
materials, and logistics are under the integrated control of CPS (Hermann et al, 2016).
Wang et al. (2016) describe the three types of integration that characterize the smart
factory: horizontal, vertical, and end-to-end engineering. Horizontal integration
throughout value networks enables various companies that are responsible for
different stages of a product’s development and overall lifecycle to collaborate more
effectively with one another. Horizontal integration supports the free exchange of
information, materials, and money. Vertical integration of subsystems within the
smart factory allows for easy reconfiguration to maintain a more flexible
manufacturing system. Vertical self-organizing subsystems may include actuators and
sensor systems, manufacturing systems, production management systems, and more
general corporate planning systems. End-to-end engineering integration allows for the
use of a consistent product model throughout the lifecycle, from production through
to servicing, maintenance, and recycling, and supports customization.
Figure 6 Three types of industry 4.0 smart factory integration (Czech, 2016, p. 17)
24
The ability to interact with surrounding systems can improve machine
intelligence and performance (Lee et al., 2014). Moreover, interoperability allows for
collaboration within horizontal networks by eliminating distance between processes,
which supports the sharing of resources, competencies, and risks and promotes
innovation. However, it also brings risks with regard to trust among different firms, as
some collaborators may lose out to opportunists as a result of information sharing, and
it can increase costs for some firms due to cost-sharing measures (Brettel et al., 2014).
Also, there are a variety of technical challenges that must be solved when attempting
to establish true integration, given the large number of factors involved. To achieve
the comprehensive interoperability required by Industry 4.0, integration must occur at
all levels, including the physical environment, communication, and computation,
which encompasses design views, methods, representations, tools, cyber components,
and specifications (Saldivar, Li, Chen, Zhan, Zhang, & Chen, 2015). Thus, integration
involves a very high level of complexity.
Vehicular design and development are becoming increasingly integrated
on a global scale as companies target worldwide markets, and manufacturers are also
seeking greater regional integration, favoring local production in response to political
pressures and the cost-effectiveness of locating factories where production is less
expensive (Sturgeon, Van Biesebroeck, & Gereffi, 2008). Increasing horizontal
integration requirements, both global and regional, could be addressed with the
widespread adoption of Industry 4.0 technologies and processes The benefits of
integration would be particularly significant for the automotive industry, given that
under the current system, components are manufactured by various vendors who use
their own hardware and software. Adopting Industry 4.0 technologies and practices
would reduce costs by allowing manufacturers to create components that can be used
with different types of vehicles (Baheti & Gill, 2011).
3.2 Virtualization
A key component of Industry 4.0 is the amalgamation of the physical and
the virtual. With virtualization, sensors and simulations are used to create a virtual
copy of a particular physical world (Hermann et al., 2016). Smart objects are
connected with one another and the Internet, and the use of cloud computing
technology allows server networks to “be virtualized as a resource pool that can
25
provide scalable computing ability and storage space on demand for big data
analytics” (Wang et al., 2016, p. 3). Virtualization allows for optimization throughout
the supply chain with the integration of workflows and services using CPS (Brettel
et al., 2014). It also eliminates boundaries between industries, enabling systems
within one industry to access the customer bases, technologies, and infrastructures of
another (Schwab, 2016).
Hodge (2011) discusses the advantages of virtualization, which include
greater efficiency, cost-effectiveness, and reliability. Efficiency and cost-effectiveness
are increased in a couple of ways. First, with virtualization, one computer can be
substituted for many computers, which reduces hardware, maintenance, and space
requirements and costs. Also, companies can use the same operating systems for
longer because the systems are supported by virtual environments rather than physical
hardware. Second, it is far easier to upgrade software, which saves time and allows
human staff to focus on production improvements. Virtualization also increases fault
tolerance and overall system reliability and makes disaster recovery easier. With
virtualization, staff can develop and test disaster recovery plans to improve the speed
and reliability of recovery, and more reliable hardware can be shared virtually at
multiple sites to reduce the likelihood of problems. Moreover, in the case of a failure,
it is relatively easy to swap in a new piece of hardware without adversely affecting the
virtual system.
Another benefit of virtualization that is particularly relevant to the
automotive industry is the ability to engage in virtual prototyping using computer-
automated design. In the past, product developers had to use a time-consuming trial-
and-error process, but with virtualization, design problems can be addressed through
advanced simulations, saving a substantial amount of time and effort (Saldivar et al.,
2015). Thus, virtualization technologies enable companies to reduce overall
development time and get their products into the marketplace more quickly, which
gives them a significant competitive advantage (Schuh, Potente, Wesch-Potente,
Weber, & Prote, 2014).
In addition to reduced cost and space requirements and improved system
reliability, virtualization can support more environmentally friendly production
(Kagermann et al., 2011). Some companies are already adopting these technologies to
26
decrease their energy usage. One example is the application of green data center
technologies whereby virtualization software enables server sharing to reduce the
overall number of servers required and their collective energy demands. Another is
the use of programs that facilitate the safe collection and recycling of hazardous
electronics (Bughin et al., 2010).
3.3 Information transparency
Industry 4.0 smart factory systems provide more complete information
transparency than traditional factory systems because a virtual copy of the physical
environment is created via sensor data, and this contextualized data is maintained and
continually updated within the information network (Marr, 2016). According to Wang
et al. (2016), because a self-organizing, vertically integrated set of information
systems within a smart factory collects enormous amounts of data on a continual basis
and sends it to information systems within the cloud, the production process itself
becomes highly transparent. Information can be accessed at any time to examine any
stage of the production process and used to support better and swifter decision making
and planning, as well as accelerating time to market, which can give companies a
significant competitive edge. Also, in the case where a company is unable to meet a
particular customer’s requirements, information transparency makes it easier to
determine how the system could be improved to avoid this problem in the future.
The information transparency in a virtualized environment also makes it
easier to monitor system performance. The health and status of the system or any of
its elements can be accessed from a single user interface, which enables and simplifies
remote management (Hodge, 2011). Continuous and transparent information access
represents a significant improvement over the traditional factory system where shop
floor supervisors provide reports well after events occur, and these reports are often
riddled with inaccuracies, leading to significant problems that are difficult to address
(Dai et al., 2012).
Tubbs (2015) notes that information transparency has significant
implications for manufactured products. Such products are becoming increasingly
intelligent due to embedded sensors that can communicate with manufacturing
networks. They know their own histories, can assess their current states and compare
them to their target states, and determine how to achieve their target states based on
27
this information. They are no longer passive objects; instead, they have become active
participants in their own development, maintenance, and recycling. These objects
provide information to machines and humans as needed, making it far easier to
determine their current states, identify faults that require correcting, and manage them
effectively throughout their lifecycles. Information transparency can also help
companies produce more environmentally friendly products, as smart products can
collect and provide information about their own CO2 footprints (Wahlster, 2014).
Figure 7 Information transparency with smart objects (Wahlster, 2014, p. 15)
Increasing information transparency will require the adoption of open
standards, which will in turn support the integration of software and the addition of
new technologies into existing structures (Tubbs, 2015). It will also require the
development of three layers, as defined by Egri, Karnok, and Váncza (2007). The first
is a common dictionary that enables software systems at various companies to
understand one another. The second is protocol, which orchestrates communication by
determining what will be communicated and when it will be transmitted. The third
layer is infrastructure, which encompasses particular instruments used to facilitate
communications.
28
Although transparency will bring many benefits by making information
more freely accessible, it also presents some problems, as noted by Marr (2016).
Among the most significant is data security, given that information transparency
provides access to more systems and individual operators. Another challenge is the
difficulty of maintaining the stability and reliability of communication systems in
CPS.
3.4 Real-time capability
One of the most significant differences between the smart factory and the
traditional factory is that the smart factory can gather and analyze data from both
individual machines and the plant as a whole in real-time. In the past, this required
gathering and analyzing data spanning several months to examine issues such as
energy consumption or machine downtime, whereas the Industry 4.0 smart factory
reports this data continually as part of its operational routine (Tubbs, 2015). Data is
gathered and analyzed by machines as it becomes available, which allows for
continuous real-time tracking and the implementation of corrective actions as soon as
a problem occurs. (Hermann et al., 2016). Thus, Industry 4.0 allows for more rapid
response, which reduces the negative impacts of failures.
Flexible, adaptable, and agile are descriptors that appear frequently in the
Industry 4.0 literature because the real-time capabilities of smart factories support
rapid shifts in production as required. The smart factory can implement changes to
correct problems or meet customer requirements without the significant delays that
occur in traditional factories (Tubbs, 2015). In particular, the widespread adoption of
RFID technology by large vehicle manufacturing companies has supported mass
customization, lean operations, and just-in-time manufacturing (Dai et al., 2012).
Despite the many advantages of real-time capability, there is one
significant challenge. Allowing data to flow freely speeds analysis and response, but
there is the risk that a continual flow of data could overwhelm networks. Thus,
improving existing wireless communication and network technologies will be
important for ensuring the success of Industry 4.0 implementations (Tubbs, 2015).
3.5 Modularity
Modularity is a key component in adaptive systems (Vogel-Heuser et al.,
2016) and a critical aspect of Industry 4.0. Adaptive systems are better able to correct
29
problems and respond to market demands, and companies that can provide
customized products enjoy a significant competitive advantage. However,
responsiveness and customization require a diverse array of specialized tools and
frequent changes to machinery settings, which are not feasible for traditional static
production systems (Bauer, Jendoubi, & Siemoneit, 2004). With virtualized systems,
hardware can be reconfigured easily because particular applications are contained as
individual modules that can be swapped out for one another in a matter of minutes, as
opposed to traditional deployment, which can take weeks (Hodge, 2011).
With the modular, scalable systems associated with Industry 4.0
technologies, smart factories can engage in both small-scale and mass production
simultaneously, as well as optimizing individual processes for each (Tubbs, 2015).
Modularity allows for customization of products to particular customer needs while
maintaining economies of scale because it supports the production of different batch
sizes. Moreover, integration of horizontal networks, which relies on modularity,
allows for increased collaboration and thus creates new opportunities. However, there
is a risk that modularity could reduce the focus on global optimization (Brettel et al.,
2014). In other words, a narrow focus on optimizing aspects of production or certain
types of production at the expense of global operations could reduce overall efficiency
and productivity.
Despite the many advantages associated with modularity, there are also a
number of technical challenges arising from problems such as differing software
component sizes and interfaces. Also, factory software engineering has been largely
project-driven over the past decades, making it difficult to restructure legacy code
from various projects to provide similar functionality, and differing platforms present
further challenges (Vogel-Heuser et al., 2016).
3.6 Decentralization and technical assistance
According to Saldivar et al. (2015, p. 5), “decentralization forms a self-
organizing emergent system [capable of] self-adaptation, self-management, and self-
diagnosis.” Within the decentralized CPS, RFID tags communicate with machines and
CPS and make decisions based on this information. Therefore, the decision-making
process is largely decentralized.
30
The decentralized CPS can determine priorities and optimize decision-
making processes due to its capacity for self-awareness, self-comparison, and real-
time monitoring (Lee et al., 2014). A network of smart objects can actually
reconfigure itself as needed without input from a human operator (Wang et al., 2016).
The technical assistance provided by machines within the Industry 4.0
smart factory goes beyond decision support and problem solving, as smart machines
can also help with tasks that are unsafe or too challenging for human operators (Marr,
2016). Within a smart factory, machines can fix their own faults without human
intervention (Vogel-Heuser et al., 2016), which decentralizes maintenance and repair
functions, because instead of having a centralized department focused on maintenance
and repair, these processes are carried out by a network of machines. This form of
technical assistance reduces demands on personnel and time lost to repair work.
However, it could also result in lost jobs, as the need for human repair and
maintenance personnel is reduced or eliminated altogether.
Decentralization of information processing is another aspect of the overall
Industry 4.0 trend toward decentralization. Both machines and the products they
manufacture provide data that can be processed remotely from any location,
eliminating the need for central processing and thus increasing the flexibility of
logistics options (Tubbs, 2015). Decentralization also creates new opportunities for
collaboration, as information and services can be shared among multiple project
stakeholders (Pisching, Junqueira, Santos Filho, & Miyagi, 2015).
Self-assessment of industry 4.0
This research uses a self-assessment instrument developed by [PWC] (2016)
to investigate the current state of implementation of Industry 4.0 in Thai automotive
parts manufacturing firms. PWC is one of the world largest consulting services
networks with more than 223,000 employees over 157 countries. Their expertise areas
include strategic implementation, leadership management, financial management and
human resource management (PWC, 2016). The full PWC (2016) instrument includes
six different areas of readiness, including: the firm’s business model, product and
service portfolio; market and customer access; value chains and process; IT
architecture; compliance, legal, risk, security and task; and organization and culture
31
(PWC, 2016). For the purposes of this research, only the first four dimensions are
used, because the focus of this research is on technical readiness for implementation.
The PWC (2016) self-assessment instrument addresses different aspects of
preparedness. In the Business Models, Product and Service portion of the self-
assessment, key issues include the mix of physical and digital products and services
offered by the firm and the extent of digitization of engineering and design processes.
Market and Customer Access focuses on questions such as sales, communication and
service channels, collection and use of customer data, and tracking and monitoring of
customer information. Value Chains and Processes deals primarily with issues of
engineering, manufacturing and supply chain. It includes, for example, the integration
and digitization of engineering and manufacturing processes, supply chain and
logistics management, and capacity planning. This area is the core of the Industry 4.0
model as presented by Schwab (2016) and others, as it focuses directly on the in-plant
production process. Supporting the Value Chains and Processes area is the IT
Architecture area, which addresses issues like the company’s technical capabilities,
use of IT support for manufacturing and supply chain processes, and development of
IT infrastructure for digital services. The Compliance, Legal, Risk, Security and Tax
dimension encompasses what might be termed regulatory and fiduciary issues,
including technical implementation of risk monitoring, management of tax
opportunities, and security issues. Finally, Organization and Culture addresses the
current Industry 4.0 capabilities and change readiness (PWC, 2016).
The self-assessment is scored using mean responses, which are then
categorized into four points of Industry 4.0 readiness (PWC, 2016). In Stage I (Digital
Novice), the firm is beginning to offer digital products/services and uses automation
in sub-processes, but remains mainly traditional and product focused. In Stage II
(Vertical Integrator), the firm begins to expand its digital and integrated offerings,
uses integrated sales channels and data analytics, and uses vertically integrated
processes flows and homogeneous IT systems. By Stage III (Horizontal Collaborator),
the firm has begun to coordinate with outside partners to create individualized
customer experiences and digital solutions. At Stage IV (Digital Champion), the firm
has fully horizontally and vertically integrated processes and systems, is fully
customer-focused, and has begun to develop new business models (PWC, 2016).
32
It is important to note that the PWC (2016) instrument has not undergone
comprehensive academic testing for reliability and validity. However, at this stage in
the development of the concept of Industry 4.0, this gap is unavoidable, because as
discussed earlier in the literature review Industry 4.0 is still under development as a
theory. Instead, the PWC (2016) self-assessment instrument is a practical assessment
of the conditions that are believed to facilitate Industry 4.0 readiness, including the
firm’s product strategies and market and customer orientation practices as well as its
hardware. There is no other instrument that has been shown to be more reliable, and
in fact most studies of Industry 4.0 implementation have used a qualitative case study
approach rather than a survey approach in the first place. Thus, while there are flaws
with the PWC (2016) instrument, it is the best available alternative at this stage. This
is an area that could use additional research in future, as the concept of Industry 4.0
develops further and more becomes known about the conditions of implementation.
Industry 4.0 implementations in automotive and related industries
The degree of industry 4.0 implementation can be defined as the current
state of implementation of Industry 4.0 in firms (Dai et al., 2012).These level can be
categorized into 4 levels based on PWC scales (PWC, 2016). These levels are Digital
Novice, Vertical Integrator, Horizontal Collaborator and Digital Champion. Baheti
and Gill (2011) argue that the cost-effective integration of components developed by
different suppliers will be the key to success for automotive manufacturing firms in
the future. This integration will require not only designing, analyzing, and verifying
components, but also developing an understanding of the ways in which vehicle
control systems interact with other subsystems such as engines and steering, and
ensuring their overall safety and performance while keeping costs as low as possible.
Industry 4.0 technologies and practices address a number of problems faced
by automotive manufacturers, including those associated with production planning,
materials distribution, and factory management (Dai et al., 2012). It has been
predicted that adopting Industry 4.0 technologies and processes could improve
productivity by 20-30% for industrial component manufacturers serving the
automotive industry (Rüßmann et al., 2015).
33
In recent years, the focus of Thailand’s automotive industry has shifted from
developing capacity in general to enhancing productivity (ASCCI, 2015). Based on
this trend, industry goals include establishing a leaner supply chain and standardizing
vehicular components and testing equipment to meet the requirements of the ASEAN
economic agreement (Thailand Automotive Institute, Ministry of Industry, 2012).
This will necessitate the development of technological infrastructure to link
companies with their supply chains, allowing them to standardize processes and
operate more efficiently (Techakanont, 2011). To increase the likelihood of achieving
these goals, stakeholders within Thailand’s automotive industry should learn from the
Industry 4.0 experiences and best practices in automotive and related industries in
other nations.
1. Case study: Honeywell specialty materials
Hodge (2011) discusses the case of Honeywell Specialty Materials, which
manufactures specialized automobile components. The company implemented fully
virtualized systems at its Geismar facility to reduce infrastructure requirements,
maintenance costs, and energy use. It switched to virtual systems for offline testing
and development, and later virtualized its operator training system once the first
implementation proved successful. Honeywell also installed its advanced process
control and related maintenance and development applications onto virtual servers,
and all of the virtual machines were loaded onto a single physical server. Benefits of
the shift to Industry 4.0 virtualization have included improved efficiency, cost-savings
for hardware and engineering, increased user friendliness, simplified system
maintenance, ease of modification and replacement of virtual computers, greater
flexibility in the areas of testing and development, and being able to achieve more
with fewer materials. Overall, the experience of Honeywell indicates that
virtualization can help manufacturing organizations increase their return on
investment.
2. Case study: Implementation of RFID technology at a manufacturing SME
RFID technology is being rapidly integrated into the manufacturing plants of
large companies and small and medium-sized enterprises (SMEs) are starting to take
notice, given the benefits this technology has provided for their larger counterparts.
However, it is difficult for smaller companies to implement Industry 4.0 technologies
34
because their resources are limited and their smaller workforces usually lack the
technical skills required for successful adoption (Dai et al., 2012).
Dai et al. (2012) conducted a case study of an SME engine valve
manufacturer in China that adopted RFID solutions. Applying RFID technology and
integrating its manufacturing and enterprise resource planning systems enabled the
company to address the problems of excessive human operator and decision-maker
requirements. Specific benefits of the RFID adoption have included improved
efficiency and quality of business processes, better decision making due to real-time
data collection and processing, simplification and integration of production planning
and execution, and reduced operational errors. Statistical analysis capabilities brought
particular benefits, including 12% greater production efficiency, a 34% inventory
reduction, a 40% product quality improvement, elimination of 88% of all paperwork,
and a 96% improvement in the likelihood of receiving information on time, as well as
a productivity increase of 2.2 million pieces and cost savings of 1 million RMB.
This case indicates that Industry 4.0 technologies and practices can provide
benefits for smaller companies as well as large multinationals. However, the company
did experience some challenges during the implementation, including resistance
among workers and difficulty training employees to use the new system due to lack of
IT skills. The company also faced technical challenges as a result of limited RFID tag
memory, RFID readers responding slowly when dealing with multiple tags (signal
jamming), and limited network bandwidth affecting communication module response
times. However, it was able to overcome all of these issues and the implementation
was ultimately successful.
This case study has particular relevance for Thailand, given that 99% of
Thai businesses are SMEs (Charoenrat & Harvey, 2013), and lack of IT skills has
been identified as a particular problem in the Thai workforce (Ghazali, Lafortune,
Latiff, Limjaroenrat, & Whitesides, 2011). Therefore, Thai firms are likely to
experience some of the same benefits and challenges as the company featured in this
case study.
3. Case study: Remote prognostics and monitoring
Lee et al. (2014) conducted a case study of a remote prognostics and
monitoring system that was developed collaboratively by a vehicle component
35
manufacturer and the Center for Intelligent Maintenance Systems. This system, which
was designed to focus on a particular diesel engine component, monitored a set of
parameters that included temperature, pressure, engine rotational speed, and fuel flow
rate for various operating points, such as idling or maximum gas exhaust temperature.
The goal was to assess engine health, identify causes of problems, and predict
remaining lifespan. Based on their observations of this particular system and trends in
big data in general, the authors predict a number of benefits from such systems,
including reduction of downtime, optimizing all aspects of management and
maintenance scheduling, ensuring machine safety, increasing information
transparency throughout the supply chain, reducing labor costs, improving working
environments, reducing energy costs through greater energy efficiency, and better
supply chain management overall.
4. Case study series: The use of CPS in industrial manufacturing
After conducting multiple qualitative case studies of industrial
manufacturing firms, Herterich, Uebernickel, and Brenner (2015) found that Industry
4.0 provided a number of benefits, including continuous equipment monitoring, the
ability to control equipment and diagnose and solve problems from remote locations,
and the capacity to optimize operations in response to sensor data. Adopting CPS also
enabled companies to conduct predictive and preemptive repair and maintenance and
gave their products the ability to order their own spare parts. In addition, with
enhanced data collection and information processing abilities, companies with CPS
were able to share or sell information. All of these benefits led to substantially
increased efficiency and service innovation for the CPS adopters.
5. Case study: Volkswagen’s RFID implementation
Volkswagen, a major automotive manufacturer, launched a Transparent
Prototype project whereby parts were labeled in accordance with industry
recommendations for all companies, and this labeling created IP addresses for them.
Once the implementation was completed, vehicle manufacturers could automatically
identify RFID-coded prototype parts even after installation, which eliminated the need
to conduct many time-consuming manual tasks that were formerly required to
document construction status during testing phases. The use of RFID and an
36
electronic data exchange now supports quick and easy information transfer between
Volkswagen AG and its suppliers, increasing efficiency (Schmidt, 2015).
6. Case study: Implementation of industry 4.0 at Bosch Rexroth Corp
Tubbs (2015) reports on the results of an Industry 4.0 implementation at a
facility that produces hydraulic valves. Each factory object now contains an RFID
chip, and intelligent stations know how the products must be assembled, what tool
settings they should use, and what processes they must apply to do so. Human
operators collect and transmit information to assembly stations via Bluetooth, and the
stations adjust themselves to the requirements of their human operators and display
instructions as required in a format customized to the needs of each worker. This shift
to Industry 4.0 has provided a number of benefits. The assembly line can now produce
batches as small as a single item and create up to 25 product variants without any
human input. Also, there is no time lost to setup or excessive stocking, which has
resulted in a 10% productivity increase and an inventory decrease of 30%.
7. Case study: The SmartFactoryKL
Hermann et al. (2016) conducted a literature review case study of the
SmartFactoryKL in Germany (Hermann et al., 2016). The SmartFactoryKL is not a
specific product, but rather an independent technology initiative of the German
Research Center for Artificial Intelligence, but it provides a good representation of
Industry 4.0’s capabilities. The authors found that the system incorporated all of the
key design principles of Industry 4.0. Its CPS, including workpiece transporters,
assembly stations, and products, are able to communicate with one another, thus
meeting the requirements of interoperability and information transparency. Processes
are virtualized, as the CPS are able to monitor the physical activities taking place in
the factory and alert a human worker in the case of problems that cannot be solved by
the machines. However, the CPS also have the capability to address many issues on
their own, in response to information provided by RFID tags, therefore decentralizing
the decision-making process. Moreover, the machines track and analyze processes in
real-time, enabling constant monitoring and immediate corrective action as needed.
The system is modular, with the option to integrate new modules immediately using
standardized software and hardware. In addition, the SmartFactoryKL is flexible
37
because its functionalities take the form of an encapsulated virtual service, which
allows for customization.
8. Case study: Comprehensive automation at an automotive OEM
Pfeiffer (2016) describes the case of a large automotive original equipment
manufacturer (OEM) in Germany that implemented Industry 4.0 technologies to the
point where the ratio of industrial robots to human workers was 1:1. Contrary to the
popular belief that comprehensive automation would eliminate the need for skilled
human workers (with the exception of a few managers and top engineers), 90% of
those working in the plant had three or more years of vocational training and were
called upon to intervene in the robotic processes 20-30 times during each shift to
prevent technical problems. The conclusion drawn from this case study was that
Industry 4.0 automation decreased the quantity of human work but increased
qualitative skill requirements due to heightened complexity.
Best practices of industry 4.0 implementation
The final topic of the literature review is a review of best practices of
Industry 4.0 implementation from other countries. Because the principles of Industry
4.0 are relatively recent and have only become implementable within the past five to
ten years (Schwab, 2016), and many are not yet implementable, there is not yet much
evidence on such best practices, which are still under development in academic and
practical research and have not yet been fully established (Vogel-Heuser & Hess,
2016). However, the origins of the Industry 4.0 paradigm in German industrial policy
and further research into Industry 4.0 implementation do provide some possible best
practices. Three of the best supported best practices are identified here. However,
these should not be considered to be the only possible best practices.
One of the recommended best practices for Industry 4.0 implementation is
standardization of systems and components (Weyer, Schmitt, Ohmer, & Gorecky,
2015). Weyer et al. (2015) point out that currently, Industry 4.0 systems are isolated
and vendor-specific, often custom designed for a specific factory or process. To
expand coverage of Industry 4.0 and truly achieve interoperability, data transparency,
virtualization, modularity and other core principles, an implementation standard must
be developed to ensure that equipment and systems from different vendors can work
38
together effectively (Weyer et al., 2015). Standards also serve several purposes for the
organization, enabling it to “1) to facilitate the delivery of the right information at the
right time, 2) to enable actions based on that information and 3) to reduce risk of
technology adoption and development (Lu, Morris, & Frechette, 2015, p. 998).”
Standards for Industry 4.0 and smart factories are still under development, with most
standards addressing a limited area such as human machine interface or cloud
manufacturing (Lu et al., 2015). Weyer et al. (2015) discuss the implementation of the
SmartFactoryKL open implementation standard, which was developed in Germany to
meet the need for modular and interoperable systems. The development of a full open
standard for Industry 4.0 smart factories is still in progress, and not all major vendors
of factory equipment have engaged with this need (Weyer et al., 2015). Regardless,
implementation and use of SmartFactoryKL or another shared standard should be
considered as a best practice, as it is in other areas of computing.
A second best practice that needs to be considered is security (Kargl, van der
Heijden, König, Valdez, & Dacier, 2014). As Kargl et al. (2014) point out, industrial
control systems have historically been more designed for physical safety than for
cyber-security, with many such systems having only rudimentary security systems
and precautions implemented. Furthermore, the long service life of industrial control
systems means that systems may still be operational but no longer able to deal with
modern security threat models such as hackers. This was not as much of a problem
when control systems and industrial machinery were isolated, but the cloud
connectivity inherent in Industry 4.0 systems creates an opportunity for external
attack through the network, which many older and even newer systems are ill-
equipped to deal with (Kargl et al., 2014). Thus, smart factory technology is
consistent with the current state of implementation of Internet of Things (IoT)
security, in that many devices continue to be unsecured or poorly secured and have
weak or no defenses against malware and other malicious attacks (Kumar, Vealey, &
Srivastava, 2016). There are a number of standardization and protocol implementation
initiatives currently ongoing that could help address this area. For example,
the International Society of Automation (ISA) is working toward a security standard
as part of its standardization initiative (Lu et al., 2015). Other security initiatives are
addressing general IoT security concerns for both industrial and consumer
39
applications (Kumar et al., 2016). Since these standards and protocols have not yet
been fully developed, it is critically important that Industry 4.0 implementations
address security issues and use standard best practices for Internet connected systems
(Kumar et al., 2016). One set of best practices for Internet-connected industrial
control systems identifies several key aspects of the system and its demands (Dacier,
Kargl, König, & Valdes, 2014). These recommendations include transitioning away
from reactive security systems and toward proactive intrusion detection and counter-
detection systems and consideration for the protection of physical systems (Dacier
et al., 2014). However, the authors acknowledge that there are many areas of security
that are still poorly understood.
A third best practice is utilization of experts and specialists for Industry 4.0
implementation at both the strategy level and the system level (Erol, Schumacher, &
Sihn, 2016). As Erol et al. (2016) pointed out, there are specific challenges related to
Industry 4.0 that are distinctly different from existing areas of organizational
expertise, including existing expertise in system design and integration. This
paradigmatic change demands that firms seek out expert knowledge for how Industry
4.0 strategies could best be used in the organization (Erol et al., 2016). There are also
requirements for specialist insight for areas like security and systems integration,
particularly with legacy systems, which most firms do not have the technical expertise
for (Slama, Puhlmann, Morrish, & Bhatnagar, 2015). Thus, an effective
implementation in most organizations will require external assistance. This best
practice is common for the implementation of large, complex IT systems, for example
enterprise resource planning (ERP) systems, which are complex and require specialist
knowledge for installation and configuration (Sun, Ni, & Lam, 2015). Thus, firms
should be familiar with the requirement for specialist expertise for Industry 4.0
technical implementation. However, as Erol et al. (2016) point out, the paradigm
change of Industry 4.0 means that firms may also require specialist assistance with
firm strategy as well, which may be less familiar.
Summary of literature
Thailand must adopt new technologies and adapt its automotive industry
processes to remain competitive within an increasingly globalized marketplace, and
40
cost management will be a key aspect of this adaptive strategy, though the industry
will also have to modify its practices to address changing social, economic, and
environmental concerns. Meeting these challenges will require improving efficiency,
productivity, and environmental friendliness by collaborating with governmental and
academic organizations; developing research and development capacity; enhancing
supply chain management; and increasing overall competency (Thailand Automotive
Institute, Ministry of Industry, 2012). Evidence from early adopters indicates that
implementing Industry 4.0 technologies and processes would likely be the most
effective way to achieve these goals.
Industry 4.0 draws upon modern technological trends such as big data, the
IoT, and the smart factory to provide a variety of benefits, including virtualization,
integration and interoperability, decentralization and technical support, modularity,
information transparency, and real-time capability through the use of wireless
network technologies. Manufacturing companies that have adopted Industry 4.0 have
achieved benefits in the areas of efficiency, productivity, and cost savings, but in
some cases have faced both worker-related and technological challenges during the
implementation phase. Also, cost of implementation may be a barrier for smaller
firms. Table 2 summarizes the benefits and challenges of Industry 4.0.
Table 2 Benefits and challenges of industry 4.0
Industry 4.0 benefits Industry 4.0 challenges
Better supply chain management
Increased productivity
Increased efficiency
Information transparency
Real-time capability
Technical assistance
Decision support
Flexibility to meet market demand
Reduced operating costs
Cost of implementation
Lack of workforce IT skills
Employee resistance
Technical problems (such as
overloaded networks)
Increased workforce training
requirements
Lost jobs for human operators
(particularly low-skill jobs)
41
Table 2 (Continued)
Industry 4.0 benefits Industry 4.0 challenges
Better environmental performance
Rapid fault identification and
correction
Better opportunities for
collaboration
Ease of system upgrading
Risk of focusing on the
optimization of individual
processes at the expense of global
optimization
Data security risks
42
CHAPTER 3
RESEARCH METHODOLOGY
This chapter provides a description of the research methodology that was
applied for this study. This research used mixed methods which employed both
quantitative and qualitative. There are 9 sections in the chapter as below;
1. Research process
2. Research philosophy
3. Research approach
4. Sampling and sample size
5. Data collection
6. Validity and reliability
7. Data analysis
8. Limitations of methods used
9. Ethical considerations
Research process
The research process was based on a balanced mixed methods design. Mixed
methods research combines qualitative and quantitative data collection and analysis
techniques in different ways (Creswell, 2013). For example, research can be weighted
toward qualitative or quantitative data, may be either qualitative-led or quantitative-
led, and the results of the two streams may be either triangulated or used as inputs for
each other (Creswell, 2013). Triangulation is a process of answering research
questions or objectives from different perspectives or from different information
(Creswell, 2013). Triangulation is appropriate for exploratory research such as this
study because it allows for evaluation of possible theories derived from qualitative
research and analysis at different levels (Creswell, 2013). In this research, a balanced
approach was used, with quantitative and qualitative data given approximately equal
weight. However, the research was qualitative-led, with interviews being conducted
prior to the surveys. This approach was chosen so that information from the
interviews could be used if necessary to inform the quantitative research, and to make
43
sure there was sufficient information collected. The results were then triangulated to
answer the research questions (figure 8).
Figure 8 The research process
The research was conducted using a cross-sectional time horizon, with firms
being interviewed and/ or surveyed on their current state of Industry 4.0 readiness.
The research primarily took an inductive logical approach, with observations focused
on the Industry 4.0 model being the main focus. Sample sizes were determined for the
qualitative and quantitative stages of research independently, although the samples
were drawn from the same population. The remaining sections of this chapter explain
in detail the research choices made to conduct the study and how these choices
influenced outcomes.
Research philosophy
This research was used a mixed methods, qualitative-led methodology, in
order to take advantage of the strengths of both qualitative and quantitative research
approaches. The detail of each approach will be explain in the next section.
1. Qualitative approach
44
Qualitative research approaches are a diverse set of research approaches that
use non-statistical procedures to analyze data that has varying degrees of
standardization (Creswell, 2013). The most common choice for qualitative research is
the use of interviews to collect data from respondents, followed by an analysis
approach such as content analysis or thematic analysis that helps explain the outcomes
(Cooper & Schindler, 2014). However, there are a wide variety of potential
approaches, such as ethnography, action research, case studies, and grounded theory,
that can be used in specific situations (Creswell, 2013). Qualitative research does have
some weaknesses; for example, results cannot be generalized across the population,
and the lack of standardization in data collection can lead to potential biases (Cooper
& Schindler, 2014). However, qualitative research excels at not just describing
situations or relationships, but explaining these relationships and situations and how
they emerge in a given context (Creswell, 2013). Qualitative approaches are best
suited to the collection and analysis of data reflecting the social world and human
behavior (Anderson, 2010). Although this research focuses on the implementation of
Industry 4.0, it is actually a study of human factors because it examines the degree to
which managers and owners of Thai firms have chosen to adopt new technologies and
practices, and their qualitative perceptions regarding the impacts of implementation,
as well as barriers to adoption. Currently, there is very little Industry 4.0 activity in
Thailand, and although the Ministry of Information and Communication Technology
plans to launch a number of initiatives to support the transition to Industry 4.0 in the
near future (Tortermvasana, 2016), a review of the literature indicates that little is
known about the human factors that affect the likelihood of success for these
initiatives. Moreover, given the novelty of the Industry 4.0 phenomenon, there is not
much information about the impacts of Industry 4.0 implementations on human
environments (Roblek, Meško, & Krapež, 2016). Prior research has identified human
factors such as employee resistance and lack of IT skills as barriers to adoption for
manufacturing SMEs (Dai et al., 2012). While there are not enough Industry 4.0
adopters in Thailand to conduct a large-scale quantitative study, valuable insights can
be gained from interviewing early adopters or those considering making the switch to
Industry 4.0.
45
2. Quantitative approach
Quantitative research approaches are those that use standardized data
collection and numeric techniques such as statistical analysis, modeling and
simulation for analysis (Creswell, 2013). In contrast to the diversity of qualitative
techniques, the two main quantitative techniques are surveys (which collect
uncontrolled data) and experiments (which control variables to observe effects on the
outcome) (Creswell, 2013). Quantitative research is typically deductive, or theory-led,
and is used to confirm existing theories rather than to extract new theories (Cooper &
Schindler, 2014). As a result, quantitative research is helpful for determining whether
previously observed relationships apply in a new context, but cannot necessarily be
used on its own to isolate new relationships (Creswell, 2013). Quantitative research,
unlike qualitative research, can be used to generalize findings across a population, and
to prove (though not explain) specific relationships (Cooper & Schindler, 2014).
Thus, to some extent the strengths and weaknesses of qualitative and quantitative
research balance each other.
In order to validate and verify the qualitative insights, a small-scale
quantitative survey was used here (Creswell & Plano Clark, 2011). The integration of
quantitative research into a qualitative study allows the researcher to confirm and test
findings from qualitative research, providing higher reliability and an assessment of
the degree of generalization possible and furthering theory development (Plano Clark
& Ivankova, 2015). Thus, the use of mixed methods help better develops
understanding of how Industry 4.0 is being incorporated into the Thai automotive
industry.
The research philosophy guiding this study is interpretivism, which is
compatible with qualitative research methods. Interpretivism assumes that
interpretation is subjective, and that phenomena can be best understood by developing
an understanding of the subjective perceptions and beliefs of particular groups of
people. The interpretivist researcher seeks to understand phenomena from the
perspectives of those who experience them, and their subjective impressions provide
the insights required to develop new theories (Goldkuhl, 2012).
46
Research approach
Deductive analysis starts with a theory that is used to generate hypotheses,
after which data is collected to either confirm or refute and revise them (Bryman,
2016). While deductive analysis is more often used with quantitative research
methods, it can be used with certain qualitative approaches. When applying deductive
logic to qualitative analysis, researchers typically use a preliminary theory that is
based on expectations arising from either professional or personal experience. They
may then develop hypotheses based on this theory or simply use the theory itself to
guide their research (Gilgun, 2008). For mixed methods, the findings derived from
qualitative research can then strengthen the theoretical basis for the quantitative
findings (Plano Clark & Ivankova, 2015). In the case of this research, the theoretical
foundation is built upon the professional experience of manufacturing representatives
and researchers who have overseen Industry 4.0 implementations.
Qualitative researchers then conduct case studies to determine whether their
theories or hypotheses are in keeping with the actual perceptions and experiences of
participants (Gilgun, 2008).
The primary advantage of qualitative research is that it puts data in context,
which allows researchers to develop an understanding of the environmental factors
that contribute to particular perceptions, experiences, and outcomes (Myers, 2013).
The main benefit of quantitative research is that it explains how generalizable findings
are (Creswell & Plano Clark, 2011). This is important when studying Thai
manufacturing firms because factors such as market and operating environments,
technological infrastructure, regulations, and many other variables are likely to play a
role in Industry 4.0 implementations and outcomes. Also, using a mixed methodology
combined with deductive logic will focus the research on particular areas of interest
while maintaining the flexibility required to pursue new discoveries not predicted by
the hypotheses (Gilgun, 2008). Maintaining such flexibility is important for this study
because Industry 4.0 is a relatively new field of inquiry, so there is the potential for
new discoveries.
47
Sampling and sample size
For the qualitative research, the sample was selected using a purposive
sampling approach whereby the researcher recruits study participants with
characteristics relevant to the research topic. Because purposive sampling is a non-
probability method, the results will not be generalizable (Bryman, 2016). However,
the goal of this research is not to produce generalizable results, but rather to develop
preliminary insights into the potential benefits and challenges of Industry 4.0
implementation for a specific industry sector in Thailand. Therefore, this research
focus on representatives of automotive parts manufacturing firms that are
implementing or contemplating the adoption of Industry 4.0 technologies and
processes (n = 20). Respondents will include a mix of technical experts, managers,
and others who play a role in the implementation process. The choice was made to
conduct 20 interviews because this is the minimum number recommended for
grounded theory research (Cresswell, 2013), and there are very few companies
implementing or even contemplating Industry 4.0 in Thailand.
Given the relatively small size of the target population, a snowball sampling
technique will be used to recruit the required number of respondents. With snowball
sampling, the researcher initially recruits a smaller group of individuals with the
required characteristics and then asks them to recommend others who fit the criteria
for the target population (Bryman, 2016).
The quantitative research is conducted at the firm level, drawing from the
same pool of respondents as the qualitative research. The sample size was calculated
using the total number of automotive firms in Thailand, which the Board of
Investment [BOI] (2015) estimates at 2,427 (including 18 assemblers/ car makers; 709
Tier 1 suppliers, and about 1,700 Tier 2 and 3 suppliers). Because the population size
was known, the sample size was calculated using the equation:
n =N
1+N*(e)2, which assumes a 95% confidence level and +/-5% confidence
interval to determine the sample size based on a proportion of the total population
(Yamane, 1967).
Calculation of the sample size results in the following: n =2,427
1+2,427*(.05)2 = 332.
Sample size does also need to be determined based on factors like access to the
48
population and the amount of time required for the project, which can limit the
number of members that can reasonably be recruited (Cooper & Schindler, 2014).
However, these are not obvious concerns here. The target sample size is n = 332
automotive firms. Convenience sampling, or selection based on availability, is used.
This strategy was selected because of the practical limitations on sample selection
particularly for Tier 2 and 3 suppliers (Bryman, 2016).
Data collection
1. Interview
Data was collected during a series of interviews conducted face-to-face or
via Skype, depending on the locations and availability of respondents. The decision
was made to use semi-structured interviews because they provide some guidance to
keep the data collection activities focused on key topics while allowing the flexibility
to change question order and pursue points of interest (Bryman, 2016).
2. Questionnaire
A self-assessment questionnaire is used for the quantitative research. The
questionnaire was developed by adaptating an existing instrument, which reinforces
reliability and validity (Bryman, 2016). The research is based on the PWC Industry
4.0-Enabling Digital Operations self-assessment questionnaire, which was developed
for manufacturing industry assessment (PWC, 2016). It assesses four areas of Industry
4.0 readiness, including Business Models, Product and Service Portfolio (Part II);
Market and Customer Access (Part III); Value Chains and Processes (Part IV); and IT
Architecture (part V). Part I collects basic company information. The adapted
questionnaire is included in the Appendix.
Validity and reliability
Face validity, or the degree to which interview questions reflect the concepts
they were designed to examine (Bryman, 2016), was assessed by expert review. This
is a common method of validation whereby subject matter experts examine a newly
created instrument to determine its overall suitability to its purpose and identify
problems such as ambiguity or incomprehensibility (Zamanzadeh et al., 2015). Once
49
the expert review has been completed, the interview guide was adjusted as necessary
for greater clarity and effectiveness. Reliability and validity of the questionnaire is
reinforced by the adaptation of the existing instrument (Bryman, 2016). It was also
triangulated with the qualitative findings to test the validity of the theoretical model
proposed (Creswell & Plano Clark, 2011).
Data analysis
Content and thematic analysis strategies were used to analyze the interview
data. Content analysis was selected because it is useful for extracting meaning from
unstructured text-based data (Bryman, 2016). Qualitative content analysis involves
systematic coding and categorizing of text-based data to identify trends and patterns,
and to characterize the overall content of participant responses or other textual
documents. Thematic analysis is slightly different, in that it seeks to identify
overarching themes (Vaismoradi, Turunen, & Bondas, 2013). Using both methods
allows for a more comprehensive examination of the data, with content analysis
providing the coding structure for the identification of recurring data categories and
thematic analysis enabling the organization of these recurring categories into cohesive
themes. Both content and thematic analysis are suitable for examining complex social
phenomena (Vaismoradi et al., 2013). Therefore, they will be useful for exploring the
implications and impacts of new technologies and processes on the people who will
be affected and the business environments in which they operate.
Descriptive statistics are used to analyze the questionnaire data. Means and
standard deviations for each of the five scales are computed, including the Actual and
Target states. Additionally, the mean difference between the Target and Actual states
is computed to understand how much work firms perceive is ahead of them to
incorporate Industry 4.0. The interpretations of the Actual and Target means are as
follows, based on PWC’s scales (PWC, 2016):
• Means 1.00 to 2.00: Digital Novice-the firm is still working on its first
digital implementations, has separate online and offline presence, product rather than
customer focus, and fragmented and siloed IT infrastructure and processes.
• Means 2.01 to 3.00: Vertical Integrator-the firm is beginning to develop a
digital product and service portfolio, multi-channel communication and distribution,
50
and integration of data flows and processes; has a homogeneous IT structure and
cross-functional collaboration, but has not yet fully addressed the challenges of
digitization.
• Means 3.01 to 4.00: Horizontal Collaborator-the firm is using integrated
customer solutions, individual customer processing, integration of data flows and
processes with external customers and partners and common architecture.
• Means 4.01 to 5.00: Digital Champion-Firm is able to develop disruptive
business models, has an integrated customer journey, uses a fully integrated partner
system including service busses and secure data exchange.
Moreover, paired t-test was used for comparing actual and target
performance in each items. To respond to the research questions, triangulation was
used. Triangulation is a process of synthesizing qualitative and quantitative results
from a mixed methods study, in which knowledge derived from qualitative and
quantitative research streams is combined into a single respond to a given research
question from different perspectives or viewpoints (Creswell, 2013). The triangulation
process is distinct from a quantitative-led or qualitative-led (theory building) mixed
methods research study, where the output of one of the methods is used in the other
(Creswell, 2013). However, it is appropriate for this study because as an exploratory
research study, it was important to consider Industry 4.0 readiness from multiple
perspectives.
Limitations of methods used
This research has a number of limitations. First, because it will apply a
cross-sectional design, it will provide information about the current situation but no
insights into changes over time. Second, the pool of potential respondents is quite
small, and due to the combination of a small sample size and non-purposive sampling
technique, the results will not be generalizable to the broader population of
automotive manufacturers. This problem relates to both the qualitative and
quantitative streams. Third, the study will focus solely on Thai automotive
manufacturers, so any insights gained may not be relevant to other types of
manufacturers or automotive manufacturers in other nations. Fourth, interviews were
conducted with single representatives of each participating company, and therefore
51
reflect only one perspective from each firm. It is possible that the perspectives of
other company representatives may differ from those of the interviewees. The same is
true for the questionnaires, which was completed by one firm representative. Fifth,
participants may choose not to disclose certain information (particularly problems or
challenges) in order to present a more positive view of their companies.
Ethical considerations
This research was not involve harmful activities or vulnerable populations,
and all participants were adults and therefore able to provide informed consent, so the
primary ethical concern for this research is maintaining participant confidentiality.
Respondents were asked to provide information about a variety of topics related to
their companies, and it is possible that some of their answers could reflect negatively
on the firms they represent. However, none of the participants were identified by
name, nor was any of their companies. Names and contact information were collected
in advance to conduct the interviews, but this information were stored in a password-
protected file and deleted once the interviews have taken place. No identifying
information were recorded in any documentation related to this research.
52
CHAPTER 4
RESULTS AND DISCUSSION
This chapter presents the results from the quantitative and qualitative
primary research, which were generated using the methodology explained in the
previous chapter. The first section of the chapter presents the results. First, the
quantitative results from the company questionnaire are presented, using a
combination of tables and discussion. Next, the qualitative interviews with automobile
industry companies implementing Industry 4.0 are presented, once again using a
combination of tabular and narrative discussion. The final section of the chapter
discusses the qualitative and quantitative results and compares them to the literature
review (chapter 2).
Results
1. Research analysis
The results were generated using a mixed methods research approach, which
incorporated a qualitative interview of Thai automobile industry firms, followed by a
quantitative survey. The mixed methods approach was chosen because it was
considered the best choice for collecting both depth and breadth of information about
the readiness for Industry 4.0 implementation in Thailand’s automobile industry. The
current state of knowledge surrounding Industry 4.0 means that there is limited
information about readiness and implementation, particularly outside the German
automobile industry or its Tier 1 suppliers in major countries like China. Furthermore,
most of the existing evidence is based on single firm case studies rather than an
industry-wide viewpoint. This meant that a wide spectrum of information about firm
readiness was desirable, encouraging the use of both quantitative and qualitative data
techniques for data collection and analysis.
The qualitative interviews were based on an interview guide designed for the
study, as derived from the literature on Industry 4.0 implementation. The interviews
were used to support and deepen the findings of the questionnaire, which was a
closed-ended instrument that did not allow for additional input or dimensions.
53
The quantitative survey was based on PWC’s (2016) self-assessment for
Industry 4.0 implementation readiness, which is one of the few available instruments
that address the firm’s internal conditions. The quantitative survey was analyzed using
descriptive statistics, which allowed for categorization of firms based on the original
interpretation provided by PWC (2016), which identifies four readiness stages.
2. Qualitative results
The second part of the research used interviews with 20 firms drawn from
the quantitative sample in order to understand the implementation process of Industry
4.0. The results for each of the 11 aspects of interest in the interviews are summarized
below.
2.1 Business background
Firms were asked briefly about the industry sector or area they were
mainly involved with (table 3). As this shows, firms participated in a wide range of
industry sectors, although most were Tier 2 suppliers or lower and were not primary
or retail firms.
Table 3 The industry sector participation
Firm Main product
1 Automotive glass industry
2 A category of automotive parts-leak detectors and sealers
3 Auto Parts Industry
4 Auto parts industry
5 Electrical auto parts industry
6 Metal cutting
7 Metal parts manufacturing for other industrial purposes
8 Auto parts manufacturing
9 Automotive parts
10 Automotive metal parts factory
11 Automotive parts
12 Electrical auto parts industry
13 Automotive belt industry
54
Table 3 (Continued)
Firm Main product
14 Car seats
15 Electrical auto parts manufacturer
16 Auto parts in-cabin active noise cancellation
17 Electrical auto parts
18 Automotive belt industry
19 Automotive industry
20 Motorcycle manufacturing, importing, exporting, and retailing
To summarize (table 4), firms were most likely to be general auto parts
manufacturers (Firms 3, 4, 8, 9, 11, 19) electrical parts manufacturers (Firms 5, 12,
15, 17), metal fabricators (Firms 6, 7, 10) or automotive belt manufacturers (Firms 13,
18). There were also firms that produced automotive glass (Firm 1), leak sealers and
detectors (Firm 2), motorcycles (Firm 20), cabin noise cancellation assemblies (Firm
16), or car seats (Firm 14). Overall, this shows that firms are either mainly engaged in
general manufacturing or they are component suppliers , some of which were highly
specialized.
55
Table 4 Summary of industry sector participation
No Industry area Firms Total
(Firms) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 Auto parts
(general) 6
2 Automotive belts 2
3 Automotive glass 1
4 Electrical parts 4
5 Leak sealers and
detectors 1
6 Metal fabrication 3
7 Motorcycle
manufacturing 1
8 Noise cancellation 1
9 Seats 1
55
56
2.2 Basic principles of industry 4.0
Participants were asked what their understanding of the basic principles
of Industry 4.0 were. All answers can be seen in Table 5. Most of the responses were
very brief and incorporated only one or a small number of the elements of Industry
4.0, focusing on the use of IT and the replacement of human workers with technology.
However, a few of the firms’ representatives offered a more complex and detailed
explanation of the Industry 4.0 principles, especially Firms 15, 17, 19, and 20.
Overall, participants were generally clear about what Industry 4.0 implied, even if
they were not certain about the technical or implementation details.
Table 5 Understanding of the basic principles of industry 4.0
Firm Answer
1 Most industry 4.0 tends to focus on IT and services rather than the old-style
related labor and workforce.
2 They are the key strategies for building Thailand 4.0
3
It is a model for changing industrial operations strategy in new countries from
industrial base manufacturing industry to advanced technology with different
service patterns.
4 It is an incorporation of technology in designing unique products and increasing
productivity toward more automation.
5 It is an application of industrial innovation.
6 Uses technology to replace humans.
7 Industry that uses advanced technology and requires fewer people to run it.
8 It uses the Internet and various innovations in production, rather than humans.
9 Uses digital media and robots to work.
10 Technological system
11 The technology used to assist the overall or different sections are the advanced
kind.
12 The technology used to assist the overall or different sections Technology used
to assist the overall or different sections and innovations.
13 It uses technology to help with decision making.
57
Table 5 (Continued)
Firm Answer
14 From my understanding, it uses IT software systems to increase efficiency in
productivity.
15
Industry 4.0 comprises 2 parts: 1) automatic production and 2) Control via the
Internet where it can be connected from and through multiple areas. The
production varies on actual demands of the customers. There is also a
technological integration named Big Data and Cloud.
16 Change the production model to incorporate technological supports in order to
maximize efficiency.
17
Efficiently integrating IoT technology helps ease communication and
connection among devices. Machines in the industry can communicate back
and forth and exchange production data. Also, they can transmit and receive
specific data from and to customer handling systems.
18 Integrates modern technology in the industry by developing and improving
various sections.
19
It is an integration of technology through the management of Big data, Cloud,
and IoT by using them to accommodate the system across all, vertically, the
production lines and horizontally, the administration.
20
Integrating digital technology and the Internet into the manufacturing process to
increase efficiency. Link the diverse needs of the customers directly to the
manufacturing process. Batch produces large amounts of products with various
styles according to customer specifications.
Although some of the participants had limited views of the process, there
were a number of shared perspectives, which are summarized below (table 6). Some
of the most complete views of Industry 4.0 included the following:
• Industry 4.0 comprises two parts, including 1) automatic production and
2) control via the Internet where it can also be connected from and through multiple
areas. The production varies on actual demands of the customers. There is also a
technological integration of Big Data and Cloud. (Firm 15)
58
• Integrating IoT technology efficiently helps ease communication and
connection among devices. Machines… can communicate back and forth and
exchange production data. In addition, they can also transmit and receive specific data
from and to customer handling systems. (Firm 17)
• It is an integration of technology through the management of big data,
cloud, and IoT by using them to accommodate the system vertically (production lines)
and horizontally (administration). (Firm 19)
In terms of general principles, the most firms identified utilization and
integration of technologies like IoT and cloud computing and utilization of
automation, robotics and other process innovations into the production process.
These principles were often associated with the reduction or replacement of the
human workforce, rather than with other goals such as more efficient resource
utilization. In contrast, firms were not as likely to identify customer focus and
responsiveness, digital media, social media or the Internet, or workforce reduction or
elimination as aspects of Industry 4.0, but all of these responses were recognized by
some firms. This suggests that the firms view production automation as the primary
aspect of Industry 4.0, with aspects of customer focus and communication technology
and integration being less important. However, this should not necessarily be
considered a gap in their understanding, since as noted above most of the firms in the
study are Tier 2 and lower suppliers of parts and components in the automobile
industry, and most of them do not deal directly with retail consumers, but instead
coordinate production activities with buyers and suppliers in the supply chain. Thus,
their use of technology within their products and services would be determined by
supply chain leader demands.
59
Table 6 Summary of the basic principles of industry 4.0
No Characteristic or principle of
industry 4.0
Firms Total
(Firms) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 Changing manufacturing and
production strategies 2
2 Customer responsiveness/focus 3
3 Integration of processes and
technologies 1
4
Promoting or improving
productivity, efficiency, or
quality
5
5 Use of IT products and services 2
6
Utilization and integration of
technological advancements
like IoT, cloud computing and
communication technologies
10
7
Utilization of automation,
robotics or industrial
innovations
8
59
60
Table 6 (Continued)
No Characteristic or principle of
industry 4.0
Firms Total
(Firms) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
8 Utilization of data for decision
making and analytics 1
9 Utilization of digital media,
social media and the Internet 3
10
Workforce reduction, reducing
or eliminating human
interaction and workers
5
60
61
2.3 Industry 4.0 in the automotive industry
Respondents were then asked their perception of the application of
Industry 4.0 in the automotive industry (table 7). These perceptions were varied and
often confused; for example, several firms confused Industry 4.0 with the older
automation paradigm (Firms 4, 5, 7, 9, 12, 14, 16, 18, 20). Few of the respondents
mentioned the distinction between older automation models, which belong to the
Industry 3.0 paradigm, and the more complex Industry 4.0 paradigm, which
incorporates the Internet and full-plant automation through smart factories. Thus,
the perception of possible applications in the automobile industry appears to be
somewhat shallow.
Table 7 Perception of application of industry 4.0
Firm Answer
1 I am still uncertain about how the industry 4.0 model will turn out, but it should
incorporate modern technology into services and processes.
2 So that each division works faster and more systematically.
3
Due to the recent industry 4.0 policy, the company had discussions and planning
on bringing in new technology and incorporating production-related technology
into the company’s operations.
4
It is used to move or grab parts. However, it is not yet able to be used for
everything since automotive parts are very small and some processes still
require human labor.
5 It applies to a great extent, for example, in assembly, and production.
6 It is used in the main production line to reduce the risk of accidents to the
employees.
7 Machine development to reduce the workforce.
8
Previously, the company would mainly assign staff to check and verify
production. Now, it uses programming instead because problems with smaller
parts can be too difficult for the human eye to detect, compared to using the
programs.
9 Improve the workflow speed by using robots.
62
Table 7 (Continued)
Firm Answer
10 Technology can be used heavily, but it would also impact the staff morale since
it is less human reliant.
11 It helps cut the cost of automotive parts manufacturing and increase the edge in
business competition.
12 Using robotic automation for dangerous work in order to help speed up
production and improve the efficiency of quality control.
13 It can be used to cut long-term production costs with higher efficiency. It also
adds value to the product in terms of design.
14 To improve production efficiency towards speed and precision.
15
Structurally, it is being implemented in the overall supply chain, for example,
order processing from tier 1, 2, and 3 are sent to the manufacturer, and the
communication feedbacks, monitored by the IT the department, are in real time.
This is done with the aim of reducing all unnecessary steps. The company has
multiple industries, but they are controlled by a single control center.
16 The production process looks better while involving automation as an attempt to
increase work efficiency.
17 This increases production efficiency and more importantly, decreases the
employment transaction cost which is currently relatively high.
18 Utilized in parts manufacturing to improve manufacturing efficiency and speed.
19 This helps increase the production efficiency and product quality to better
match and tailor to each consumer’s needs.
20 Use it to monitor machine operations for the entire system of the industry.
Many of the respondents had a very limited view of the use of Industry
4.0, mainly focusing on production and operations automation (Firms 4, 5, 6, 8, 9, 10,
12, 13, 14, 16, 18). For example:
• “Previously, the company would mostly assign the staff to check and
verify production. Now, it uses programming instead because smaller parts can be too
difficult for human eyes to detect problems compared to using the programs.”
(Firm 8)
63
Respondents were also likely to cite the role of Industry 4.0 in improving
production efficiency, speed and quality (Firms 2, 8, 11, 12, 14, 17, 18). For example:
• [Industry 4.0] helps increase production efficiency and product quality
in order to better match and tailor to each consumer’s needs. (Firm 19)
A third common perception of Industry 4.0 in the automotive industry has
an effect on the workforce, which can be both positive and negative. These two
conflicting opinions are shown in comments from different firms:
• “[Industry 4.0] is used in the main production line to reduce the risk of
accident for employees.” (Firm 6)
• “Machine development to reduce the workforce.” (Firm 7)
• “Technology can be used heavily but it also impacts staff morale
because of less human reliance.” (Firm 10)
Only one firm’s representative identified the potential for vertical and
horizontal integration of operations across the firm and suppliers, stating:
• “The company has multiple industries but all are controlled by a single
control center.” (Firm 15)
In summary, the participants’ views of Industry 4.0 in the automotive
industry is limited primarily to automation of operations within the plant, and only has
a limited viewpoint on the expansion of control and integration into the broader
supply chain. Based on the scale used in the quantitative study, this is consistent with
a Digital Novice perspective. It is also more consistent with the Industry 3.0 paradigm
stage (Schwab, 2016). This suggests that the Thai automobile industry may be
operating even further back in terms of industrial development than expected, with a
limited implementation of automation especially at smaller suppliers. While this does
not prevent Industry 4.0 implementation (and may actually ease it, since there would
be fewer legacy systems to integrate), it does mean that there is a limited
understanding of the Industry 4.0 model.
64
Table 8 Summary of perspective on application of industry 4.0 in the automotive industry
No
Perspective of application of
industry 4.0 in the automotive
industry
Firms Total
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 (Firms)
1 Incorporation of modern
technology into operations 2
2 Increasing efficiency, speed,
and quality of production 8
3
Automation of production for
speed, accuracy, efficiency or
safety
11
4
Effects on workforce
(workforce reduction, injury
prevention)
4
5 Cutting costs 3
6 Increasing competitive
advantage 1
7 Centralized control of
operations and supply chain 3
64
65
2.4 History of industry 4.0 implementation
The fourth question firms were asked was about their firms’ history of
Industry 4.0 implementation (table 9). In almost every case, implementation has not
started, although some firms are undergoing feasibility studies and other serious steps
toward implementation. The firms that identified some implementation of Industry
4.0, once again, appear to be referring to standard automation under an Industry 3.0
paradigm, rather than the full Industry 4.0 model. Firm 15’s response suggests that
there has not been any movement toward Industry 4.0 development in the automotive
industry at all, with most firms just moving toward initial automation. Thus, it is
possible that these firms’ experiences are indicative of most firms in Thailand.
Table 9 Firms’ history of industry 4.0 implementation
Firm Answer
1 It has not started yet, but there is a plan to integrate new software and
technology into the industry.
2 A serious case study has just been initiated.
3
The implementation has not started yet since the government has just set the
policy to allow each company to change from old to new implementations.
However, the mentioned implementation has been scheduled.
4 It has been partially implemented, for example, using electric vehicles as a
system to move and transport the auto parts.
5 There is already some. It is using electric vehicles to move and transport parts
through the production line.
6 The company has already been implementing machinery for ten years.
7 There has been some research and planning.
8 Industry 4.0 was introduced in early 2016.
9 It started ten years ago, in accordance with the mother company in Japan.
10 The company stopped the addition of staff recruitment in 2015.
11 It is already begun by being implemented in the production section first
because it is about manufacturing parts.
66
Table 9 (Continued)
Firm Answer
12 It has already begun, starting with the dangerous tasks involving heat and those
in relation to quality control.
13 Currently, the company has set up a task force and continues implementing
advanced technology to accommodate industry 4.0.
14
There has been some IT staff recruitment, mainly sponsored by the public
sector. In the future, we expect to completely house our operation to gain a
competitive advantage in business.
15
There has not been industry 4.0 integration. The company uses workers to
handle the manual operations. However, there is also some mixture between
workers and machines as a move toward semi-automation. In the future, it
hopes to introduce complete automation, but this may not happen in the very
near future.
16
Study more about Big Data, Cloud, and IoT management. Right now, we have
assigned a team of high-level executives to ensure the work is in accordance
with the industry 4.0 policy obtained from the mother company in Japan.
17 Now, the company has brought in machines that support and are compatible
with Big Data, Cloud, and IoT.
18 It has partially started in the parts manufacturing process.
19 The company started some implementations last year.
20 We have begun the automation system with a partial Internet connection to
some manufacturing processes in 2013.
While the oldest implementations date back at least 10 years (Firms 6,
10), and there are a small number of firms that have implemented within the past three
years (Firms 8, 10, 19). Most of the firms are either pre-implementation or have
partially implemented aspects such as plant automation but have not yet implemented
the full Industry 4.0 process. This may be the reason for the emphasis on plant
automation discussed above, since automation of operations is the first stage in the
67
process. There is some evidence that firms are responding to external pressures. For
example:
• “The implementation has not started yet since the government has just
set the policy to allow each company to change from old to new implementations.”
(Firm 3)
• “It started 10 years ago in accordance with our parent company in
Japan.” (Firm 9)
Thus, government and supply chain partner pressures could influence
Industry 4.0 adoption.
68
Table 10 Summary of firm history of industry 4.0 implementation
No Implementation stage Firms Total
(Firms) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 Pre-planning 1
2
Pre-implementation
(Feasibility study and
planning stages)
6
3
Partial implementation of
plant automation or key tasks,
recruitment and plant tooling
7
4 New implementation
(1-3 years) 3
5 Mature implementation
(4+ years) 3
68
69
2.5 Reasons for implementing industry 4.0
Respondents were also asked why their firms were implementing Industry
4.0 practices (table 11). The most common reasons include increasing efficiency and
implementing modern technology, although in some cases the firms have introduced
basic automation (such as electric carts in Firm 5, safety equipment in Firm 6, and
quality control tools in Firms 8 and 9) as a way of reducing strain on workers while
improving the quality of production. In a few cases, the impetus came from outside
sources, like supply chain partners (Firm 16) or growing demands for technology
from customers (Firm 19). This suggests that the possible influences for Industry 4.0
implementation (or as discussed above, what could more properly be termed Industry
3.0 implementation) come from a variety of internal and external sources.
Table 11 Reasons for implementing industry 4.0
Firm Answer
1 Because it is essential to upgrade the industry’s efficiency.
2 So that we can get the laborers ready to operate modern technology.
3 Because it is all about governmental policy encouraging conformation.
4 Because relying on electric vehicles when moving and transporting goods helps to
save manpower and time.
5 Because at first, the staff had to walk a great deal to deliver parts to other areas in
the production processes.
6
For safety purposes in some areas, where it would be too dangerous to let the
employees work. When the robot is damaged, it can be repaired. Therefore, it is
less harmful and reduces the need for staff.
7 To improve the production rate and reduce the cost.
8
Due to the sensitive nature of some tasks, using humans would produce greater
errors (from fatigue or other various obstacles). Therefore, the company introduced
innovations to replace the human workforce (It is currently under trial due to the
high cost).
9 Due to human errors in work, there had to be some supportive measures.
70
Table 11 (Continued)
Firm Answer
10
Current operating staffs are loyal, and have been working with the company for
many years, and are planning to continue working until their retirement. Everyone
has his/her set of special skills. They can be reassigned to other tasks while
introducing machines in the production lines. A new machine can replace as many
as six staff.
11 To be able to survive in the industrial world’s highly competitive environment.
Staying still means falling behind.
12 Because it enables the company to work in a highly dangerous environment with
higher safety standards compared to using humans.
13 To follow the public sector’s industrial policies.
14 Public sector started to support it.
15 Probably in the next five years.
16 Due to the nature of the policy, which was set by the mother company, we have a
clear goal to move forward to become a leader in auto parts manufacturing.
17 It can manufacture the product according to consumers’ specifications with the
same high-level production efficiency and in batches of large quantities.
18 To catch up with the trend and competition.
19
The last year’s implementations happened because we saw a tendency that
consumers started to rely on the technology more and each consumer clearly has
his/her specific needs.
20 To maximize the manufacturing process and control and maintain product quality
to pass criteria of a high standard.
The most common reason for implementing Industry 4.0 was to capture
improved efficiency, for example modernizing the production process, increasing
production rates, or saving time and money (Firms 1, 2, 4, 5, 7, 17, 20). Related to
this, a few firms identified improved quality as the reason for implementation (Firms
8, 9, 20). The second most common reason was to either reduce labor requirements or
better utilize production capacity and skills (Firms 6, 8, 9, 10). A related reason was
71
improved workforce safety (Firms 6, 12). Thus, for most firms, internal pressures to
improve operations were the main reason for implementing Industry 4.0.
A third cluster of reasons for implementation related to the external
environment. This could include, for example, growing support from public sector
bodies and government policy (Firms 3, 13, 14) or requirements from supply value
chain partners like parent companies or customers (Firms 16, 17, 19). A few firms
also identified gaining or maintaining competitive advantage as the reason for
implementation (Firms 11, 18). Given the role of Industry 4.0 in horizontal and
vertical integration of firms and customers, it is surprising that more firms did not cite
this as their primary motivation.
72
Table 12 Summary of reasons for the firm implementing industry 4.0
No Reasons for implementation
Firms Total
(Firms) 1 2 3 4 5 6 7 8 9 1
0
1
1
1
2 13
1
4
1
5
1
6 17
1
8
1
9 20
1
Improved efficiency (production
modernization, increased production
rates, saving time, reducing cost)
6
2 Improved quality
3 Reducing labor demands, better
utilization of labor capacity 4
4 Improved safety 1
5 Government or public sector support and
policy 3
6 Competitive advantage, keeping up with
competition 2
7 Requirements from supply chain partners
(parent company, customers) 3
72
73
2.6 Process of implementing industry 4.0
Respondents were asked about the process of implementing Industry 4.0
(table 13). Most of these responses are vague about what the process would look like,
or focus on the technical details of implementation such as upgrading software (Firm
2). This lack of detailed information could be due to different reasons, like avoiding
giving away trade secrets. However, given the lack of clear understanding of what
Industry 4.0 entails, it is also possible that the informants did not have a clear
understanding of the implementation process or that the firms had not developed a
clear action plan for Industry 4.0 implementation. This would be consistent with the
self-assessment outcomes, which indicated that most firms were still in the digital
novice or vertical integrator stage of development.
Table 13 Implementation process of industry 4.0
Firm Answer
1 It can be expected that incorporating software and technology would be in
servicing, marketing, manufacturing, and logistics
2 If it were to be implemented, it should be done by communication, by upgrading
the existing software to a better version and increasing machine usage.
3
It has not been implemented. Furthermore, there needs to be sufficient planning
beforehand. However, there have been some prior discussions on what direction
the company should adapt according to governmental policy.
4
The company has partially begun using electric vehicles in goods transportation,
and now there is the TPS project to help reduce work procedures. However, it
would almost be impossible to completely rely on robots and technology since the
auto parts industry involves small parts.
5
The decision to do so was intended to reduce employees’ walking activity and to
cut time and improve productivity. The company, therefore, decided to introduce
electric vehicles to deal with transportation as opposed to manpower.
6 Some machines are already in place. There are currently four machines.
7 Begin by planning the application in each section.
8 It begins with the production line to reduce human error.
9 Begin with the interior production line to reduce errors produced by the staff.
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Table 13 (Continued)
Firm Answer
10 Train the staff to operate the machines.
11 It has to start with company policy. Convince the executives to see the importance
of it and to approve budget allocation to invest in technological development.
12 The company has started replacing the human workforce with robots on more
dangerous tasks.
13
The company is sending staff for training offshore in countries that utilize
advanced technology so that they bring back knowledge to exchange with the
organization.
14 We send the employees for additional training. Now there is a specific task force
designed to accommodate this policy.
15 Right now, we need to study more in order to implement full automation.
16
Open the recruitment to young adults who are equipped with technology-related
knowledge. Send those who have the right potential for training and workshops.
Maximize the training consistency and quantity by scheduling generation-by-
generation training.
17
Provide sufficient information and training to the staff. Locate a software company
who is ready and has the right understanding of the company’s industrial tasks. Get
the specific company to help with analysis and design.
18
We have already started the implementation in various industrial divisions, for
instance, the production division has introduced highly efficient robots into their
manufacturing process, and the IT division has replaced old software with new.
19
Our company has upgraded some assembly lines with full automation. In the
future, we will check if a certain production line can still be functional. I have to
admit that the technological cost of equipment and devices requires a lump-sum
investment.
20 We have introduced the automation system with a partial Internet connection on
some production line processes for motorcycle engines and body frame production.
This question should be interpreted carefully, since many of the firms
have not yet begun implementing Industry 4.0. As with previous questions, the most
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common aspect of implementing Industry 4.0 in the firm was automation of
manufacturing and operations (Firms 4, 5, 6, 8, 9, 10, 12, 15, 18, 19, and 20). This
was followed by strategic planning and aspects of implementation such as budgeting,
recruitment and policy planning (Firms 3, 11, 14, 16, and 19) and training and
knowledge transfer within the firm (Firms 10, 13, 14, 16, and 17). Development of
software for non-operational aspects of integration (Firms 1, 7, 17, and 18) or
operational aspects (Firms 2, 17, and 18) were less common. The responses suggest
that the initial stage of operations automation is either incomplete or has not started
within these firms. This is consistent with the previous responses, which indicate a
low level of Industry 4.0 maturity.
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Table 14 Summary of implementation process of industry 4.0
No Aspect of implementing
industry 4.0
Firms Total
(Firms) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 Automation of manufacturing and
operations 11
2
Development and integration of
software and technology into
business activities (non-
production)
3
3
Development and integration or
upgrade of software and
technologies into manufacturing
and operations
3
4
Strategic planning, recruitment,
policy development and
budgeting
5
5 Training and knowledge transfer 5
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2.7 Benefits of implementing industry 4.0
Respondents were asked what the perceived benefits of implementing
Industry 4.0 were for the firm (table 15). As expected given the limited view of
Industry 4.0, most of the responses were focused on issues like operational efficiency
(time and cost), quality improvement, and workforce reduction. One response (Firm
3) stood out because of its emphasis on government policy and obtaining government
support, which suggests a broader, longer term strategic view of Industry 4.0
implementation. However, this was unusual, and most of the benefits identified were
based on implementation of basic automation.
Table 15 Benefits of implementing industry 4.0
Firm Answer
1 It can accurately and precisely help with the job and reduce workforce-related
problems.
2 I think it would make the company better equipped to catch up with other
countries.
3
I think there is because if we follow national policy, there will be incoming
governmental support to enhance the company’s development which would be
totally beneficial.
4 It is useful in terms of workforce, time, and cost.
5 The employment transaction cost is reduced.
6 Reduce the need for staff and improve safety in operations.
7 It reduces the employment transaction cost.
8 It helps reduce the product defect rate to a certain level but not completely. The
staff is now able to do other jobs like document-related.
9 The precision is relatively effective. It can help reduce human errors.
10 The workflow goes faster, it’s more productive, less erroneous, and the result can
be anticipated or calculated in advance.
11 Help the company to survive the technological era and business competition.
12 Quality control (since using a human to do so is highly inefficient) and dangerous
tasks.
13 To make the production faster and more efficient.
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Table 15 (Continued)
Firm Answer
14 The work becomes faster and easier.
15 It can keep the cost of production low, and thus it can compete with low-cost
countries.
16 Reduce business loss and production process problems by integrating Big Data and
using loT to create an all-in-one network connection.
17 Decrease mistakes due to human error and production time.
18 This speeds up the work with fewer mistakes.
19
Applying industry 4.0 can certainly project a long-term result of being able to meet
the needs of the consumer, reduce the chances of a production error, decrease
production time, and track back the data to create a further manufacturing plan
according to what was previously entered into the system.
20 We were able to control the entire manufacturing system with no defect. The
product quality is good, and the system requires less staff to operate.
The most common benefit was increased production quality (Firms 1, 8,
9, 10, 12, 17, 18, 19, 20), followed by reduced workforce problems and labor costs
(Firms 1, 4, 5, 6, 7, 8, 9, 20) and reduced production time (Firms 4, 10, 13, 14, 17, 18,
19, and 20). In contrast, relatively few firms identified factors like improved
efficiency or production capacity (Firms 10, 13, 14, 16, and 19), competitive
advantage (Firms 2, 11, and 15) or increased control and predictability (Firms 10, 19,
and 20). Some representative comments included:
• “Applying Industry 4.0 can [create] a long-term result of being able to
meet custom needs of the consumer, reduce chances of production error, decrease
production time, and track data to create manufacturing plans according to what we
previously entered into the system.” (Firm 19)
• “The workflow goes faster, is more productive, less erroneous, and the
result can be anticipated or calculated in advance.” (Firm 10)
• “I think if we follow national policy, there will be incoming
government supports to enhance the company’s development, which is totally
beneficial.” (Firm 3)
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Table 16 Summary of benefits of implementing industry 4.0
No Benefit of industry 4.0
implementation
Firms Total
(Firms) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 Customization 1
2 Increased competitive advantage 3
3 Increased control and predictability 3
4 Increased government support 1
5 Increased production capacity,
efficiency 5
6 Increased quality 9
7 Increased safety 2
8 Reduced cost 3
9 Reduced production time 7
10 Reduced workforce problems and
labor costs 7
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2.8 Disadvantages of implementing industry 4.0
Firms were also asked about the disadvantages of implementing Industry
4.0 in their firms (table 17). Unsurprisingly, the majority of drawbacks related to
effects on the company’s existing staff morale and performance, but relatively few
firms indicated that high technological demands or cost were important drawbacks.
This could be the result of acknowledged resource limitations (knowledge and
financing) or it could be because of greater concern about the company’s staff.
Table 17 Drawbacks of implementing industry 4.0
Firm Answer
1
The industrial impact should be positive in terms of advancement, speed, and
accuracy. However, there may also be some downsides such as the concerns of
employees who might be affected by employment reduction which may further
cause labor issues.
2 I think it would positively impact the company by making the communication and
operation more systematic and modern.
3 Looking at the governmental support, it would benefit our organization better.
4
It can affect people’s minds. Since the company has integrated new technology into
the workflow, it appears to have more workers than necessary. As a result, the
company launched a voluntary layoff program. When some people resign, the
people remaining may feel somewhat insecure.
5 It allows the staff to be more productive.
6 There might be layoffs or staff being reallocated to other sections.
7
The employees should develop the necessary skills to catch up with the technology
to be able to operate those even more advanced. However, there will consequently
be staff layoffs.
8 It affects the feelings of staff because they may feel less valuable.
9 No negative impacts.
10 There is no significant impact.
11 It is about the vision, funding, and preparing the staff to reduce the tendency of
going against the decision.
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Table 17 (Continued)
Firm Answer
12 Reducing the number of employees and the chances of policy violation by the
employees.
13
Due to the costly expenditure of importing new machinery, the process seems to
move rather slowly. Also, there is a need to monitor the economic situation at the
time of making such a decision.
14 Many sections of the company comprise an older generation of employees who are
reluctant to accept change. It will take time.
15
Industry 4.0 requires technological implementation. However, Thailand is not a
high-tech country yet. We still need to import the tools, which might not yet be
worthwhile considering the fact that the machines will need onsite support or
maintenance.
16 The first phase is about old staff adjustment, but since we have been preparing for
it from time to time, there should be less impact.
17 The technological cost is rather high. Therefore, it requires a certain period of time
to implement.
18 In terms of workforce, when introducing more robots, we might need less staff.
This impacts their mental health and may reduce their work efficiency.
19
The company experienced no negative impact on its employees since the
automation integration was introduced to totally new production lines. However, if
we were to reconsider making a change to the existing production lines, it would
obviously affect them. However, we plan to reassign them to other tasks. Budget-
wise, there should be no problem because we have long-term planning, especially
for this matter.
20 It requires a large investment and the staff are not yet aware of what Industry 4.0 is
about.
By far the most important concerns were about the firm’s workforce.
Most commonly, firms were concerned about the impact of layoffs or workforce
reduction (Firms 4, 5, 6, 12, 18) or other concerns, like staff insecurity and poor
morale, lack of training and knowledge, and change resistance (Firms 1, 4, 7, 8, 11,
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12, 14, 16, 18, and 19). Relatedly, the need for staff to gain new skills was also a
concern (Firm 6, 7, and 8). Some representative concerns included:
• “It rather affects people’s minds. Since the company has integrated new
technology into the workflow, it appears to have more workers than necessary. As a
result, the company launches a voluntary layoff program. When some people resign,
the people remaining may feel somewhat insecure.” (Firm 4)
• “It affects the feelings of the staff because they may feel less valuable.”
(Firm 8)
• “many sections of the company comprise an older generation of
employees who have difficulty accepting change. It will take time.” (Firm 14)
Two firms were primarily concerned with the cost and time of
implementation (Firms 13 and 17), while two firms suggested there would be no
impact (Firms 9 and 10). Thus, by far the most important disadvantage of Industry 4.0
implementation was seen as the negative effects on the firm’s employees and the
potential that these employees could resist the change.
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Table 18 Summary of drawbacks of implementing industry 4.0
No Disadvantage of industry 4.0
implementation
Firm Total
(Firms) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 Development time 2
2
Employee concerns and labor issues
(insecurity, lack of training, poor
morale, change resistance)
10
3 Investment expense 4
4 Lack of tools and technology 1
5 Layoffs 5
6 Need for staff to develop new skills 3
7 No significant impact 2
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2.9 Rating of industry 4.0 implementation
Respondents were asked to rate their firm’s Industry 4.0 implementation
on a scale of 1-10 (table 19), and to give the reasons for their rating. These ratings
cannot be directly compared to the ratings derived from the quantitative research,
since here respondents were free to interpret their own scale and ratings. The
suggested conversion scale is provided above in the qualitative results.
Table 19 Rating of the firm’s industry 4.0 implementation
Firm Answer
1 It is at level 1 since the company is not utilizing state-of-the-art technology but
rather, still relying on manpower.
2 It is at level 8 because the company tends to focus on technology utilization.
3
I think it is at level 4 because, in order to truly understand each other, we must
proceed in the same fashion or format for the entire organization rather than only
having a few executives who know what to do. We are discussing and planning as
a much as possible as a team, to understand the direction of Industry 4.0 and what
we can do to the company strategy.
4
It is at level 4 because some robot usage within the paper system is still required.
Formerly, employees had to monitor every step of the cutting process. Now it
requires fewer people to do the automation.
5
Currently at level 2 because the implementation is currently only partially in place,
but in the future, it is expected to be as high as 7-8. However, it depends on how
much integration the company decides on. The additional Industry 4.0 technique
that I think we should be incorporating is the technology to cut and lathe auto parts.
6 It is around level 5 because there is some machine usage but not extensive due to
the less complex nature of the work process.
7 On level 6 because some research projects have already been conducted.
8 It is at level 3 since it has just started, but it also has already been a while.
9 It is at level 6 since it started a long while ago and utilized computers in most of its
work processes.
10 It is between level 5-6 since it is under development towards advancement.
11 It is at level 5-6. Partially started but not completed yet.
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Table 19 (Continued)
Firm Answer
12 Partially used.
13 Level 7
14 Level 5-6
15 Below level 5
16 Level 6-7
17 Level 6-7.
18 It is at level 7 since the company uses machines and robots, but it might not seem
to be as ready since it still commits many mistakes.
19 Level 8-9
20 It is at level 4 since the implementation is only partial to some of the production
processes.
This question revealed a broad variation in the perception of
implementation standards. Most firms ranked themselves between 5 and 7 (Firms 5, 6,
7, 9, 10, 11, 12 13, 14, 16, 17, and 18), followed by level 1 to 4 (Firms 1, 3, 4, 8, 15,
and 20). Only a few firms rated themselves as performing at higher levels (8 to 10)
(Firms 2 and 19). The responses to this question indicated a wide variety of perceived
ratings. For example, Firm 2 rated itself on level 8, even though when asked where
the firm was in terms of implementation (Section 4.1.2.4) it stated it was in the pre-
planning stages. Another example is:
• “It is level 7, since the company uses machines and robots but it might
not seem to be as ready since it still commits many mistakes.” (Firm 18, rated as
partial implementation previously)
In contrast, Firms 6, 9, and 20, which rated themselves between 4 and 6
on this scale due to incomplete implementation, were the only firms that actually had
a mature implementation of Industry 4.0. It is not certain what this gap implies.
However, it is possible that firms at the beginning of the implementation process do
not always have a strong understanding of the full requirements of Industry 4.0 or
how much work it will be to implement. However, some firms do have a more
realistic rating of their own performance. For example:
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• “It’s on level 1, since the company is not utilizing state of the art
technology, but still relying on manpower.” (Firm 1, rated as pre-implementation
previously)
• “Currently on level 2, because the implementation is still partial. In the
future it is expected to be as high as level 7-8 but it all depends on how much
integration the company decides to incorporate.” (Firm 4, pre-implementation)
Overall, these results suggest that firms may necessarily have a strong
grasp of the scope of implementation required for Industry 4.0. This is consistent with
earlier responses, which indicate that the firms are mainly considering the
implementation as a problem of manufacturing and operations automation, rather than
a full integration of the firm’s operations.
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Table 20 Overall rating of the firm’s industry 4.0 implementation
Industry 4.0
implementation rating
Firm Total
(Firms) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 to 4 6
5 to 7 12
8 to 10 2
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2.10 Recommendations for Other Companies Implementing Industry 4.0
The final question interviewees were asked was about their
recommendations for other companies beginning to implement Industry 4.0 (table 21).
These recommendations showed a high level of concern with the knowledge,
information availability, and cost of implementation of Industry 4.0. These concerns
are consistent with the current state of implementation, with most firms having
serious resource constraints in these areas.
Table 21 Recommendations for other firms implementing industry 4.0
Firm Answer
1 There will be information exchanges in terms of knowledge and experience with
those who are interested.
2 If they have sufficient knowledge and experience, they should focus on being
prepared and getting ready to tackle any problems that might occur.
3
Each company is suggested to firstly, try to understand what Industry 4.0 is.
Secondly, the government should foster the learning and study of each company’s
developmental status and progress. Finally, set the developmental program
according to relevant policies.
4
It helps to reduce employment expenditure. Nowadays, salaries rise on a regular
basis, and once a certain type of welfare is provided for the employees, it can never
be reduced or taken away. Therefore, implementing automation helps to reduce
this particular cost because the technological cost is a one-off payment, and does
not increase over time
5
I would advise considering the appropriate ratio/proportion when incorporating
technology since it also frees up the staff previously needed. The proper ratio, in
my opinion, would be 30-70%
6 It is not yet at the level to be able to give advice to other companies.
7
Keep in mind that industry 4.0 might not be applicable for every company. If one
wants to implement it, one has to carefully study the advantages and disadvantages
that would affect the company.
8 Use the experience gained using Industry 4.0 to advise others.
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Table 21 (Continued)
Firm Answer
9 I still need to make further studies in order to be able to give advice since it only
became available in Thailand this year.
10 Giving advice based on experience and encounters that we face at work.
11 Starting from learning, understanding, and discovering a profitable way to do it for
the company.
12 It should begin with the tasks that may cause danger or may easily create errors.
Look at the cost and profit ratio and see if it is worth the investment.
13
Primarily, I would suggest setting up a team to explore the pros and cons and the
work processes so that it reduces time in trial and error with the actual
implementation.
14 I would suggest having extra technological training.
15 I would suggest planning step by step and seeing if Thai technology can provide
the necessary support.
16
I would firstly, suggest reviewing the policy of the organization to set the strategy
and objectives to conform to this matter. Secondly, providing the vision and
knowledge which are both essential elements to stimulate organizational readiness.
17 I would suggest looking at what the organization lacks in terms of readiness and
get it ready.
18 Give advice on what is already known.
19
A company wishing to apply this principle to their organizational development has
to plan ahead for a certain amount of time because the staff has to be
knowledgeable enough. Technological investment is costly thus without proper
planning a systematic implementation will never happen.
20
A company must study what Industry 4.0 is and understand the production process
for the entire industry to be able to analyze and identify which part of the process it
can be applied to.
Three respondents indicated the firm was too new to the process to give
meaningful advice (Firms 5, 6, and 9). Firm 9 stated that since the technology only
arrived in Thailand this year, it was too early for them to give advice. However, other
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firms had some meaningful advice and recommendations. Some examples of the
advice given include:
• “I would advise firms to consider the appropriate ratio/proportion when
incorporating technology, since it also frees up the staff previously needed.” Firm 4)
• “Keep in mind that Industry 4.0 might not be applicable for every
company. One has to carefully study the advantages and disadvantages that would
affect the company.” (Firm 7)
• “It should begin with the tasks that may cause danger or may easily
create errors. Look at the cost and profit ratio and see if it’s worth the investment.”
(Firm 12)
• “I would first suggest reviewing the policy of the organization to set the
strategy and objectives. Secondly, providing the vision and knowledge which are both
essential elements to stimulate organizational readiness.” (Firm 16)
• “Technological investment is costly, thus without proper planning, a
systematic implementation won’t happen.” (Firm 19)
The most important recommendations included seeking out knowledge
and information from firms experienced in Industry 4.0 implementation (Firms 1, 2, 8,
10 and 18) and making sure that the implementation is planned step-by-step and
integrated into the firm’s broader strategy objectives (Firms 13, 15, 16, and 19). These
recommendations are consistent with the need to understand Industry 4.0 and what it
requires of the industry, and the need to make sure that the firm’s implementation is
appropriate for the firm and consistent with the firm’s strategy and objectives.
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Table 22 Summary of recommendations for other firms implementing industry 4.0
No Recommendation for implementation Firms Total
(Firms) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1
Exchange knowledge and information with
experienced firms based on their implementation
experience.
5
2 Seek out more information to understand what
Industry 4.0 is. 3
3 Consider the appropriate extent of implementation
for the firm. 3
4
Identify areas that would provide a good cost-
benefit ratio for the firm to target initial
implementation.
3
5 Seek out additional technology training. 2
6 Step-by-step planning and integration of Industry
4.0 into the firm’s broader strategy. 4
7 Identify gaps in the organization’s readiness. 1
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2.11 Summary of qualitative findings
Representatives of twenty firms in the automotive industry were
interviewed to understand the current situation of Industry 4.0 in the automotive
industry of Thailand. The firms represented different areas of the industry, although
most were in the automotive parts industry (Section 4.1.2.1). Most of the firms were
in pre-implementation or partial implementation stages of Industry 4.0 (Section
4.1.2.4). For example, Firm 3 stated “The implementation has not started yet since the
government has just set the policy to allow each company to change from old to new
implementations. However, the mentioned implementation has been scheduled.”
However, firms had an inconsistent view of their current stage of implementation,
with many participants in pre-implementation or partial implementation rating
themselves in mid-implementation (Section 4.1.2.9).
For many participants, their understanding of Industry 4.0 was limited to
manufacturing and operations automation, and relatively few firms had a good
understanding of the broader context of Industry 4.0, such as horizontal or vertical
integration (Section 4.1.2.2). This suggests that in terms of the scale used in the
quantitative research, the majority of firms were in the Digital Novice or Vertical
Integrator stage of Industry 4.0 implementation (Section 4.1.1.1). This is supported by
the participants’ understanding of the purpose of Industry 4.0 implementation in the
automotive industry (Section 4.1.2.3). For example, most participants identified
benefits like production automation to improve speed, efficiency, quality or other
factors and increasing the efficiency, speed and productivity of the manufacturing
process. There were few firms that were aware of broader reasons for implementation,
such as vertical or horizontal integration with supply chain partners or customer
benefits. These perceptions of the role of Industry 4.0 were consistent with the
reasons the participants gave for their own firm’s implementation (Section 4.1.2.5).
The most important reason was improved production efficiency, with a secondary
concern of workforce reduction and increased labor efficiency. In terms of the actual
process of implementation (Section 4.1.2.6), most firms were focused on the
automation of production and operations, with secondary concerns for strategic
planning and employee training. For example Firm 1 stated “Because it is essential to
upgrade the industry’s efficiency.”
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Benefits of implementing Industry 4.0 were consistent with the focus on
automation, including increased efficiency and quality (Section 4.1.2.7). The
predominant concern or disadvantage for implementation was the effects on the
workforce, including the potential for layoffs and resulting poor morale and change
resistance (Section 4.1.2.8). The most important recommendation for implementing
firms is to seek out information and advice from firms that have already implemented
the process, and to consider the strategic implementations and requirements of the
firm (Section 4.1.2.10).
3. Quantitative results
3.1 Company information and scoring details
A total of 332 firms participated in the study. All firms indicated that they
were using Industry 4.0 practices at least to some extent. Information including
number of employees (table 23) and annual revenue (table 24) was collected. Most of
the firms were large firms (209 firms, 63%), with the next largest group being
medium firms (102 firms, 30.7%). Only 21 firms (6.3%) are classed as small firms.
The same pattern is observed when considering annual revenue. The largest group had
revenues of BHT5 million or more (204 firms, 61.4%). A further 92 firms (27.7%)
had revenues of BHT500,000 to BHT5 million. Only 36 firms (10.8%) had revenue
under BHT500,000. These findings indicate that at least in Thailand, Industry 4.0
practices are primarily the domain of large firms, with much less participation by
small and medium firms.
Table 23 Firm information: number of employees
Number of employees Frequency Percent
Under 50 (Small) 21 6.3
51 to 200 (Medium) 102 30.7
200+ (Large) 209 63.0
Total 332 100.0
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Table 24 Firm information: annual revenue
Annual revenue Frequency Percent
Under 500,000 baht 36 10.8
500,000 to 5,000,000 baht 92 27.7
5,000,000 baht+ 204 61.4
Total 332 100.0
The firm questionnaire collected information on four areas of Industry 4.0
implementation, including business models, products and services; market and
customer access; value chains and processes; and IT architecture. Each of these areas
is discussed individually below. However, a brief overview of the scoring system is
provided here for interpretation purposes. Data was collected based on a five-point
Likert scale. Means falling into a given range are interpreted as follows:
• 1.00 to 2.00: Digital Novice. The firm is still working on its first digital
implementations, has separate offline and online presence, product focus instead of
customer focus, and fragmented and siloed IT infrastructure and process.
• 2.01 to 3.00: Vertical Integrator. The firm is beginning to develop a
digital product and service portfolio, multichannel communication and distribution,
and integration of data flows and processes. It has homogeneous IT structures and
cross-functional collaboration, but has not yet fully addressed the challenges of
digitization.
• 3.01 to 4.00: Horizontal Collaborator. The firm uses integrated customer
solutions, individual customer processing, integration of data flows and processes
with external customers and partners, and common architecture.
• 4.01 to 5.00: Digital Champion. The firm is able to develop disruptive
business models, has an integrated customer journey, and uses a fully integrated
partner system including service busses and secure data exchange.
Note: Firms included in the interviews were also asked to estimate their
readiness for Industry 4.0 implementation, this time on a scale of 1 (completely
unready) to 10 (completely ready). Although a direct comparison is not possible, it is
recommended that the following comparison should be used:
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Quantitative: Qualitative: Meaning
1.00-2.00 1-2 Completely unready
2.01-3.00 3-5 Beginning stage
3.01-4.00 6-8 Implementing stage
4.01-5.00 9-10. Completely ready
For each of the items discussed below, respondents were asked about the
firm’s actual performance (the company’s current status) and target goals (the
company’s goal state within five years). In each of the sections, the actual and goal
state of different aspects of Industry 4.0 implementation are presented. In general, the
firms are performing at the Vertical Integrator or Horizontal Collaborator level, with
the most advanced performance seen in the Value Chains and Processes focus. Target
goals for the next five years are, on average, to see the firm advanced to Digital
Champion level.
3.2 Business models, products and services
The first focus of Industry 4.0 is Business Models, Products and Services.
This focus addresses the firm’s choices of product/market focus and use of
digitization, data and collaboration. The firms were asked five questions about their
business models, products and services (table 25). The firms’ mean performance in
most categories is moderate, indicating the firms are ranked as either Vertical
Integrator or Horizontal Collaborator. Items where firms are currently ranked as
Vertical Integrators include: contribution of digital features, products and services to
the portfolio; digitization of products in the portfolio; and digitization of the life cycle
phases of products in the portfolio. Firms are currently ranked as Horizontal
Collaborators in three areas, including: customer individualization of products; use
and analysis of data; and collaboration with partners, suppliers and clients in
product/service development. This indicates that while firms have not on average
implemented Industry 4.0 principles in the Business Models, Products and Services
focus, they are in the process of doing this. It also indicates that digitization per se is
less advanced than developing customer focus and collaboration. In terms of five-year
goal performance, on average firms expect to have reached the Digital Champion
stage of maturity for the Business Models, Products and Services focus. The lowest
ranking item in terms of actual or current performance was the level of digitalization
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and automation of the firm’s products (M = 2.85, SD = 1.00). The highest scoring
item was the level of cooperation with partners, suppliers and customers to develop
products and services (M = 3.55, SD = 1.06). This suggests that the firms are on
average more comfortable working with other firms than with the actual
implementation of Industry 4.0 principles. Given the structure of the automobile
industry, with tightly integrated multi-level supply chains that implement
technological and IT changes in concert (Brettel et al., 2014), this may not be very
surprising. Moreover, paired t-test results show that there is a significant difference
between target and actual performances of all items (p-value = 0.00).
97
Table 25 Descriptive statistics: business models, products and services
No Statement
Actual Target Pair t-test
Mean SD Mean value
interpretation Mean SD
Mean value
interpretation
t P-value
1
Overall, what is the level of adoption of digital characteristics or
automation system for your company’s products and services in
order to add more values?
2.86 1.00 Vertical
integrator 4.01 0.95
Digital
champion
-21.93 0.00
2
On average, what is the level of digitalization of your company’s
products (e.g., RFID, sensor, IoT connection, smart product), or
the level of automation which your company’s products enter?
2.94 1.00 Vertical
integrator 4.04 0.99
Digital
champion
-21.85 0.00
3 What is the level of unique characteristics of your company’s
products that meet and satisfy customer demands? 3.32 0.99
Horizontal
collaborator 4.30 0.80
Digital
champion
-19.72 0.00
4
Overall, what is the level of digitalization or automation for your
products? (digitalization and integration of planning, engineering,
manufacturing, service, and recycling)?
2.85 1.00 Vertical
integrator 4.03 0.94
Digital
champion
-23.15 0.00
5 What is the level of importance of data usage and analysis for
your company? 3.40 0.99
Horizontal
collaborator 4.27 0.81
Digital
champion
-18.35 0.00
6 What is the level of cooperation with partners, suppliers and
customers to develop your company’s products and services? 3.55 1.06
Horizontal
collaborator 4.43 0.78
Digital
champion
-17.90 0.00
97
98
3.3 Market and customer access
The second focus of Industry 4.0 is Market and Customer Focus, which
addresses the firm’s use of channels and digital tools for reaching customers and
markets. There were six items in this focus (table 26). Once again, the firm’s mean
actual performance falls primarily into the Vertical Integrator and Horizontal
Collaborator categories, although in this case the responses are somewhat more
advanced than the Business Models, Products and Services focus. The two items that
firms are currently performing at the Vertical Integrator level include integration of
multiple channels for customer interaction and communication and digital enablement
of the sales force. In contrast, aspects including use of multiple integrated sales
channels, use of dynamic and customer-tailored pricing, analysis and use of customer
data, and collaboration with partners for customer access are all at the Horizontal
Collaboration range. In terms of future performance, firms aim to be at the Digital
Champion level for all Market and Customer Focus aspects in five years. Overall,
these results indicate that firms are developing their channel integration, digital
enablement of sales, and customer focus, and have strong goals for improvement. The
lowest ranking individual items in terms of the current performance included the level
of channel integration (M = 2.99, SD = 1.06) and the level of developing and
improving digital and automation systems to improve sales volume (M = 2.99, SD =
1.04). To a certain context, this may not be surprising, given that firms are not
operating through direct consumer sales, and as a result may not be as concerned with
retail or consumer channel integration. The highest scoring item, once again, related
to the level of cooperation with partners to gain access to customers (M = 3.29, SD =
0.97). This once again draws on the industry’s existing structure, with tightly
integrated, collaborative supply chains. When comparing actual and target
performance. It shows a significance difference (p-value=0.00).
99
Table 26 Descriptive statistics: market and customer access
No Statement
Actual Target Pair t-test
Mean SD Mean value
interpretation Mean SD
Mean value
interpretation
t P-value
1 What is the level of adoption of integrated multi-channel
distribution strategy to sell your company’s products? 3.10 0.99
Horizontal
collaborator 4.16 0.84
Digital
champion
-2.19 0.00
2
What is the level of channel integration (e.g., a website, blog,
social media) in order for your company to establish
interactions for distributing news, receiving comments, etc.
with your customers?
2.99 1.06 Vertical
integrator 4.11 0.96
Digital
champion
-19.70 0.00
3
What is the level of developing or improving digital system
or automation system to increase sales volume (mobile
devices, access to related systems, full-scale sales)?
2.99 1.04 Vertical
integrator 4.01 0.99
Digital
champion
-19.71 0.00
4 What is the level of flexibility and satisfying customer
demands of your company’s pricing system? 3.24 0.91
Horizontal
collaborator 4.20 0.78
Digital
champion
-19.46 0.00
5 What is the level of customer data analysis to get insight into
your customers? 3.24 0.95
Horizontal
collaborator 4.26 0.83
Digital
champion
-20.39 0.00
6
What is the level of cooperation with partners to gain access
to customers, and the level of access from customers to your
company’s products?
3.29 0.97 Horizontal
collaborator 4.22 0.81
Digital
champion
-19.52 0.00
99
100
3.4 Value chains and processes
The third focus of Industry 4.0 is Value Chains and Processes (table 27),
which address the digitization of the firm’s production processes and relationships.
There were five items in this area. This is the area that showed the strongest
development, with means for all items falling into the Horizontal Collaborator
category. The strongest performing item was end-to-end IT-enabled planning and
steering through the forecasting, production and warehouse planning, and logistics
processes. This was followed by the degree of digitization of the horizontal value
chain, the degree of digitization of the vertical value chain and real-time view of
production and dynamic reaction capabilities (scoring the same mean), and production
equipment digitization. Unsurprisingly, the mean target goal within five years for all
of these items fell into the Digital Champion category. These figures indicate that the
Value Chains and Processes focus is the area that may have received the most
attention during implementation of Industry 4.0, and is the area that is most
consistently performing among the firms. The findings imply that the firms will have
an easier time in developing more advanced implementations of Industry 4.0 for this
area compared to others. The lowest scoring individual item for current performance
was the level of digitalization and automation systems for company production
systems (M = 3.07, SD = 1.04). This is consistent with the previous two areas, as
there has also been a low level of digitalization and automation of products and sales
channels. The highest scoring item was the level of company planning for the entire
IT system and process change (M = 3.20, SD = 1.01). Ultimately, this is not much
higher than the lowest scoring item, which suggests that this area of implementation is
an area of consistency. Moreover, paired t-test result also indicated a significant
difference between actual and target performance of all items (p-value = 0.00).
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Table 27 Descriptive statistics: value chains and processes
No Statement
Actual Target Paired t-test
Mean SD Mean value
interpretation Mean SD
Mean value
interpretation
t P-value
1
What is the level of your company’s making use of Artificial Intelligence
(AI) to process data for research and development as well as advanced
instruments?
3.12 0.99 Horizontal
collaborator 4.17 0.85 Digital champion -19.67 0.00
2
What is the level of on-demand manufacturing and capability to flexibly
satisfy the change, or the level of adoption of Flexible Manufacturing
Systems or allocating jobs or reducing waste?
3.12 0.87 Horizontal
collaborator 4.14 0.80 Digital champion -20.80 0.00
3
What is the level of your company’s planning with an entire IT system,
and the level of process change, ranging from sales forecast during
production to warehouse and logistics planning?
3.20 1.01 Horizontal
collaborator 4.23 0.89 Digital champion -20.52 0.00
4
What is the level of digitalization or automation system of your
company’s production system which links together and is controlled by
computer software?
3.07 1.04 Horizontal
collaborator 4.22 0.82 Digital champion -21.41 0.00
5
What is the level of using IT systems to manage your company’s vertical
value chain from receiving orders from customers, working with
suppliers, to production and logistics, and is the system flexible enough to
satisfy specific requirements and is it capable of real-time, active
connection with a production system to manage equipment and related
parties?
3.13 0.99 Horizontal
collaborator 4.17 0.86 Digital champion -19.15 0.00
101
102
3.5 IT architecture
The final Industry 4.0 focus is IT Architecture, addressing the extent to
which the firm’s IT architecture is integrated with its business processes. There were
six items that addressed this focus (table 28). As with most other focuses, firms’ mean
actual performance generally falls between Vertical Integrator and Horizontal
Collaborator performance levels. Items where the firms are achieving the Vertical
Integrator performance level include IT architecture addressing requirements of
digitization and 4.0 and the use of manufacturing execution systems for
manufacturing control. Items where the firms are performing at the Horizontal
Collaborator level on average include: maturity of IT and data architecture for
manufacturing, product and client data collection, aggregation and analysis; using
new technologies in business operations; fulfilling IT-related business requirements
effectively; and IT integration with customers, suppliers, and fulfillment partners.
Unsurprisingly given the firm’s performance goals for other Industry 4.0 focuses, the
firms’ mean target goal for five years fall entirely into the Digital Champion
performance level. The two lowest scoring items for current performance on this
scale relates to the level of overall requirements support (M = 3.00, SD = 0.98) and
the level of adoption of IT systems for manufacturing and production processes (M =
3.00, SD = 1.04). These low scores are indicative of IT system implementation issues,
which are identified in the qualitative interviews as a problem for the firms. The
highest scoring item in this scale was the level of importance of new technologies for
the firm (M = 3.21, SD = 1.08). Thus, firms are recognizing the importance of new
technologies, even if they do not yet have the resources to implement them. When
comparing actual and target performance. It shows a significance difference of all
items (p-value=0.00). Thus, firms are recognizing the importance of new
technologies, even if they do not yet have the resources to implement them.
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Table 28 Descriptive statistics: IT architecture
No Statement
Actual Target Paired t-test
Mean SD Mean value
interpretation Mean SD
Mean value
interpretation
t p-value
1
What is the level of overall requirements support by your IT
architecture for digitalization and automation systems as part
of Industry 4.0?
3.00 0.98 Vertical
integrator 4.17 0.80
Digital
champion
-23.36 0.00
2
What is the level of adoption of IT systems for manufacturing
or equivalent processes to manage production processes, or the
level of product design capable of assembly by using multi-
purpose, centralized controllable industry robots?
3.00 1.04 Vertical
integrator 4.15 0.91
Digital
champion
-21.90 0.00
3
What is the level of readiness of your IT architecture and data
to rapidly gather data, analyze, process and present clear
information leading to real-time decision-making about
production, products and customers?
3.02 1.01 Horizontal
collaborator 4.14 0.88
Digital
champion
-21.66 0.00
4
What is the level of importance of new technologies, such as a
social media, mobile devices, cloud computing and analysis, or
cloud storage, for running your business?
3.21 1.08 Horizontal
collaborator 4.20 0.88
Digital
champion
-17.49 0.00
103
104
Table 28 (Continued)
No Statement
Actual Target Paired t-test
Mean SD Mean value
interpretation Mean SD
Mean value
interpretation
t p-value
5
What is the speed of your IT related departments’ response to
business requirements under specified time, budget and
quality? For example, using software to process data real-time
for transport route planning, tracking fleet by using GPS to
know the status while transporting and to adjust route regarding
to costs and time.
3.10 1.03 Horizontal
collaborator 4.18 0.86
Digital
champion
-22.37 0.00
6
What is the level of integrating IT systems or transmitting data
through a computer network to understand overall process
status of the factory, and updating every processing step to the
center, which then distributes data to customers, suppliers and
partners?
3.10 0.95 Horizontal
collaborator 4.23 0.79
Digital
champion
-22.32 0.00
104
105
CHAPTER 5
DISCUSSION AND IMPLICATION
Discussion
As the results above showed, the Thai automotive industry is not presently at
a high state of application of the principles of Industry 4.0. In this section, these
results are discussed and examined together with the literature review, in order to
reveal how consistent the present state of implementation and views of participants
are with the theoretical principle. The discussion follows the research questions.
1. Basic principles and application of industry 4.0 (Objective 1)
The basic principles of Industry 4.0 in the views of the participants in the
qualitative study focused on manufacturing automation for improved efficiency and
quality control, with only a few participants mentioning aspects like big data, IoT or
integration across the value chain. This is a very shallow understanding of Industry
4.0 as compared to the theoretical basis. For example, no participants in the interviews
identified the idea of cyber-physical systems, or connections between the
manufacturing equipment and the Internet to enable communications and
interconnected interoperativity (Kagermann et al., 2011), or the ability of the
machines themselves to learn and improve their function (Lee et al., 2014). Instead,
the perception of Industry 4.0 expressed in the interviews was more consistent with
systems thinking, which underlies what Schwab (2016) identifies as Industry 3.0. This
stage of manufacturing production is related to aspects such as automation and
centralized control of the factory floor (Slack & Lewis, 2011). However, it does not
incorporate concepts such as use of IoT-connected devices for monitoring and control
(Xia et al., 2012; Gubbi et al., 2013), collection of big data and deployment of
analytics systems to improve productivity (Lee et al., 2014), or the development of a
smart factory (Radziwon et al., 2014), which incorporates CPS to monitor and
manage physical processes (Marr, 2016). In part, these gaps could be because many of
the firms are only at the beginning of their Industry 4.0 implementation and do not yet
have the basic knowledge and skills for implementation, which is a known problem
for Thailand’s automotive industry generally. It could also be because the industry has
106
only recently begun to receive attention from global partners to improve efficiency
and productivity (Thailand Automotive Institute, Ministry of Industry, 2012).
Therefore, it may not have the extent of automation required in place to take Industry
4.0 to the next level of the smart factory.
2. Current state of implementation of industry 4.0 (Objective 2)
The qualitative and quantitative studies revealed an interesting split in
readiness for Industry 4.0. The quantitative study, which mainly consisted of large
firms, suggested that firms were mainly in the Vertical Integrator or Horizontal
Collaborator stage of implementation, and aimed to be at the Digital Champion stage
in five years, according to PWC’s self-evaluation framework (PWC, 2016). However,
the qualitative study, which included firms of all sizes, showed that firms had a much
more mixed position. While some firms had been implementing Industry 4.0
principles for up to ten years (commonly under the guidance of parent companies or
foreign partners), others were in the pre-implementation stage or had only just begun
to introduce manufacturing automation into their factories. This suggests that, there is
a limited implementation of Industry 4.0 principles at this time, which is consistent
with the limited understanding of the principles discussed above. It is unclear from the
literature review whether this situation is common or not, as most of the literature on
Industry 4.0 is either theoretical in nature (Lee et al., 2014; Schwab, 2016) or is based
on single-case studies. Lee et al. (2014) and Schwab (2016) very much present
Industry 4.0 as a future manufacturing condition, rather than examining it as a concept
that is broadly in place in the manufacturing industry. Most of the other studies are
similar. For example, while Rüßmann et al. (2015) anticipate savings across the
industry, they did not actually survey the automotive industry to understand the
current implementation state. The emphasis on manufacturing automation and
increased productivity is consistent with the broader goals of the Thai automotive
industry, which has recently undertaken a drive to improve productivity (ASCCI,
2015). However, while Techakanont (2011) identifies a need to link firms in the
value chain, this was not reflected strongly in the results either, potentially because
the required communications infrastructure is not yet present. Overall, the current
state of implementation of Industry 4.0 in Thailand’s automotive industry is weak,
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and while some large firms may have a full implementation, many smaller firms are
still working on the required basic automation and communication principles.
3. Positive and negative impacts of implementation (Objective 3)
Information about the positive and negative impacts of implementation came
from the qualitative interviews only. The most important benefits identified by the
participants were increased production quality, reduction in workforce problems and
labor costs, reduced production time, improved production capacity and efficiency,
and potential benefits to competitive advantage. These benefits are broadly consistent
with the benefits identified in the literature review. For example, Lee et al. (2014)
stated that smart factories could not only detect flaws and potential problems in
production, but could also solve the problems through a combination of sensor data,
analytics and predictive capabilities. Hodge (2011) noted that Honeywell did achieve
improved efficiency. The use of automation to handle tasks that are not safe or too
challenging for human operators is also acknowledged (Marr, 2016). However, there
were some advantages that were not identified. For example, few firms identified
vertical and horizontal interoperability and integration (Hermann et al., 2016), which
would allow the company to respond to changes in supply and demand effectively and
flexibly (Radziwon et al., 2014; Wang et al., 2016). Firms also did not identify the
real-time capabilities provided by Industry 4.0 implementations (Hermann et al.,
2016; Tubbs, 2015). This may be related to the low level of Industry 4.0 maturity,
since most firms were still in the stage of initial automation of the factory floor and
had not begun to consider external connections. Overall, most of the benefits
identified could be achieved with a standard Industry 3.0 implementation of
automated assembly lines (Schwab, 2016), and do not represent true benefits
specifically of Industry 4.0 at all.
Disadvantages of Industry 4.0 implementation were mainly concerned with
human factors, including the impact of layoffs on the workforce, staff insecurity, poor
morale and change resistance, and the need for staff to acquire new skills. Only two
firms identified the investment costs and lack of human resources associated with
implementation as a major concern. The human impact of Industry 4.0 is only
superficially considered in the theoretical discussion of the practice, for example in
observations that humans would not be required to repair, adjust or reconfigure a
108
network of smart objects (Dai et al., 2012; Vogel-Heuser et al., 2016; Wang et al.,
2016). Concerns about cost were acknowledged in the literature, with an emphasis on
cost-effective integration (Baheti & Gill, 2011). Thus, it seems that the concerns of
the Thai automotive firms in regard to human resources are either out of step with the
concerns of global operators or have simply been forgotten in the rush to improve
technological superiority. This should be a concern for future research, since it may
have cultural or philosophical foundations that will be important for future
understanding of the workplace.
4. Comparison of implementation in Thailand to best practice (Objective 4)
The qualitative study provided limited insight into implementation of
Industry 4.0 in the firms, mainly because most of the firms were either in the pre-
implementation stage (still studying feasibility or making implementation plans) or a
partial implementation stage (mainly concerned with deployment of standalone
automated machinery for specific tasks such as quality control or tasks dangerous to
human operators). This is not generally consistent with the implementation of
Industry 4.0 in other situations. Dai et al. (2012), who studied implementation at an
SME in China, provide the most accurate comparison for the firms in this study. The
factory studied by Dai et al. (2012) was an engine valve manufacturer who deployed
RFID technology, integrating this tool into the firm’s manufacturing and ERP systems
in order to reduce the need for a human operator and the decision-making time for
production. RFID technology, in comparison to many other forms of Industry 4.0
cyber-physical systems, is a relatively lightweight technology that can be deployed
easily and relatively cheaply into an existing manufacturing system (Dai et al., 2012),
making it ideal for the SME with limited resources. Furthermore, Dai et al. (2012)
showed that the firm gained many of the desirable benefits of Industry 4.0
implementation, including increased productivity, efficiency and product quality
improvement. Thus, implementation of RFID systems could be highly beneficial for
the firms in the study. However, none of the firms that were interviewed identified
RFID as a priority. This outcome suggests that firms are not implementing Industry
4.0 according to best practices or according to what would be beneficial to their own
needs, which could be related to a lack of understanding about the concept or its
109
principles. This could be a problem for future implementation, particularly for smaller
firms with limited knowledge and financial resources.
5. Manufacturer’s needs for implementation (Objective 5)
The qualitative interviews identified some manufacturers’ needs for
information. The most prevalent recommendation was that firms should seek out
knowledge and information from other firms that have experience in Industry 4.0
implementation processes. Training and knowledge transfer were also identified as
critical needs for implementation. Second, it was recommended that firms should
assess their implementation needs and consider to what extent Industry 4.0 was right
for them. The need for these recommendations was evident in the overall review of
implementation, which showed that firms had a limited grasp on the type of
technologies they could implement and how they should be connected internally as
well as to vertical and horizontal value chain partners. Thus, these are reliable
suggestions for meeting a manufacturer’s implementation needs.
The need for implementation knowledge could be a major barrier for Thai
automotive firms, considering the relative scarcity of implementation and the known
issues with appropriate human resources availability. For example, it is known that
the automotive industry has a shortage of people with knowledge and skills related to
process automation and analytic systems, which has negatively affected
implementation (APPM, 2016). This problem has been exacerbated by a lack of
formal support for Industry 4.0 by the government (APPM, 2016). This gap could be
filled through collaboration with global partners. For example, it is known that
German automotive firms have deployed Industry 4.0 principles in collaboration with
suppliers in some countries such as China (Kinkel et al., 2015). Thus, knowledge
transfer from international supply chain partners is one viable approach to improving
Industry 4.0 implementation in China. Firms should therefore seek out their supply
chain partners and encourage knowledge sharing for implementation.
Firm implementations could be improved by using specialist consultants in
Industry 4.0 strategy and technical implementation, as suggested in the best practices
of the literature review (Erol et al., 2016; Slama et al., 2015; Sun et al., 2015). Expert
consultants can provide not just technical implementation assistance, but also support
the firm in its development of strategies to make the best use of Industry 4.0
110
principles and practices. The use of specialist consultants could help firms overcome
some of the problems that were identified in the interviews, such as lack of human
resources and technical capabilities for implementation. Specialist consultants could
help firms implement standards and protocols to ensure interoperability and modular
operation of its selected systems, which is a further best practice that is required for
effective broader implementation (Lu et al., 2015; Weyer et al., 2015). In particular,
specialist consultants will know which standards would be appropriate for
implementation and be able to advise the firm on how these standards could be
implemented. Specialist consultants would also be able to help the firm with security
threat models for their new systems, the third best practice identified (Dacier et al.,
2014; Kargl et al., 2014; Kumar et al., 2016). As Dacier et al. (2014), the security
threat model of Internet-connected industrial control systems of the type of that
Industry 4.0 is built on is a new model, and there are multiple dimensions of physical
and cyber security that need to be taken into account. The interviews strongly
suggested that firms do not have sufficient technical expertise to implement systems
security or design effectively, which is a major challenge for the firms in this study.
As noted in the literature review, best practices for Industry 4.0 implementation are
underdeveloped and still a matter for further research (Vogel-Heuser & Hess, 2016).
However, even the best practices that have been identified would be helpful in
improving the firms’ ability to implement Industry 4.0 effectively.
Conclusion
This research studied the current state of implementation of Industry 4.0
principles in the automotive industry in Thailand. This research was timely because,
although Industry 4.0 has only recently been articulated as a holistic theory, the
automotive industry is one of the best positioned for its implementation due to
existing high levels of automation and manufacturing-information systems integration
(Gruber, 2014). The German automotive industry, where the idea began, has also been
promoting Industry 4.0 in its supplier relationships (Kinkel et al., 2015). Thus, even
though there are some significant barriers to implementation in Thailand, like a lack
of capital resources and appropriate human resources (APPM, 2016), it was
111
worthwhile to consider the extent to which Industry 4.0 had been implemented
already and what the future may hold for the industry.
The research began with a literature review, which helped to establish a
conceptual framework and to identify the basic principles of Industry 4.0 and its use
in the automobile industry (Objective 1). This review showed that Industry 4.0 is built
on the concept of cyber-physical systems, in which Internet of Things (IoT) enabled
devices and big data are used to implement smart factories of interconnected devices,
systems, and even materials. The core concepts of interoperability and integration,
virtualization, information transparency, real-time capabilities, modularity, and
decentralization and technical assistance form the basis for these systems. The
literature review showed that best practices for Industry 4.0 are in their infancy,
although some best practices, including the use of standards, security practices, and
use of specialist consultants could be identified.
The next step of primary research was to identify the state of Industry 4.0
implementation in the Thai automobile industry (Objectives 2 through 5). The
research was designed as a mixed methods study. The research incorporated a self-
assessment survey of Thai firms (n = 332), which was designed to assess the degree
of Industry 4.0 readiness in the industry. It also included a series of interviews with
top managers at automotive firms (n = 20), which was designed to assess knowledge
and implementation aspects of Industry 4.0.
The self-assessment instrument was based on PWC’s firm self-assessment
instrument, and included aspects of Business Model/ Products/ Service Plan, Market
and Customer Accessibility, Supply Chain and Manufacturing Process, and IT
Architecture (PWC, 2016). The instrument was designed using a rating system, where
firms assessed where they were currently and where they aimed to be within five
years. On average, firms were in an intermediate stage of Industry 4.0 readiness,
acting as Vertical Integrators or Horizontal Collaborators. At this stage of
implementation, the firms have typically begun to implement automation and
production systems, use online sales channels, and so on, but have not fully integrated
IoT capabilities, federated systems with suppliers, or implemented a full smart
factory. While the firms do aim to be at the Digital Champion stage on average in all
four categories within five years, the results of the interview do raise questions about
112
whether this is possible. The interviews showed that many firms do not have a very
strong grasp on the principles of Industry 4.0, although some firms did have a very
clear idea about it. The participants in the interviews had a weak understanding of
what Industry 4.0 entailed, with only a few firms identifying aspects such as value
chain integration, big data and IoT in their discussion of the principles. Furthermore,
many firms had not even begun implementation, or had only partial implementation
(for example automation of jobs where there were safety issues). This gap could be
caused by firm size; the majority of firms in the quantitative survey were large firms,
while there were a mixture of firm sizes represented in the qualitative study. Overall,
firms were at a very low level of implementation compared to best practice, although
firms rated their performance wildly. This could be because of a poor understanding
of Industry 4.0. At present, simple automation without the cloud-based connectivity,
RFID and other elements of a full Industry 4.0 implementation is the norm, and many
participants viewed Industry 4.0 as only factory automation. Operationally, firms also
seemed to be focused only on production automation, and were at the beginning
stages of introducing automated quality control, production, and operations assistance
such as electric vehicles to their assembly lines. The respondents, who mostly though
not entirely came from small-scale Tier 3 and Tier 4 suppliers of individual
components, did not report strong connections to their customer companies or other
facilitating conditions that would help further implementation. The main benefits
identified by the firms were increased production efficiency, quality and speed,
reduced cost, and increased employee safety. While participants generally viewed
Industry 4.0 as positive, the most commonly mentioned benefit was reduced labor
costs due to automation, rather than more efficient production or other benefits. The
major drawbacks were also personnel related, including the need for layoffs, change
resistance and potential poor morale. It was also viewed as very expensive. While
participants did generally agree that Industry 4.0 would be welcomed, it was not clear
that most participants fully understood the implications of the implementation. Given
the gap in implementation knowledge, the most important thing that firms need to do
is to study the principles of Industry 4.0 and understand what the concept means and
how it applies to their current operational systems. This may require firms to hire
external experts and consultants, especially to provide the required IT knowledge and
113
support for implementation. The firms also need to consider the investment cost,
especially in factories that have limited automation to date. Perhaps most importantly,
firms need to consider a broader scope of integration, moving away from
manufacturing automation and looking toward developing a connected smart factory
with integration into horizontal and vertical value chain partners. When firms do
move toward Industry 4.0 implementation, the use of specialist consultants could
alleviate problems of technical knowledge and ensure that the implementation is
effective.
In conclusion, Thailand’s automobile industry is at the beginning stages of
Industry 4.0 implementation. Although standalone mechanization is commonplace
(but not ubiquitous) in the industry, many firms are not yet ready to move beyond this
level of automation to create a full cyber-physical system. Barriers such as firm size
and resources, which limit the available technical expertise and financial capital
required to implement Industry 4.0, are likely to be difficult to overcome. If
Thailand’s automobile industry is to implement Industry 4.0 fully, it will require
assistance from global supply chain partners who have the resources needed for
implementation.
Knowledge contribution
This study has generated novel knowledge on the application of Industry 4.0
across an entire industry (the Thai automobile industry). Most previous studies of
Industry 4.0 have been either theoretical in nature (e.g. Baheti & Gill, 2011; Rüßmann
et al., 2015, and others) or based on case studies of implementation at companies in
China and Germany (e.g. Dai et al., 2012; Hermann et al., 2016; Herterich et al.,
2015; Hodge, 2011; Lee et al., 2014; Schmidt, 2015; Pfeiffer, 2016). These previous
theoretical explorations and studies have all been valuable because they have set out
the principles of Industry 4.0 on a theoretical level and have explored how they are
implemented in individual firms. However, there has not to date been much
examination of how the principles of Industry 4.0 could spread and mature across an
entire industry. This research contributes by studying Thailand’s automobile industry,
which while it is healthy has not received the attention from global automobile brands
that China has. This means that the implementation conditions revealed here are
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probably more representative of the general stage of the automobile industry’s general
supply chain than case studies in Chinese firms.
This research is exploratory research and focuses on an early stage of
implementation and maturity of Industry 4.0, which as the research showed is not
likely to last very long. In fact, the firms in the quantitative survey expect that they
will have reached full implementation of Industry 4.0 within the next five years.
While these results are more representative of large firms than of small ones, even the
small firms interviewed in the qualitative research showed increasing changes toward
connected factories and use of integrated automation. Thus, the conditions reviewed
here are not likely to last very long. Since the automotive industry is likely to be one
of the first to implement Industry 4.0 in Thailand, this research offers a useful
perspective on the beginning stages of implementation, which can then be tested by
examining other industries. This research does provide some possible insight into
Industry 4.0 implementation and the challenges of resource inequality between large
and small firms, which could if explored further lead to a more robust resource-based
theory of Industry 4.0 implementation. At this stage, however, these findings are
preliminary. The results do not yet provide enough information to develop a new
theory or model of Industry 4.0. Additional research is required to develop an
understanding of implementation within supply chains, in lower tier automotive
suppliers and specialist firms, and in other areas before a robust theory of Industry 4.0
implementation can be developed.
Research implications and contributions
One of the biggest implications of the research is that the concept of
Industry 4.0 is only partially implemented and poorly understood in Thailand’s
automotive industry. The theoretical concept of Industry 4.0 spans a broad area of
concern, including production automation and integrated communications, use of
Internet of Things and big data, and integration across the firm’s entire value chain
from suppliers to customers, extending both horizontally and vertically. This implies
connections and integration between the firm and its consumers, producers, and
technology partners, incorporating a range of data to improve the firm’s operations
from new product development to marketing and logistics. However, the firms
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involved in the qualitative study were focused on automation of operations, especially
manufacturing (although it also included logistics automation to a lesser extent). Very
few of the firms interviewed addressed issues of IoT and big data, communications, or
even horizontal and vertical relationships. While manufacturing automation is likely
to improve the efficiency and productivity of the firms as indicated by the firms, the
potential benefits of Industry 4.0 are not well understood and do not appear to be an
important part of the consideration. For practice, this suggests that firms may not be
recognizing or targeting the potential benefits of Industry 4.0. However, for Industry
4.0 theorists, there is a stronger difficulty, which is that the idea has not gained full
traction within the industry. This suggests that much more communication about the
concept is required.
A second implication of the research is that there may be cultural and/or
economic barriers, as well as firm resource barriers, to implementation of Industry 4.0
principles. This research took place in an emerging market that, although it has one of
the largest automobile industries in the world, still has issues with expanding capital
and infrastructure. However, it also has a distinctly different culture than Germany,
where the Industry 4.0 model was established. Most of the firms in the quantitative
study were large firms, with a much smaller group of medium firms and very few
small firms participating. This is unsurprising given the investment capital and human
resources required to implement Industry 4.0. However, it could also be problematic
when it comes to implementing Industry 4.0 across the value chain, particularly for
industries like the automobile industry where tier 3 and 4 suppliers are likely to be
small firms. The qualitative interviews provided some information about why mainly
large firms may be implementing Industry 4.0 principles. For example, firms cited
lack of human resources (especially technological knowledge) and the need for
investment funds as problems of implementation. There was also a very strong
concern for the workforce impact of the implementation. While automation was seen
as a potential way to improve safety, it was also seen as a potential challenge to the
workforce’s security and mental health and a cause for layoffs. Although this study
did not address this aspect specifically, the fact that workforce effects are not strongly
considered in the literature on Industry 4.0 could indicate a cultural difference in
terms of consideration of the workforce and its effects. For example, it is possible that
116
cultural differences between Thailand and Germany or the United States influence the
extent of concern for the workforce and possible workforce displacement, which were
central to many of the interviewee’s responses. These barriers and other findings
could also be economic in origin, related to the specific economic limitations and
structures of an automobile industry in a developing country. For example, the lack of
human resources for complex systems implementation as required in Industry 4.0
could be related to a general labor shortage in technological areas, which can be
commonly found in developing countries. Of course, these types of limitations could
be found in tier 3 and 4 suppliers in any country, since these firms tend to be smaller
than tier 1 and 2 firms and are therefore more resource-constrained. The lack of
human resources could be somewhat alleviated by using best practices like utilizing
consultant expertise, but this would still be expensive for the firm. There has been
little research into Industry 4.0 implementation in small firms to date, making it
difficult to determine whether this is a problem characteristic of developing country
firms, small firms, or both. However, since resource inequality between large and
small firms could impede Industry 4.0 implementation through global supply chains,
this is a question worth considering for both practice and research.
This research also raises a theoretical implication about the Industry 4.0
model and its scope of application and potentially even applicability. Like many new
models of industrial action and production, Industry 4.0 is presented optimistically as
a model that can be deployed across the value chain of the manufacturing industry.
However, the point at which this may occur may be a long time in the future for firms
in the automotive industry, especially third and fourth tier suppliers. As this research
has shown, many of these suppliers are only now entering the stage of automating
their production equipment, and development of a full cyber-physical system is likely
to be far in the future. The limited amount of research on the Industry 4.0 model also
suggests that Tier 1 and Tier 2 suppliers have not yet acted to transfer the technology
and knowledge required for Industry 4.0 implementation along their supply chains.
This research demonstrates that at present, the idea of Industry 4.0 is mainly only
fully applicable in the large, technologically advanced manufacturers. Although
lower-level suppliers are aware of the concept (in some cases), the amount of
financial capital and technical knowledge required to implement Industry 4.0 is likely
117
to continue to be out of reach of smaller firms for some time. This raises several
questions. The first question is to what extent Tier 1 firms such as BMW, where the
concept originated, are going to transfer knowledge and resources down their supply
chain to enable implementation (if at all). The second question is to what extent an
Industry 4.0 system can be said to be in operation in one firm if the firms it interacts
with are not using the model. This is not simply a theoretical question regarding
networks of systems, but a real practical concern given the automobile industry’s
reliance on tightly integrated supply chain operations. These questions cannot be
answered given the current state of research, but should be a serious concern for
operators within the industry.
Research limitations
There are a number of limitations in this study.
1. The research was an exploratory study of a new manufacturing and
operations concept and its implementation in a developing country. Thus, there is
limited theoretical strength to the concept of Industry 4.0, for example in
understanding the enablers and barriers to implementation of Industry 4.0 principles.
This lack of theoretical strength was even reflected in the findings, where the firm’s
representatives mainly were considering industrial automation as the central concept
rather than connected communications and integrated operations. This suggests that
the automotive industry, at least in Thailand, is not highly aware of Industry 4.0 and
the admittedly ambiguous boundary between Industry 3.0 and Industry 4.0. This is not
surprising given the relative novelty of the model, but it does pose a problem for
implementation of the model in actual practice.
2. The study will have limited application geographically and temporally.
Different market conditions and industry structures in other countries could result in
different implementation stages and factors; for example, it is likely that the German
automotive industry, where the concept of Industry 4.0 began, has far more
integration and maturity of application. At the time, it is not even clear that the Thai
automotive industry is operating at Industry 3.0 levels, given that many firms were
only beginning to explore more than rudimentary automation. However, it is likely
118
that Thailand’s automotive industry will have increasingly mature Industry 4.0
implementations over time, so these results will not be reliable indefinitely.
3. Although it is likely that other industries have also implemented similar
concepts, the results from this study only apply to the automotive industry.
4. It is not clear whether the Industry 4.0 implementation stage of the firms
in this study is due to the firm’s resources, the structure of the global automobile
industry, or cultural factors specific to Thailand. In part, this is due to a lack of deep
research into Industry 4.0 implementation. For example, there has been little research
into Industry 4.0 implementation in tier 3 and 4 automobile industry suppliers or other
small firms, which would help differentiate economic effects from cultural effects.
5. There is also a lack of development in other areas, such as best practices,
which make it difficult to identify how firms could implement Industry 4.0
effectively. These limitations mean that the recommendations that can be made in this
study for firm implementation are highly limited, and the research must be positioned
as one of what will hopefully be many exploratory studies as the industry 4.0 model
matures.
Recommendations for future research
The concept of Industry 4.0 is very new, as it has only been proposed and
developed within the past few years. This means that there is little information about
how it is being implemented in firms, outside of the handful of large and cash-rich
high-technology firms it is based on. While this study has provided an exploratory
study on how Industry 4.0 is implemented in a given industry (the Thai manufacturing
industry), the low level of adoption means that it is difficult to determine the effects of
factors like government policies, supply chain partners, and underlying
communications and technology infrastructure and human resources in
implementation. Thus, this is an opportunity for further research. The
recommendation for future includes;
1. Conduct exploratory and descriptive research to examine Industry 4.0
implementation in other contexts and to conduct theory development regarding how
and why firms adopt Industry 4.0. In particular, areas of concern include the cultural,
economic, and industrial context of Industry 4.0 and consideration of the resource
119
implications of its adoption. Such research could include, for example, consideration
of the resource inequalities of firms at different levels of the supply chain or in
different industries in Industry 4.0 adoption. It could also include the impact of
culture on Industry 4.0 implementation. For example, do differences in concern for
employees or expectations of lifetime employment influence willingness to
implement Industry 4.0?
2. There are also significant gaps in the practical knowledge regarding
Industry 4.0 and its implementation. Currently, issues such as standards and security
practices are at the fore, as a general reflection of Industry 4.0’s extension of big data
and IoT paradigms. However, as Industry 4.0 becomes more widespread, more
concern for problems like integration of legacy systems and cyber-physical security
threat models will need to be applied in the literature. At this stage, more general and
exploratory research is needed to theorize and develop the concept.
120
REFERENCES
Anderson, C. (2010). Presenting and evaluating qualitative research. American
Journal of Pharmaceutical Education, 74(8). Retrieved from
http://www.ncbi.nlm.nih.gov/pmc/articles/ PMC2987281
Asia Pacific Plant Management Magazine [APPM]. (2016). Industry 4.0: A guideline
towards the future for the Thai industrial sector. Retrieved from
http://ttim.co.th/news/industry-4-0
Automotive Supply Chain Competitiveness Initiative [ASCCI]. (2015). Lessons for
South Africa from the Thai automotive industry’s success. Retrieved from
http://ascci.co.za/wp-content/uploads/2016/03/ ASCCI_
Quarterly_October_December_2015.pdf
Baheti, R., & Gill, H. (2011). Cyber-physical systems. The Impact of Control
Technology, 12, 161-166.
Bauer, M., Jendoubi, L., & Siemoneit, O. (2004, June). Smart factory-mobile
computing in production environments. Retrieved from
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.111.2116
&rep=rep1&type=pdf#page=18
Birks, M., & Mills, J. (2015). Grounded theory: A practical guide. London: Sage.
Board of Investment [BOI] (2015). Thailand global green automotive production
base. Retrieved from http://www.boi.go.th/upload/content/BOI-
brochure%202015-automotive-20150325_70298.pdf
Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a
cultural, technological, and scholarly phenomenon. Information,
Communication & Society, 15(5), 662-679.
Brettel, M., Friederichsen, N., Keller, M., & Rosenberg, M. (2014). How
virtualization, decentralization and network building change the
manufacturing landscape: An Industry 4.0. International Journal of
Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing
Engineering, 8(1), 37-44.
Bryman, A. (2016). Social research methods. Oxford: Oxford University Press.
121
Bughin, J., Chui, M., & Manyika, J. (2010). Clouds, big data, and smart assets: Ten
tech-enabled business trends to watch. McKinsey Quarterly, 56(1), 75-86.
Business dictionary. (2017). Retrieved from http://www.businessdictionary.com
Charoenrat, T., & Harvie, C. (2013). Technical efficiency of Thai manufacturing
SMEs: a stochastic frontier analysis. Australasian Accounting Business &
Finance Journal, 7(1), 99-122.
Chu, M., & Chaichalearmmongkol, N. (2015, March 24). Falling sales threaten
Thailand’s car-making supremacy in Southeast Asia. Retrieved from
http://www.wsj.com/articles/falling-sales-threaten-thailands-car-making-
supremacy-in-southeast-asia-1427217924
Cooper, D. R., & Schindler, P. S. (2014). Business research methods (12th ed.). New
York: McGraw-Hill/ Irwin.
Creswell, J. W. (2013). Qualitative inquiry and research design: Choosing among
five approaches. Thousand Oaks, CA: Sage.
Creswell, J. W., & Plano Clark, V. L. (2011). Designing and conducting mixed
methods research (2nd ed.). Thousand Oaks, CA: Sage.
Czech, J. (2016). Industry 4.0. Poland-competitive advantages. Retrieved from
http://www.digitalthailand.in.th/drive/Industrial
Rovolution/Jacek%20Czech%20-%20Industry%204.0.pdf
Dacier, M., Kargl, F., König, H., & Valdes, A. (2014). Network attack detection and
defense: Securing industrial control systems for critical infrastructures.
Retrieved from http://doc.utwente.nl/93351/1/2014-11-dagstuhl-14292.pdf
Dai, Q., Zhong, R., Huang, G. Q., Qu, T., Zhang, T., & Luo, T. Y. (2012). Radio
frequency identification-enabled real-time manufacturing execution system:
a case study in an automotive part manufacturer. International Journal of
Computer Integrated Manufacturing, 25(1), 51-65.
Egri, P., Karnok, D., & Váncza, J. (2007, November). Information sharing in
cooperative production networks. In Proceedings of IFAC Workshop on
Manufacturing Modelling, Management and Control, (pp. 115-120).
Retrieved from http://www.sztaki.hu/~egri/publications/Egri2007a.pdf
122
Erol, S., Schumacher, A., & Sihn, W. (2016). Strategic guidance towards industry
4.0-a three-stage process model. Retrieved from
https://www.researchgate.net/profile/Selim_Erol/publication/286937652_Str
ategic_guidance_towards_Industry_40_-_a_three-
stage_process_model/links/5671898308ae90f7843f2d27.pdf
Ghazali, N. A., Lafortune, E., Latiff, M. K. M., Limjaroenrat, P., & Whitesides, E.
(2011). Thailand automotive cluster. Retrieved from
http://www.isc.hbs.edu/resources/courses/moc-course-at-harvard/
Documents/pdf/student-projects/Thailand_Automotive_2011.pdf
Gilgun, J. F. (2008). Deductive qualitative analysis as middle ground.Retrieved from
https://www.amazon.ca/Deductive-Qualitative-Analysis-Middle-Ground-
ebook/dp/B014TRE2J4
Goldkuhl, G. (2012). Pragmatism vs. interpretivism in qualitative information systems
research. European Journal of Information Systems, 21(2), 135-146.
Greengard, S. (2015). The internet of things. Cambridge, MA: MIT Press.
Gruber, F. E. (2014). Industry 4.0: A best practice project of the automotive industry.
In Digital product and process development systems (pp. 36-40). New York:
Springer.
Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of things (IoT):
A vision, architectural elements, and future directions. Future Generation
Computer Systems, 29(7), 1645-1660.
Gupta, A. K., & Arora, S. K. (2013). Industrial automation and robotics (2nd ed.).
Boston, MA: University Science Press.
Hermann, M., Pentek, T., & Otto, B. (2016). Design principles for industries 4.0
scenarios. In 2016 49th Hawaii international conference on system sciences
(HICSS) (pp. 1530-1604). Koloa, HI: IEEE.
Herterich, M. M., Uebernickel, F., & Brenner, W. (2015). The impact of cyber-
physical systems on industrial services in manufacturing. Procedia CIRP,
30, 323-328.
123
Hodge, P. (2011). Virtualization 101: Understanding how to do more with less.
Retrieved from https://ww2.isa.org/standards-and-publications/isa-
publications/intech-magazine/2011/august/system-integration-virtualization-
101-understanding-how-to-do-more-with-less/
International Trade Administration. (2016). Top markets report automotive parts
country case study. Retrieved from http://trade.gov/topmarkets/pdf/
autoparts_thailand.pdf.
Kagermann, H., Lukas, W., & Wahlster, W. (2011). Industrie 4.0: MIt dem internet
der dinge auf dem weg zur 4. industriellen revolution. Retrieved from
http://www.vdi-nachrichten.com/Technik-Gesellschaft/Industrie-40-Mit-
Internet-Dinge-Weg-4-industriellen-Revolution
Kargl, F., van der Heijden, R. W., König, H., Valdez, A., & Dacier, M. C. (2014).
Insights on the security and dependability of industrial control systems.
IEEE Security and Privacy, 12(6), 75-78.
Kinkel, S., Lichtner, R., Hochdörffer, J., & Rurhmann, S. (2015). Fields and patterns
of German companies’ operation and collaboration strategies in China
(unpublished manuscript). Retrieved from
https://www.researchgate.net/profile/Steffen_Kinkel/publication/281004060
_Fields_and_patterns_of_German_companies’_operation_and_collaboration
_strategies_in_China/links/55d0910208ae6a881385e3fc.pdf
Kopetz, H. (2011). Internet of things. New York: Springer.
Kortuem, G., Kawsar, F., Sundramoorthy, V., & Fitton, D. (2010). Smart objects as
building blocks for the internet of things. IEEE Internet Computing, 14(1),
44-51.
Kuhn, T. (2012). The structure of scientific revolutions (4th ed.). Chicago, IL:
University of Chicago Press.
Kumar, S. A., Vealey, T., & Srivastava, H. (2016). Security in internet of things:
challenges, solutions, and future directions. In 2016 49th Hawaii
international conference on system sciences (pp. 5772-5781). Honolulu, HI:
IEEE.
124
Lee, E. A. (2008, May). Cyber physical systems: Design challenges. In 2008 11th
IEEE International symposium on object and component-oriented real-time
distributed computing (ISORC) (pp. 363-369). n.p.
Lee, J., Bagheri, B., & Kao, H. (2015). A cyber-physical systems architecture for
industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18-23.
Lee, J., Kao, H. A., & Yang, S. (2014). Service innovation and smart analytics for
industry 4.0 and big data environment. Procedia CIRP, 16, 3-8.
Littler, C. R. (1978). Understanding taylorism. British Journal of Sociology, 29(2),
185-202.
Lu, Y., Morris, K. C., & Frechette, S. (2015). Standards landscape and directions for
smart manufacturing systems. In 2015 IEEE International conference on
automation science and engineering (CASE) (pp. 998-1004). Gothenburg,
Sweden: IEEE.
Marr, B. (2016, June 20). What everyone must know about industry 4.0. Retrieved
from http://www.forbes.com/sites/bernardmarr/2016/06/20/what-everyone-
must-know-about-industry-4-0/#42fcf3bb4e3b
Marsh, P. (2012). The new industrial revolution: Consumers, globalization and the
end of mass production. New Haven, CT: Yale University Press.
Maxwell, G. A., & Drummond, S. K. (2010). Automotive industry: Technical
challenges, design issues and global economic crisis. Hauppage, NY: Nova
Science.
Minelli, M., Chambers, M., & Dhiraj, A. (2012). Big data, big analytics: Emerging
business intelligence and analytic trends for today’s business. Hoboken, NJ:
John Wiley and Sons.
Myers, M. D. (2013). Qualitative research in business and management. London:
Sage.
Pfeiffer, S. (2016). Robots, Industry 4.0 and humans, or why assembly work is more
than routine work. Societies, 6(2), 16-41.
Pisching, M. A., Junqueira, F., Santos Filho, D. J., & Miyagi, P. E. (2015, April).
Service composition in the cloud-based manufacturing focused on the
industry 4.0. In Doctoral conference on computing, electrical and industrial
systems (pp. 65-72). Lisbon: Springer International Publishing.
125
Plano Clark, V. L., & Ivankova, N. V. (2015). Mixed methods research: A guide to
the field. Thousand Oaks, CA: Sage.
Pricewaterhouse Coopers [PWC]. (2016). Industry 4.0-enabling digital operations
self-assessment. Retrieved from https://i40-self-
assessment.pwc.de/i40/landing/
Radziwon, A., Bilberg, A., Bogers, M., & Madsen, E. S. (2014). The smart factory:
exploring adaptive and flexible manufacturing solutions. Procedia
Engineering, 69, 1184-1190.
Roblek, V., Meško, M., & Krapež, A. (2016). A complex view of Industry 4.0.
Retrieved from http://sgo.sagepub.com/content/6/2/2158244016653987
Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., & Harnisch,
M. (2015). Industry 4.0: The future of productivity and growth in
manufacturing industries. Boston: Boston Consulting Group.
Saldivar, A. A. F., Li, Y., Chen, W. N., Zhan, Z. H., Zhang, J., & Chen, L. Y. (2015).
Industry 4.0 with cyber-physical integration: A design and manufacture
perspective. Retrieved from http://eprints.gla.ac.uk/112439/1/112439.pdf
Schmidt, M. (2015). Volkswagen AG forges ahead with transparent prototype.
Retrieved from http://www.rfidjournal.com/articles/view?13601
Schwab, K. (2016). The fourth industrial revolution. Geneva, Switzerland: World
Economic Forum.
Schuh, G., Potente, T., Wesch-Potente, C., Weber, A. R., & Prote, J. (2014).
Collaboration mechanisms to increase productivity in the context of
Industrie 4.0. Procedia CIRP, 19, 51-56.
Slack, N., & Lewis, M. (2011). Operations strategy (3rd ed.). New York: Financial
Times Prentice Hall.
Slama, D., Puhlmann, F., Morrish, J., & Bhatnagar, R. M. (2015). Enterprise IoT:
Strategies and best practices for connected products and services.
San Francisco, CA: O’Reilly Media.
Stearns, P. N. (2012). The industrial revolution in world history. Denver, CO:
Westview.
126
Sturgeon, T., Van Biesebroeck, J., & Gereffi, G. (2008). Value chains, networks and
clusters: Reframing the global automotive industry. Journal of Economic
Geography, 8(3), 297-321.
Sun, H., Ni, W., & Lam, R. (2015). A step-by-step performance assessment and
improvement method for ERP implementation: Action case studies in
Chinese companies. Computers in Industry, 68, 40-52.
Techakanont, K. (2011). Thailand automotive parts industry. In M. Kagami, (Ed.),
Intermediate goods trade in East Asia: Economic deepening through
FTAs/EPAs, BRC research report no. 5. Bangkok: Bangkok Research
Center, IDE-JETRO.
Teddlie, C., & Tashakkori, A. (2009). Foundations of mixed methods research:
Integrating quantitative and qualitative methods in the social and behavioral
sciences. Thousand Oaks, CA: Sage.
Thailand Automotive Institute, Ministry of Industry. (2012). Master plan for
automotive industry 2012-2016. Retrieved from http://www.thaiauto.or.th/
2012/backoffice/file_upload/research/11125561430391.pdf
Tortermvasana, K. (2016). ICT pushes drive toward “Thailand 4.0”. Retrieved from
http://www.bangkokpost.com/business/telecom/ 990257/ict-pushes-drive-
towards-thailand-4-0
Tubbs, A. (2015). Connect the potential of Industry 4.0 with real manufacturing.
Retrieved from http://www.csemag.com/single-article/connect-the-potential-
of-industry-40-with-real-manufacturing/e3d84cb04edb154ddf082137
5c8842af.html
Vaismoradi, M., Turunen, H., & Bondas, T. (2013). Content analysis and thematic
analysis: Implications for conducting a qualitative descriptive study.
Nursing & Health Sciences, 15(3), 398-405.
Vogel-Heuser, B., & Hess, D. (2016). Industry 4.0-Prerequisites and visions. IEEE
Transactions on Automation Science and Engineering , 13(2), 411-413.
Vogel-Heuser, B., Rösch, S., Fischer, J., Simon, T., Ulewicz, S., & Folmer, J. (2016).
Fault handling in PLC-based industry 4.0 automated production systems as
a basis for restart and self-configuration and its evaluation. Retrieved from
Scientific Research: http://file.scirp.org/pdf/ JSEA_2016012714262700.pdf
127
Wahlster, W. (2014, October 20). Industry 4.0, the fourth industrial revolution based
on smart factories. Retrieved from http://www.slideshare.net/
SPRICOMUNICA/basque-industry-40-the-fourth-industrial-revolution-
based-on-smart-factories
Wang, S., Wan, J., Li, D., & Zhang, C. (2016). Implementing smart factory of
industrie 4.0: an outlook. Retrieved from http://www.hindawi.com/
journals/ijdsn/2016/3159805/
Weyer, S., Schmitt, M., Ohmer, M., & Gorecky, D. (2015). Towards Industry 4.0
-Standardization as the crucial challenge for highly modular, multi-vendor
production systems. IFAC-PapersOnLine, 48(3), 579-583.
Wiener, N. (1961). Cybernetics: Or control and communication in the animal and the
machine (2nd ed.). Cambridge, MA: MIT.
Xia, F., Yang, L. T., Wang, L., & Vinel, A. (2012). Internet of things. International
Journal of Communication Systems, 25(9), 1101.
Yamane, T. (1967). Elementary sampling theory. Englewood Cliffs, NJ: Prentice-
Hall.
Zamanzadeh, V., Ghahramanian, A., Rassouli, M., Abbaszadeh, A., Alavi-Majd, H.,
& Nikanfar, A. R. (2015). Design and implementation content validity
study: Development of an instrument for measuring patient-centered
communication. Journal of caring sciences, 4(2), 165-178.
Zikmund, W. G., Babin, B. J., Carr, J. C., & Griffin, M. (2013). Business research
methods (9th ed.). Mason, OH: South-Western Cengage.
128
APPENDICES
129
Self-assessment questionnaire
A degree of industry 4.0 strategies implementation and practices
in Thai automotive manufacturers in Thailand
…………………………………………………………………………………………
This self-assessment questionnaire aims to explore technology adoption in
Thailand’s automotive industry in accordance with strategies and practices of Industry
4.0. This questionnaire is a part of the thesis tiled “A Degree of industry 4.0 strategies
implementation and practices among Automotive Manufacturers in Thailand”. The
researcher would like to request assistance on answering the research questionnaire
honesty to benefit academics and future applications.
The researcher looks forward to your cooperation. Thank you in advance.
Section I: Company background
Please tick next to items corresponding to your company.
1.Number of Employees o Less than 50 persons (Small)
o 51-200 persons (Medium)
o More than 200 persons (Large)
2.Annual Income o Less than 500,000 baht
o 500,000-5,000,000 baht
o More than 5,000,000 baht
For Section II-V, please rate items below on a score from 1 to 5, with 1
being lowest and 5 being highest.
Please rate on the score that is closed to your current company’s
situation in ‘Today’ column and what you expect the situation in 5 years ahead
in ‘5 Years Ahead’ column by specifying 1, 2, 3, 4 or 5.
130
Section II: Business model, product and service planning
No Item Today
5
Years
ahead
1
Overall, what is the level of adoption of digital
characteristics or automation system for your company’s
products and services in order to add more values?
2
On average, what is the level of digitalization of your
company’s products (e.g., RFID, sensor, IoT connection,
smart product), or the level of automation which your
company’s products enter?
3 What is the level of unique characteristics of your
company’s products that meet satisfy customer demands?
4
Overall, what is the level of digitalization or automation
for your products? (digitalization and integration of
planning, engineering, manufacturing, service, and
recycling)?
5 What is the level of importance on data usage and analysis
for your company?
6
What is the level of cooperation with partners, suppliers
and customers to develop your company’s products and
services?
131
Section III: Market and access to customers
No Item Today
5
Years
ahead
1 What is the level of adoption of integrated multi-channel
distribution strategy to sell your company’s products?
2
What is the level of channel integration (e.g., a website,
blog, social media) in order for your company to
establish interactions for distributing news, receiving
comments, etc. with your customers?
3
What is the level of developing or improving digital
system or automation system to increase sales volume
(mobile devices, access to related systems, full-scale
sales)?
4 What is the level of flexibility and satisfying customer
demands of your company’s pricing system?
5 What is the level of customer data analysis to get insight
into your customers?
6
What is the level of cooperation with partners to gain
access to customers, and the level of access from
customers to your company’s products?
132
Section IV: Value chain and process
No Item Today
5
Years
ahead
1
What is the level of your company’s making use of
Artificial Intelligence (AI) to process data for research
and development as well as advanced instruments?
2
What is the level of on-demand manufacturing and
capability to flexibly satisfy the change, or the level of
adoption of Flexible Manufacturing System or allocating
jobs on reduce the waste?
3
What is the level of your company’s planning with an
entire IT system, and the level of process change, ranging
from sales forecast during production to warehouse and
logistics planning?
4
What is the level of digitalization or automation system of
your company’s production system which links together
and is controlled by computer software?
5
What is the level of using IT systems to manage your
company’s vertical value chain from receiving orders
from customers, working with suppliers, to production
and logistics, and the system is flexible to satisfy specific
requirements and is capable of real-time, active
connecting a production system to manage equipment and
related parties?
133
Section V: IT architecture
No Item Today
5
Years
ahead
1
What is the level of overall requirements support by your
IT architecture for digitalization and automation system as
part of Industry 4.0?
2
What is the level of adoption of IT system for
manufacturing or equivalent process to manage production
process, or the level of product design capable of assembly
by using multi-purpose, centralized controllable industry
robots?
3
What is the level of readiness of your IT architecture and
data to rapidly gather data, analyze, process and present
clear information leading to real-time decision making
about production, products and customers.
4
What is the level of importance of new technologies, such
as a social media, mobile device, cloud computing and
analysis, or cloud storage, for running your business?
5
What is the speed of your IT related departments’ response
to business requirements under specified time, budget and
quality? For example, using software to process data real-
time for transport route is planning, tracking fleet by using
GPS to know the status while transporting and to adjust
route regarding to costs and time.
6
What is the level of integrating IT systems or transmitting
data through a computer network to understand overall
process status of the factory, and updating every processing
step to the center, which then distributes data to customers,
suppliers and partners?
134
Interview questions (Semi-structured)
1. Please briefly explain about your business?
2. Based on your understanding, what are the basic principles of Industry
4.0?
3. In your opinion, how does Industry 4.0 apply within the automotive
industry?
4. How long has your company been implementing Industry 4.0?
5. Why did your company decide to implement Industry 4.0?
6. How does your company implement Industry 4.0 (such as process,
consultant)?
7. What are the benefits that you think you can gain from implementing
Industry 4.0?
8. What are the negative impacts that you think you can gain from
implementing Industry 4.0?
9. How would you rate your company regarding Industry 4.0 implement
(range from 1 to 10)? And please explain why?
10. If other company wants to implement Industry 4.0, what would you
recommend them?
135
BIOGRAPHY
Name Miss Nuchon Meechamna
Date of birth August 03, 1982
Place of birth Chonburi, Thailand
Present address 95/76 Moo 1, Samet, Mueang District,
Chonburi Province 20000
Position held
2006-2009 Officer, Human Resource Planning Section
DENSO INTERNATIONAL ASIA CO., LTD.
2009-2014 Officer, Human Resource Management Section
DENSO INTERNATIONAL ASIA CO., LTD.
2014-2015 Senior Officer,
Human Resource Development Section
DENSO INTERNATIONAL ASIA CO., LTD.
2015-present Senior officer, Associate Relations
& Associate Service Department,
DENSO (THAILAND) CO., LTD.
Education
2000-2004 Bachelor of Business Administration (B.B.A.),
Personnel Management,
Faculty of Humanities and Social Sciences,
Burapha University
2005-2008 Master of Business Administration (M.B.A.),
Graduate School of Commerce,
Burapha University
2011-2016 Doctor of Business Administration (D.B.A.)
Graduate School of Commerce,
Burapha University
