Chapter 01: Introduction
1.1.
Research Title
Impact
of industry 4.0 adoption on the employee productivity: A study from Sri Lankan
manufacturing sector
1.2.
Background of the study
The advancement of technology has created radical
changes in the way how organisations perform their business operations. These
changes can be interpreted as industrial revolutions
Throughout the years, traditional
businesses are confronting new business issues in today's volatile economy due
to progressive globalisation, mass customisation, and competitive business
settings. As a result, the demand for faster delivery times, more efficient and
automated processes, higher quality, and customized products have become
compelling business needs (Simmert et al. 2019). Industry 4.0 provides
solutions to those uprising issues and refurbish the manufacturing operations
in many ways (Boggess,
2022). Whereas it converted the traditional
manufacturing model into new landscape where factories became smart factories.
Industry 4.0 enables faster, more flexible and efficient processes to
manufacture higher-quality goods at lower prices by allowing data to be
collected and analysed across equipment (Rubmann et al., 2022). Industry
4.0 enable digitisation via the use of sensors, software, connectivity and big
data analytics which resulted more efficient factories while facilitating flexible
business models (Zheng et al., 2020). Although I4.0 allows
real-time monitoring of the entire manufacturing process where the user has the
visibility of total organization's value chain including the materials used in
production, the materials' supply at various stages, the materials' origins,
and the various activities involved with productions (Zadjali and Ullah, 2021).
These technological advancements are
progressively being adopted by the worldwide labour market as new technology
makes it easier for companies to automate routine tasks and could disrupt the
balance between job responsibilities completed by humans and those completed by
machines and algorithms. Thus, industry 4.0 applications created massive
changes in the way how employees perform their tasks and duties (Ejsmont, 2021). Workforce productivity is important to the success of any
organisation. Humans may upskill, take on new responsibilities, offer higher
value, and focus less on repetitive jobs because of industry 4.0 adoption and automated
manufacturing facilities (Hiskey, 2017). Although employees could therefore
expect to be employed in more interesting and challenging roles in the future, facilitating
their personal development and growth. There are several aspects of employee
productivity that can be impacted with the adoption of industry 4.0 technology
to any manufacturing organisation. As
smart technology becomes more ubiquitous, it is necessary to evaluate the
influence that use will have on the workforce. These influence on the employee
productivity can be either positive or negative. Therefore, this research aims
to analyse how industry 4.0 technologies affected employee productivity in
manufacturing organisations.
1.3.
Problem statement
Industry 4.0, as a current trend in
manufacturing technology for automation and data sharing, it revolutionises the
operations of production lines, supply chains, and product portfolios. Although
it is disrupting how organisations make and transport items today and
transforming the manufacturing workplace of the future. Industry 4.0 envisions
much higher productivity, efficiency, and self-managing manufacturing processes
in which people, machines, equipment, logistical systems, and work-in-process
components directly connect and collaborate using low-cost mass production
efficiencies to achieve higher customisations. Thus, as a result of the fast-paced
environment created by technology adoption, transforms how employees perform
their duties and change their behaviours and responsibilities. Such changes
impact on employee productivity either negatively or positively. Although no
organisation can be fully automated by eliminating total human operations as
human intervention is necessary in handling technology, decision making and
etc. Therefore, this study will analyse the impact of industry 4.0 technology
adoption on employee productivity of the manufacturing sector. The results of
the study are applicable to the manufacturing sector in finding out employee
productivity variations based on the industry 4.0 adoption thus be able to
improve their employee productivity.
1.4.
Problem Justification
For a variety of reasons, the Fourth
Industrial Revolution, or Industry 4.0, is vitally relevant and becoming
increasingly significant in manufacturing. Industry 4.0 is the next wave of
technology that will drive operational efficiency. Failure to implement Fourth
Industrial revolutionary technologies will force firms to lag behind, as their
activities will not be digitally adequate to strive intense competition.
Industry 4.0 technology aids in the management and optimization of all elements
of the production and supply chain activities of any manufacturing
organization. It gives access to the real-time data and insights that need to
make better, faster business choices, which helps to run the entire operation
more efficiently and profitably.
As new technology makes it easier for firms
to automate ordinary operations, these technological improvements are gradually
being accepted by the global labour market, potentially disrupting the balance
between work duties fulfilled by humans and those completed by machines and
algorithms. As a result, industry 4.0 applications have resulted in significant
changes in the way how employees perform their tasks and duties (Jesmond,
2021). The productivity of a company's workforce is critical to its success.
Because of industry 4.0 adoption and automated production facilities, humans be
able to upskill, take on new tasks, provide more value, and focus less on
repetitive activities (Hiskey, 2017). As smart technology becomes more common,
it's important to assess the impact it'll have on the workforce. The impact of
this on the employee productivity can be either positive or negative. The
analysis of the influence of industry 4.0 adoption on employee productivity is
important to manufacturing companies, as it allows them to assess their
performance and see how their workforce needs to adapt or be trained.
1.5.
Objectives of the study
1.
To finding out industry 4.0
adoption level (based on 6 dimensions) of Sri Lankan manufacturing sector.
2.
To determine the relationship
between the industry 4.0 adoption and the employee productivity
3.
To finding out industry 4.0
applications that could be adopted in future based on the current adoption
level in manufacturing sector of Sri Lanka.
1.6.
Significance of the study
The industry 4.0 is becoming increasingly
important in production for a variety of reasons. As it is the next generation
of technology improvement, it helps organisations to improve their operational
efficiency. Failure to deploy breakthrough industry 4.0 technologies, would
cause businesses to fall behind, since their activities will not be digitally
adequate to compete in today's market. Worldwide manufacturing organisations
adopt this industry 4.0 technologies with the aim of improving their efficiency
and profitability. Even though technology has a significant impact on the type
and features of the workforce, it is important to study the impact of it on the
employee productivity. I4 technological applications, modifies or destroys traditional
vocations, while new ones are being established. Thus, to remain employable,
people in these evolving occupations must refresh their knowledge and skills
where new technology can be used to address those. It is important to identify
how the employee productivity change in related to the skill gaps, attitudinal
and behavioural issues, employee engagement, satisfaction etc., where new
technological applications can be applied to resolve such issues. Therefore,
given the impression that impact of industry 4.0 applications to the employee
productivity is a cyclical process where new innovations or disruptions could
resolve the risen productivity issues. Thus, it is important to analyse the
impact caused to the employee productivity by industry 4.0 applications with
the aim of improving organisational productivity.
1.7.
Scope of the study
This study is a comparative analysis based
on the concept of industry 4.0 applications. The proposed study will be
conducted for a population derived from the manufacturing companies in Sri
Lanka. Such that the population comprises of 10 manufacturing companies which
located in the BOI (board of investors) zone in Biyagama, Sri Lanka. Even
though there are several manufacturers listed in Sri Lanka, this study will be
conducted for manufacturing companies which located in Biyagama BOI zone with
the practical limitations and time constraints. For the analysis, total of 500
responses will be collected from a closed-ended descriptive questionnaire as
the research instrument. The survey conducts on the employees of different companies,
departments, different age limits and employees with different experience
levels.
Chapter 02: Literature Review
2.1.
Industry 4.0
With technological advancements, many
sectors are heading toward the smart factory, where information is more
accurate and work is easier and more efficient. Massive changes in the way
industries operate have occurred during the last few decades as a result of
significant technological transformations (Vaidya et al., 2018). This is an area where knowledge is always evolving, and new
inventive technologies are increasingly entering the market.
Major shifts in the technological landscape
are described as the industrial revolutions. Humans have gone through four
industrial revolutions so far. The first revolution brought mechanization with
the steam power, the second brought mass production with the light of electric
energy, and the third revolution introduced electronics, telecommunications,
and computers (Butt,
2020). Fourth industrial revolution is the move towards
digitisation, smart technology that enables the use of Internet of Things
(IoT), Cyber-Physical Systems (CPS), big data analytics, cloud computing and
cognitive computing (Grencikova and Kordos, 2020). Cyber physical systems which is a main
component of the industry 4.0, it connects infrastructure, physical objectives,
human actors, machines, and processes across organizational boundaries,
enabling the fusion of the physical and virtual worlds by utilizing sensors,
actuators, and computation power to transmit data in real-time for
decentralized decision-making processes (Zheng et al., 2020). Industry 4.0 technologies have the potential to provide considerable
benefits to the manufacturing value chain. Improved productivity and
efficiency, more knowledge sharing and collaborative working, flexibility and
agility, simpler regulatory compliance, better customer experience, lower
costs, and higher revenues are just a few of the advantages (Khan and Turowski, 2016).
As Butt (2020) stated companies
can use six design concepts in implementing industry 4.0 applications. Those
are namely interoperability, virtualization, decentralization, real-time
capability, service orientation, and modularity. The ability of a company's
systems and employees to interact, exchange data, and coordinate actions is
referred to as interoperability (Bughin, et al., 2018).
Virtualization is associated with the monitoring of physical processes by a
single virtual resource from numerous physical resources, or multiple virtual
resources from a single physical resource. Decentralization is the process of
moving away from a central system and toward system components to reduce risks
and increase operational flexibility. Real-time capability refers to the
ability to collect and process data in real time so that educated and timely
decisions can be made. The capacity to employ big data analytics to generate a
predictive analysis that can aid in decision-making (Ejsmont, 2021). Service orientation refers to the capacity to employ big data
analytics to obtain a predictive analysis that can aid in better understanding
customers' demands. Modularity refers to a company's capacity to adapt to
changing requirements and industry demands (Butt, 2020).
2.2.
Industry 4.0 applications in the
manufacturing sector
Industry 4.0 is about revolutionising the way how
organization functions are performed and grows, not merely investing in a new
technology and tools to boost industrial efficiency. Industry 4.0 technologies
are rapidly adopted by manufacturing organisations in world wide due to
increased cost efficiency, labour productivity, democratize data, operational
efficiency, visibility, ROI, reduced cycle times etc. (Sima et al., 2020).
Traditional assembly lines are synchronous, with
established workflows based on production work orders that are processed
through corporate business systems. Each manufacturing station is synced with
the assembly line and receives centrally notified production steps. Industry
4.0, on the other hand, is built on asynchronous manufacturing, with components
in the production flow using auto identification technology to advise each
machine and operator at each step of the production process what needs to be done
to achieve the personalized end product (Lydon, 2022).
There are nine main technological advancements that
form the foundation for Industry 4.0. By applying the nine pillars, isolated,
optimized cells will come together as a fully integrated, automated, and
optimized production flow, leading to greater efficiencies and changing
traditional production relationships among suppliers, producers, and customers.
The nine pillars are namely, big data and analytics, autonomous robots, simulation, horizontal and vertical
system integration, the industrial internet of things, cybersecurity, the cloud,
additive manufacturing, augmented reality (Rubmann et al., 2022).
Big data analytics are based on massive data sets which
has lately become popular in the manufacturing industry and used to enhance
production quality, conserve energy, and extend the life of equipment. To
support real-time decision making in an Industry 4.0 setting, the gathering and
complete evaluation of data from a variety of sources such as production
equipment, systems, enterprise and customer-management systems will become
standard (Butt, 2020). Many businesses have long employed autonomous robots to
complete complex tasks, but robots are evolving to become even more useful.
They're becoming more self-sufficient, adaptable, and cooperative. Robots will
eventually interact with one another and work securely alongside people,
learning from them. These robots will be less expensive and have a wider
variety of capabilities than those currently in use in production (Demira et
al., 2019).
3-D simulations of products, materials, and
manufacturing processes are already employed in the plant operations. These
simulations will use real-time data to create a virtual model of the physical
world, which could include equipment, products, and people. This allows workers
to test and refine machine settings for the next product in line before the
physical changeover, reducing machine setup times and improving quality. This
concept can be employed in employee training and development where employee
being able to experience virtual world scenarios as a substitute for the real
world applications (Ali android Xie, 2021).
when cross-company, universal data-integration networks
expand and enable genuinely automated value chains, organizations, departments,
functions, and capacities will become much more cohesive with Industry 4.0 (Rubmann,
et al., 2022). In embeded computing, sensors
and field devices with limited intelligence, as well as automation controllers,
are often structured in a vertical automation pyramid, feeding into an overall
manufacturing-process control system (Khan and Turowski, 2016). However, with
the Industrial Internet of Things, more equipment will be enriched with
embedded computing and connected utilizing common protocols, including
incomplete products. With Industry 4.0's growing connection and usage of
standard communications protocols, the need to protect important industrial
systems and manufacturing lines from cybersecurity attacks has grown
significantly (Gajdzik et al., 2021).
Companies are currently employing cloud-based software
for some enterprise and analytics applications, but Industry 4.0 will
necessitate increasing data sharing across sites and company boundaries for
more production-related endeavors (Vaidya et al., 2018). Additive manufacturing
methods will be widely employed as part of Industry 4.0 to make small
quantities of bespoke products with construction advantages such as
complicated, lightweight designs (Rubmann, et al., 2022). A number of services
are supported by augmented-reality-based systems, including picking parts in a
warehouse and delivering repair instructions to mobile devices (Vaidya et al.,
2018).
2.3.
Industry 4.0 and employee
productivity
As new technology makes it easier for firms
to automate ordinary operations, these technological improvements are gradually
being accepted by the global labour market, potentially disrupting the balance
between work duties fulfilled by humans and those completed by machines and
algorithms. As a result, industry 4.0 applications have resulted in significant
changes in the way how employees perform their tasks and duties (Jesmond,
2021). The productivity of a company's workforce is critical to its success.
Because of industry 4.0 adoption and automated production facilities, humans be
able to upskill, take on new tasks, provide more value, and focus less on
repetitive activities (Hiskey, 2017). Industry 4.0 necessitates a cultural
shift in how humans interact with machines. Employees in an industry 4.0 world
will not only be able to collaborate across departments, sharing real-time data
and insights to make accurate workplace decisions, but they will also be able
to have some of their tasks automated by machines, freeing them up to work on
new, less tedious tasks and tight delivery timescales ( Grencikova and Kordos,
2020).
Every business will aim to ensure that all
employees involved in the organization's activities can give the maximum degree
of work productivity feasible in order to achieve the previously established
organizational goals (Suhardi, 2020). Work productivity, in conclusion,
is a comparison of the effectiveness of producing output vs the efficient use
of input, all of which are opinions on enhancing the quality of employees desired
or required to meet organizational objectives. The employee productivity
analysis tool can use amount and time, quality, efficiency and effectiveness to
measure the growth in employee job productivity (Hanaysha, 2016).
Individuals or groups can demonstrate high
levels of productivity at work. A person's personality often sets himself in
many types of attitudes, methods of thinking, and ways of acting on numerous
factors that affect a person's productivity. A supportive environment, such as
the presence of adequate business facilities, a quiet area, recognition of the
ideas of other colleagues, leaders who understand the needs of employees, and
technology available, influences the degree of productivity obtained by a
person (Grencikova
and Kordos, 2020). Employees' work productivity is
influenced by a variety of factors, the first of which is related to their
quality and physical abilities, which includes their degree of education,
training, work motivation, work ethic, and mental and physical talents. Second,
in the form of supporting facilities, such as the work environment (production,
production facilities and equipment, level of work safety), and staff welfare
(management and industrial relations) (Suhardi, 2020).
It is important to analyse how these
productivity aspects are impacted by the industry 4.0 technological
applications. Technology applications that impact on employee skill development
and training can be identified as self-learning equipment facilities,
simulation models, augmented reality & virtual reality applications (Rubmann, et al., 2015). Technological applications that impact on work motivations are
automation, decentralisation, working facility improvement and empowered
employees (Angel,
2021). Thus, work ethics are impacted by industry 4.0
applications such as cyber security, data governance and access control. Work
quality is impacted by use of IoT, 3D inspection, predictive quality analytics
and Machine Vision Quality Control (Angel, 2021). Industry 4.0
applications of wearable IoT device, active leading indicator systems, PSIM
systems and BIM create and impact on the work safety of manufacturing
environment (Forcina,
2021). The work efficiency is impacted by the industry 4.0 technologies and
concepts such as SMED times, autonomous robots, asynchronous manufacturing, flexibility
and agility (Kumar et al., 2020).
The
employee productivity can be measured by the factors of employee engagement, satisfaction
and job stress as explained by Panwar and
Agrawal (2021).
Chapter 03: Research Methodology
3.1.
Research philosophy
This study will
follow a deductive approach where primary data will be collected and analysed
via a structed close ended questionnaire. Therefore, a quantitative study will
be carried out to test the hypothesis developed based on the findings from the
review of literature.
3.2.
Conceptual framework
Independent variable Employee productivity skills development & Training Work quality Work efficiency Industry 4.0 adoption work motivation Work safety Work ethics Level of technology adoption on, H1 H2 H3 H4 H5 H6 H7 Figure 1: Conceptual framework Dependent variable
3.3.
List of Hypothesis
Based on the
conceptual framework following hypotheses have been developed. This research
tests seven hypotheses to achieve the research objectives.
Table 1: Hypothesis
|
No |
Hypothesis |
|
|
1 |
Ho1 |
There is no significance relationship between level of technology
adoption in skill development & training to employee productivity |
|
Ha1 |
There is a significance relationship between level of technology
adoption in skill development & training to employee productivity |
|
|
2 |
Ho2 |
There is no significance relationship between level of technology
adoption in work motivation to employee productivity |
|
Ha2 |
There is a significance relationship between level of technology
adoption in work motivation to employee productivity |
|
|
3 |
Ho3 |
There is no significance relationship between level of technology
adoption in work ethics to employee productivity |
|
Ha3 |
There is a significance relationship between level of technology
adoption in work ethics to employee productivity |
|
|
4 |
Ho4 |
There is no significance relationship between level of technology
adoption in work quality to employee productivity |
|
Ha4 |
There is a significance relationship between level of technology
adoption in work quality to employee productivity |
|
|
5 |
Ho5 |
There is no significance relationship between level of technology
adoption in work safety to employee productivity |
|
Ha5 |
There is a significance relationship between level of technology
adoption in work safety to employee productivity |
|
|
6 6 |
Ho6 |
There is no significance relationship between level of technology
adoption in work efficiency to employee productivity |
|
Ha6 |
There is a significance relationship between level of technology
adoption in work efficiency to employee productivity |
|
|
7 |
Ho7 |
There is no significance relationship between technology adoption to
employee productivity |
|
Ha7 |
There is a significance relationship between technology adoption to
employee productivity |
|
3.4.
Population
The population of this study is the
employees who are currently being employed at the 10 manufacturing companies
which located in BOI zone, Biyagama, Sri Lanka. The population is around 10000
employees from 10 different companies.
3.5.
Sample
The sampling method for the study will be
stratified simple random sampling. The selected 10 companies will be
categorized into groups according to their business sectors. Business sectors
are apparel, dipped products, food, medical devices, auto components. 50
responds will be collected from the employees of each of the company. Total of
500 responds will be collected for the quantitative analysis.
3.6.
Research Strategy
The study will be carried out in a quantitative
approach where data will be gathered using a self-administered structured
questionnaire. To achieve the objectives of the study primary data collection
method will be employed. Questionnaire, interviews will be conducted as the
primary data collection method.
Under the quantitative study, the collected
data will be analysed through Structured Equation Modelling (SEM) to accept or
reject each hypothesis developed. A software will be used for analysing
purposes such as SPSS. Each relationship in the conceptual model will be tested
and validated thus multiple regression analysis, correlation analysis and
reliability analysis will be used to interpret the final outcome of the
research.
3.7.
Operationalisation of variables
In the table below, the operational
definitions of the variables utilized in this investigation are listed. The
researcher selects how to quantify the variables in the study using an
operational definition. The dependent and independent variables utilized in
this study were specified in relation to the study objectives as stated in the
literature.
Table 2 : Operationalisation of
variables
|
Type of the variable |
Name of the variable |
Dimension |
Indicator |
Operational Definition |
Measurement |
|
Independent variable Independent variable Independent variable |
Industry 4.0 adoption Industry 4.0 adoption Industry 4.0 adoption |
level of technology adoption on skills development
& Training |
Self-learning equipment facilities |
These facilitates the employee
self-learning via smart devices |
Likert Scale |
|
Use of simulation models |
Simulations models are used to interpret
the real world in a virtual scenario |
Likert Scale |
|||
|
Use of Augmented reality |
Augmented reality creates an interactive
experience of the real world |
Likert Scale |
|||
|
Use of Virtual reality |
Virtual reality creates a simulated
environment where new equipment can be tested |
Likert Scale |
|||
|
level of technology adoption on work
motivation level of technology adoption on work
motivation |
Automation of processes |
Automated process reduces the repetitive
tasks performed by the employees |
Likert Scale |
||
|
Decentralisation |
Decentralisation creates a centralised knowledge
portal that facilitates decision making |
Likert Scale |
|||
|
working facility improvement |
working environment can be improved via
the technology applications |
Likert Scale |
|||
|
Empowered employee |
Employees can be empowered by assigning
new responsibilities to handle machines, make decisions etc |
Likert Scale |
|||
|
level of technology adoption on work
ethics |
Data governance |
Sensitive data protection and storage
ensured by data governance |
Likert Scale |
||
|
Cyber security |
Cyber security protects the systems from
threats caused by internet |
Likert Scale |
|||
|
Access controls |
System access is controlled by face
recognition, retinal scanning and fingerprint validations |
Likert Scale |
|||
|
level of technology adoption on work
quality |
Use of IoT |
IoT helps to create interconnected
environment where all the data are shared |
Likert Scale |
||
|
3D inspection |
3D inspections help to identify defects
accurately without human intervention |
Likert Scale |
|||
|
Predictive Quality Analytics |
PQA is a tool used by manufacturers to
forecast the quality of the products, components, and materials, that are
already in the production process |
Likert Scale |
|||
|
Machine Vision Quality Control |
These technologies enable an automation
of inspection with consistent and accurate inspection results |
Likert Scale |
|||
|
level of technology adoption on work
safety |
Wearable IoT device & Active leading
indicator systems |
ALIs enable for the monitoring of heart
rate and body temperature, with the system alerting operators when these
values approach a critical level. |
Likert Scale |
||
|
use of PSIM systems |
The PSIM system can offer operators with
tailored and real-time instructions based on their physical attributes and
location on the manufacturing site. |
Likert Scale |
|||
|
BIM - Building Information Modelling |
It is possible to develop a device that
can detect and communicate data about environmental conditions, as well as
visualize production life cycle management. |
Likert Scale |
|||
|
level of technology adoption on work
efficiency level of technology adoption on work
efficiency |
Reduced SMED times |
Change over times are reduced by
technology applications |
Likert Scale |
||
|
Autonomous robots |
Autonomous robots are self-sufficient
machines that can manage their tasks intelligently without the need for a
human operator. |
Likert Scale |
|||
|
Asynchronous manufacturing |
components in the production flow using
auto identification technology to inform each machine and operator what needs
to be done to produce the customized end product at each step of the
production process |
Likert Scale |
|||
|
Flexibility and Agility |
I4 support production flexibility and
agility where rapid changes to market conditions can be applied |
Likert Scale |
|||
|
Dependent variable Dependent variable |
Employee productivity Employee productivity |
Employee engagement |
Engagement time |
the level of enthusiasm and dedication a
worker feels toward their job |
Likert Scale |
|
Satisfaction |
Employee satisfaction |
Job satisfaction is a metric that
assesses how content and happy people are with their jobs. |
Likert Scale |
||
|
Job stress |
Employee stress level |
How stressed are the employees in
performing their job |
Likert Scale |
3.8.
Time frame
Table 3: Time plan
|
No |
Activity |
Month 1 |
Month 2 |
Month 3 |
Month 4 |
||||||||||||
|
1 |
Selecting a research
topic |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
2 |
Review of literature |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
3 |
Preparation of the
research proposal |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
4 |
Submission of the
research proposal |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
5 |
Data collection and
analysis |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
6 |
Testing and
Validation |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
7 |
Preparation of the
final report |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
8 |
Submission of
preliminary soft-bound dissertation |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
9 |
Final viva
presentation |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Chapter 04: Conclusion
4.1.
Conclusion
In manufacturing context, people, machines,
equipment, logistical systems, and work-in-process components all communicate
and cooperate directly with the adoption of industry 4.0, resulting in much
higher efficiency, and self-managing production processes. These technology
applications changed how employees perform their tasks and create an impact on
their productivity. Thus, this study conducted to analyse the relationship between
industry 4.0 adoption towards employee productivity. Thus, it follows a
quantitative model where primary data will be collected based on the
respondents of Sri Lankan manufacturing sector. The results of this study is
applicable in any sector to improve their employee productivity based on level
of technology adoption.
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