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Internet of Things Applications in Future Manufacturing

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Future manufacturing is driven by a number of emerging requirements including: • The need for a shift from capacity to capability, which aims at increasing manufacturing flexibility towards responding to variable market demand and achieving high-levels of customer fulfillment. • Support for new production models, beyond mass production. Factories of the future prescribe a transition from conventional make-to-stock (MTS) to emerging make-to-order (MTO), configure-to-order (CTO) and engineer-to-order (ETO) production models. The support of these models can render manufacturers more demand driven. For example, such production models are a key prerequisite for supporting mass customization, as a means of increasing variety with only minimal increase in production costs. • A trend towards profitable proximity sourcing and production, which enables the development of modular products based on common platforms and configurable options. This trend requires also the adoption of hybrid production and sourcing strategies towards producing modular platforms centrally, based on the participation of suppliers, distributors and retailers. As part of this trend, stakeholders are able to tailor final products locally in order to better serve local customer demand.
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5
Internet of Things Applications
in Future Manufacturing
John Soldatos1, Sergio Gusmeroli2, Pedro Malo3and Giovanni Di Orio3
1Athens Information Technology, Greece
2Politecnico di Milano, Italy
3UNINOVA & FCT NOVA, Portugal
5.1 Introduction
Future manufacturing is driven by a number of emerging requirements
including:
The need for a shift from capacity to capability, which aims at increas-
ing manufacturing flexibility towards responding to variable market
demand and achieving high-levels of customer fulfillment.
Support for new production models, beyond mass production. Facto-
ries of the future prescribe a transition from conventional make-to-stock
(MTS) to emerging make-to-order (MTO), configure-to-order (CTO)
and engineer-to-order (ETO) production models. The support of these
models can render manufacturers more demand driven. For example,
such production models are a key prerequisite for supporting mass
customization, as a means of increasing variety with only minimal
increase in production costs.
A trend towards profitable proximity sourcing and production, which
enables the development of modular products based on common plat-
forms and configurable options. This trend requires also the adoption of
hybrid production and sourcing strategies towards producing modular
platforms centrally, based on the participation of suppliers, distributors
and retailers. As part of this trend, stakeholders are able to tailor final
products locally in order to better serve local customer demand.
153
154 Internet of Things Applications in Future Manufacturing
Improved workforce engagement, through enabling people to remain at
the heart of the future factory, while empowering them to take efficient
decisions despite the ever-increasing operational complexity of future
factories. Workforce engagement in the factories of the future is typically
associated with higher levels of collaboration between workers within the
same plant, but also across different plants.
The advent of future internet technologies, including cloud computing and the
Internet of Things (IoT), provides essential support to fulfilling these require-
ments and enhancing the efficiency and performance of factory processes.
Indeed, nowadays manufacturers are increasingly deploying Future Internet
(FI) technologies (such as cloud computing, IoT and Cyber-Physical Systems
(CPS) in the shop floor. These technologies are at the heart of the fourth
industrial revolution (Industrie 4.0) and enable a deeper meshing of virtual and
physical machines, which could drive the transformation and the optimisation
of the manufacturing value chain, including all touch-points from suppliers
to customers. Furthermore, they enable the inter-connection of products,
people, processes and infrastructures, towards more automated, intelligent and
streamlined manufacturing processes. Future internet technologies are also
gradually deployed in the shopfloor, as a means of transforming conventional
centralized automation models (e.g., SCADA (Supervisory Control and Data
Acquisition), MES (Manufacturing Execution Systems), ERP (Enterprise
Resource Planning)) on powerful central servers) towards more decentralized
models that provide flexibility in the deployment of advanced manufacturing
technology.
The application of future internet technologies in general and of the IoT
in particular, in the scope of future manufacturing, can be classified in two
broad categories:
IoT-based virtual manufacturing applications, which exploit IoT
and cloud technologies in order to connect stakeholders, products
and plants in a virtual manufacturing chain. Virtual manufacturing
applications enable connected supply chains, informed manufacturing
plants comprising informed people, informed products, informed pro-
cesses, and informed infrastructures, thus enabling the streamlining of
manufacturing processes.
IoT-based factory automation, focusing on the decentralization of the
factory automation pyramid towards facilitating the integration of new
systems, including production stations and new technologies such as
sensors, Radio Frequency Identification (RFID) and 3D printing. Such
5.1 Introduction 155
integration could greatly boost manufacturing quality and performance,
while at the same time enabling increased responsiveness to external
triggers and customer demands.
Within the above-mentioned categories of IoT deployments (i.e. IoT in the
virtual manufacturing chains and IoT for factory automation), several IoT
added-value applications can been supported. Prominent examples of such
applications include connected supply chains that are responsive to customer
demands, proactive maintenance of infrastructure based on preventive and
condition-based monitoring, recycling, integration of bartering processes in
virtual manufacturing chains, increased automation through interconnection
of the shopfloor with the topfloor, as well as management and monitoring of
critical assets. These applications can have tangible benefits on the competi-
tiveness of manufacturers, through impacting production quality, time and
cost. Nevertheless, deployments are still in their infancy for a number of
reasons including:
Lack of track record and large scale pilots: Despite the proclaimed
benefits of IoT deployments in manufacturing, there are still only a
limited number of deployments. Hence manufacturers seek for tangible
showcases, while solutions providers are trying to build track record and
reputation.
Manufacturers’ reluctance: Manufacturers are rather conservative
when it comes to adopting digital technology. This reluctance is inten-
sified given that several past deployments of digital technologies (e.g.
Service Oriented Architectures (SOA), Intelligent Agents) have failed
to demonstrate tangible improvements in quality, time and cost at the
same time.
Absence of a smooth migration path: Factories and production pro-
cesses cannot change overnight. Manufacturers are therefore seeking for
a smooth migration path from existing deployments to emerging future
internet technologies based ones.
Technical and Technological challenges: A range of technical chal-
lenges still exist, including the lack of standards, the fact that security
and privacy solutions are in their infancy, as well as the poor use of data
analytics technologies. Emerging deployments and pilots are expected
to demonstrate tangible improvements in these technological areas as a
prerequisite step for moving them into production deployment.
In order to confront the above-listed challenges, IoT experts and manufacturers
are still undertaking intensive R&D and standardization activities. Such
156 Internet of Things Applications in Future Manufacturing
research is undertaken within the IERC cluster, given that several topics dealt
within the cluster are applicable to future factories. Moreover, the Alliance
for IoT Innovation (AIOTI) has established a working group (WG) (namely
WG11), which is dedicated to smart manufacturing based on IoT technologies.
Likewise, a significant number of projects of the FP7 and H2020 programme
have been dealing with the application and deployment of advanced IoT
technologies for factory automation and virtual manufacturing chains. The
rest of this chapter presents several of these initiatives in the form of IoT
technologies and related applications. In particular, the chapter illustrates IoT
technologies that can support virtual manufacturing chains and decentralized
factory automation, including related future internet technologies such as
edge/cloud computing and BigData analytics. Furthermore, characteristic
IoT applications are presented. The various technologies and applications
include work undertaken in recent FP7 and H2020 projects, including FP7
FITMAN (www.fitman-fi.eu), FP7 ProaSense (http://www.proasense.eu),
H2020 MANTIS (http://mantis-project.eu), H2020 BeInCPPS (http://www.
beincpps.eu/), as well as the H2020 FAR-EDGE initiative. The chapter is
structured as follows: The second section of the chapter following this
introduction illustrates the role of IoT technologies in the scope of EU’s digital
industry agenda with particular emphasis on the use of IoT platforms (includ-
ing FITMAN and FIWARE) for virtual manufacturing. The third section is
devoted to decentralized factory automation based on IoT technologies. A set
of representative applications, including applications deployed in FP7 and
H2020 projects are presented in the fourth section. Finally, the fifth section
is the concluding one, which provides also directions for further research and
experimentation, including ideas for large-scale pilots.
5.2 EU Initiatives and IoT Platforms for Digital
Manufacturing
5.2.1 Future Manufacturing Value Chains
The manufacturing Industry has recently evolved from rigid, static, hierarchi-
cal value chains to more flexible, open and peer-to-peer value ecosystems.
Moreover, the added value produced by manufacturing (15% of the overall
GDP (Gross Domestic Product) in the 28 EU Member States) has dramati-
cally changed its pattern, where production and assembly of physical goods
has constantly decreased its value added, in favor of pre-production and
post-production activities.
5.2 EU Initiatives and IoT Platforms for Digital Manufacturing 157
The so-called SMILE challenge (Figure 5.1) is also emphasizing the
role of ICT in this radical transformation of manufacturing value chains.
In the central production stages, IoT is mostly at the service of Factory
Automation and represents the major vehicle for connecting Real World (and
its Cyber Physical Production Systems) with Digital-Virtual worlds in a green
sustainable economy; in the pre-Production stages, closed loop collaboration
ecosystems for new product-service design as well as Digital-to-Real world 3D
printing ecosystems have been enabled by IoT and support creative economy;
in the post-Production stages, new IoT-driven business models, supporting
service- sharing- circular- economy, have been developed with success with
the aim to compensate the loss of jobs derived from factory automation. For all
the stages, it is necessary to proceed with the formation of new competencies
and curricula centered on IoT and its related digital technologies, in order to
attract young talents to Manufacturing and to up- re-skill existing workforce
(blue and white collar workers).
Following paragraphs discuss the relevance of IoT in Manufacturing
Value Chains, in consideration of two major events, which have characterized
this year (i.e. 2016): the “Digitising EU” Industry policy communication
and the enormous success of Industrie 4.0 initiatives and projects. A bi-
directional convergence and innovation reference framework for digitizing
EU Manufacturing value chains through IoT adoption is also proposed.
5.2.2 Recent EU Research Initiatives in Virtual Manufacturing
In commissioner Oettinger’s speech at Hannover Messe on April 14th 2015,
four main pillars for Europe’s digital future were identified: i) Digital Inno-
vation Hubs; ii) Leadership in platforms for Digital Industry; iii) Closing the
digital skills gap and iv) Smart Regulation for Smart Industry.
On this basis, DG CNECT elaborated a yin-yang metaphor (Figure 5.2) to
pictorially represent the two main challenges for achieving a strong EU Digital
sector (against the GAFA US dominance) supporting a pervasive digitalization
of EU industry.The “Collaborative Manufacturing and Logistics” FoF11 2016
call was partly focused on digital automation platforms for collaborative
manufacturing processes, i.e. addressing together the first two pillars of
Mr. Oettinger’s speech: EU leadership in digital platforms to digitize EU
manufacturing and logistics industries.
Many of the new FoF11 projects (currently under Grant Preparation phase
in DG CNECT) are based on FIWARE and FITMAN Industrial IoT platform
(Figure 5.3) and will bring new ideas and contributions to the IoT (IERC)
158 Internet of Things Applications in Future Manufacturing
Figure 5.1 Role of ICT in the transformation of manufacturing value chains.
5.2 EU Initiatives and IoT Platforms for Digital Manufacturing 159
Figure 5.2 Elements of industry digitization according to EU’s vision.
Cluster in the next 2–3 years. They will also adhere and support AIOTI
WG11 Smart Manufacturing, which is currently chaired by EFFRA(European
Factories of the Future Research Association).
The convergence and coordination between IoT-focused projects (super-
vised by IoT European Platforms Initiative (EPI)) and other DG CNECT
initiatives such as the aforementioned FIWARE (FITMAN), many FoF ICT
projects such as I4MS BEinCPPS (based also on OpenIoT open source
platform) and CPS/SAE initiatives represents the real challenge in the coming
years for the IoT for Manufacturing domain of IERC.
In fact, the common research topic to be addressed by all the projects in
the area of IoT-driven Digital Manufacturing Value Chains lies in the inter-
relation between the different aspects of IT (Information Technology, in this
160 Internet of Things Applications in Future Manufacturing
Figure 5.3 FP7 FITMAN and FIWARE projects include several IoT building blocks for
digital manufacturing and virtual manufacturing chains.
case represented by IoT and CPS areas) and OT (Operation Technology) tech-
nology (in this case represented by Manufacturing Industries): stakeholders,
reference architectures, platforms, physical and human resources, innovation
and business models.
5.2.3 Levels of Manufacturing Digitization
The recent EU communication about Digitising EU Industry of April 19th
2016 is exactly addressing this key topic, which is also the key topic for this
interest group in IERC. The purpose of this Communication is to reinforce the
EU’s competitiveness in digital technologies and to ensure that every industry
in Europe, in whichever sector, wherever situated, and no matter of what
size can fully benefit from digital innovations. The communication aims at
overcoming current barriers (e.g. high- vs. low-tech sectors, frontrunners vs.
hesitators EU Countries, micro vs. small vs. large multinational enterprises),
which prevents all EU manufacturing industries to achieve the following three
progressive evolutionary levels of digitalization:
Digital Products: driven by the development of the IoT to smart con-
nected objects, it includes developments of markets like the connected
car, wearables or smart home appliances.
5.2 EU Initiatives and IoT Platforms for Digital Manufacturing 161
Digital Processes: driven by the development of IoT-enabled CPS, it
includes Industrie 4.0, the further spread of automation in production
and the full integration of simulation and data analytics over the full
cycle from product design to end of life (circular economy).
Digital Business Models: driven by service-oriented IoT-based busi-
ness models, it includes the re-shuffling the value chains and blur-
ring boundaries between products and services with the final aim to
increase profitability by up to 5.3% and employment by up to 30%
(in 2020).
According to the same EU communication, the achievement of this threefold
objective is enabled by Digital Platforms, i.e. initiatives aiming at combining
digital technologies, notably IoT, big data and cloud, autonomous systems and
artificial intelligence, and 3D printing, into integration platforms addressing
cross-sector challenges. In particular, leadership in IoT has recently seen
an investment of the Commission in demand-driven large-scale pilots and
lighthouse initiatives in areas such as smart cities, smart living environments,
driverless cars, wearables, mobile health and agro-food. The investment
will address notably open platforms cutting across sectors and accelerate
innovation by companies and communities of developers, building on existing
open service platforms, such as FIWARE. The accompanying staff-working
document on IoT outlines among others standardisation and regulation chal-
lenges and opportunities for IoT and the role of theAlliance for IoT Innovations
(AIOTI).
The Digitising Industry initiatives are aimed at a pervasive adoption of
Information Technologies (IT) into Operations Technologies (OT), so they all
implement the ITOT way to do it. There is another perspective of the same
topic (or the other side of the coin): the perspective of Manufacturing Industry,
from OTIT migration journey. This viewpoint is mostly represented by
the German Industrie 4.0 and its subsequent EU-wide regional and national
initiatives.
5.2.4 Industrie 4.0 Principles for CPS Manufacturing
The key focus of Industrie 4.0 is in the adoption of Cyber Physical Production
Systems and in the consequent enablement of IoT and IoS applications
(Figure 5.4).
Recently, several analysts identified so called “Industrie 4.0 readiness
levels” to help manufacturing industries and especially SMEs to unleash the
162 Internet of Things Applications in Future Manufacturing
Figure 5.4 Industrial revolution steps: towards industrie 4.0.
5.2 EU Initiatives and IoT Platforms for Digital Manufacturing 163
full potential of digitalization of products, processes and business processes.
In its most recent publications and in its speech at the World Manufacturing
Forum 2016 in Barcelona, Max Blanchet, Senior Partner Automotive Industry,
Process and Materials, Roland Berger, presented its model and the undoubtable
benefits to Manufacturing Industry, deriving from a full adoption of seven key
principles:
From Mass Production to Mass Customisation.
From volume Scale Effect to localized & flexible Units.
From planned Make to Stock to dynamic Make to Order.
From Product to Usage.
From Cost driven to ROCE (Return on Capital Employed) driven.
From Taylorism to flexible work organization.
From hard working conditions to attractive work spaces.
The implementation of these seven principles in the manufacturing industry
implies a migration of its resources towards IoT and the new IT.
As underlined before, the main research issue to be addressed in the
Collaborative Digital Manufacturing Industry domain is the development of
a bi-directional, win-win symbiotic model between IT and OT, in this case
between IoT and Manufacturing. In this perspective, Europe is already playing
a leading role worldwide in several so-called Key Enabling Technologies
(KETs), such as micro- and nano-electronics, nanotechnology, industrial
biotechnology, advanced materials, photonics, and advanced manufacturing
technologies. When talking about bridging the Valley of Death between
Research and Innovation, even at the small scale, such KETs are able per
se to achieve a strong and immediate impact. In fact, in the first and second
Phases of the I4MS initiative, some high-impact KETs (such as laser, robotics,
High Performance Computing (HPC) simulation and CPS) have been and
are being successfully transferred to Industry and SMEs in particular via
a consistent ecosystem of local, small scale, almost independent champion
experiments, grouped in Innovation Hubs. In the KETs domains, Technology
Transfer approaches are based on increasing the TRL at the supply side,
and on experimented lead-by-example success stories and best practices at
the demand side, in order to give evidence to the whole ecosystem of the
business benefits achieved. Once the effectiveness of the new KETs has been
experimented on the field, the main barriers to their full adoption are mostly
economic and financial: where and how to find the relevant resources to cover
the sometimes huge investments required.
164 Internet of Things Applications in Future Manufacturing
5.2.5 Digital Manufacturing and IoT Platforms
In terms of IoT, several Reference Architectures and Digital Platforms have
been developed in diverse Research & Innovation actions at EU, National
and Regional level, with the common aim to digitize manufacturing and
logistics collaborative business processes. In the EC-funded FP7 and H2020
landscape, several R&I projects have been funded addressing the digitalization
of manufacturing and logistics industries, not just in the Factories of the
Future PPP (especially the recent C2Net, CREMA projects about Cloud
Manufacturing and the Product Service System cluster), but also in other
research environments such as Net Innovation (e.g. FIWARE for Industry,
FITMAN and the Sensing Enterprise cluster), Cyber-Physical Systems (e.g.
many H2020 ICT1 projects and the Smart Anything Everywhere cluster),
IoT (e.g., 2015 Clusterbook of IERC Chapter 5, the AIOTI WG11 Smart
Manufacturing and several “IoT for Manufacturing” workshops held at the
recent and coming IoT WEEK events), Cloud Computing and Big Data (e.g.
FIWARE PPP, IDS and BDVA) and evenTechnology Enhanced Learning (e.g.
the TEL cluster for Manufacturing).
Many of these initiatives have been presented during several workshops
organized along 2015 by DG CNECT. In particular, during the workshop of
5–6 October 2015, also initiatives not coming from EC-funded initiatives
have been successfully demonstrated and discussed, such as Industry 4.0
RAMI (Reference Architecture Model Industrie 4.0) [1], Virtual Fort Knox,
Industrial Data Space and the US Industrial Internet Reference Architecture
IIRA. More recently, the newborn BEinCPPS InnovationAction in FoF I4MS
phase II is aiming to integrate several of these platforms and to connect them
via RAMI reference architecture also to National/Regional initiatives such as
Virtual Fort Knox and Industrial Data Space.
However, the flourishing in EU of such an ecosystem of Research-driven
IoT driven Platforms has not yet led to a successful and effective digitalization
of all the aspects and resources of manufacturing and logistics industries
involved in collaborative business processes: this is mainly due to the hetero-
geneity of the IT supply side (too many technologies and too many reference
architectures, impossible to integrate into a common digital platform) and
to the heterogeneity of the domains to be addressed and transformed in the
Industry demand side (not just production systems, but also organizational,
human resources, educational, business and just ultimately IT systems). Is
IoT properly addressing the issues of data ownership and IPR management?
Is Cloud Manufacturing a real opportunity for all manufacturing business
processes, also those to be executed in real time? Have performance and
5.2 EU Initiatives and IoT Platforms for Digital Manufacturing 165
security issues been solved? Is the Industrie 4.0 revolution based on CPPSs
easy to be implemented in low-tech SMEs may be located in Eastern EU? If
we look at the technological supply side, many of the above issues have been
“solved” with advanced ICT solutions, but are the manufacturing industries
ready for this revolution? Is there any Digital Platform to support their internal
transformations, evolution to the new technologies?
In fact, when speaking of IoT-oriented Digital Platforms unleashing the
full potential of collaborative business processes along the whole supply
chain of manufacturing and logistics stakeholders, the process of digitizing
industry requires complex, multi-domain and multi-disciplinary Large Scale
Pilots (LSP) and cannot be effectively supported by simply putting in place
mono-directional technology adoption initiatives based on increasing TRLs
and Technology Transfer approaches.
In the case of Large Scale Pilots for Digital Platforms, TRLs are in fact not
an absolute metric and often are dependent on contextual information, which
cannot be ignored, such as size, sector, domain, digital literacy, location of the
industries and their supply chains.
Moreover, as already said, often the activation of a huge ecosystem of
Technology Transfer experiments is not the most effective option to create
impact, in the presence of not well-prepared target industries and with respect
to more holistic approaches like the creation of cross-domain interlinked
regional ecosystems and Large Scale Pilots.
On the contrary, such a merely technology-driven approach risks to deepen
the Digital Divide among industries, by favoring the excellence of leading
edge champions, but offering inadequate support to lagging behind and low-
tech industries. If not well prepared and conducted just via a mono-directional
TRL-based technology transfer approach, Digital Automation risks to sharpen
the divide between Eastern-Western EU Countries; between high- and low-
tech sectors; between large multinational and local SMEs and mid-caps
manufacturing industries.
More recently, in particular inside theAIOTI WG2 Innovation Ecosystem
community led by PHILIPS and ELASTICENGINE, a new approach has been
proposed: the appropriate way to measure the impact of these early adopter
models would have to account for:
The level of risk;
The number of potential early adopters;
Potential to yield data from early adoption; and finally
The technology readiness.
166 Internet of Things Applications in Future Manufacturing
We call them Market Adoption Readiness Levels (MARLs). This interesting
approach for the very first time poses the increase of TRL as just one of the
factors (the fourth one) to achieve innovation and not the unique way to impact.
However, such an approach is mostly targeting consumer-centric and creative
industries and needs substantial improvements and extensions to be applied
to manufacturing domain, but in any case it is a quite promising starting point
for a holistic approach to digital transformation of EU industry.
5.2.6 Maturity Model for IoT in Manufacturing
As indicated in the following picture, a Manufacturing Adoption Readiness
Model:
A first dimension considers the size and the investment capability of the
manufacturing industry and its collaborative supply chain. Sometimes
micro enterprises ecosystems are the fastest and most disruptive innova-
tors, but they find difficult to create a real impact in the society, due to
scarcity of investments. On the other side, large multi-national industries
are seen as champions and archetypes for ICT-driven innovation, but
often their migration processes are slow and bureaucratic. Economic
feasibility and sustainability is the major maturity criterion addressed in
this dimension;
A second dimension considers the sector and industrial domain and its
ICT awareness, where high-tech industries have already familiarity with
certain technologies and young talented employees well prepared with
respect to digital skills. On the contrary, low-tech industries heavily
depend on knowledge and experiences of aging workers and engineers
and the migration assumes in many cases the meaning of a generational
knowledge transfer. Social sustainability is the major criterion addressed
in this dimension.
A third dimension considers the political and societal environment where
the manufacturing supply chain operates. According to the Industry 4.0
readiness quadrant developed by Roland Berger consultants, four clusters
of EU Countries could be identified according to two orthogonal vari-
ables: the Industry 4.0 readiness index (including degree of automation,
workforce skills, innovation intensity and high value-added collaborative
value networks) and the manufacturing vs. GDP ratio (the target 20% in
2020 for EU-28 countries according to former Commissioner Tajani’s
agenda). Hesitators are countries (such as Spain, Portugal and Estonia,
plus presumably some EU associated countries like Serbia and Turkey)
5.2 EU Initiatives and IoT Platforms for Digital Manufacturing 167
with low readiness level and low GDP ratio; Traditionalist Countries
(such as Italy, Poland, Croatia, Hungary, Slovenia) have a solid tradition
in manufacturing – high GDP ratio – but a low readiness level and
penetration of ICT into manufacturing industry; Potentialist Countries
(such as UK, France, Denmark and the Netherlands) are good in ICT
innovation but their manufacturing industry is not as developed as needed
to achieve a deep societal impact; finally Frontrunners Countries (such as
Germany, Ireland, Sweden and Austria) are leading edge environments
where manufacturing digital innovation and societal impact are both well
developed. Political sustainability is the major criterion addressed in this
dimension.
A reference architecture for IoT-driven Digital Industry Collaborative Ecosys-
tems could be inspired by the Industrie 4.0 RAMI, where hierarchical levels
(from single components, to devices, to the whole connected world) are
crossed with abstraction layers (from assets data, to information, to business
knowledge) along the lifecycle of product typology and product instances
(things lifecycle).
A first dimension of the IoTRAMI (Figure 5.5) (hierarchical technological
levels, Y axis) considers technological assets and platforms, where Smart
Networks, CPSs, IoT, Cloud, Big Data and Applications Marketplaces are
considered.
This dimension is crossed with the second dimension (abstraction layers,
Z axis) of the different types of Connected Factory resources involved in
the migration processes: production resources, human resources, business
resources, organizational resources and IT resources.
The third dimension (lifecycle, X axis) represents the evolution of dig-
italization patterns from smart products and production shop floors (digital
inside, smart connected objects), to intelligent digitized M&L process (shop
floor automation, energy optimization, preventive maintenance), to new busi-
ness opportunities and innovation models (servitisation, sharing and circular
economy), enabled by the migration to ICT.
In conclusion, the success of IoT-driven Digital Manufacturing Value
Chains (Figure 5.6) depends on the simultaneous and coordinated implemen-
tation of a digitising Industry ITOT roadmap aiming at increasing the TRL
of IoT solutions and to extend the number of early adopters and success stories
in manufacturing through Large Scale Pilots and of a migration to Industrie 4.0
OTIT roadmap aiming at evolving manufacturing value chains’ resources
towards IoT and its technologies, by considering multi-dimensional maturity
models and reference architectures derived from RAMI 4.0.
168 Internet of Things Applications in Future Manufacturing
Figure 5.5 Dimensions of the reference architecture model industrie 4.0.
5.3 Digital Factory Automation
5.3.1 Business Drivers
Globalization has created a new and unprecedented landscape changing signif-
icantly the way manufacturing companies operate and compete: one of fierce
competition, shorter response time to market opportunities and competitor’s
actions, increased product variations and rapid changes in product demand
are only some challenges faced by manufacturing companies of today. As in
other domains, production market has deeply felt the effects of globalization
on all different layers [2–4]. The increasing demand for new, high quality
and highly customized products at low cost and minimum time-to-market
delay is radically changing the way production systems are designed and
deployed. Success in such turbulent and unpredictable environment requires
production systems able to rapidly respond and adapt to changing markets
and costumer’s needs. To capitalize on the key markets opportunities and
winning the competition for markets share, manufacturing companies are
caught between the growing needs for:
5.3 Digital Factory Automation 169
Figure 5.6 Manufacturing company functional hierarchical decomposition according to the ISA-95/IEC62264 standard.
170 Internet of Things Applications in Future Manufacturing
implementing more and more exclusive, efficient and sustainable pro-
duction systems to assure a more efficient and effective management
of the resources and to produce innovative and appellative customized
products as quickly as possible with reduced costs while preserving
product quality;
creating new sources of value by providing new integrated product-
service solutions to the customer [5].
In order to meet these demands, manufacturing companies are progressively
understanding that they need to be internally and externally agile, i.e. agility
must be spread to different and several areas of a manufacturing company
from devices data management at shop floor level rising up to business data
management while going beyond the individual company boundaries to intra
enterprises data management at organization level. Therefore, agility implies
being more than simply flexible and lean [6].
Flexibility refers to the ability exhibited by a company that is able to adjust
itself to produce a predetermined range of solutions or products [7, 8], while
lean essentially means producing without waste [9]. On the other hand, agility
relates to operating efficiently in a competitive environment dominated by
change and uncertainty [10].
Thus, an agile manufacturing company should be capable to detect the
rapidly changing needs of the marketplace and propagate these needs to the
lower levels of the company in order to shift quickly among products and
models or between products [11]. Therefore, it is a top down enterprise wide
effort that supports time-to-market attributes of competitiveness [12].
Thus, to be agile a manufacturing company needs a totally integrated
approach i.e. to integrate product and process design, engineering and man-
ufacturing with marketing and sale in a holistic and global perspective.
Such holistic and global vision is not properly covered in the manufacturing
company of today.
5.3.2 IoT Techniques for the Virtualization of Automation
Pyramid
The vision of decentralizing the automation pyramid towards gaining addi-
tional flexibility in integrating new technologies and devices, while improving
performance and quality is not new. Earlier efforts towards the decentral-
ization of the factory automation systems have focused on the adaptation
and deployment of SOA (Service Oriented Architecture) architectures for
CPS and IoT devices [13]. However, SOA architectures tend to be heavy-
weight and rather inefficient for real-time problems, and therefore cannot be
5.3 Digital Factory Automation 171
deployed in the shopfloor without appropriate enhancements. Furthermore,
SOA deployments tend to focus on specific application functionalities and are
not suitable for implementing shared situation awareness across all shopfloor
applications. In recent years, the advent of edge computing architectures has
provided a compelling value proposition for decentralizing factory automation
systems, through the placement of data processing and control functions at
the very edge of the network. Edge computing is one of the most prominent
options for implementing IoT architectures that involve industrial automation
and real-time control [14]. Nevertheless, the adoption of decentralized archi-
tectures (including edge computing) and IoT/CPS systems from manufacturers
remains low for a number of reasons, including:
Lack of a well-defined and smooth migration path to distributing
and virtualizing the automation pyramid: The vast majority of manu-
facturers has heavily invested in their legacy automation architectures
and are quite conservative in adopting new technologies, especially given
the absence of a concrete and smooth migration path from conventional
centralized systems to decentralized factory automation architectures.
The virtualization of the automation pyramid could greatly benefit from
a phased approach, which will facilitate migration, while also ensuring
that the transition accelerates production, improves production quality
and results in a positive ROI (Return-on-Investment).
IoT/CPS deployments and standards still in their infancy: IoT/CPS
deployments in manufacturing are still in their infancy. They tend to
be overly focused on unidirectional data collection from sensors for
remote monitoring purposes, while being divorced from the embedded
and real-time nature of plant automation problems. At the same time,
they tend to ignore the physical aspects of automation i.e. they pay
limited emphasis on CPS aspects. Furthermore, despite the emergence of
edge/fog computing architecture proposals for manufacturing (e.g., [14])
their implementation is still in its infancy.
Lack of shared situational awareness and semantic interoperability:
There is a lack of semantic interoperability across the heterogeneous
components, devices and systems that comprise CPS-based automation
environments for manufacturing. Distributed IoT/CPS components pro-
vide non-interoperable data and services, which is a set-back to creating
sophisticated production automation workflows.
Lack of open, secure and standards-based platforms for decentral-
ized factory automation: The distribution of automation functions in
the shopfloor is usually implemented on an ad-hoc fashion, which may
172 Internet of Things Applications in Future Manufacturing
not comply with emerging architecture standards (such as the Refer-
ence Architecture Model Industrie 4.0). There is a lack of architectural
blueprints for decentralized factory automation based on future internet
technologies. Furthermore, emerging future internet platforms (such as
FIWARE) have a horizontal nature and are not built exclusively for
manufacturing domain (e.g., they do not address real-time requirements,
complex security requirements and physical processes that characterize
the FoF etc).
The advent of edge computing architectures, in conjunction with the emer-
gence of IoT/CPS manufacturing as part of Industrie 4.0, promise to provide
solutions for highly scalable distributed control problems which are subject
to stringent real-time constraints. In particular, edge computing architectures
are appropriate for processing or filtering large amounts of data at the edge
of the network, as well as for performing large scale analysis of real-time
data [15]. A digital automation platform based on edge computing and IoT
technologies in the main objective of the H2020 FAR-EDGE project, which is
currently in its contracting stage. This platform will comprise digital models
of the plant, based on a proper compilation of reference/models and schemas
for the digital representation of factory assets and processes (e.g., IEC-61987),
notably reference schemas specified as part of RAMI. The platform will
achieve distributed real-time control and semantic interoperability based on
the replication and management of the state of the factory at the logical edges
of the network and in a trustworthy way. The digital model of the factory and
its secure sharing and distribution across the servers of the edge computing
architecture will provide a foundation for the development of an operating
system for factory automation, which could support a wide range of plant
automation and control activities.
5.3.3 CPS-based Factory Simulation
The successful deployment of IoT analytics technologies in the shop floor
hinges on the availability of digital datasets suitable for verification and
validation of complex behaviors. The availability of such data cannot be taken
for granted. The development of simulation services based on appropriate
digital representations of plant could alleviate this limitation. Such simulation
services need to consider the IoT architecture of the digital automation system
along with the digital models of the representation of the plant.
The challenge lies not only to align the simulator with these models, but
also to enable their sharing and synchronization across different automation
processes.
5.3 Digital Factory Automation 173
5.3.4 IoT/CPS Production Workflows – Systems-of-Systems
Automation
The next generation of industrial infrastructures are expected to be complex
System-of-Systems (SoS) that will empower a new generation of industrial
applications and associated services that are actually too hardly to implement
and/or too costly to deploy [16]. There are several definition of a SoS in
the literature, however, the definition that best fits the considered application
context/domain is is the one provided in where SoS are defined as: [17]
large-scale integrated systems that are heterogeneous and independently
operable on their own, but are networked together for a common goal. The
goal may be cost, performance, robustness, and so on”. The state-of-the-art
industrial automation solutions are known for their plethora of heterogeneous
smart equipment encompassing distinct functions, form factors, network inter-
faces and I/O (Input/Output) specifications supported by dissimilar software
and hardware platforms [18]. Such systems are designed, implemented and
deployed to fulfill two main objectives:
To convert raw materials, components, or parts into finished goods that
meet a customer’s expectations or specifications.
To perform the conversion effectively and efficiently to guarantee a
certain level of performance, robustness and reliability while keeping
the costs low.
To do that, coordination, collaboration and, thus, integration and interoperabil-
ity are extremely critical issues. Several efforts have been made towards the
structural and architectural definition and characterization of a manufacturing
company and its production management system as pointed in [19]. Among
the others, the most popular and still practical applied is the set of definitions
embodied into the ISA-95/IEC62264 standard (see Figure 5.6).
According to this standard manufacturing companies and their production
systems (process plus factory) are organized into a five level hierarchical
model also known as “automation pyramid”. Besides this representation, the
standard also provides a set of directives and guidelines for manufacturing
operations management such as primary & secondary processes, quality
assurance, etc. Even if the ISA-95/IEC62264 is the wider used approach
for modelling manufacturing companies, nowadays it does not show all the
intricacies of the applications, the communication protocols, and – more in
general – of the several solutions present at each one of the five levels.
Heterogeneity in terms of hardware and software – as well as – data distribution
(transmission of information from several signal sources) and information
174 Internet of Things Applications in Future Manufacturing
processing are not fully covered by the ISA-95/IEC62264 standard. In fact
it defines an information exchange framework to facilitate integration of
business applications with the manufacturing control applications within a
manufacturing company [20]. However, lower levels of the pyramid are not
addressed implying that the automation pyramid – as it is – has significant
limitations regarding the increased complexity of modern networked automa-
tion systems [21], and – in particular – it has limitations when it is used to
support:
a) The integration of new technologies and devices and their lifecycle
management;
b) The handling of the information flow along the overall automation
pyramid from the lower level to the higher ones (company visibility);
c) The handling of the information flow coming from intelligent devices
spread all over the living environment that could be used as fundamental
feedback shared inside the automation pyramid.
The a), b), and c) limitations can be easily considered as different per-
spectives under the main umbrella of system integration research stream.
In manufacturing, system integration can be addressed and instantiated at
different levels of a company and, thus, with different levels of abstraction
according to the context of application [22]. Each level presents a peculiar
perspective about integration in general, and data integration in particular.
Current technological trends in both industrial and living environments are
pushing more and more to the idea of pervasive and ubiquitous computing
while offering – at the same time – a huge opportunity to link information
sources to information receivers/users. Future internet technologies – such as
IoT and CPS – facilitate the deployment of advanced solutions in plant floor,
as well as, day to day applications while promoting the meshing of virtual
and physical devices and the interconnection of products, people, processes
and infrastructures within the manufacturing value chain. The deployment
of IoT/CPS-based systems is enabling the creation of a common virtualized
space to facilitate the data acquisition process across multiple heterogeneous
and geographically distributed data sources while facilitating the collaboration
at large scale. It is necessary to comprehend that today’s problem is no longer
networking (protocols, connectivity, etc.) nor it is hardware (CPU/memory
power is already there, at low-cost and low-power consumption) but rather
it is on how to link disparate heterogeneous data sources – that are typically
acquired from distinct vendors – to the specific needs and interaction forms
of applications and platforms.
5.4 IoT Applications for Manufacturing 175
Designing and operating such complex systems requires from one side
the presence of a generic reference model together with models, descriptions,
guidance and specifications that can be used as key building blocks for
deriving IoT/CPS-based architecture. From the other side, the increasing
number of devices with advanced network capabilities is forcing the pres-
ence of intelligent middleware and more in general platforms where the
whole enterprise is part of and where its internal components/devices can be
easily discovered, added/removed/replaced and dynamically (re-)configured
according to the business needs during the system operations and especially
during the re-engineering interventions [16, 23, 24]. Several research initia-
tives and/or projects have been conducted to facilitate the interoperability
of heterogeneous data sources. The IoT-A (http://www.iot-a.eu) project has
addressed the IoT architecture and proposed a reference model as a response
to a galaxy of of solutions somehow related to the world of intercommunicating
and smart objects. These solutions show little or no interoperability capabilities
as usually they are developed for specific challenges in mind, following spe-
cific requirements [25]. The Arrowhead (http://www.arrowhead.eu) project is
aimed to provide an intelligent middleware/platform that can be used to allow
the virtualization of physical machines into services. It includes principles on
how to design SOA-based systems, guidelines for its documentation and a
software framework capable of supporting its implementations. As a matter
of fact, one of the main challenges of the Arrowhead project is the design and
development of a framework to enable interoperability between systems that
are natively based on different technologies. Most of the specifications are
based on the models and outcomes provided by the FP7 IoT-A project.
5.4 IoT Applications for Manufacturing
5.4.1 Proactive Maintenance
As stated in [26], maintenance activities and procedures are always on high
pressure from the top management levels of a manufacturing company to
guarantee cost reduction while keeping the perfect working conditions of the
machines and equipment installed in a production system, and in order to
assure a certain degree of continuity in the productive process and – at the
same time – the safety of the people that are part of it.
To do that, several policies and strategies for maintenance have been
defined, developed and adopted, namely:
Corrective Maintenance (CM);
Preventive Maintenance (PM);
176 Internet of Things Applications in Future Manufacturing
Predictive Maintenance (PdM); and
Proactive Maintenance (PrM).
In fact, maintenance owes its development essentially to the industrial progress
in the recent centuries and to the growing need for manufacturing companies
to be competitive [27].
Corrective Maintenance also called Run-to-failure reactive maintenance
is the oldest policy and envisions the repair of a failure whenever it happens.
It implies that a plant using run-to-failure management does not spend any
money on maintenance until a machine or system fails to operate [28].
Preventive Maintenance is a time-driven policy and envisions the
advanced definition of the time of intervention in order to anticipate the failure
of complex system [27]. In preventive maintenance management, machine
repairs or rebuilds are scheduled based on the mean time to failure (MTTF)
statistic [28].
Predictive Maintenance also called condition-based maintenance is a pol-
icy that envisions the regular monitoring of machine and equipment conditions
to understand their operating condition and schedule maintenance interven-
tions only when they are really needed. In predictive maintenance manage-
ment, machine repairs and/or rebuilds – i.e. maintenance interventions – are
programmed in real-time avoiding unforeseen downtimes and their related
implications [27]. As stated in [28]: Predictive maintenance is a philosophy
or attitude that, simply stated, uses the actual operating condition of plant
equipment and systems to optimize total plant operation. Finally, proactive
maintenance is a totally policy that is not “failure” oriented like the others.
As a matter of fact, proactive maintenance envisions not the minimization
of the machine/equipment downtime but the continuous monitoring of the
machine and equipment conditions with the main objective of identifying the
root causes of a possible failure and/or machine breakdown and proactively
schedule maintenance intervention to correct the abnormal values of the
root causes. Thus, in proactive maintenance policy the minimization of the
downtime is only the consequence of a strategy that is aimed to improve
the machine/equipment health during its lifecycle and to assure overall high
production system productivity, reliability, robustness while paradoxically
reducing the number of maintenance intervention [29]. Proactive maintenance
is a necessary state in the main path to effective maintenance. It has not been
thought as an alternative to predictive maintenance but as a complementary
approach to predictive maintenance in the direction of effective maintenance
(Figure 5.7).
5.4 IoT Applications for Manufacturing 177
The successful implementation of proactive maintenance strategies str-
ictly depends on the availability of an efficient and effective monitoring infra-
structure that can gather relevant operational data from the machine/equipment
combine and analyze this data to identify possible breakdowns and their
root causes. However, current industrial monitoring and control solutions are
extremely “bit-oriented” making hard and painful the process of predicting
failures and detecting root causes. However, manufacturing companies are
betting on the application of intelligent and more integrated monitoring and
control solution to reduce maintenance problems, production line downtimes
and reduction of production line operational costs while guarantying a more
efficient management of the manufacturing resources [30].
In this context, IoT/CPS technologies can enable the design and devel-
opment of advanced monitoring strategies and thus maintenance policies
by adding additional monitoring capabilities to industrial machines and
equipment providing in such a way the following functionalities:
Integration of secondary processes within the main control: IoT/CPS
based technologies can be deployed in order to provide more data
about machine and equipment during their operation. Such information
can be used to model the machine/equipment behavior for the sake of
failures/breakdowns detection;
Modernization of low-tech production systems: IoT/CPS based tech-
nologies can be deployed in low-tech production processes, i.e. produc-
tion processes that are not natively ready for industry 4.0, and make them
industry 4.0 compatible.
IT/OT Integration: IoT/CPS technologies can easily provide data to
all the layers of the automation pyramid enabling a true cross-layer
integration.
Maintenance engagement: IoT/CPS technologies can enable a better
engagement of the maintenance department in the health of the overall
production system.
5.4.2 Mass Customization
The deployment of IoT technologies in virtual manufacturing chains and
decentralized factory automation systems enables reduction of the production
batch side and facilitates mass-customization. IoT devices can be deployed
across the supply chain (e.g., even at retail locations) in order to obtain insights
on customers’ preferences. At the same time, the flexible integration of new
178 Internet of Things Applications in Future Manufacturing
technologies (such as stations, sensors and devices) facilitates the reduction
of the batch size. Overall, IoT supports mass-customization across all points
of the supply chain.
5.4.3 Reshoring
Decentralized IoT-based factory automation can enable European manufac-
turers to re-shore activities from low-labour countries back to the EU, which
could have a positive impact on both employment and GDP (Gross Domestic
Product). In particular, IoT enables reshoring through facilitating integration
with advanced manufacturing technologies (e.g., IoT, 3D printing, robots, etc.)
thus rendering manufacturing a far less labour intensive process. In this way,
IoT enables a shift of manufacturing from low labour locations to locations
with higher proximity to demand and innovation, which are the factors that
will determine future locations for manufacturing.
5.4.4 Safe Human Workplaces and HMIs
The scope of Industrie 4.0 includes the dynamic adaptation, reconfiguration
and streamlining of manufacturing processes. This reconfiguration occurs in
response to variations in demand, while taking into account the status of
the plant floor (e.g., machines, tools, control systems) in order to optimize
the production workflow. Nevertheless, such adaptive and reconfigurable
processes tend to neglect the human factor, given that they do not adequately
take into account the employees’profile characteristics (such as age, disability,
gender and skills). For example, RAMI, does not make any provisions
for managing workforce profiles and human-centred processes. Likewise,
Industrie 4.0 roadmaps are overly focused on technological issues and pay less
attention on the ever important human and social factors (e.g., requirements for
human-centred manufacturing). Overall, factory workers (include elderly and
disabled workers) are still expected to fully adapt to the operations of machines
and automation systems, even in cases of manufacturing workplaces with poor
ergonomics.
In order to address these limitations, there is a need to devise technologies
and processes that could invert the above loop i.e. put factory automation in
the human workforce loop. Such technologies and processes could lead to a
number of important benefits for manufacturers and for the society as whole,
including: (A) Optimal integration among human and technical resources
towards enhancing workforce performance and satisfaction; (B) Confronting
5.5 Future Outlook and Conclusions 179
the manufacturing skills gap; (C) Leveraging those individual worker capabili-
ties that are most advantageous to the manufacturing process, while addressing
important social factors (e.g., ageing and/or other handicapped groups) and
ensuring health and safety at work; (D) Introduction of new flexible models
of work and organization. Overall, there is a clear need for blending leading
edge production automation technologies with state-of-the-art methodolo-
gies for human-centred processes and workplaces, including techniques for
the adaptation of the physical workplace to the workers’ characteristics
and skills.
IoT technologies can enable manufacturers to support advanced
ergonomics and novel models of work and organization through providing
support for the following functionalities:
Human-centred production scheduling (notably in terms of work-
force allocation):IoTtechnologies (such as RFID tags) can be deployed
in order to provide access to the users’profile and context, thus enhancing
conventional techniques for distributing tasks among workers in order
to take into account the (evolving) profile and capabilities of the worker,
including his/her knowledge, skills, age, disabilities and more.
Workplace Adaptation: IoT devices such as sensors and PLCs (Pro-
grammable Logic Controllers) can provide the means for adapting the
factory workplace operation and physical configuration (i.e. in terms
of automation levels and physical world devices’ configuration) to the
characteristics, needs and capabilities of the workers, with a view to
maximizing their performance and the overall productivity of the plant,
while also maximising worker satisfaction.
Worker’s engagement in the adaptation process: IoT technologies can
enable the comparison of the performance of a worker in a given task
with the corresponding performance of skilled workers, in order to fine-
tune the task distribution and workplace adaptation processes. Feedback
on the performance of workers will be derived based on RFID tags
and wearable devices, which can provide information about the workers
stress, fatigue, sleepiness, and more.
Enhanced Workers’ Safety and Well-Being: The deployment and
use of IoT wearables (such as Fitbit devices) can enable the tracking
of the workers’ activity levels. Fitbit data can be accordingly used to
enhanced workers’ safety and reduce healthcare and insurance costs for
the manufacturers.
180 Internet of Things Applications in Future Manufacturing
5.5 Future Outlook and Conclusions
5.5.1 Outlook and Directions for Future Research and Pilots
Previous paragraphs have presented a range of IoT technologies that can be
used for streamlining manufacturing operations and for decentralizing factory
automation. Despite the development of these technologies, there are still
technological challenges especially in the following areas:
Security and Privacy: IoT data in the shop floor varies in terms
of volume and velocity, while including structured, unstructured and
semi-structured data sources. At the same time, IoT deployments in
manufacturing comprise multiple devices, which must be secured on
the network. Holistic multi-layer approaches to security are therefore
required in order to ensure safeguarding of personal data and control over
the flow and exchange of sensitive information across the manufacturing
chains and/or the shopfloor industrial network.
BigData Analytics: Manufacturers need to convert data into actionable
insight. Given the large volume of data, this is a significant challenge.
The generation of business critical insights based on these data is still
in its infancy, since data stemming from the manufacturing environment
tends to be largely underutilized.
Adoption of Edge Computing: Novel factory automation architectures
have been largely based on the SOA and Intelligent Agents paradigm,
in-line with standards such as the IEC 61499 Function Block. Emerging
edge computing architectures have distinct advantages for the imple-
mentation of decentralized architectures, yet they have not been widely
deployed yet.
Need for Standards-based Reference Implementations: Recently,
standards based organizations (such as the industrial Internet Consor-
tium) have produced reference architectures for industrial automation and
the integration of digital enterprise systems in the manufacturing chain.
The provision of reference implementation of these standards will pave
the way for their wider adoption and sustainable use by manufacturers.
Beyond the need to address these technical challenges, there is also a need for
large-scale pilot deployments, which will combine several of the applications
outlined in the previous section, in a way that considers their interactions
and synergies. For example, proactive maintenance can give rise to effective
production (re)scheduling, which could be also driven by information about
customer demands (received via IoT devices). Likewise, IoT supported supply
chain operations can drive the reconfiguration of production recipes, along
Bibliography 181
with the scheduling of production. Moreover, the development of human
centric workplaces requires the blending of adaptive human-centric processes
(including appropriate HMIs (Human Machine Intefaces)) into IoT based
factory automation architectures. Up to date, pilot deployments have been
addressing only a fraction of the above listed applications, without a systematic
consideration of their interactions under the prism of a standards-based
reference implementation.
In addition to integrated pilots, large scale secure and privacy friendly
deployments need to be evaluated in terms of quality, time and cost. Manu-
facturers need tangible evidence and benchmarks about IoT’s ability to lead
to improvements across these three axis, in order to provide a compelling
proposition for adoption.
5.5.2 Conclusions
In this chapter, we have presented how IoT can transform manufacturing
towards aligning to emerging trends such as proximity sourcing, support for
flexible production models, human-centred manufacturing and more. We have
also illustrated tangible deployments of IoT technologies based on recent
FP7 and H2020 projects, notably projects focusing on the factories of the
future. Despite these deployments, both technology and operational challenges
exist. Reference implementation of standards compliant architectures for
digital manufacturing based on IoT technologies could successfully address
these challenges. Likewise, large-scale pilots combining the benefits of IoT
deployments could also boost the confrontation of both technological and
business/operational challenges.
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