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Key Success Factors for the Implementation of
Digital Technologies in the Context of Industry 4.0
NVH and Information Systems
Virtual Vehicle Research GmbH
NVH and Information Systems
Virtual Vehicle Research GmbH
Infineon Technologies Dresden GmbH
& Co. KG
NVH and Information Systems
Virtual Vehicle Research GmbH
Abstract— The implementation of digital technologies in
general and in the context of Industry 4.0 in particular is a socio-
organisational challenge. After an introduction into Industry 4.0
and a summary of its key enabling technologies, this paper pre-
sents a qualitative analysis of key factors that can influence the
success of implementing digital technologies into organizations
from the literature as well as from a single industrial case study
at a semiconductor manufacturer. Identified key success factors
are assigned to two different non-technical dimensions, manage-
ment, and processes (organizational dimension), as well as cor-
porate culture and employees (social dimension). They include
for example, the creation of a common understanding of the as-
is situation, the challenge to be solved and the envisaged to-be
situation after technology implementation among all relevant
stakeholders. Equally important, however, is active support of
top management to promote implementation projects and a ded-
icated project manager with sufficient technical expertise in all
related technical domains.
Keywords—Success factors, industry 4.0, digitalisation
I. INTRODUCTION AND MOTIVATION
Referring to a Fourth Industrial Revolution in which the
worlds of production and network connectivity are integrated
through information and communications technology (ICT),
Industry 4.0 is an umbrella term for a wide variety of tech-
nologies, methods and applications for improving production
processes and their supporting processes , , . Origi-
nally initiated in Germany, Industry 4.0 can generally be in-
terpreted as the digitization or digital transformation of the
industry, which is taking place as a result of the increasing
adoption of digital technologies in the manufacturing sector.
Digitization as the progressive convergence of the real
and virtual world is becoming the main driver for innovation
and change in all sectors of our economy . Major social,
economic and political triggers for Industry 4.0 are in partic-
ular shorter development and innovation periods, individual-
isation on demand and increasing individualisation of prod-
ucts and – in extreme cases – of ‘batch size one’, higher flex-
ibility in development and production, decentralisation in-
volving a reduction of organisational hierarchies, and re-
source efficiency due to an increase of prices, as well as a
social change in the context of ecological aspects requiring
an intensified focus on environmental sustainability .
The digital transformation of manufacturing companies is
being decisively shaped by numerous players from the ICT
industry. From this perspective, Industry 4.0 describes the on-
going "informatisation" of traditional factories. In connection
with Industry 4.0, Internet of Things, Cyber-Physical Produc-
tion Systems, Big Data and Artificial Intelligence represent
possible key technological components . In further,
broader definition attempts, Industry 4.0 is even referred to
as complete digitization over the entire product life cycle, in
which, beginning with concept definition over product devel-
opment, production, use and service, action and decision-rel-
evant knowledge is collected and integrated in a way that pur-
poseful analyses are made possible , .
From this perspective, Industry 4.0 does not exclusively
refer to the production process, but also includes upstream
processes such as product development, or downstream pro-
cesses such as product operation and service. According to a
pioneering strategy document by Kagermann et. al , three
integration features can be linked to the Industry 4.0 concept:
Horizontal integration refers to the integration of different
ICT systems in the various stages of the manufacturing and
planning processes in a company, vertical integration refers
to the integration of different IT systems at various hierar-
chical levels (e.g., actuator and sensor level, manufacturing
and execution level, production level and corporate planning
level) and end-to-end digitization refers to the integration of
different IT systems across the entire engineering process, so
that the digital and real worlds are integrated across the entire
supply chain of a product and even across companies .
Implementing digital technologies such as the Internet of
Things, Digital Twins, Augmented and Virtual Reality, Mo-
bile and Collaborative technologies or Artificial Intelligence
in organizations is a complex challenge, because organisa-
tions are socio-technical systems. As knowledge about the
factors that contribute to the success of technology imple-
mentation projects is fundamental, the authors pose the fol-
lowing research question: What are key success factors for
the implementation of digital technologies in the context of
Industry 4.0? To answer this research question, the authors
first conduct a literature review to derive key success factors
and then pursue a single case study to validate them.
II. THEORETICAL BACKGROUND
A. Internet of Things and Digital Twins
As an umbrella term for the interconnectedness of physi-
cal objects by using internet technologies, Internet of Things
technologies play a central role in the context of Industry 4.0,
too . Therefore, many speak of an Industrial Internet or
an Industrial Internet of Things ,  and refer in sum-
mary to the increased networking of production machines
with machines or the networking of the production process
with the product to be manufactured.
In the context of digitization, the digital twin is playing an
increasingly important role. A digital twin is able to connect
the physical (real) world with the digital world . In gen-
eral, digital twin has mutated into a main concept in connec-
tion with the Industry 4.0 wave. A digital twin provides vir-
tual representations of systems along their life cycles  and
can be described as the intelligent, digital image of a real
product or process . For example, a digital production
twin can digitally map the production process and thus enable
a targeted, controlling , planning  and intervention in
the production process, if the digital twin is continuously syn-
chronized with the physical production environment. A digi-
tal twin uses data provided by sensors installed in the produc-
tion process on the one hand and technologies from the Inter-
net of Things on the other. Wireless sensor networks (WSN)
and Radio-frequency identification (RFID) are used to form
an extensive network for supporting IoT  for the further
deployment of services. The future state of a fully connected
manufacturing system that operates primarily without human
workforce by generating, transferring, receiving and pro-
cessing the necessary data to perform all the tasks required to
manufacture all types of goods is often referred to as a Smart
B. Augmented and Virtual Reality
The augmentation of the physical environment through
digital information (Augmented Reality) and the interaction of
a user in a completely virtual world (Virtual Reality) show a
promising potential. Augmented reality has become an inte-
gral part of Industry 4.0, as it enables workers to access digital
information and overlay that information with the physical
world . With Augmented, Virtual and Mixed Reality tech-
nologies, knowledge-based tasks of production employees can
be supported by using modern assistance systems  and
thereby can exert a positive influence on productivity. By im-
proving the supply of information at work while leaving both
hands free and not restricting employees in their manual work
practices, data glasses are often today as innovative and useful
devices for employees in production, service and quality man-
agement . For example, data glasses can be used to pro-
vide production workers with helpful information for the com-
pletion of their tasks, as quality information, 3D exploded
views or other supporting additional information can be easily
displayed in a person’s field of view .
C. Mobile and Collaborative Technologies
Recent developments in digitization, such as social soft-
ware and mobile technologies, also offer promising opportu-
nities to help knowledge workers in production environments
by supporting knowledge processes, decision making and so-
cial interaction , . In collaborative production environ-
ments, improving knowledge building, decision making and
social interaction between team members is an important is-
sue. Mobile technologies enable a social network based col-
laborative problem-solving method utilizing for instance root
cause analysis to formalise problem solving instructions and
expert opinions described in natural language, given that em-
ployees overcome their hesitations to share knowledge, as-
sure their commitment to report new problems and avoid dis-
tracting them with the use of mobile devices on the shop floor
. Thereby experienced engineering can identify causes for
a particular problem while more inexperienced people could
take advantage of the mobile app to identify solutions that
were successful in the past.
D. Machine Learning and Artificial Intelligence
Operating complex production machines requires not
only technical knowledge about the machine to be operated,
but also domain experience and process knowledge Artificial
intelligence requires to integrate human process knowledge
into models to (semi-)automate decision making. Therefore,
Artificial intelligence technologies such as Machine Learning
or Deep Learning are increasingly in demand in production
environments. The aim is to identify, for example, faults in
production processes such as vibrations that can lead to devi-
ations in product quality and manifest themselves in a short-
ened product life or in severe application problems . An
almost unmanageable number of existing Machine Learning
methods are contrasted with an equally large number of pos-
sible areas of use and application , . Therefore, expe-
rienced industrial data scientists are a prerequisite to address
identified problems with machine learning approaches. The
use of data science and modern methods of data analysis
plays an important role in the analysis of production data
. For instance, Stanisavljevic et al.  have shown that
different engine configurations can be identified from analys-
ing values measured on an engine test bench using methods
from data science.
E. Organisations as socio-technical systems
In the context of Industry 4.0 and the digital transfor-
mation of companies, the role of human beings is also closely
examined. Due to the rapid pace of technological change, it
can be clearly seen that the role of employees in the manu-
facturing industry is constantly changing. The increasing au-
tomation of manufacturing processes has already dramati-
cally reduced the amount of manual work, while the growing
complexity of manufacturing systems and processes requires
the remaining employees to have both increasingly broader
and deeper qualifications .
In order to emphasize that the human being should also
be at the centre of tomorrow's factories, there is a constant
debate about the additional empowerment of humans through
digital technologies , , . Information and commu-
nication technology can help employees in collaborative pro-
duction environments , for example, because digital tech-
nologies better support problem-solving and decision-making
processes, or because access to action and decision-relevant
information is further improved for employees . How-
ever, digital technologies cannot only support human work,
but also partially or even completely automate it. To generate
cost savings, individual components of knowledge work are
increasingly being automated under the guise of "robotic pro-
cess automation" in addition to the classic production process
automation in factories . Similar to the robots in classical
production automation, the standardizable parts of
knowledge work usually performed by humans is conducted
by software agents, which often use modern data analysis and
Digital technologies such as Augmented or Mixed Real-
ity, Machine Learning and Artificial Intelligence, Digital
Twins or Robotic Process Automation for knowledge-based
work processes are playing a key role in the digital transfor-
mation of companies . Because modern work always in-
volves a structured interaction of people with information and
communication technologies, the digitalisation of industry
can by no means be reduced to a mere technical dimension.
In companies, people work together in an organized manner
to fulfil tasks within an organizational structure. Digital tech-
nologies can help people to perform their tasks better. Never-
theless, some technologies push existing forms of organiza-
tion to their limits. Therefore, the organization of work and
the technologies used in the work must be aligned.
Enterprises are socio-technical systems, and the introduc-
tion of digital technologies in companies is a socio-technical
undertaking: Because Industry 4.0 implementation projects
always have to involve people from a wide range of different
fields and disciplines , it is important, for example, to de-
termine the benefits of Industry 4.0 projects in advance at the
three levels, people, organization, and technology , 
and to communicate them to the key stakeholders accord-
ingly. This should ensure that the technical solutions devel-
oped in implementation projects can be transferred as
smoothly as possible into productive operation. The techno-
logical solutions must also be tightly integrated into organi-
zational processes and structures. It is obvious that although
digital technologies and digital design of work can signifi-
cantly modernize work processes, stakeholders must be ac-
tively involved in these change processes and must be con-
vinced to adopt the new solution approaches accordingly
F. Implications from Information Systems Research
Although terms such as digital technologies, digital trans-
formation, or digital innovation have arrived in the German-
speaking mainstream in recent years, many findings from es-
tablished research on information systems can be applied to
the introduction of digital technologies in the context of In-
dustry 4.0, too. For example, it must always be borne in mind
that in addition to the design of technical systems to support
information and knowledge sharing, the design of organiza-
tional measures is at least as important . There is no doubt
that digitization and digital transformation affect not only the
technological work environment, but also a wide range of so-
cial aspects such as processes, organizational structures, em-
ployee skills and corporate culture .
Well-known, frequently cited explanatory models from
information system research, such as the technology ac-
ceptance model  or the information system success model
,  already provide a large number of indications of
success factors for the introduction of new technologies and
solutions. From the technology acceptance model, essential
factors such as perceived usability and perceived usefulness
of the technology to be implemented can be derived ,
while the information system success model focuses on the
necessity of the positive effects of introduced information
systems on the levels of employees ("individual impact") and
organization ("organizational impact"). Information quality,
system quality and service quality also play a major role in
the success of the introduction of information systems, since
these aspects influence the intention whether people will use
an information system or not , . Thus, both models
explain from different perspectives the relationship between
the information system, the development of a behavioural in-
tention to use the information system and the stimulated first
or continuing use of the information system.
From both models it can be deduced that non-technical
factors in particular play a driving role in the success of a new
technical solution with end users. Furthermore, the expected
success of introducing information systems also depends
much on how these information systems are perceived by us-
ers in advance. It is primarily the user perception that deter-
mines the initial intention to use a novel system . There-
fore, measures to stimulate a positive user perception in ad-
vance are required to enhance the success of implementation
III. A REVIEW OF KEY SUCCESS FACTORS
The successful design of digital workflows or digitally
supported workflows usually requires an integrated, interdis-
ciplinary, participative and agile approach that enables the
identification, analysis and best possible support of human
work practices and their context in a predominantly digital
environment . Therefore, the implementation of techno-
logical solutions always requires a compatible and optimal
design of the associated physical workflows. Only then can
employees successfully integrate digital solutions into their
work practices. Offered digital technologies must therefore
go hand in hand with digital work processes. The socio-tech-
nical design of a new, digital solution must be evaluated with
the help of a suitable methodology .
The introduction of digital technologies in the context of
Industry 4.0 is complex, since development and introduction
projects usually must involve people from a wide range of
disciplines and fields . To create a consistent picture for
all involved actors already before the factual implementation
phase and eliminate communication problems between stake-
holders coming from different disciplines and using a differ-
ent vocabulary, a systematic approach for the documentation
and structuring of Industry 4.0 use cases to be communicated
to the involved stakeholders should be applied . This use
case description includes the actual situation with the cur-
rently used technology, the organizational challenge the en-
visaged digital solution should solve, the expected target sit-
uation after implementing the solution and the expected ben-
efits for different stakeholders gained from the introduction.
Having this information at hand before starting the imple-
mentation project will increase the probability that the in-
volved stakeholders will support the implementation project
Richter et al.  rely on a careful survey of the current
situation and a coordinated definition of the target situation
in the sense of a ‘digital work design’ - in cooperation with
the relevant stakeholders - and recommend the use of per-
sonas, activity scenarios and early, non-functional proto-
types for solution validation. This user-centred approach fo-
cuses on the current working practices of employees, but with
the strong intention to optimally adapt them later. To support
work practices in the best possible way, or to transfer them
into digitally supported work practices, the physical work
practices must first be captured and understood in depth.
Through such a process, employees can best learn how to use
new technology and have their work practices best supported
by digital technologies .
As with many digital technologies, and especially those
designed to support communication and interaction between
employees, it is important that stakeholders are involved
early in the development and deployment process and can
quickly perceive the benefits (from first-time use). The active
persuasion of users by the project management plays a sig-
nificant role in this process . Here, the project manage-
ment can even take on the role of a solution ambassador and
encourage employees to try out and then continue using the
new solution through targeted actions such as individual
training, usage incentives or rapid transfer of user require-
ments into the solution .
Success factors that specifically apply to the introduction
of knowledge-based documentation systems include the ini-
tial provision of a sufficiently high number of useful
knowledge contributions immediately after the solution
rollout as well as the motivation of suitable persons in the
company who act as multipliers and in turn motivate other
employees to use and contribute to the new solution ,
. Digital technologies must be compatible with organiza-
tional processes. A further success factor is the successive
preparation of the organization for the change that the intro-
duction of digital technologies will bring, including the in-
volvement and training of users and the adaptation and rede-
sign of organizational processes to ensure that technology and
processes are aligned .
The role of the management in the introduction of digital
technologies is central : However, a distinction must be
made between different levels such as management attention
to an implementation project, management support, and the
direct use of the new solution by the management . The
strongest impact on implementation success is achieved when
the management is perceived by employees not only as solu-
tion promoter, but even as an active user .
The existence of a concrete and understandable digitali-
zation strategy helps to better classify digitalization projects
within the company and to set the appropriate framework to
implement them , . Designing such a strategy is a
management task. In any strategy development, it is im-
portant to work out which goals should be associated with the
introduction of digital technologies in the company and then
set the appropriate mechanism to monitor goal achievement.
Two approaches to the introduction of digital technologies
can be distinguished, but they can also be combined . In
an exploration-centred strategy, the exact way in which the
solution should be used by the users is initially left to the us-
ers within the framework of a participatory approach and ap-
plication scenarios are gradually identified by users them-
selves, whereas in the promotion-centred strategy, the new
solution and its modes of use are communicated to users in a
coordinated manner from the very beginning, with the close
support of management, and then specifically trained.
The corporate culture plays a significant role in the suc-
cess of the introduction of technological solutions , ,
. An open, innovative, and participatory culture in which
information is shared is much better suited than a closed, in-
novation-hostile culture in which information and knowledge
are, in extreme cases, understood as a personal good and
therefore not shared at all . Since implementation pro-
cesses are usually controlled by the management, but affect
employees strongly, the way employees and managers inter-
act and communicate with each other plays an essential role.
As a rule, shaping the corporate culture is a management
task. Managers must exemplify a culture based on trust and
mutual appreciation . If the corporate culture is not open,
employees may see a new solution as a threat to their job.
IV. INDUSTRIAL CASE STUDY: WAFER PRODUCTION
A. As-is situation and challenge
The case company is a semiconductor manufacturer that
produces high-quality chips with complex production tech-
nology based on 200/ 300 mm silicon wafers for innovative
applications in automotive electronics, security, smart cards,
energy management and multimarket applications.
The development of new products requires a high degree
of domain expertise and knowledge about the interrelation-
ships and effects of individual manufacturing sub-processes
on the manufactured product. The impact of new or modified
process sequences must therefore be repeatedly tested and
evaluated by conducting experiments on the production line.
Experiment management is an important business process in
the case company because experience management creates
the fundament for faster and more efficient product develop-
ment, but also for the associated production process develop-
ment. The new experiment management system should work
across several production sites - in the sense of a Smart Fac-
tory Cluster. Manual interventions by process operators dur-
ing the conduction should no longer be necessary and should
be replaced by an automated control system. The virtualiza-
tion of experiment management (i.e., the implementation of a
new digital technology) will empower employees to plan and
control experiments at other locations, or to plan and control
these experiments completely independently of their physical
workstations, e.g., also during a business trip or from the
The goal was to create a tool that is easy and intuitive to
use, with which experiments can be planned and performed.
Mechanisms were integrated into the tool, which can detect
possible errors in the planning of experiments at an early
stage or ultimately prevent them. Thus, more complex pro-
duction processes across several production sites should be
possible, but also controllable. The users of the new system
included people who have some form of control over the pro-
B. Key success factors of the implementation project
The automation of production and production processes
is a strategically anchored project in the case company to in-
crease quality and efficiency and to develop and produce in a
more resource-saving way. The implementation project de-
scribed here is also part of this strategic framework. In the
case company, all key stakeholders, both individuals and in-
terest groups, were involved in the development process. This
was to consider the complexity of the topic, which makes it
no longer possible for individual persons to have an overview
of the entire topic.
The new technical solution will affect many processes and
employees in the company. It was therefore important that
these highly motivated employees were involved in the defi-
nition of requirements and approaches to solutions at an early
stage. In this sense, it was important to disregard hierarchical
company structures and to organize the project development
team in a very flexible way. Thereby, it was possible to put
together a highly motivated team with the appropriate ex-
perts from various fields, such as IT and MES systems and
production at the connected sites.
A matrix organization, consisting of a line and a project
organization, enabled the participating experts to fulfill both
their daily business in the company and to participate in the
implementation project. For this to work smoothly, it was im-
portant to provide experts involved with sufficient resources
and, above all, time for the implementation project, so that
they could provide the best possible support for the imple-
mentation project. Nevertheless, or especially in such a pro-
ject organization it was necessary to push the project results
and not to lose sight of the project goals. This project coordi-
nation and management task was performed by a dedicated
project manager who, although not an expert in all technical
areas, had sufficient technical understanding to be able to me-
diate in conflict situations. Even if this may not appear effi-
cient at first glance, a development team must also be able to
pursue approaches to solutions in a complex environment that
ultimately prove unsuccessful.
V. SUMMARY AND CONCLUSION
Although the Covid-19 pandemic has helped to spread the
use of digital technologies, especially computer-based collab-
oration tools, enormously due to the "lockdowns" imposed by
governments around the world , a "normal situation" in
companies requires a well thought-out and structured intro-
duction process in order to be successful. While Covid#19
left companies and employees no other option to use remote
collaboration technologies that were already "available" as
infrastructure, implementation projects in the field of Indus-
try 4.0 must be treated in a differentiated way .
After a short introduction into Industry 4.0, the role of
digital technologies in the context of Industry 4.0 and the
presentation of concrete, relevant digital technologies, this
paper has presented key success factors that – if considerate
– can significantly improve the implementation of digital
technologies. Knowledge of success factors and the adapta-
tion of one's own actions regarding the identified success fac-
tors can increase the success of implementation projects.
However, there is - as usual in success factor research - no
guarantee that success will be increased. While this paper was
a qualitative study of key success factors based on a literature
review and a single-case study, future works should carefully
examine several cases to validate key success factors and also
try to quantify them.
Fig. 1. Key success factors for digital technology implementation projects
Identified key success factors (cf. Fig 1) can be assigned
to two different non-technical domains, management, and
processes (organizational domain), as well as corporate cul-
ture and employees (social domain), while technologies
themselves cannot considered a success factor. Identified key
success factors include the adaptation of the relevant (manual
and digital) processes, the initiation of training and persua-
sion processes among all users, the involvement of all rele-
vant stakeholders in the implementation process, and the ac-
tive support of the management on shaping an innovation-
friendly corporate culture. Furthermore, a holistic under-
standing of the current situation, the organizational challenge,
and the expected target situation after the implementation of
digital technologies is to be formed by involving all internal
and external stakeholders in this discussion process. Future
studies should explore multiple use cases to derive further
success factors and try to quantify them. However, the digital
technologies themselves are not success factors, as success
factors are factors that should facilitate the adoption of tech-
nologies. The authors' notion of key success factors as socio-
organisational constructs reflects the interesting combination
of people, organisation, and technology.
The iDev40 project has received funding from the ECSEL
Joint Undertaking (JU) under grant agreement No 783163.
The JU receives support from the European Union’s Horizon
2020 research and innovation programme. It is co-funded by
the consortium members, grants from Austria, Germany, Bel-
gium, Italy, Spain and Romania.
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