Internet of Things based Product-Service System
in the Maritime Industrial Sector
Islam Abusohyon and Flavio Tonelli
DIME - Department of Mechanical Engineering, Energetics, Management and Transportation, Polytechnic School,
University of Genoa, Italy
Keywords: Product-Service Systems, Internet of Things (IoT), Cyber Physical Systems (CPS), Value-added Data,
Smart Control Framework.
Abstract: The continuous progress of technology affects all aspects of life and business, dynamically. In order to take
the advantaging of technology development, business owners need to adapt themselves with the changes
stemming from it. Digitalization of production and service processes is one of the directions that alignment
with it will bring many privileges. Internet of Things, cyber-physical system, and artificial intelligence are
the popular components of digitalization that constantly undergo evolution. Utilizing these advanced
components enables business owners to transform the product-centric processes to smart control digital
service-oriented ones. The main motivation of current research work is analysis a theoretical thematic of
literature on IoT and CPS servitization topics to shed the light on the main areas that the researchers are
focusing on since 2009 and bridge the gap that exists in the literature regarding the implementation of these
technologies in the remote monitoring processes in the maritime sector. The result of the literature
examination revealed five dominant areas. Through utilizing these disclosed areas, a ten-step approach
block diagram for IoT-based ‘smart product servitization’ was designed. The proposed framework supports
companies to take the first steps toward remote monitoring servitization through the implementation of IoT
and CPS to produce a fully integrated smart monitor system to improve assets’ health and performance and
reduce costs and waiting time. Moreover, a case study of a smart injector for marine engine is analysed to
propose a working framework supporting the implementation of IoT and CPS to communicate the added-
value data within the smart system built on five modules: process control module, process diagnosis module,
healing module, storage module, and human interaction module.
The industry 4.0 revolution has brought a substantial
paradigm shift in industrial practices by propelling
these practices toward automation and digitalization
of the industrial processes. The integration of the
digital, and physical worlds along with the
expanding utilization of modern technologies such
as artificial intelligence, cloud computing, and
Internet of Things (IoT), are the principal
characteristics of fourth industrial revolution which
enable organizations to achieve self-monitoring
capability and consequently reduce the number of
human interventions (Heiner L. et al. 2014).
Therefore, the implementation of these digital
technologies forces the transformation of traditional
manufacturing into digital manufacturing (Tonelli et
al. 2017; Kristen et al. 2018; Marco et al. 2018;
Tonelli F. et al. 2019 & Damiani L. et al. 2017) and
to deliver a more customized, smart and service-
based offerings (Shum et al. 2008). And in order to
do so, many infrastructures require a combination of
these new technologies to adapt themselves with
changes stemming from Industry 4.0 revolution
(Damiani L. et al. 2018) such technologies
originated from different disciplines. Among them,
IoT and Cyber Physical System (CPS) are gaining
vast attention from a wide range of industries (Li D.
et al. 2018).
The “CPS,” and “IoT,” mechanism emanate from
distinct origins, but some common features can be
found in their definition. As a matter of fact, the
main stimulus of both is integrating digital
capabilities, containing network connectivity and
computational capability, with physical devices and
systems for the purpose of data collection and
Abusohyon, I. and Tonelli, F.
Internet of Things based Product-Service System in the Maritime Industrial Sector.
DOI: 10.5220/0010423501770184
In Proceedings of the 6th International Conference on Internet of Things, Big Data and Security (IoTBDS 2021), pages 177-184
ISBN: 978-989-758-504-3
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
exchanging (Christopher G. et al. 2019; Sumayya M.
et al. 2015; Keyur K. Sunil M. 2016). And according
to the survey that was conducted by Yang L. in
2017, CPS and IoT technologies are the way toward
innovated and integrated smart systems.
With the implementation of artificial intelligence
and machine learning algorithms, IoT has
accelerated this evolution by using sensors to extract
data throughout the lifecycle of the product, in order
to create value and knowledge from the huge
amount of the collected data, such as the knowledge
of the product performance and conditions (Marco
C. et al. 2019), and this has enabled the
transformation in several industries to move toward
selling this data to get more revenue and higher
margins instead of selling their final product
(Suliman A. et al. 2018; Bianchi N. at al. 2009;
Taticchi P., et al. 2009 & Enrico S. 2019), and this
process of creating additional values by adding
services to products to achieve financial and
strategic benefits, is called servitization (Natalia K.
et al., 2014). One of the ways to achieve data
monetization could be by providing fee-based
structures for value-added data services (Baecker J.
et al., 2020).
Servitization has paved its way through
manufacturing industries by proving its capabilities
in being an effective strategy that provides not only
financial advantage (Andrea T. and Enrico S. 2019),
but competitive advantage as well, by providing
competitive offerings to manufacturing companies
(Natalia K. et al., 2014). In the shipping industry,
servitization has helped in decreasing the cost and
increasing the revenue (Pagoropoulos A. et. al.,
One of the industries that are witnessing the
servitization trend through the implementation of
IoT, CPS and intelligent data analytics is the
maritime industry, by improving the services
provided to their products such as maintenance,
repair and performance services (Moritz S. et al.,
2016). And what characterizes the maritime sector is
that systems in this sector are complex systems that
generate huge amount of data continuously which
needs to be analysed, used and stored in a very
effective manner based on the integrated
implementation of internet of things and CPS
technologies (Sullivana B. et. al., 2020)
Lokukaluge P. & Brage M. in 2019 highlighted
the importance of the modern internet of things in
facilitating the management and the control of ship
vessels through the smart aggregation and analysis
of real-time data from various ships in different
locations, to digitalize the shipping industry.
Moreover, asset owners in the maritime sector are
looking for reducing cost with smart maintenance,
which is the main reason why the maritime domain
is witnessing digital transformation especially in the
smart remote control and automation of processes
through the extraction of valuable knowledge of the
large stream of data collected from sensors and
actuators. Therefore, the ability to access data
remotely from hard-to-reach assets and handling
these huge datasets is so important in the maritime
domain because it helps in achieving a cost-effective
maintenance and higher performance for the assets.
However, the research and interest in the maritime
domain is still new while a lot of research is
focusing on the manufacturing domain in the past
few years (Taylor N. et al., 2020), without a clear
framework for the remote monitoring through the
implementation of these technologies.
The rest of the paper is structured as follows.
Section 2 presents the assumptions, hypotheses, and
research questions formulation; section 3 presents
the case study; section 4 discusses the framework
formulation and the results; and section 5 provides
some conclusions.
For an in-depth investigation of the literature in
order to understand better the implementation of IoT
and CPS in servitization, a group of articles were
collected and analysed using Scopus database. After
deciding about the articles that are related to this
topic, a theoretical thematic approach was used to
extract the important data and finally categorizing
the final list of articles according to the important
extracted data (Vaismoradi M. et al., 2013).
The first step of the literature analysis started
with the search for papers’ titles, abstracts, and
keywords: “Internet of things” using the time frame
2008-2019. This search was done to get an idea
about the size of the existing literature that is related
to Internet of Things, the result shows 70.247 of
articles, which reflects the importance that this topic
is getting from the researchers. After that, to narrow
this literature and focus it on the articles that
discussed the implementation of this technology
within a specific device, another search was
conducted but this time with the key words “IoT”
IoTBDS 2021 - 6th International Conference on Internet of Things, Big Data and Security
and “physical device”, and this reduced the list to
12.119. Speaking specifically about the situation of
the IoT in the Maritime industry, “IoT”, “Physical
device”, and “Maritime” were used as keywords on
Scopus, and this time the result shows just 75
articles showing that the implementation of “IoT” is
still a new area of interest in maritime sector
deserving research effort.
A more focused research refinement was done to
understand the effect of implementing IoT, CPS, and
operational research (OR) algorithms and
servitization concept as the following:
The analysis was done, as mentioned above,
within a fixed time range (2008-2019), carrying the
following steps:
Scopus searching for papers’ titles,
abstracts, and keywords: “cyber physical
system” and “operational research
Filtering the results and make them limited
to “English articles that were published in
“Journals” only in the field of “Engineering,
and Computer Science”, in the window
frame from 2008-2019; a list of 143 articles
was the result.
By analysing the corpus data, a list of 36 articles
were defined as the most related ones for this
research according to a proper research questions
and assumptions formulation process.
Table 1 is the result of the analysis of this list of
articles identifying five areas of interest and
development on which researchers focus the most.
Table 1: Main Research Priorities.
Research Area Percentage of
IoT real-time data analysis 38%
Product control and management 22%
Cyber security 19%
Integrated design and manufacturing
Human and CPS interaction in
After analysing these categories, the following
five assumptions (A) were formulated:
A1: Cyber physical environment can integrate
digital product design and manufacturing processes
for higher quality and lower cost operations.
A2: Transforming product-centric processes to
service-oriented ones can be done with the support
of digital technologies.
A3: The digital transformation can be achieved
by the integration and the development of IoT, CPS,
with other physical devices.
A4: Operational research technologies “AI and
ML” can be used to support data intelligence
development and analytics into this application area.
A5: Improved field service, maintenance and
decision making is possible because of more
information about when and how their product is
being used.
According to the first assumption A1 and the
fifth one A5, the approved advantages of
servitization, companies in the maritime sector are
moving toward the implementation of this strategy.
And this has pathed the way to the formulation of
the first hypothesis:
H1: There is an increasing interest of the
maritime sector in servitization.
Moreover, based on the second A2 and the third
A3 assumptions, the secure implementation of
digital technologies facilitates the transformation of
the traditional companies to digital service-oriented
ones. Therefore, the following two hypotheses were
H2: Engines and related systems (subsystems)
will benefit from the integration of IoT, CPS, and
OR techniques.
H3: Cyber security issues will increase in
Finally, since services, maintenance and
decisions can be improved by the right use of data
A5, the following hypothesis has been formulated:
H4: The success of servitization depends highly
on data understanding.
These assumptions and hypothesis lead to the
formulation of the following research questions
High level question:
RQ1: How IoT-enhanced CPS and servitization can
add value to a manufacturer or a components
provider in the maritime sector?
First level questions (Product level):
RQ1.1: How to use the collected real-time data RTD
from IoT sensors?
RQ1.2: What is the effect of the tardiness in
aggregating IoT-data?
Internet of Things based Product-Service System in the Maritime Industrial Sector
RQ1.3: Who are the beneficiaries of the aggregated
Second level question (Manufacturing process
RQ2: How to improve product manufacturing
process by using operational collected data?
In order to test and validate results obtained in
section 2, a 10-step approach for the development of
IoT-based product design for servitization was
proposed accordingly by detailing the previously
identified five areas represented in Table 1 and the
results of the analysis of the articles found in the
literature. Figure 1 shows the 10 main blocks in this
Figure 1: IoT-Based Smart Product for effective
servitization block diagram.
Moreover, the framework delivered by Yoval C. &
Gonen S. in 2020, was validated by implementing it
in OMT-Digital Smart fuel Injection System. OMT
has created the start-up company OMT Digital to
quickly evolve its offer to also include services
based on its products. To achieve this goal, the two
companies have worked together to create an
intelligent injector able to communicate its operative
characteristics to a local processing unit for
performing fast data analytics and providing
immediate feedback to the engine control unit and to
the engine room crew, as well as transmitting the
processed data to a cloud-based storage for further
analysis and knowledge generation (Marco C. &
Marco F., 2019). As a result, a smart process control
framework was elaborated.
The aim of OMT Digital is to be able to transform
traditional mechanical injectors into smart injectors
to be able to share, process, and store data for further
analysis to improve injectors’ performance and
increase its lifetime. Their smart system is made of
different layers, so there is a fully digital layer where
the injectors are connected in IoT system to the Hub.
In this layer the digitization of the analogue signal
takes place. After that comes the processing of the
signal, which is done in the fog node, and here is
where the algorithms run, and the reduction of data
occurs -process raw data to extract value added data-
and present directly to users’ interface the status of
the health of the system. And then the same data
further reduced is sent to the cloud when the
connection allows.
The IoT intelligence-related services/data
provided to the user are divided into three categories
C1: drift compensation and product
development “automation system”. OMT has
GUI so they can see how all the injectors “in the
world” are operating and get important data to
help them develop the product further. This is
one of the benefits that the “injector
manufacturer” can get from this automation
system (i.e. getting numerous data from all the
operating injectors).
C2: on-board maintenance so if there is a
problem in the injector; it needs to be fixed
quickly. The smart system here discovers the
abnormality and advises about the possible
actions “on the user interface” that need to be
taken in order to solve the problem. This leads
to an easier and faster maintenance for the
C3: condition-based maintenance which means
the ability to measure the status of the injector
over time to predict failure before it occurs and
calculate its remaining life to decide what
maintenance needs to be done and when.
IoTBDS 2021 - 6th International Conference on Internet of Things, Big Data and Security
By taking into account the analysis of the literature
and OMT-Digital case and pursuing improvements
to the process control framework proposed by Yoval
C. & Gonen S. in 2019, a smart controller
framework for the fuel injection system FIS of
OMT-Digital is presented. Figure 2 describes its
main five modules and information flows in which
the model operates.
Figure 2: Smart controller framework for fuel injection
4.1 Process Control Module
This module is responsible on collecting the data
“mainly the temperature and the solenoid current”
and sending it to the “process diagnosis” control.
The injectors are connected to the system, and
whenever the injector is activated by the control unit
of the engine then this triggers data generation and
acquisition. Moreover, there is an analogue sensor in
the injector, and there is an analogue to digital
convertor, and then this data is sent to the Hub and
then sent via internet to this compute (fog node). So,
there isn’t any particular algorithm in this phase, it’s
just data coming into the sensor and then delivered
to the fog node.
4.2 Process Diagnosis Module
The processing of the collected data is done in this
module. The collected data is sent to the fog node,
then the fog node will machine learning algorithms”
or classical digital signal processing algorithms, in
order to process the data and transform it into a more
value-added data. The other level of data processing
is done in the cloud.
In the ship, the real-time analysis is taking place,
which means that if there is something changing
significantly, then this will immediately be
transferred with an alarm to people who are in
charge on the ship will be informed, so they can
perform immediate actions to the resulted changes.
On the other hand, the development of the
algorithms to estimate the components’ lifetime is
done on the cloud.
4.3 Healing Module
Based on the required type of intervention, the
corrective actions can either be performed
automatically as part of the smart control or through
manual intervention of a human expert. For
example, if there is a delay in the performance of the
injectors, the system can compensate for this defect
automatically by itself. However, in the case of more
complex faults, the intervention of human is
4.4 Storage Module
Here is where the data get stored, and it can be
stored in two ways, in the fog node for up to a month
of a capacity for local analytics on the ship, and once
there is internet connection, the data get
automatically stored in a data lake in the cloud. It’s
used to give lifetime prediction and to give access to
the data by different stakeholders
4.5 Human Interaction Module
This platform is used as one direction information
flow to provide the user with the valuable data. The
stakeholders can get access to the cloud with the
graphical user interface of OMT “OMT GUI”, and
the health of a specific engine with some KPIs can
be reviewed to the crew on the ship through alarms
on the platform which reflects if there is something
wrong, and the ways to solve this issue is provided
to the operator through the platform as well. And
this is done locally without the cloud connection
The main discussion areas in this research are the
importance of internet of things technology in
manufacturing comes from its ability to collect real
time data and extract valuable knowledge from this
huge amount of data which can be supported
through the implementation of smart IoT-based
servitization framework which was presented in this
research together with a 10-steps approach diagram.
These are highly connected to the assumptions and
hypothesis that were mentioned before, so were used
to answer the research questions formulated earlier
in this research.
Internet of Things based Product-Service System in the Maritime Industrial Sector
Speaking about the first hypothesis H1: There
is an increasing interest of the maritime sector in
servitization” which is directly linked to the first and
fifth assumptions A1: Cyber physical environment
for integrating digital product design and
manufacturing processes for higher quality and
lower cost operations, A5: Improved field service,
maintenance and decision making is possible
because of more information about when and how
their product is being used”, these were tested
positive through the outcomes achieved by the
company under investigation in this study after
implementing the smart control fuel injection system
proposed earlier. OMT is a company in the maritime
sector which is achieving important benefits from
implementing this IoT-based servitization
framework within their operations. They start to
build a good reputation in the market for having this
technology “the smart control system”, and seen as a
technologically advanced injection system
developer, and this attracts a lot of new costumers
for the company “indirect revenue”, as well as the
financial revenue from selling the service provided
by the smart monitor FIS “direct revenue”. Also,
having the technology of IoT-based servitization
smart monitor system gives OMT the possibility to
gain more projects of this kind and to be able to
digitalize other products and not only the injector.
These benefits also provide an answer to the higher-
level question formulated earlier in this research:
RQ1: How IoT-enhanced CPS and servitization
can add value to a manufacturer or a components
provider in the maritime sector?
The second hypotheses “H2: Engines and related
systems (subsystems) will benefit from the
integration of IoT, CPS, and OR techniques”, which
was extracted from the second and third assumptions
A2: Transforming product-centric processes
service-oriented ones through the help of digital
technologies implementation, A3: The digital
transformation can be achieved by the integration
and the development of IoT, CPS, with other
physical devices”, also found support since the IoT-
based framework that was implemented in a CPS
environment to support the smart fuel injection
system produced by the OMT-Digital, showed that
all the data were fed to machine learning and
artificial intelligence algorithms to enable the
prediction of the injector lifetime depending on the
actual conditions of use; this also answers the
second research question:
RQ2: How to improve product manufacturing
process by using operational collected data?
Regarding the third hypotheses H3: Cyber
security issues will increase in importance”, this is
tested positive since the high amount of collected
and shared data needs to be processed in a secured
way otherwise the risk of cyber-attacks will increase
dramatically to the point where the implementation
of these new technologies will affect negatively on
the company. On the other side, if these real-time
data were treated securely, this will expose any
abnormalities that might occur. In the case study in
this research, the collected real-time data “RTD”
helped OMT in the detection of injector operation
anomalies such as delayed start of injection, which is
linked to higher fuel consumption, and their
compensation by the control unit to keep the engine
operating optimally. So, the RTD is used to detect
the performance and the lifetime of the injector, and
this data can support decision makers who are either
the ship stakeholders or the engineers in the crew of
the ship and guide them toward better understanding
of the performance of the injectors and therefore,
better maintenance. This answers the following
RQ1.1: How to use the collected real-time data
RTD from IoT sensors?
RQ1.3: Who are the beneficiaries of the
aggregated RTD?
However, since the proposed framework in this
research doesn’t cover the effect of the tardiness in
the detection of the real-time data, this leaves the
following question without an answer:
RQ1.2: What is the effect of the tardiness in
aggregating IoT-data?
Therefore, for being able to answer this question,
a further development to the proposed framework
can be suggested and then validated in other real-
case scenarios.
Finally, understanding the value of the collected
data and being able to extract knowledge out of it
and transform this huge amount of data into valuable
services, is the heart of servitization, and this
supports the final hypotheses
H4: The success of
servitization depends highly on data understanding”
and its related assumption “A4: Operational research
technologiesAI and ML are used to support data
intelligence development and analytics”
Moreover, the 10-steps approach diagram and
the smart control framework for the case under
investigation developed by this research can be
considered as the first steps toward implementing
IoT-based servitization concept in a CPS
environment to collect and analyse data for further
development and improvement in product
performance and maintenance. However, the smart
IoTBDS 2021 - 6th International Conference on Internet of Things, Big Data and Security
controller FIS framework proposed by this research
differs from the one described by Yoval C. & Gonen
S. in 2020, since it’s considering the storage process
of the collected value-added data, which is a new
module that was not covered in their framework.
Also, the healing module is mainly responsible on
performing the automatic intervention but can’t send
any updates or modifications to the machine learning
weights in the process control module. Moreover,
the human interaction platform in the framework
presented here is used just like a tool to provide
information to the operator, so the operator can’t
send any data to the other modules within the
framework. Finally, their framework assumes that
the sensors practice self-awareness and maintain
their own reliability, while it’s not the case of the
sensor developed by OMT-Digital.
With the evolution in the requirements of more
integrated and connected world, companies are
moving toward servitization and smart monitoring of
their assets to satisfy their customer’s needs.
However, smart monitoring and servitization
through the implementation of IoT and CPS
technologies in the marine sector, has remained
under-researched in literature.
In this research, we aimed to propose a
framework and approach to support companies in
remote mentoring and improving hard-to-reach
assets health and performance.
This paper introduces a ten-steps approach and a
framework to support the smart implementation of
IoT and CPS in the manufacturing companies in
order to be able to catch and communicate the
added-value data within the system in real time, and
this helps in servitization and the digital
manufacturing. It also shows that the majority of the
articles are focusing on the role of IoT real-time data
in supporting decisions. And this is exactly the main
idea behind the use of IoT data in service-oriented
manufacturing. Detailing these five areas resulted in
the formulation of a IoT-based servitization block
diagram that was implemented within OMT-Digital
boundaries, and one of its main features is the
“storage module” since this feature was neglected by
the researchers in the literature who produced
similar process control frameworks. The proposed
framework supports manufacturing companies who
want to take the first steps toward smart monitoring
through digitalization and servitization by the smart
implementation of IoT and CPS in the
manufacturing companies to produce a fully
integrated smart control system starting from the
aggregation of information to the storage of value-
added data in real time. Moreover, a case study of a
smart injector for marine engine is analysed to
propose a working framework supporting the
implementation of IoT and CPS to communicate the
added-value data within the smart monitoring
system built on five modules: process control
module, process diagnosis module, healing module,
storage module, and human interaction module.
Three important constraints limit the
generalizability of the framework presented in this
research. Firstly, the aggregation of data was mainly
focused on the first stages of the digitalization
process, because the smart system investigated in
this research was not yet acquired by so many
customers in the maritime sector, which made it
difficult to follow the complete servitization strategy
till the end of the product’s lifecycle. Future work
could include a longitudinal study for the complete
investigation of the servitization process by the
implementation of IoT and CPS. Secondly, the focus
of this research is on the maritime industry, future
research could include pursuing improvements to the
framework and validating it in other industries.
Heiner L., Peter F., Thomas F., Michael H. (2014).
Industry 4.0. Business and information systems
Demartini M., Tonelli F., Orlandi I., Anguitta D. (2017).
A manufacturing value modeling methodology
(MVMM): A value mapping and assessment
framework for sustainable manufacturing. 4th
International Conference on Sustainable Design and
Kristen L., Sven P., Kristin V. (2018). Drivers of digital
transformation in manufacturing. Hawaii international
conference on system sciences.
Marco S., Carlo A., Fabrizio D. (2018). How digital
transformation is reshaping the manufacturing industry
value chain: The new digital manufacturing ecosystem
applied to a casy study from the food industry.
Springer International Publishing AG.
Tonelli F., Galluccio F., Mattis P., Abusohyon I., Lepratti
R., Demartini M. (2019). Closed-Loop Manufacturing
for Aerospace Industry: An Integrated PLM-MOM
Solution to Support the Wing Box Assembly Process.
Advances in Production Management Systems.
Towards Smart Production Management Systems pp
Damiani L., Demartini M., Cassettari L., G., Revetria R.,
Tonelli F. (2017). Digitalization of manufacturing
Internet of Things based Product-Service System in the Maritime Industrial Sector
execution systems: The core technology for realizing
future smart factories. Proceedings of the Summer
School Francesco Turco.
Shum, Kwok L., Watanabe, Chihiro (2008). The effects of
technological trajectory in product centric firms upon
the transition to smart service provision. The case of
smart solar photovoltaic. Journal of services research.
Damiani L., Demartini M., Guizzi G., Revetria R., Tonelli
F. (2018). Augmented and virtual reality applications
in industrial systems: A qualitative review towards the
industry 4.0 era. IFAC-Papers OnLine p.624-630.
Li D., Eric L., Ling L. (2018). Industry 4.0: state of the art
and future trend. International journal of production
Christopher G., Martin B., David W., Edward G. (2019).
Cyber physical systems and internet of things.
National Institute of Standards and Technology
Special Publication
Somayya M, R. Ramaswamy, Siddharth T. (2015).
Internet of Things (IoT): A Literature Review. Journal
of Computer and Communications.
Keyur K. & Sunil M. (2016). A New Approach to
Integrate Internet-of-Things and Software-as-a-Service
Model for Logistic Systems: A Case Study.
International Journal of Engineering Science and
Yang L. (2017). Cyber Physical System (CPS)-Based
Industry 4.0: A Survey. Journal of Industrial
Integration and Management
Marco C., Francesco C., Marco F., Marco L. (2019). Fuel
Injection 4.0: The Intelligent Injector and Data
Analytics by OMT Enable Performance Drift
Compensation and Condition-Based Maintenance. The
29th CIMAC World Congress 2019 in Vancouver,
Suliman A., Husain Z., Abououf M., Alblooshi M., &
Salah K. (2018). Monetization of IoT data using smart
contracts. IET Digital Library.
Bianchi N. P., Evans S., Revetria R., Tonelli F. (2009).
Influencing factors of successful transitions towards
product-service systems: A simulation approach.
International Journal of Mathmetics and Computers in
Taticchi P., Tonelli f., Starnini E. (2009). A Framework
for Evaluating Product-Service Systems Strategies.
Proceedings of the 10th WSEAS Int. Conference on
mathematics and computers in business and
Natalia k., Sebastian k., Michal G. (2014). Servitization
Strategies and Product-Service-Systems. IEEE 10
world congress on services.
Baecker J., Engret M., Krcmar H. (2020). Business
Strategies for Data Monetization: Deriving Insights
from Practice. In book: WI2020 Zentrale Tracks
Andrea T. & Enrico S. (2019). Exploring the relationship
between the product-service system and profitability.
Journal of management and governance.
Sullivana B., Desaib S., Solec J., Rossia M., Ramundoa
L., Terzi S. (2020). Maritime 4.0 Opportunities in
Digitalization and Advanced Manufacturing for Vessel
Development. Procedia Manufacturing
Volume 42, Pages 246-253
Pagoropoulos A., Kjær L., Bejbro J., Andersen1 and
McAloone T. (2017). The influence of costs and
benefits’ analysis on service strategy formulation:
Learnings from the shipping industry. Cogent
Moritz S., Carl R., Bjornar H., Klaus-Dieter T. (2016).
Utilising the Internet of Things for the Management of
Through-life Engineering Services on Marine
Auxiliaries. The 5th International Conference on
Through-life Engineering Services.
Lokukaluge P. & Brage M. (2020). Ship performance and
navigation information under high dimensional digital
models. Journal of Marine Science and Technology.
Taylor N., Human C., Kruger K., Bekker A. Basson A.
(2020). Comparison of Digital Twin Development in
Manufacturing and Maritime Domains. In book:
Service Oriented, Holonic and Multi-agent
Manufacturing Systems for Industry of the Future
Vaismoradi M., Taurunen H. & Bondas T. (2013). Content
analysis and thematic analysis: Implications for
conducting a qualitative descriptive study. Nursing
and Health Sciences, 15, 398–405
Yoval C., & Gonen S. (2020). Framework for smart
process controller implementation in an industry 4.0
Marco C. & Marco F. (2019). Towards the digital engine:
the OMT smart injector enables performance
monitoring and condition-based maintenance. 29
CIMAC World Congress.
IoTBDS 2021 - 6th International Conference on Internet of Things, Big Data and Security