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Conceptualizing Industry 4.0
readiness model dimensions:
an exploratory sequential
mixed-method study
Jiju Antony
Department of Industrial and Systems Engineering, Khalifa University,
Abu Dhabi, United Arab Emirates
Michael Sony
Mechanical and Marine, Namibia University of Science and Technology,
Windhoek, Namibia, and
Olivia McDermott
College of Science and Engineering, National University of Ireland, Galway, Ireland
Abstract
Purpose –Organizations use Industry 4.0 readiness models to evaluate their preparedness prior to the
implementation of Industry 4.0. Though there are many studies on Industry 4.0 readiness models, the
dimensions of readiness differ. Besides, there is no study empirically validating the readiness model in different
sectors or types of organization. The purpose of this study is to conceptualize the dimensions of theIndustry 4.0
readiness model and subsequently evaluate the criticality of these dimensions in manufacturing, service, small
and medium-sized enterprises (SMEs) and large enterprises (LEs).
Design/methodology/approach –The study uses an exploratory sequential mixed method design. In phase
one, 37 senior managers participated through a purposive sampling frame. In phase two, 70 senior managers
participated in an online survey.
Findings –The results of the study indicated that the Industry 4.0 readiness model has 10 dimensions.
Further, the criticality of the dimensions as applied to different sectors and type of organizations is put forward.
This study will help manufacturing, services, SMEs and LEs to evaluate Industry 4.0 readiness before
commencing the deployment of Industry 4.0.
Practical implications –The findings can be very beneficial for Industry 4.0 practitioners and senior
managers in different organisations to understand what readiness dimensions need to be considered prior to
implementation of Industry 4.0 technology.
Originality/value –This paper makes an attempt to conceptualize the Industry 4.0 readiness model and
utilizes an exploratory mixed method for critically evaluating the dimensions related to the model.
Keywords Industry 4.0, Readiness model, Industry 4.0 implementation, Interviews, Surveys
Paper type Research paper
1. Introduction
Industry 4.0 represents a new trend inautomation and data exchange and many organizations
are trying to implement it across the globe (Caiado et al., 2021;V
an
eet al.,2021). The first step
before implementing Industry 4.0 is to assess whether an organization is ready to deploy
Industry 4.0 (Krishnan et al., 2021;Rajnai and Kocsis, 2018). The readiness model of Industry 4.0
is described as the “degree to which organizations can take advantage of Industry 4.0
technologies”(Hizam-Hanafiah et al.,2020). There are many Industry 4.0 readiness models
developed in both practitioner and academic literature (Sony and Naik, 2019b). Within these
models, organizations can be classified as being in a not ready or almost ready state
(Hizam-Hanafiah et al., 2020). These models vary concerning Industry 4.0 model dimensions
(Hizam-Hanafiah et al.,2020;Gokalp et al.,2017). There is currently a common understanding
of the dimensions of the Industry 4.0 readiness model (Sony and Naik, 2019b).
Industry 4.0
readiness
model
dimensions
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1754-2731.htm
Received 20 June 2021
Revised 30 June 2021
Accepted 30 June 2021
The TQM Journal
© Emerald Publishing Limited
1754-2731
DOI 10.1108/TQM-06-2021-0180
Hizam-Hanafiah et al. (2020) identify key six dimensions of the Industry 4.0 readiness model
through a systematic literature review (Hizam-Hanafiah et al.,2020). A similar study was done
by Sony and Naik (2019a) through a systematicliterature review. The authors had identified six
broad themes of the generic Industry 4.0 readiness model. These two studies were systematic
literature reviews and need empirical validation as regards to the dimensionality from a
pragmatic point of view (Acioli et al.,2021;Hizam-Hanafiah et al.,2020;Sony and Naik, 2019b).
There is also a need to explore if there are any additional dimensions of Industry 4.0 from the
perceptions of both manufacturing and service organizations as well as small and medium-
sized enterprises (SMEs) and large enterprises (LEs). Thus the authors intend to explore the
research question; “What are the dimensions of the Industry 4.0 readiness model”?Industry 4.0
readiness dimensions criticality may vary in manufacturing and service organizations (Sony
and Aithal, 2020c) due to the inherent differences in these organizations. Besides, the
dimensions criticality may also vary based on whether it is an SME or LE, as the challenges
these organizations face would be different (Brozzi et al., 2018;Stentoft et al., 2021). Thus, the
authors intend to explore the second research question;“What are the very critical dimensions of
the Industry 4.0 model in the manufacturing and service sectors and in SMEs and LEs?”.
The paper is organized as follows. In the next section a brief review of the literature of the
Industry 4.0 readiness model is carried out, which is followed by the research design. The
findings of a qualitative study are delineated subsequently followed by a ranking of
the dimensions using a quantitative study. It is followed by the implications of organizations
conclusions, limitations of the research and directions for the future research.
2. Background
There has been an exponential growth in the number of studies in relation to Industry 4.0
readiness models (Botha, 2018). Most of these models are complex, less pragmatic and also do
not take into account the changing goals of the organization (Hizam-Hanafiah et al., 2020).
Felch et al. (2019) conducted a detailed analysis in terms of the model’s applicability to
business practice. They suggest that not all Industry 4.0 readiness models are relevant or
applicative. Some of these models are designed for specific industries and others are generic
(Felch et al., 2019). They further suggest that empirical validation of these models has not
been conducted. Though these models have contributed to expanding the understanding of
Industry 4.0 readiness models, however, there is a possibility that these models can vary in
terms of short, medium or long-term purpose or benefits (Erol et al., 2016). Another challenge
to study these Industry 4.0 readiness models is that they are proprietary properties of
organizations and institutions, hence not available in the public domain (Hizam-Hanafiah
et al., 2020). From the perspective of previous important studies, Brozzi has studied the
Industry 4.0 readiness model from its applicability in SME, however, empirical validation was
not done (Brozzi et al., 2018). Sony and Naik (2019a) through a literature review proposed six
dimensions namely top management involvement and commitment, employee adaptability
with Industry 4.0, smart products and services, the extent of digitization of the supply chain,
level of digitization of the organization and readiness of organization strategy (Sony and
Naik, 2019b). They also proposed how these factors are interrelated. Hizam-Hanafiah et al.
(2020) through a literature review suggested technology, people, strategy, leadership,
process, and innovation as dimensions of Industry 4.0 readiness models (Hizam-Hanafiah
et al., 2020). Both these models are derived from the literature review and have not been
empirically tested. Besides, as Industry 4.0 readiness models will be used by organizations,
for this reason, it is pertinent to examine the dimensions of the Industry 4.0 readiness model
from the perspective of a pragmatic approach. Thus, this study intends to conceptualize
Industry 4.0 dimensions from an organizational perspective to compare or contrast the
applicability of the dimensions from the organization’s viewpoint.
TQM
3. Research design
As previous studies were not conclusive on the dimensionality of the Industry 4.0 readiness
model (Hizam-Hanafiah et al., 2020), exploratory sequential mixed method design (Cameron,
2009) was used. As the phenomenon under study was complex, different methods are needed
for exploring the construct dimensionality (Byrne and Humble, 2007). This research first
conducts a qualitative study to explore the dimensions of the Industry 4.0 readiness model
grounded in data. Subsequently, this model is quantitatively evaluated in a different context.
The methodology used in this study is explicated in Figure 1. As there is no conceptual clarity
as regards to the conceptualisation of Industry 4.0 readiness model dimensions, this study
used a grounded theory methodology to explore the dimensionality of the construct in the
qualitative phase. Grounded theory methodology is primarily developed to derive an
explanation about a phenomenon that was non-existent or where the theoretical explanation
was inadequate (Charmaz and Belgrave, 2015;Strauss and Corbin, 1994a).
The next phase was a quantitative phase which was designed to understand the criticality
of Industry 4.0 readiness model dimensions in both manufacturing and service sectors as well
as in SMEs and LEs.
3.1 Sampling procedure and data collection
The sampling procedure for qualitative data collection primarily revolved around interviews.
Senior managers with expertise in Industry 4.0 and working in manufacturing, services, SMEs
and LEs were chosen in this study.Senior managers withfive years of experience were chosen
in this study, because they are directly involved in decision-making about various aspects of
Industry 4.0 in their organizations, as such the information will be more accurate (Antony and
Sony, 2021;Sony et al., 2020). This study utilized the concept of theoretical sampling (Strauss
and Corbin, 1994a). The details about theparticipants were obtained from LinkedIn because it
is one of the most widely used networking sites for professionals (Power, 2015). A personal
message was sent to all potential participants outlining the objectives of the study and
requesting their voluntary participation in this study. If a participant agrees to participate, an
online interview was conducted. The interviews started with demographic questions around
the participants experience in implementing Industry 4.0, followed by open-ended questions
such as “How can one determine if an organization is ready to implement Industry 4.0?”. Open-
ended probing questions were subsequently asked as regards various facets of information
expressed by the respondents during the study. The interviews were summarized verbatim
and shown to participants within 24 h to confirm the transcription and to ensure the validity of
the data. Pseudo names were assigned to participants such as P1 for participant 1. The data
saturation concept was used to ascertain the sample size (Guest et al., 2006). In grounded
theory methodology, data collection and analysis is interlayered (Charmaz and Belgrave,
2015) and hence when no new conceptual themes of Industry 4.0 readiness model dimensions
were not emerging it was suggested that data saturation was reached. Figure 2 depicts the
demographic profile of respondents. SMEs was classified as an organization with less than
250 employees and above 250 (O’Regan and Ghobadian, 2004) were classified as LEs.
In the second phase, the authors used an online survey which was directed to senior
management professionals working in manufacturing and service organizations which were
either SMEs or LEs. The online survey questionnaire was designed based on the results of
Interpretaon
of qual and
quant results
Quant findings
Quant data
analysis
Quant data
collecon
Developing the
Industry 4.0
readiness
model
Qual findings
Qual data
analysis
Qual data
collecon
Source(s): (Adapted) (Creswell, 1999)
Figure 1.
Exploratory sequential
mixed method
Industry 4.0
readiness
model
dimensions
qualitative study in terms of the dimensions. The first section was the demographics. The
readiness success factors were tabulated and given to respondents. The 7-point Likert scale
was used (Dawes, 2002;Sullivan and Artino Jr, 2013). Seven distinct categories were used
from “Strongly Disagree”to “Strongly Agree”to capture the responses of the respondents on
the Industry 4.0 readiness factors. A seven-point Likert scale is easy to understand and use by
the respondents and it has good psychometric properties (Allen and Seaman, 2007).
Moreover, as senior management professionals are busy, unnecessary long questionnaires
may not be attractive to them. Besides, the short nature of the questionnaire scaffolds
respondents in answering the survey in a short period. The revised online survey link was
sent out to 250 senior managers who are working in their respective organisations in roles
such as Director and Vice President levels. The contacts were obtained through LinkedIn and
each of the respondents was contacted through email. A similar research methodology was
adopted in previous studies (Antony et al., 2019;Antony et al., 2020). The authors used two
criteria in the selection of such subject matter expert; (1) all respondents should have a
minimum of five years’experience in their role for implementing Industry 4.0 projects, (2)
should be working in an organisation as a Technology or Quality Director or similar senior
position. Setting such criteria enabled the authors to glean knowledge from high calibre
experts within the survey participants, who are responsible for Industry 4.0 in their
respective organisations. A total of 70 responses were collated over 13 weeks yielding a
response rate of 28%. Easterby-Smith et al. (2012) argue that a 20% survey response rate is
widely considered to be sufficient. The sample characteristics are given in Table 1.
28
9
26
11 13
24
14
23
0
5
10
15
20
25
30
Chart Title
Row labels LE SME Grand total
Manufacturing 22 19 41
Female 7 3 10
Male 15 16 31
Service 19 10 29
Female 5 3 8
Male 14 7 21
Grand total 41 29 70
Figure 2.
Demographic profile of
respondents
Table 1.
Sample Characteristics
of the
quantitative study
TQM
The internal consistency of the 10 Industry 4.0 readiness factors was assessed using
Cronbach’s alpha which tests to see if multiple-question Likert scale surveys are reliable.
Cronbach’s alpha was found to be 0.787. A value of above 0.7 indicates higher internal
consistency of the scale (Nunnally, 1994) and gave the researchers confidence that the test
designed was accurately measuring the variables of interest. Besides, none of the items
correlated in the scale fell below 0.3, indicating a positive consistency of the scale (Hair
et al., 2014).
3.2 Data analysis
For the qualitative data analysis, this study followed grounded theory methodology (Glaser
et al., 1968). Three techniques of open coding (creating a list of themes within data), axial
coding (categorizing or linking subcategories of themes) and selective coding (condensing of
specific or excessive categories into higher-order themes) (Hastings et al., 2021). Open coding
consisted of identifying individual meaning units, in axial coding these were categorized, or
sub-categorized and selective master themes were linked. The data was verified using the
member checking technique, memoing to track the themes while coding and triangulation by
multiple investigators (Strauss and Corbin, 1994b;Creswell and Poth, 2016;Merriam and
Tisdell, 2015). Microsoft Excel was used for qualitative data analysis because it has a feature
of text processing that is used in qualitative data analysis (Bree and Gallagher, 2016;Meyer
and Avery, 2009). The 10 dimensions of Industry 4.0 unearthed in this study were subjected
to quantitative analysis in phase 2. Cronbach’s alpha was calculated for checking the internal
consistency of the scale that is, how closely related a set of items are as a group. The mean
scores were normalized to identify the most critical readiness factor (Adabre and Chan, 2019)
s. Mann Whitney Utest was performed to test the difference between the groups in
manufacturing, service sectors and between the SME and LE categories. Mann Whitney U
test was used as the data did not follow, the normal distribution and it is one of widely
suggested non parametric test as a non-parametric alternative to the t-test for independent
samples (Milenovic, 2011).
4. Results and discussion
First, the results of a qualitative study in terms of the dimensions of the Industry 4.0 readiness
model are discussed and subsequently, the rankings based on quantitative study are
explicated.
4.1 Qualitative study results: ten dimensions of Industry 4.0 readiness model
The ten dimensions of Industry 4.0 are technology readiness, employee adaptability with
Industry 4.0, smart products and services, digitalisation of supply chains, extent of the digital
transformation of the organization, readiness of Industry 4.0 organization strategy,
innovative Industry 4.0 business model, leadership and top management support for
Industry 4.0, organizational culture, and employee reward and recognition systems.
4.1.1 Technology readiness. The respondents in this study remarked on the importance of
how ready an organization should be to implement the technologies of Industry 4.0. There are
many technologies used in Industry 4.0 such as IoT, RFID, Smart manufacturing, digital
twins, Cloud Computing and Robotics (Masood and Sonntag, 2020). The technology readiness
of the organization depends on how well an organization is ready to implement the
technologies of Industry 4.0 in their respective organization to meet the objectives of
the organization. This is an important component as organizational success in the
implementation of Industry 4.0 will depend on managing these technologies (Sony and Aithal,
2020a). The respondents in this study echoed similar remarks as explicated in Table 2.
Industry 4.0
readiness
model
dimensions
The quotes are verbatim and indicates participant number (P number), as pseudo names are
given for anonymity.
The technology readiness of the organization will help in deciding to acquire, develop,
customise and transition to Industry 4.0 technology. The circular economy based model
suggests that the resources stay in the system as it experiences one of the 10 Rs of
sustainability (Refuse, Rethink, Reduce, Reuse, Repair, Refurbish, Remanufacture,
Repurpose, Recycle and Recover) (Bag et al., 2021). Industry 4.0 technologies help to
overcome these challenges in the 10 Rs system and hence the degree of technology readiness
an organization demonstrates in each of the 10 Rs will help the organization to be successful.
4.1.2 Employee adaptability with Industry 4.0. Through automation and data integration,
Industry 4.0 digitally transforms an organization to meet its goals. Increased automation in
conventional wisdom will suggest workerless production or less human interaction (Sony
and Aithal, 2020a). Recent studies suggest that implementation of Industry 4.0 will result in
employees requiring a new skill set, however, employees will be critical for the success of
Industry 4.0 (Dworschak and Zaiser, 2014;Hecklau et al., 2016;Weyer et al., 2015). Industry
4.0, thus will have social (human-related) and technical (non-human) components coming
together to pursue a common goal commonly known as a socio-technical system (Avis, 2018).
Therefore, employees are as important as technology for the success of Industry 4.0 (Sony
and Naik, 2020). However, with the introduction of manufacturing automation and COBOTS,
low-skilled workers will temporarily suffer from a lack of employment opportunities.
Similarly, advances in artificial intelligence, machine learning and software automation will
impact human employability in an organization.
At present, the work which is carried out by university graduates will be done by
machines and powerful algorithms (Ford, 2009). Routine jobs will be overtaken by machines
and also highly skilled jobs will require pattern recognition and cognitive non-routine tasks
(Bonekamp and Sure, 2015). Most of lower-level operational jobs will be taken over by CPS.
The implementation of Industry 4.0 results in higher process integration and cross-functional
perspectives, resulting in the breakdown of hierarchical levels and decentralisation (Fettig
et al., 2018). Therefore, Industry 4.0 will have an impact on all levels of employment and
respondent’s response are elucidated in Table 3.
4.1.3 Smart products and services. The extent to which an organizations product or service
is smart plays an important part in deciding how ready an organization is to implement
“Well, in my opinion, an organization which is ready to accept, understand, implement and adapt the
technology to meet the goals of the organization will be the readiest to implement Industry 4.0. To cite an
instance if an organization wants to implement vertical integration, however, the systems which the
organization uses at present should be compatible for vertical integration. If the existing organization
structures are centralised, it will require immense work on the part of the organization to first put a technology-
enabled system into practice. How well an organization adapts to the technology enables how well a new
organizational structure will determine the success of the implementation of Industry 4.0”P31
“Industry 4.0 requires the implementation of new technology such as IoT, CPS, Cloud computing, COBOTS to
name a few. This will require organizations to adopt these technologies during different phases of
implementation of Industry 4.0. The organizations should have adequate technology capability to acquire and
use these technologies for their benefits”P23
“An organization which can use new technology will be more ready to implement Industry 4.0. To cite an
instance smart sensor should be strategically used to acquire data in real-time and the data should be
transmitted strategically so that organizations benefit from it. Industry 4.0 is a leap an organization takes
towards automation and data exchange in various organizational activities. So, technology is the central tenant
for the application of Industry 4.0”P13
“An Industry 4.0 ready organization will be in a position to acquire new technology and continuously use the
technology for the success of the organization. It is one thing to acquire new technology and another thing as
regards to the continued use of new technology”P12
Table 2.
Excerpts of
respondents on
technology readiness
TQM
Industry 4.0 (Lichtblau et al., 2015;Sony and Naik, 2019b). The three components of a smart
product are (1) physical components such as electrical and mechanical elements (2) smart
components such as microprocessors, sensors, data storage, controls, software, embedded
operating system and digital user interface and (3) connectivity components such as ports,
antennae, protocols and networks enable communication between the product and the
product cloud, which are run on remote servers and contains products external operating
systems (Porter and Heppelmann, 2015). If the organization’s products have all three
components built in, the more ready the organization would be to implement Industry 4.0
(Lichtblau et al., 2015;Porter and Heppelmann, 2015;Sony and Naik, 2019b).
The three core elements of a smart service are real-time data collection, continuous
communication and interactive feedback (Allmendinger and Lombreglia, 2005). The
intelligent object of the smart service could be an individual customer (e.g. health
monitoring), a group of customers (e.g. family home monitoring) or a firm (e.g. monitoring of
industrial equipment). Organizations can make use of the information gathered through
intelligent objects to improve their service offerings and let customers benefit from
customized service features (W€
underlich et al., 2015). The extent to which the organization’s
service is smart will be an indicator, to understand how ready an organization is to implement
Industry 4.0 (see Table 4).
4.1.4 Digitalisation of supply chains. Digitalisation has encompassed not only smart
products and services but also the handling of the supply chain (Nasiri et al., 2020). Supply
chains are defined as a “series of interconnected activities that involve the coordination,
planning and controlling of products and services between suppliers and customers”
(B€
uy€
uk€
ozkan and G€
oçer, 2018). The traditional supply chain is made of a series of discrete
siloed steps from supplier to consumer. The digitalisation of supply chains will result in
breaking down the walls and transforming the supply chain into an integrated system that
would run flawlessly. The digital supply chain could be defined as a “bundle of interconnected
activities that are involved in supply chain processes between the supplier and customers, which
are handled with novel technologies”(B€
uy€
uk€
ozkan and G€
oçer, 2018).
The digital supply chain is built on both digital transformation and smart technologies.
The role of digital technologies in the digital transformation of the supply chain is the key
element and hence organizations need to improve the level of technical adaptability and
“Employees will be key in the implementation of Industry 4.0. An organization whose employees are open to
learning new skills will perform better than other organizations while implementing Industry 4.0. It is not
possible to lay off employees while implementing Industry 4.0 and recruit new ones. It will have a social,
operational, economic impact on the organization, so the organization which has a good employee adaptability
program will be more ready to implement Industry 4.0 than an organisation which does not have”P14
“Technology is important but the organization whose employees are creative, or they value creativity in
problem-solving will be ready to implement Industry 4.0. We need people with the drive to unlearn and relearn
new things while implementing Industry 4.0. The solutions to new age problems will not be straightforward or
linear, but rather non-linear, complex, and sometimes difficult to solve. Therefore, creative employees or
employees who can learn to be creative will survive this onslaught from technology”P31
“There will be a variety of technical skills an employee should possess. Besides, soft skills such as teamwork,
negotiation, conflict resolution etc will be the need of the hour. I feel employees need to have both technical and
soft skills for the success of Industry 4.0 implementation”P29
“Employees will have to work with employees from a different department and different fields from their own.
They will have to gel with employees from different cultures, values, and belief systems. Hence, employees will
have to learn to adapt to working in such environments”P20
“The stress of working in a fully automated environment will be different than working in a manual
environment. Therefore, employees will have to deal with such stresses to cope with the new Industry 4.0
environment”P33
Table 3.
Excerpts of
respondents on
employee adaptability
with Industry 4.0
Industry 4.0
readiness
model
dimensions
implementation of digital technologies (Frank et al., 2019;Pramanik et al., 2019). Therefore,
organizations whose supply chains are digitalized stand a better chance of being ready to
implement Industry 4.0. The respondents in this study remarked on the importance of this
and it is explicated in Table 5.
4.1.5 Extent of the digital transformation of the organization. The digital transformation of
an organization can be viewed as the degree of integration of digital technologies into all
business areas of the organization (Verhoef et al., 2021). It fundamentally changes the way an
organization carries out day to day business (Schwertner, 2017). It could be viewed as the
usage of digital technologies to transform different functional departments and associated
systems and processes within these functions of organizations such as production,
purchasing, marketing, accounting, HR and finance (Sony and Aithal, 2020b). The three
phases of the digital transformation of the organization are digitization, digitalization and
digital transformation (Verhoef et al., 2021).
Digitization is transforming analogue data of an organization into a digital format and
transmitting such information (Loebbecke and Picot, 2015). Digitalization is the use of ICT to
alter existing business processes (Li et al., 2016). This results in new socio-technical
organization structures with digital artefacts. The focus of digitalization of the organization
“Industry 4.0 implementation will be a success if the products of the organization are smart and has features
such as self-configuration, self-diagnosis etc. If you are going to automate the organization and supply chain,
what about the product? So, if the organizations existing products are smart, there is a high chance that the
organization is ready for Industry 4.0. Suppose you are a motorcycle manufacturer, if your product is not smart,
then an organization will not be able to harness the full potential of Industry 4.0. The same is the case with
service, if you do not design services based on real-time data analytics, once cannot achieve the full potential of
Industry 4.0”P20
“One needs to understand the existing product portfolio, if your existing portfolio consists of smart products
and services, then it is easier for organizations to migrate to Industry 4.0 compared to organizations whose
products are not smart. The smart products offer a unique opportunity to tailor services based on the usage
data. Industry 4.0 should improve the customer experience and satisfaction”P36
“An Organization should have products which can transmit or share information about itself, environment and
its users. Besides, it should be able to monitor and take actions if it notices any discrepancies while working”
P28
“Imagine you are going to your favourite hotel and the room intelligently senses your presence, and adjust
things like room temperature, digital channel contents of your TV, your favourite food is suggested and so on.
Services in the modern world are changing with the advent of new technology”P21
“The supply chain is the key element for the success of any organization. If the supply chains are digitised it will
result in better coordination, agility, transparency, it would be a demand-driven system, the cash flow will be
increased due to faster supply chains and so on. If an organizations existing supply chain is digitised, it stands a
better chance of being ready to implement Industry 4.0”P24
“Digital supply chains are a remarkable phenomenon however most supply chains though digitalised in parts
have not lived up to the expectation. The challenge for the implementation of Industry 4.0 is strategic
digitisation of the supply chain using the three principles of Industry such as horizontal, vertical and end-to-end
integration”P27
“The technology has changed the supply chains from reducing transaction costs to innovation in production
and distribution. The traditional supply chains which were linear have become now dynamic, agile, and
responsive with the use of technology. Besides the transparency and coordination within the supply chains
have improved a lot”P10
“Supply chain digitization is not just automating one task, but rather a holistic transformation of the supply
chain. It looks at the integration of different organizations in the supply chain using digital technologies and
modelling it using digital twins”P6
Table 4.
Excerpts of
respondents on smart
products and services
Table 5.
Excerpts of
respondents on
digitalization of supply
chains
TQM
is to improve business processes to improve the customer experience (Verhoef et al., 2021).
Digital transformation describes organization-wide new thinking which results in new
business models. It introduces a new business model by implementing new business logic to
create and capture value (Pagani and Pardo, 2017). The digital transformation of an
organization impacts the whole organization rather than changing simple organizational
processes or tasks. The extent to which an organization is digitally transformed will
determine how ready an organization is to implement Industry 4.0. The excerpts of the
respondents are given in Table 6.
4.1.6 Readiness of Industry 4.0 organization strategy. Industry 4.0 can be thought of as a
digital container filled with many technologies, principles, and management systems
(Chiarini et al., 2020). At times organizations are disoriented when implementing Industry 4.0.
Thus, for Industry 4.0 to be successful organizations must devise the strategy and deploy
technologies, principles, and systems to strategically achieve them. Another challenge for
Industry 4.0 implementation is the lack of an implementation model or road map (Chiarini
et al., 2020).
Industry 4.0 implementation changes the long term relationship in terms of (1)
organization and nature (in terms of resource efficiency and sustainability in
manufacturing systems) (2) Organization and local communities (increased integration of
customers in the design process, reach of wider customer base etc.) (3) organization and value
chains (enabling mass customisation due to distributed and responsive manufacturing and
collaborative processes) and (4) Organizations and humans (in terms of improved human-
machine interfaces and work conditions) (Santos et al., 2017;Sony and Naik, 2019b). Another
point to consider while designing the organizational strategy is the use of big data for
competitive advantage (Sony and Naik, 2019b). Thus, the readiness of organization strategy
to implement Industry 4.0 must consider these factors while implementing Industry 4.0. The
excerpts of the respondents are given in Table 7.
4.1.7 Innovative Industry 4.0 business model. Though there has been a large number of
studies on technological aspects of Industry 4.0 (Weking et al., 2020), studies have also shown
that businesses are struggling with profit after implementation of new technologies without a
proper business model (Abdelkafi et al., 2013). Not only is product and service innovation
important, but also innovation in the business model which will help to translate the same
into profits (Weking et al., 2020). “A mediocre technology pursued within a great business model
may be more valuable than a great technology exploited via a mediocre business model”
(Chesbrough, 2010).
Manufacturers do not know or understand Industry 4.0 business models (Sarvari et al.,
2018) or they are not able to change the traditional business to Industry 4.0 requirements
“One needs to understand the present state of the organization in terms of the use of digital technologies. If the
organization has just started using digital technologies without any changes to the way it does business, then
the initiative may not be a success”P18
“There is a high chance of readiness for an organization to implement Industry 4.0 if all departments within an
organization are integrated using the digital medium. This will help in planning, organising, controlling,
leading, and coordinating various activities within an organization”P14
“Digital connectivity of an organization will determine how ready an organization is to implement Industry 4.0.
If only a few activities in the organization is done digitally, such organizations will have a lot of work to do
before the implementation of Industry 4.0”P35
“Most organizations have some degree of automation in their production lines and other departments are
digitally connected. But there is no flow of information and information is still regulated by the relevant
departmental heads. Such organizations are still in the primary phase and it will require some effort before they
can conceive and implement Industry 4.0”P34
Table 6.
Excerpts of
respondents on the
digital transformation
of the organization
Industry 4.0
readiness
model
dimensions
(Weking et al., 2020). One of the reasons why firms are not able to transform the existing
business models is because they do not understand their existing business models (Johnson
et al., 2008). It is not only imperative that technology is deployed in an organization rather we
need to customise the business models which will help the organization to grow. An
organization that has a business model which can translate the technology to business ideas
will thrive in the marketplace. Hence a technology ready business model will be a readiness
factor for Industry 4.0 implementation. The excerpts of the respondents are given in Table 8.
4.1.8 Leadership and top management support for Industry 4.0. Industry 4.0
implementation calls for the digital transformation of the organization (Verhoef et al.,
2021). Leadership will be the most important aspect in guiding the organization firstly in the
digital transformation process and later in leading the organization in the digital environment
(Sony and Aithal, 2020a). The leadership characteristics which a leader should possess would
be (1) visionary, (2) networking intelligence, (3) adaptable, (4) motivating coach, (5) digital
intelligence, (6) complexity master, (7) social intelligence, (8) democratic delegative, (9) agile,
(10) learning from errors, (11) role model, (12) diversity champion, (13) decisive courageous,
(14) creativity, (15) openness, (16) self-awareness, (17) ambidexterity, (18) knowledge-oriented,
(19) digital talent scout, (20) employee-oriented, (21) business intelligence, (22) lifelong learner
and (23) ethical (Klein, 2020).
Industry 4.0 is a radical change initiative because there would drastic reengineering of
business processes, supply chains, strategies, business plans etc (de Sousa Jabbour et al.,
2018;Sony and Aithal, 2020a). The leader would be the most important person who will guide
the change within the organization as a change leader (By, 2020). Industry 4.0 implementation
will lead to reorganization of existing work and therefore, the employees would be
reorganized, retrained and reallocated (Bonekamp and Sure, 2015). A strong leader will lead
the employees and other stakeholders in a goal-directed manner to meet the objectives of the
“Industry 4.0 implementation is not just technology, rather it is using concepts such as crowdsourcing,
personalisation, servitization, IoT in their business models. Just by implementing IoTs or CPS in the
organization will not change anything rather organizations should translate these technologies into a business
model which will help other organizations”P17
“There is a huge opportunity for the organizations to use the advanced technologies of Industry 4.0 across their
entire value chains, processes by using technology-mediated operational excellence, finding new business
growth areas using technology, and incorporating technological breakthrough in manner employees,
customers and other stakeholders create value. The strong business model will the organizations to succeed in
the marketplace”P23
“Industry 4.0 business models need to be simple and answer the question; how the organization will provide
products and service which the customer wants, and, in a manner, they want? Therefore, if an organization does
not play with the existing business model, no way it can create success”P37
“An organization should define the Industry 4.0 implementation strategy in terms of the customers,
marketplace, their core competencies, competitors and their weaknesses. Industry 4.0 is not just technology
implementation rather using technology strategically to achieve organizational goals and objectives”P27
“There should be a plan for converting implementation of Industry 4.0 to a long-term competitive advantage. It
is not just a short-term thing. We need to understand one thing that implementation of Industry 4.0 will lead to
new customers, employees with new skills, a new relationship with society and so on. Therefore, an
organization needs to understand the importance of strategy for the success of Industry 4.0”P24
“Industry 4.0 implementation means an organization will have a huge amount of data. What an organization
does with the data is most important. The organization should have a strategy to use the data and convert it to a
unique business opportunity which will create a competitive advantage for the organization in long term”P23
Table 8.
Excerpts of
respondents on
innovative Industry 4.0
business model
Table 7.
Excerpts of
respondents on the
readiness of
organization strategy
TQM
organization. In addition, another aspect is the top management support not only in terms of
resource allocation, but also understanding the strategic importance of Industry 4.0 and
taking tactical decisions for the successful implementation of Industry 4.0 (Sony and Aithal,
2020a). Furthermore, top management support can help to get necessary resources such as
facility, capital, IT, and human resources. The excerpts of the respondents are given in
Table 9.
4.1.9 Organizational culture. Organizational culture is defined as “the pattern of values,
norms, beliefs, attitudes and assumptions that may not have been articulated but that shape how
people in organizations behave and things get done. It can be expressed through the medium of a
prevailing management style in the organization”(Armstrong and Stephens, 2005). An
innovative organizational culture will help an organization to transition towards Industry 4.0
implementation because having such a culture instils in the organization an environment that
will encourage risky behaviour, an environment that will support new work behaviour, accepts
new challenges and supports creative work (Ziaei Nafchi and Mohelsk
a, 2020).
Most of the trivial and routine jobs would be done by machines in the Industry 4.0 era and
jobs which require higher-order thinking will be left for humans (Sony and Aithal, 2020a).
Organizational culture will have a significant impact on creativity and innovation.
Employees who are creative and innovative will be more effective if the organizational
culture supports them (Shanker et al., 2017). An innovative culture in an organization
supports and encourages creative work, and it will further enable one to face new challenges
(Ziaei Nafchi and Mohelsk
a, 2020). The excerpts of the respondents are given in Table 10.
4.1.10 Employee reward and recognition system. People have basic needs and if these needs
are not met then it becomes difficult for them to advance in their occupations. Besides,
“An organization which promotes creativity does not reprimand employees for thinking out of the box even if
they fail to solve the problem. It is a supportive environment which will enable employees to find new solutions.
Such organizations will create an environment which will help employees to innovate for every problem rather
than using the same solutions”P17
“An organization which is open, flat and has few hierarchies, with everyone encouraging new ways to solve an
issue at work will be able to transition to Industry 4.0 more quickly than others. This is because one can share
ideas immediately without having to go through hierarchies and it will create a culture of cooperation and
innovation”P26
“To bring in disruptive smart products and services, we need organizations who will encourage innovation. It
should be in the DNA of the organization to be innovative and think of new ways for making products and
services which will benefit the organization”P11
“The leader will be the most important person while implementing Industry 4.0 because he would be someone
whom everyone will be looking up to. So, I suggest organizations who have a visionary and strong leader will be
ready to implement Industry 4.0”P4
“Strong visionary leaders will drive the organization towards successful implementation of Industry 4.0. It will
face stiff implementation hurdles from stakeholders, and only strong leader will be able to instil the vision of big
picture”P11
“I cannot say what type of leadership style will be best for implementation of Industry 4.0, rather I would say a
leader with a strong vision towards Industry 4.0, integrity, ability to motivate employees and others to embrace
Industry 4.0, somebody who acts as per the situation will be a good leader for the digital transformation”P32
“A leader in this era should motivate the employees to unlearn and relearn. This is going to be a major
challenge, especially in developed countries where you have a mature workforce. If the organization wants to
sustain the competitive advantage, then the leader should be able to take swift decisions, be agile, be a coach for
the employees and above all a guide through the implementation of Industry 4.0 as well after that”P15
Table 10.
Excerpts of
respondents on
organizational culture
Table 9.
Excerpts of
respondents on
leadership and top
management support
Industry 4.0
readiness
model
dimensions
meeting their personal needs helps them to self-actualize which can further motivate them to
improve their performance (Marshall et al., 2015). Industry 4.0 is a socio-technical system
wherein social (human) and technical factors come together in a goal-directed manner to meet
the objectives of the organization (Davies et al., 2017;Sony and Naik, 2020). Therefore, a
reward and recognition system while implementing Industry 4.0 will bring out the best in
human elements which will help organizations (Aithal and Sony, 2020). The excerpts of
respondents are given in Table 11.
4.2 Quantitative study results: ranking of Industry 4.0 readiness dimensions
Industry 4.0 readiness factors were examined for both manufacturing and service
organizations. The ten Industry 4.0 readiness factors were ranked based on the mean
scores and are depicted in Tables 12 and 13. To determine the exact criticality of the readiness
factor, a methodology suggested by Adabre et al. (2019) was followed. The mean score was
normalized. The readiness factors whose normalized scores above 0.5 were considered to be
“Organizations should have both monetary and non-monetary reward systems which will help the employees
to remain motivated while implementing different facets of Industry 4.0. Industry 4.0 implementation is a
complex task, and the organizations should motivate the employees by rewarding and recognising their
achievements. It will help them sustain their performance and others will be motivated to perform better”P20
“Recognising employees who have shown willingness to work hard while implementing Industry 4.0 will help
them to be motivated to sustain their efforts as Industry 4.0 is a long journey. The employee motivation levels
should be very high and hence recognition will go a long way in sustaining it”P14
Readiness factor dimensions Manf Normalisation Rank
Technology readiness 6.22 1 1
Readiness of Industry 4.0 organization strategy 5.59 0.72 2
Organizational culture 5.12 0.51 3
Leadership and top management support for Industry 4.0 5.07 0.48 4
Digitalisation of supply chains 4.83 0.38 5
Innovative Industry 4.0 business model 4.78 0.36 6
Employee adaptability with Industry 4.0 4.41 0.19 7
Extent of digital transformation of organization 4.21 0.10 8
Employee reward and recognition 4.12 0.06 9
Smart products and services 3.97 0 10
Readiness factor dimensions Service Normalisation Rank
Employee adaptability with Industry 4.0 6.21 1 1
Technology readiness 5.72 0.79 2
Organizational culture 5.27 0.60 3
Innovative Industry 4.0 business model 5.14 0.54 4
Leadership and top management support for Industry 4.0 5.07 0.51 5
Employee reward and recognition 4.86 0.42 6
Readiness of Industry 4.0 organization strategy 4.83 0.40 7
Extent of digital transformation of organization 4.79 0.39 8
Smart products and services 4.31 0.18 9
Digitalisation of supply chains 3.90 0 10
Table 11.
Excerpts of
respondents on
employee reward
systems and
recognition
Table 12.
Ranking of the
manufacturing sector
Table 13.
Ranking in the service
sector
TQM
critical readiness factors. Normalized value 5(meanminimum mean)/(maximum mean
minimum mean). The normalized scores greater than 0.5 is considered as critical readiness
factor (Adabre and Chan, 2019;Osei-Kyei and Chan, 2017). The ranking for the
manufacturing sector.
In the manufacturing sector, the most critical readiness factors were technology readiness,
the readiness of Industry 4.0 organizational strategy and Organizational culture. The
integration of CPS with production, logistics and other related functions of production will
transform the modern organization into an Industry 4.0 factory (Lee et al., 2015); hence, the
technological readiness of the organization to adapt and apply the technology in the
organization is very important for the successful implementation of Industry 4.0.
In the service sector, the most critical readiness factors were employee adaptability with
Industry 4.0, technology readiness, organizational culture, and an innovative business model.
The ranking of critical success factors in service sectors is given in Table 13. The
simultaneous production-consumption nature of service warrants employees to be the key
element in delivering the service (Parasuraman et al., 1985;Sony and Mekoth, 2012) therefore
employee adaptability with Industry 4.0 is important for its success.
The ranking for small and medium scale industries suggests technology readiness, the
readiness of Industry 4.0 organizational strategy and organizational culture. Technology
readiness is the highest-ranked critical Industry 4.0 readiness factor for both SMEs and LEs.
The ranking of SMEs and LEs is explicated in Tables 14 and 15.
In large enterprises, the ranking suggests technology readiness, employee adaptability
with Industry 4.0, organizational culture, the readiness of organizational strategy, leadership
and top management support, and an innovative Industry 4.0 business model as the very
critical Industry 4.0 readiness factors.
Readiness factors SME (mean scores) Normalisation Rank
Technology readiness 5.89 1 1
Readiness of Industry 4.0 organization strategy 5.137 0.63 2
Organizational culture 4.89 0.51 3
Employee adaptability with Industry 4.0 4.79 0.46 4
Leadership and top management support for Industry 4.0 4.68 0.41 5
Innovative Industry 4.0 business model 4.44 0.3 6
Digitalisation of supply chains 4.20 0.18 7
Extent of digital transformation of organization 4.10 0.13 8
Employee reward and recognition 4.03 0.1 9
Smart products and services 3.82 0 10
Readiness factors LE (mean scores) Normalisation Rank
Technology readiness 6.09 1 1
Employee adaptability with Industry 4.0 5.41 0.61 2
Organizational culture 5.39 0.60 3
Readiness of Industry 4.0 organization strategy 5.36 0.58 4
Leadership and top management support for Industry 4.0 5.34 0.57 5
Innovative Industry 4.0 business model 5.26 0.53 6
Extent of digital transformation of organization 4.70 0.21 7
Employee reward and recognition 4.70 0.21 8
Digitalisation of supply chains 4.60 0.16 9
Smart products and services 4.31 0 10
Table 14.
Ranking of readiness
factors in SMEs
Table 15.
Ranking of readiness
factors in LEs
Industry 4.0
readiness
model
dimensions
As the data is non-normal, nonparametric tests were used to test the difference in groups
(Milenovic, 2011) of Industry 4.0 readiness factors in manufacturing and service sectors. The
group difference was statistically different using Mann Whitney UTest p-Value and is
explicated in Table 16. Technology readiness, the readiness of Industry 4.0 organizational
strategy and digitization of supply chains had higher mean scores compared to a service
organization. This indicates in manufacturing these dimensions are very vital compared to
services. It could be because there is tangibility of output in manufacturing, inventory,
automated production etc (Chase et al., 1998), therefore technology, strategy and supply chain
play a major role in manufacturing. However, employee adaptability with Industry 4.0, extent
of digital transformation of the organization and employee reward and recognition the mean
scores were higher for the service sector compared to manufacturing organizations. Services
are labour intensive, simultaneous production consumption, service induced variability and
hence employees are key in success of services (Allmendinger and Lombreglia, 2005;Chase
et al., 1998;Sony and Mekoth, 2012). Therefore, in service industry employee adaptability and
reward and recognition were the key factors. Most of manufacturing organizations are
automated compared to services (Frohm et al., 2008;Parschau and Hauge, 2020), therefore,
respondents felt that in service extent of digital transformation of organization is important
compared to manufacturing.
Similarly, the testing for mean score of SME and LE explicated in Table 17 suggest that
organizational culture, leadership and top management support for Industry 4.0, innovative
Manf Service Mann Whitney Utest pvalue
Technology readiness 6.22 5.72 0.01**
Readiness of Industry 4.0 organization strategy 5.59 4.83 0.00**
Organizational culture 5.12 5.28 0.50
Leadership and top management support for Industry 4.0 5.07 5.07 0.95
Digitalisation of supply chains 4.83 3.90 0.01**
Innovative Industry 4.0 business model 4.78 5.14 0.23
Employee adaptability with Industry 4.0 4.41 6.21 0.00**
Extent of digital transformation of organization 4.22 4.79 0.046**
Employee reward and recognition 4.12 4.86 0.01**
Smart products and services 3.98 4.31 0.24
Note(s): ** Significant difference at 5%
Readiness factors
SME
(mean)
LE
(mean)
Mann Whitney Utest
pvalue
Technology readiness 5.90 6.10 0.30
Readiness of Industry 4.0 organization strategy 5.14 5.37 0.35
Organizational culture 4.90 5.39 0.01**
Leadership and top management support for
Industry 4.0
4.69 5.34 0.07
Digitalisation of supply chains 4.21 4.61 0.04**
Innovative Industry 4.0 business model 4.45 5.27 0.00**
Employee adaptability with Industry 4.0 4.79 5.41 0.14
Extent of digital transformation of organization 4.10 4.71 0.04**
Employee reward and recognition 4.03 4.71 0.02**
Smart products and services 3.83 4.32 0.10
Note(s): ** Significant difference at 5%
Table 16.
Mean score difference
in manufacturing and
service
Table 17.
Mean score difference
in SMEs and LEs
TQM
Industry 4.0 business model, the extent of the digital transformation of the organization, and
employee reward and recognition were higher for LEs and different statistically significant
compared with SMEs.
This could be because LEs tend to offer more products and services to a variety of
customers compared to SMEs who focus on niche markets (Nicholas et al., 2011;Perrini et al.,
2007). Therefore, LEs need an innovative business model, organizational culture and
leadership and top management support. Besides, LEs have more than 250 employees
(Ayandibu and Houghton, 2017); hence, for such as large force to be motivated an
organization needs employee reward and recognition system which is dynamic. LEs are large
organizations and needs to be automated and digitalized for increased productivity and
efficiency (Bessen et al., 2020;Craig and Noori, 1985); therefore, the extent of digital
transformation plays a major role as a readiness factor.
5. Implication for the organization
This study has uncovered ten dimensions of Industry 4.0 readiness model. Further it has
discovered critical dimensions in manufacturing, service, SME and LEs. Organizations
before implementing Industry 4.0 can use these dimensions to assess whether they are ready
to implement Industry 4.0. Each of the dimensions can be used by the organizations to assess
the current state. To cite an instance the organizational culture, organizations can conduct a
self-assessment study to first understand whether their existing organizational culture is
ready to implement Industry 4.0. This is can be done through a survey and also through focus
group discussions. Subsequently, depending upon the present state, a future strategic and
tactical plan may be devised so that organizations culture may be improved. Similarly, such
an exercise can be done with other dimensions too. Also depending on the type of sector or
size of the organization the importance of the weightage attached to these dimensions should
vary as per the critical dimensions in the context of application. To cite an instance if the
organization is a service organization and employee reward and recognition dimensions has a
lower score. This is a cause of concern for service organizations and needs to be corrected on a
priority compared to manufacturing organization. Therefore, this framework of readiness
factors will help organizations as an investigative, readiness and sustenance tool for the
successful implementation of Industry 4.0
6. Conclusion and further work
The study proposed a conceptualisation of the readiness dimensions for Industry 4.0.
Utilizing qualitative methods, the study explored the ten dimensions of the Industry 4.0
readiness framework. Subsequently, the criticality of the dimensions in manufacturing,
service, SMEs and LEs were found in the quantitative study. The difference in the dimensions
in manufacturing and service and between SMEs and LEs was also explored in this study.
The limitation of the study is that the items are measured on a single-item scale. Since the sub-
dimensions are homogenous constructs the single item scale was used for simplicity.
However, a future study can explore the same topic as a multidimensional item scale. Another
study would be longitudinal analysis of the dimension pre, during and post-implementation
of Industry 4.0 to understand the time-oriented behaviour of Industry 4.0 readiness models.
The authors are carrying out case studies wherein this model will be used to assess Industry
4.0 readiness and study the efficacy of the model in predicting the successful implementation
of Industry 4.0.
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Research, Vol. 15, pp. 70-83.
Corresponding author
Jiju Antony can be contacted at: Jiju346@googlemail.com
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