201501-Factory of The Future-IEC

White Paper
Factory of the future

Executive summary
By tradition, manufacturing has been thought of automation. Over time, the food industry as
to be a process that turns raw materials into well as pharmaceutical and other manufacturing
physical products, and the factory, in managing companies has also heavily relied on automation to
fragmented communications protocols and produce more and at lower cost. This often results
automation practices, is the structure where in higher end quality and reliability throughout the
manufacturing happens. Today, drivers such assembly chain to the advantage of the consumer.
as technology, sustainability, optimization and
The ultimate goal of the factory of the future is
the need to meet customer demands have once
to interconnect every step of the manufacturing
again encouraged the transformation of the
process. Factories are organizing an unprecedented
manufacturing industry, to become adaptive, fully
technical integration of systems across domains,
connected and even cognizant of its own power
hierarchy, geographic boundaries, value chains
quality. This transformation is characterized by the
and life cycle phases. This integration will only be
globalization of value chains in organizations, with
a success if the technology is supported by global
the goal of increasing competitive advantages,
consensus-based standards. Internet of Things
creating more value add-ons and reducing costs
(IoT) standards in particular will facilitate industrial
through comprehensive sourcing. In support of
automation, and many initiatives (too many to list
this notion, one of the most significant trends in
here) in the IoT standardization arena are currently
manufacturing is the makeover from industrial
underway. To keep up with the rapid pace of
Ethernet and industrial wireless communications
advancing technology, manufacturers will also need
to that of improved information technology (IT)
to invest in both digital technologies and highly
solutions involving the union of conventional
skilled technical talent to reap the benefits offered
automation with cyber-physical systems combining
by the fast-paced factories. Worker safety and
communications, information and communication
data security are other important matters needing
technology (ICT), data and physical elements and
constantly to be addressed.
the ability to connect devices to one another. This
IT transformation, which shifts the manufacturing So what will the factory of the future look like and
process from a patchwork of isolated silos to a how will it be put into action? This White Paper will
nimble, seamless and fully integrated system of assess the potential worldwide needs, benefits,
systems (SoS) matching end user requirements in concepts and preconditions for the factory of the
the manufacturing process, can be described as future, while identifying the business trends in
factory of the future (FoF). related technologies as well as looking at market
readiness.
The advantages of having automated systems have
been quickly recognized by industry. Due to the Section 2 leads with the current manufacturing
rapid evolution of IT in the second part of the 20th environment and its evolution across the centuries.
century, engineers are able to create increasingly The benefits of having multiple, bi-directional
complex control systems and integrate the factory value chains are essential as well as supporting
floor. The automotive industry, for instance, has information optimization across organizational
been transformed radically by the development boundaries.
3
Executive summary
Section 3 provides a brief background on

manufacturing paradigms throughout history Acknowledgments
and examines various regional concepts of
This White Paper has been prepared by the
new manufacturing initiatives, their underlying
Factory of the future project team in the IEC Market
technologies and preconditions and their impact
Strategy Board (MSB), with a major contribution
on different facets of the manufacturing area.
being furnished by the project partner, the
Section 4 examines the driving technologies for Fraunhofer Institute for Manufacturing Engineering
implementation of factory of the future concepts. and Automation IPA. The project team met on
Technical challenges and preconditions – many 3 occasions: October 2014 in Cleveland, January
things are promised early, but take time to become 2015 in Stuttgart and April 2015 in Pittsburgh, and
existent – are also underscored as well as how to held a number of on-line conference calls. The
enable the necessary technologies. project team includes:
Section 5 balances the adoption of new Mr. Daryll Fogal, Project Leader, IEC MSB
technologies with the prerequisites for market Member, Tyco International
readiness. Ms. Ursula Rauschecker, Project Partner Leader,
Fraunhofer IPA
Section 6 envisages the future landscape, with
consideration being given to enabling technologies Mr. Peter Lanctot, Project Administrator, IEC
as well as some of the specific challenges involved. Mr. Andreas Bildstein, Fraunhofer IPA
Section 7 concludes with a list of recommendations Mr. Mark Burhop, Siemens

for addressing the requirements related to data, Dr. Arquimedes Canedo, Siemens
people, technology and standards for factories of Mr. Kai Cui, Haier Group
the future.
Mr. Teruaki Ito, Mitsubishi Electric
Mr. Benoit Jacquemin, Schneider Electric
Mr. Kevin J. Lippert, Eaton Corporation
Mr. Andy Macaleer, SAP
Mr. Alec McMillan, Rockwell Automation
Dr. Youichi Nonaka, Hitachi
Mr. Noritaka Okuda, Mitsubishi Electric
Mr. Ken Sambu, Mitsubishi Electric
Ms. Veronika Schmid-Lutz, SAP
Mr. Haibo Shi, Shenyang Institute of Automation
(CN)
Dr. Kazuhiko Tsutsumi, IEC MSB Member,
Mitsubishi Electric
Mr. Chris G. Walker, Eaton Corporation
Mr. Chunxi Wang, Instrumentation Technology
and Economy Institute (CN)
Mr. Yang Wang, Huawei Technologies
Ms. Shi Xiaonan, Mitsubishi Electric
4
Table of contents
List of abbreviations 7
Glossary 9
Section 1 Introduction 11
1.1 Scope of this White Paper 11
Section 2 Current manufacturing environment 14
Section 3 Concepts of the factory of the future 17

3.1 Open value chain 17
3.2 Flexible production 18
3.3 Human-centered manufacturing 18
3.4 Business models 20
3.4.1 Crowdsourcing 20
3.4.2 Anything-as-a-service 21
3.4.3 Symbiotic ecosystem 22
3.5 Local initiatives 22
3.5.1 Advanced manufacturing (US) 23
3.5.2 e-Factory (Japan) 23
3.5.3 Industrie 4.0 (Germany) 24
3.5.4 Intelligent Manufacturing (China) 25
Section 4 Driving technologies 26

4.1 Technology challenges/needs 26
4.1.1 Connectivity and interoperability 26
4.1.2 Seamless factory of the future system integration 28
4.1.3 Architecture for integrating existing systems 28
4.1.4 Modelling and simulation 29
4.1.5 Security and safety 30
5
Table of contents
4.2 Enabling technologies 32

4.2.1 Internet of Things and machine-to-machine communication 33
4.2.2 Cloud-based application infrastructure and middleware 34
4.2.3 Data analytics 35
4.2.4 Smart robotics 37
4.2.5 Integrated product-production simulation 38
4.2.6 Additive manufacturing/3D printing 40
4.2.7 Additional factory of the future technologies 40
Section 5 Market readiness 41

5.1 Implementation of a systems perspective 41
5.2 Overcome “resistance to change” in traditional production environments 41
5.3 Financial issues 42
5.4 Migration strategies 42
Section 6 Predictions 43
Section 7 Conclusions and recommendations 45

7.1 General 45
7.2 Data 46
7.3 People 46
7.4 Technology 47
7.5 Standards 48
Annex A – References 49
6
List of abbreviations
Technical and AI artificial intelligence
scientific terms AIM application infrastructure and middleware
AM additive manufacturing
AVM adaptive vehicle make
BOM bill of materials
CAD computer-aided design
CAx computer-aided technologies
CEP complex event processing
CNC computer numerical control
CPPS cyber-physical production system
CPS cyber-physical system
DCS distributed control system
EDI electronic data interchange
ERP enterprise resource planning
ESP event stream processing
FoF factory of the future
HMI human-machine interface
ICT information and communication technology
IoT Internet of Things
IT information technology
M2M machine to machine
MEMS microelectromechanical system
MES manufacturing execution system
NFC near field communication
PLC programmable logic controller
QMS quality management software
R&D research and development
ROI return on investment
7
List of abbreviations
SCADA supervisory control and data acquisition
SIM subscriber identity module
SoS system of systems
WBS work breakdown structure
XaaS anything-as-a-service
Organizations, AMO Advanced Manufacturing Office

institutions and
AMP Advanced Manufacturing Partnership
companies
IEC International Electrotechnical Commission
IIC Industrial Internet Consortium
MSB Market Strategy Board (of the IEC)
NCOIC Network Centric Operations Industry Consortium
SMLC Smart Manufacturing Leadership Coalition
VDMA Verband Deutscher Maschinen- und Anlagenbau

(German Engineering Association)
ZVEI Zentralverband Elektrotechnik- und Elektronikindustrie
8
Glossary
cyber-physical systems horizontal integration
CPS supply chain integration into a holistic IT landscape
smart systems that encompass computational between different stages of production and the
components (i.e. hardware and software) and respective resource and information flow within
physical components seamlessly integrated and a factory and across companies along the value
closely interacting to sense the changing state of chain
the real world
vertical integration
Internet of Things information integration and system interoperability
IoT across technological and business levels in pro-
infrastructure, technologies and applications that duction and logistics (sensor, control, production,
bridge the gap between the real world and the manufacturing, execution, production planning
virtual world and management level)
additive manufacturing
fully automated production of a product from a
virtual model through 3D printing or use of similar
technologies
9
Section 1
Introduction
What will the production world of the future participating in the value chain, as well as
look like? How will humans and machines providing the ability to deduce the optimal value
communicate with each other? Will our working chain processes from this data at the demand of
worlds be adaptable to our needs? In the factory the individual customer. Through the interaction
of the future humans will have to come to terms of humans, objects and systems a dynamic, real-
with an increasingly complex world of processes, time optimized and self-organizing value chain
machines and components. This will require new will evolve. This value chain can be multi-vendor
operating concepts for optimized human-machine capable and can be adjusted for different business
operations. Nimble, adaptive and intelligent aims, such as costs, availability and resource
manufacturing processes will be the measurement consumption.
of success. The combination of “virtual” and “real”
The factory of the future will increase global
in order to get a full view of the complete value
competitiveness and will require an unprecedented
chain will allow factories to produce more rapidly,
integration of systems across domains, hierarchy
more efficiently and with greater output using boundaries and life cycle phases. Many factors
fewer resources. Businesses will also be able can contribute to establishing factories of the
to respond more quickly to the market, serving future, but consensus-based standards are
increased demand for individual products. indispensable in this process.
At present, the majority of manufacturing plants IEC International Standards help improve plant
and production facilities around the world are safety, security and availability and constitute
putting into place systems that will make them the foundation to enhance product reliability and
adaptive, fully connected, analytical and more quality. The IEC provides a platform to companies,
efficient. These new manufacturing systems are industries and governments for meeting,
introducing a new industrial revolution, called discussing and developing the International
factory of the future (FoF). This model marks Standards they require.
the beginning of a new phase of manufacturing
characterized by complete automation and
involving an increased use of technology and 1.1 Scope of this White Paper
field devices in and outside of the manufacturing
This White Paper evaluates how manufacturers,
facility. It represents the convergence of the
workers and customers will have to come to terms
mechanical age initiated by the industrial revolution
with an increasingly complex world of processes,
and the digital age, in which massive amounts of
machines and components. This will require new
information can be stored and then retrieved from
operating concepts for optimized human-machine
data banks in the blink of an eye.
cooperation. Increased efficiency, reduced time-
Factories of the future are oriented toward to-market and greater flexibility will improve a
ensuring the availability of all relevant information factory’s ability to compete. Manufacturers not
in real time through the connectivity of all elements only need to enable shorter time to market but
11
Introduction
also have to increase efficiency by reducing their follows that cost reduction measures introduced as
operating costs, minimize the utilization of natural the result of regulatory and consumer pressures are
resources and improve the safety of their products pushing companies to use energy more efficiently.
and that of their workers.
Enhanced compatibility levels can only be
This White Paper describes how factories of the achieved through the existence of consistent
future will use a system of systems (SoS) approach international standards ensuring that components
in which the product to be manufactured will from different suppliers and technologies can
have available all of the data necessary for interact seamlessly. Continued development of
its manufacturing requirements. The resulting common standards will ensure that data can flow
self-organization of networked manufacturing between automation systems without requiring
equipment will take into account the entire value an expensive conversion or interpretation of the
added chain, with the manufacturing sequence meaning of the data if the logic is not commonly
being determined on a flexible basis, depending understood. IEC International Standards enable
on the current situation, and with the human common terminologies and procedures to ensure
being remaining essential as the creative planner, that organizations and businesses can efficiently
supervisor and decision maker of the process. communicate and collaborate.
The global smart factory market is expected to There are many initiatives underway, such as smart
total nearly USD 67 billion by 2020, increasing manufacturing, Industrie 4.0, e-Factory or Intelligent
at a compound annual growth rate of 6% from Manufacturing; however this White Paper is not
2014 to 2020 [1]. Communication, automation, about a specific programme but about a future
robotics and virtual simulation will change the (global) manufacturing in the long term.
product sector as we know it today. What will the
This White Paper is the seventh in a series whose
production world of the future look like? How will
purpose is to ensure that the IEC can continue
humans and machines communicate with each
to address global problems in electrotechnology
other, and what role will our thoughts play?
through its International Standards and
The developed world is confronted with economic Conformity Assessment services. The White
and monetary constraints that make it harder to Papers are developed by the IEC Market Strategy
maintain the production levels of recent years, Board (MSB), responsible for analyzing and
while developing countries are recording a understanding the IEC’s stakeholder environment,
rapid increase in output. The result is that for in order to prepare the IEC to strategically face the
those industrialized countries looking to remain future.
competitive, one element, often neglected in the
The main objectives of this White Paper are:
past but now an integral part of any bill of materials
(BOM) calculation, is the cost of the energy used §§ To assess potential worldwide needs and
to produce the goods. In manufacturing, energy benefits for the factory of the future
has always been viewed as a cost of doing §§ To identify the concepts and trends in related
business, an expense to be controlled and a large technologies and markets including value
contributor to indirect costs. For example, many chains
production lines continue to operate during holiday
§§ To review and assess the driving technologies
breaks and weekends, even in the absence of any
and their impact
workers. Since the industrial sector – which uses
roughly 30% to 40% of total world energy – is §§ To predict the future landscape of manufactur-
highly sensitive to changing economic conditions, it ing, taking into account the sometimes con-
12
Introduction
tradictory factors of market readiness versus

technology maturity
§§ To encourage the use of international stan-

dards needed to support widespread com-
mercialization of the supporting technologies
for factories of the future
13
Section 2
Current manufacturing environment
It is obvious that the economy is an important As a result, manufacturing paradigms have also
aspect of society, and as the economy has evolved across the centuries. Figure 2-1 shows the
evolved over time, so have societies. Over the past development from craft manufacturing to mass
millennia, several major social transformations production, which made a wide variety of products
have determined the course of humanity, including available for a wide range of people, followed by
the agricultural, industrial and information and a shift back towards specialized and diversified
service revolutions. From the extensive changes
production in order to reflect the individual needs
introduced by those eras, it can be seen that as
of customers – but on a more efficient and high-
shifts to a new industrial base have occurred,
tech level.
business models and manufacturing systems
have adapted respectively, since manufacturing However, addressing product demands does
demands are always related to the needs of not on its own make manufacturing companies
societies. competitive. It should be considered that currently
per variant
Product volume
Mass produc+on
1955
1980
Globaliza+on
2000 Regionaliza+o
n
1913
Man
ual p 1850
rodu
c+on

Product variety
Figure 2-1 | Evolution of production [2]
14
manufacturing industries are undergoing rapid well. In the manufacturing domain, this means that
changes, which are driven by globalization and workplaces will have to be adapted appropriately,
the exploitation of the early and late phases of for example by adding intelligent assistance
production chains, as it is shown by the smile systems to enable workers to focus on creative
curve in Figure 2-2, since manufacturing has and value-adding tasks and achieve a reduction
become the least value-adding process in the of routine and stress-intensive labour, and to
provision of products. facilitate the transfer of knowledge among workers
and manufacturing systems as a whole.
A close relation exists among strategies to add
value and related societies – not only with regard The importance of such knowledge and skills is
to the kind of value added that people are willing cumulative, as products, systems and business
to pay for, but also with respect to the kind of environments become more and more complex
jobs that create value. For example, it is the case and technology-intensive. This is leading to a
that manufacturing employment is decreasing trend of perceiving knowledge as capital, with the
globally, especially when compared to the overall goal of using and exploiting information across
level of manufacturing added value, which is traditional boundaries as successfully as possible.
increasing. This especially applies to high-wage A company’s ability to manage and use the
countries, where the real output per labour hour knowledge about market, product, and production
in manufacturing could be increased by reducing environment will increasingly exert an influence on
labour intensity through manufacturing automation its competitiveness and capacity for innovation.
and the transformation of workers into highly-
For this reason, the exploitation of appropriate IT
skilled experts.
systems in manufacturing is essential. Depending
In this context, socio-economic trends such as on their degree of maturity, such systems support
demographic changes have to be considered as the management of knowledge and complexity
Higher
Value added
Concept/R&D Sales/aGer service
Branding MarkeDng
Design DistribuDon
Manufacturing
manufacturing creates the least value
Lower
Produc+on chain
Time
Figure 2-2 | Smile curve of value added in production industries [3]
15
throughout value chains, i.e. the full range of value

adding activities in production across multiple
organization units, via visualization, integration and
connection and intelligent analysis of production
systems.
With today’s globalization explosion, it is clear that

companies cannot survive without recognizing
and integrating a multitude of value chains. Every
supplier and every customer demands nuances
that force companies to function as a link in any
number of chains, and those chains must be
viewed from a global perspective.
While dealing with multiple value chains, it is

important to recognize that a company’s value
chain is a cornerstone of its business success.
Diversity and technical advances are to be
maintained by determining core competencies,
ensuring effective outsourcing where appropriate
and engaging in benchmarking and best practices.
In other words, it is necessary to strive for supply
chain excellence through visibility, collaboration,
synthesis and velocity.
In modern production ecosystems, value chains

need to be bi-directional, with every link supporting
the flow not only of goods but of information as
well. Information silos must be broken down
within and between partners, if supply chain and
production processes are to be optimized across
organizational boundaries.
16
Section 3
Concepts of the factory of the future
Trends in manufacturing are moving towards these changes, value chain systems need to
seamless integration of physical and digital become more adaptable, agile and resilient
worlds in order to enable fast integration, and need to be optimized with regard to
feedback and control loops throughout distributed capital expenditure. Accordingly, suppliers
manufacturing infrastructures. As Mark Watson, have to provide flexible machinery, which spreads
senior technology analyst at the global information investments across a wide customer base, and
company IHS, explains, “stand-alone plants can need to be flexibly integrated into value chains,
also communicate with other factory sites, merging which results in a modularization of the latter.
vast industrial infrastructures already in place
This keeps switching costs low and limits
with cloud computing and IoT. The end result is a
transaction-specific investments, even though
complex but vibrant ecosystem of self-regulating
buyer-supplier interactions can be very complex [5].
machines and sites, able to customize output,
Value chain modularization also lowers the
optimally allocate resources and offer a seamless
threshold for new market entrants, who previously
interface between the physical and virtual worlds
had to invest large capital expenditures,
of construction, assembly and production.” [4]
accumulate decades of experience and build
This overlay requires integrity and consistency of solid reputations before they could venture into a
distributed data throughout the whole product and technology- and capital-intensive market [6].
production lifecycle. To ensure this, digitization
Progress in IT development and its application
and interlinkage of distributed manufacturing
to the logistics industry enables close-to-real-
systems constitute key measures for implementing
time numerical simulation and optimization
the factory of the future, for example by integrating
of value chain planning and execution, while
new kinds of production equipment that will be
highly interconnected with one other and that will taking into consideration information such as bills
widely organize themselves, while offering a new of materials (BOM) and work breakdown structures
form of decision-making support based on real- (WBS), which represent the final product and
time production data arising from the production value chain structure, engineering data, such
equipment and the products themselves. These as product specs, product design model and
new concepts of manufacturing in the factory of process parameters, and operational data as it is
the future, and in related business models and gathered from customer inquiries, design works,
technologies, will be examined within the following productions, logistics, installations, utilizations and
sub-sections. maintenances.
As a result, manufacturing processes, production

paths and resource management will no longer
3.1 Open value chain have to be handled by human beings, as machines
As the demand for personalized products and IT systems themselves will determine the best
increases, product lifecycles are becoming shorter way forward: the value chain controls itself. In
and shorter. To respond to requests arising from the process, appropriate algorithms are required,
17
which support transparent and fair decision However, not all adaptions can be implemented
making in order to determine global optimums. by means of material or parameter adjustments.
It will also be necessary to reconfigure machines
in certain cases. In doing so, it is essential to
3.2 Flexible production utilize standardized mechanical, electrical, and
Not only do value chains as a whole have to IT interfaces as well as virtual commissioning
become more flexible, singular production systems techniques in order to minimize efforts for the
also have to adapt to fast-changing customer setup, configuration, commissioning, and ramp-up
demands. Figure 3-1 gives an overview of the of manufacturing equipment.
kinds of flexibility which manufacturing systems To evaluate and improve production configurations,
have to provide in order to adapt to changing it is necessary to execute related data analytics
market environments. and simulations based on actual and up-to-date
Individual product specifications have to be information from the shop floor. For this reason,
transferred to production plans, working the factory of the future has to integrate various
instructions, and machine configurations which sensor systems that provide close-to-real-time
are to be distributed to the respective facilities. In data and ensure that the analysis models used
the factory of the future, this process takes place represent the actual state of manufacturing
automatically by means of appropriate IT interfaces systems.
and planning tools, which integrate related design
and manufacturing execution systems and extract
respective manufacturing settings from product
3.3 Human-centered manufacturing
configurations by means of intelligent mapping IT systems can introduce new relations between
mechanisms. humans and the workplace into the factory of the
future. Figure 3-2 shows a use case of the relation
Kind of flexibility Explana+on

Volume Range of output levels that a firm can economically produce
products
Product/variant Time it takes to add or subsDtute new parts into the system
New design Speed at which products can be designed and introduced into the
system
Market (locaDon/Dme) Ability of the manufacturing system to adapt to changes in the
market environment
Delivery Ability of the system to respond to changes in delivery requests
Process Number of different parts that can be produced without incurring a
major setup
AutomaDon Extent to which flexibility is housed in the automaDon
(computerizaDon) of manufacturing technologies
Figure 3-1 | Kinds of manufacturing flexibility (excerpt) [7]
18
between humans and factories comparing past supports dynamic arrangement of work-time
and future associations. schedules, so that personal schedules will be
more respected. Also the sharing of knowledge
In the past, the relation between human and
across platforms will be enhanced and learning
factory was relatively fixed. In a factory, the
cycles will be shortened due to data storage,
manufacturing schedule was created according to
semantic technologies and the ability of the
a business plan and a workforce was assembled.
worker to merge and analyze the company’s
Workers adjusted their life to the manufacturing
experiences with his/her own experiences for
schedule and sacrificed their personal schedules
the creation of new ideas. Additionally, smart
and sometimes their health. Productivity was
robotic technologies will be able to contribute
restricted by the degree to which workers could
to improvement of ergonomics in production to
unite their minds with the factory.
help address the needs of workers and support
Furthermore, in past human-factory relationships, them in load intensive and routine tasks, which
the manufacturing knowledge was amassed in the will provide workers with the opportunity to
factory. Therefore the reallocation of the acquired focus on knowledge-intensive activities. Also
knowledge to other factories was difficult, and customer integration, which enables customer-
manufacturing flexibility was restricted due to this specific, or customer-driven product design
local knowledge accumulation, which led to a and faster joint innovation cycles, should be
muffling of the productivity of the company. mentioned as a concept of focusing on humans
Future human-factory relations will become more in manufacturing.
flexible through the use of advanced IT that
Past Future
Factories
Factory
Schedules
Factory
knowledge Company knowledge
Manufacturing
schedule Individual Individual
knowledge knowledge
Work force
Private
schedule
Figure 3-2 | Relation between humans and factories in the past and in the future
19
3.4 Business models a customer or factory operator announces

order conditions on the site of a crowdsourcing
The increasing adoption of information and
service, such as engineering supports, temporal
communication technologies (ICT) in the
human resource employing, purchasing parts or
manufacturing domain not only leads to more
facilities, etc.
efficient and technologically sophisticated
production systems, but also enables the In response, a member of the crowdsourcing
implementation of innovative business models. platform proposes a plan to implement the order,
These business models are mainly driven by potentially including quotations, and gets it if the
collaboration among manufacturing stakeholders, plan satisfies the customer or factory operator.
who have a different set of skills and expertise The term crowdsourcing is a blend of “crowd”
enabled and supported by new technologies. and “outsourcing” and describes the process
An example of new technologies supporting of obtaining ideas, services or content from a
innovative business models are micro factories. large, collaborative group of participants rather
A micro factory is an international concept which than from traditionally specified employees,
encompasses the creation of miniaturized units contractors or suppliers. That is to say, the key
enabler of crowdsourcing utilization is not a
or hybrid processes integrated with metrology,
top-down management, but rather cooperation
material handling and assembly to create the
between parties with respect to one another, so
capability of producing small and high-precision
a management policy change is requested for this
products in a fully-automated manner, while
new tool application.
offering the advantage of savings on both costs
and resources. There are 5 main reasons leading manufacturers
to leverage crowdsourcing:
Many micro factory activities are underway in this
regard in Asia, especially in Japan, where micro- 1) To innovate via new perspectives and ideas
electromechanical systems (MEMS) micronizing coming from talent outside of the company
both machine tool and machining technologies 2) To research new concepts during the idea
are expediting the application of such technolo- and development phases with people who are
gies in electronic component production, fluid likely to use the company’s products
machinery, construction component production
3) To design new products with better alignment
and semiconductor packaging.
to the customers’ needs
The main benefits of micro factories are cost
4) To fine tune the design and concept of
efficiency, flexible production solutions, and easy
products before they are launched onto the
management of production processes, increased
market, using direct feedback from potential
productivity speed, and human resource
customers
cultivation. The following sub-sections give an
overview of some of the new business models that 5) To flexibly integrate manufacturers for the
may arise from the digitalization of production. production of new products or prototypes,
for which customers do not have their own
facilities
3.4.1 Crowdsourcing
The latter motivation in particular is closely related
Crowdsourcing is an ordering operation to the maker movement, which is a source of
addressed to an unspecified number of people. (small-scale) entrepreneurship, as it is based on
In factory operations, as shown in Figure 3-3, do-it-yourself communities and platforms pushed
20
Engineering
Method Materials assets

Human resources Material
Asset
People
People Material
Crowdsourcing
pla]orm
Assets Method
People Material People Material
Asset Method
Factory A Asset Method
Factory C
People Material
Asset Method Factory B
Figure 3-3 | Crowdsourcing
forward by 3D printing and other fabrication Adaptive Vehicle Make (AVM) programme which
technologies. attempts to create revolutionary approaches to the
However, several challenges must be ad- design, verification, and manufacturing of complex
dressed before crowdsourcing becomes a main- defense systems and vehicles [10].
stream process in manufacturing. The European
Union has identified 3 obstacles: the fear of change
3.4.2 Anything-as-a-service
and unawareness by organizations adopting
crowdsourced manufacturing solutions, intellec- Similar to crowdsourcing business models,
tual property issues and a lack of design-sharing service orientation is finding its way into the
technologies [8]. manufacturing domain. Service orientation is
Examples of companies or platforms which applied to manufacturing ecosystems in order to
already exploit the crowdsourcing principles are increase their flexibility, as services are thus able
for example Local Motors, which created the first to be consumed on demand, which addresses the
crowdsourced production vehicle in the space trend towards faster reactions to changing market
of 18 months, about 5 times faster than the needs. However, anything-as-a-service (XaaS) is
traditional development process [9], or DARPA’s not restricted to product design and production,
21
as is the case for crowdsourcing. It can involve the structures and constituent elements, decentralized
entire product lifecycle, including product design, symbiotic systems provide an environment for
manufacturing, usage, maintenance and scrap mutually accommodating the use of limited
or recycling, and cannot only provide services resources between multiple autonomous systems,
to be executed by other persons, but also those according to local and global system objectives
implemented by integrating IoT components. as well as internal and external changes in the
environment (see Figure 3-4).
So it adds aspects such as product-service in-
tegration to the business model options, which is In order to maintain and continue this
achieved by embedding intelligence and connec- accommodation of resources between multiple
tivity into both industrial and consumer products, systems in a stable manner, the system providing
allowing manufacturers to leverage their knowl- the resources has to determine autonomously
edge of the product, or to gather additional knowl- whether or not it can provide accommodations
edge from intelligent products, in order to provide without significantly harming its ability to reach its
additional value-added services. It also enables own objectives. To realize that technologies such
them to transform their experience with the cus- as distributed decision making and collaborative
tomer from a one-time transaction to an ongoing platforms are needed.
relationship. This can provide a critical new source
of revenue in aftermarket services or can com-
pletely change the manufacturer’s business mod- 3.5 Local initiatives
el to one that provides performance guarantees, Various local initiatives exist to address the
(semi-)automates product maintenance or even challenges that arise from factory of the future
sells its product as a service. concepts. Many of these are focusing on common
topics such as efficiency improvements and
personalization in production. Depending on
3.4.3 Symbiotic ecosystem
the societal and industrial environment of the
In further considering crowdsourcing, XaaS respective regions or countries, other additional
and the extended degree of integration and key aspects such as sustainability or quality play
servitization related to both, attention is focused a role. To achieve the overall objectives involved,
also on other domains involving manufacturing all of the initiatives propose to exploit technologies
ecosystems, such as energy and Smart Cities. As such as IoT, additive manufacturing, and data
a result, global platforms which integrate diverse analytics.
ecosystems in such a way as to consider the
However, even though there is a considerable
impacts they have on one another and to exploit
degree of congruency among the objectives and
resulting synergies enable the improvement of
technological approaches pursued in all of the
infrastructures beyond pure production system
initiatives, an ongoing fragmentation exists with
and production network perspectives.
regard to target groups (e.g. small or large compa-
”Symbiotic” is a biological term that describes nies, focus on business models or manufacturing
multiple types of organisms living together in technology, etc.) funding policies, and standard-
a mutually reciprocal relationship, in which the ization. Thus multiple bodies such as the Indus-
organisms do not harm each other, but rather live trial Internet Consortium (IIC), Japan’s e-Factory,
close together while providing each other with as well as the German Industrie 4.0 platform are
various benefits. While accepting the inevitability each defining a reference architecture model for
of constant change in external environments, overall factory of the future infrastructures. The
22
CollaboraDve XaaS pla]orm
Industrial producDon
Alignment of supply
logisDcs , workers
schedules, etc.
OpDmizaDon Urban environment

of energy consumpDon
Power grids
Figure 3-4 | Symbiotic ecosystem
following sub-sections give an overview of some the basis of the initiatives sponsored by the
of the major initiatives currently ongoing in the Advanced Manufacturing Office (AMO) and the
context of factory of the future. various innovation hubs being established around
the US [13].
The concepts behind advanced manufacturing are

3.5.1 Advanced manufacturing (US)
also often referred to as smart manufacturing or
In the US, several initiatives such as the Smart smart production, and focus on smart products
Manufacturing Leadership Coalition (SMLC) [11] and objects in the production environment, which
or the Industrial Internet Consortium (IIC) [12] are support product design, scheduling, dispatching,
promoting the concept of advanced manufacturing, and process execution throughout factories and
which is based on the integration of advanced new production networks in order to increase efficiency
technologies such as IoT into the manufacturing and enable individualization of products.
area to improve produced goods and manufacturing
processes.
3.5.2 e-Factory (Japan)
A significant amount of study and work has been
done by the Advanced Manufacturing Partnership The e-Factory concept from Japan is achieving
(AMP), a steering committee reporting to the an advanced use of the industrial internet with
US President’s Council of Advisors on Science regard to both manufacturing control and data
and Technology. Their recommendations describe analytics, with the aim of effecting an optimization
23
of productivity and energy conservation. The automation of knowledge work, IoT, cloud
e-Factory approach helps to make the factory truly services, 3D printing, etc. These are applied to
visible, measurable and manageable with the help respond to future market needs and to implement
of emerging technologies (see Figure 3-5). new business models.
As more data than ever before will be generated To realize the next generation e-Factory ap-
by equipment, devices, sensors and other ICT proach, a multi-company organizational structure
equipment, big data analytics will have the power has been formed to enable cooperation between
to dramatically alter the competitive landscape assemblies of companies. This partner alliance
of manufacturing in the future. Combining is aimed at joint product development, manu-
manufacturing control and big data analytics facturing, and marketing, as well as solution in-
novation for the entire supply chain. Meanwhile,
through the industrial internet will produce huge
governmental organizations have also launched
opportunities in all manufacturing areas.
investigation and studies to support the industrial
Moving from current implementation to future companies undertaking such activities.
creations, the next generation e-Factory is
targeting the entire networked manufacturing
supply chain, its operational efficiency and 3.5.3 Industrie 4.0 (Germany)
its innovation, by considering and integrating Industrie 4.0, the 4th industrial revolution, is
information technologies as well as enabling a enabled by a networked economy and powered
continuous improvement of physical systems and by smart devices, technologies and processes
pushing forward collaboration between humans. that are seamlessly connected. The vision
The potential significance of the next generation for the 4th industrial revolution is for cyber-
e-Factory approach is indeed broad: enabling physical production systems which provide
technologies include sensing, smart robotics, digital representation, intelligent services and
ProducDvity Advantage of emerging technologies Energy conservaDon
Reduced equipment standby Dme, shorter ReducDon of energy

Improvement of operaDng tact Dme, greater facility performance,
rate shorter lead-‐Dme consumpDon
Improvement of producDon Shorter producDon Dme, opDmum energy Improvement of energy
efficiency supply based on systemaDc operaDons consumpDon efficiency
IntroducDon of high-‐efficiency equipment, PromoDon of power-‐saving

ReducDon of product cost greater management of power usage technologies
Reduce frequency of troubles and pre-‐

MinimizaDon of quality loss producDon Dme loss, eliminaDon of MinimizaDon of energy loss
wastefulness (idle operaDons) and rejects
Figure 3-5 | e-Factory objectives
24
interoperable interfaces in order to support 3.5.4 Intelligent Manufacturing (China)

flexible and networked production environments.
China is pushing forward its Intelligent
Smart embedded devices will begin to work
Manufacturing initiative, which will drive all
together seamlessly, for example via the IoT, and manufacturing business execution by merging
centralized factory control systems will give way to ICT, automation technology and manufacturing
decentralized intelligence, as machine-to-machine technology. The core of the idea behind Intelligent
communication hits the shop floor. Manufacturing is to gain information from a
The Industrie 4.0 vision is not limited to automation ubiquitous measurement of sensor data in order to
of a single production facility. It incorporates achieve automatic real-time processing as well as
integration across core functions, from production, intelligent optimization decision-making. Intelligent
material sourcing, supply chain and warehousing Manufacturing realizes horizontal integration
all the way to sale of the final product. This high across an enterprise’s production network,
level of integration and visibility across business vertical integration through the enterprise’s device,
processes, connected with new technologies will control and management layers, and all product
enable greater operational efficiency, responsive lifecycle integration, from product design through
manufacturing, and improved product design. production to sale.
The target of Intelligent Manufacturing is to

While smart devices can in many ways
improve product innovation ability, gain quick
optimize manufacturing, they conversely make
market response ability and enhance automatic,
manufacturing far more complex. The level of
intelligent, flexible and highly efficient production
complexity this creates is immense, because it
processes and approaches across national
not only concerns isolated smart devices, but
manufacturing industries. Furthermore this initiative
involves the whole manufacturing environment,
focuses on the transformation of manufacturing
including various other smart devices, machines
towards a modern manufacturing model involving
and IT systems, which are interacting across
an industry with a high-end value chain. It thereby
organizational boundaries.
promotes advanced manufacturing technology,
Industrie 4.0 and its underlying technologies the transformation and upgrading of traditional
will not only automate and optimize the existing industries and the nurturing and development of
business processes of companies, it will also strategic emerging industries.
open new opportunities and transform the way
To implement this goal, China has established
companies interact with customers, suppliers,
the Made in China 2015 strategy, which aims
employees and governments. Examples of this are
at innovation, quality and efficiency in the
emerging business models based on usage and
manufacturing domain.
metering.
To push forward Industrie 4.0 applications,

there exists a broad community encompassing
industrial associations in Germany such as VDMA,
Bitkom, and ZVEI [14], large companies and
research organizations. Driven by this community,
governmental initiatives such as national or
regional studies and research programmes have
been launched, in addition to the efforts being
undertaken by industrial companies.
25
Section 4
Driving technologies
The implementation of factory of the future interoperability has to be established on various

concepts requires appropriate technologies to levels:
support the seamless integration of manufacturing
§§ On the physical level when assembling and
systems in order to enable information exchange
connecting manufacturing equipment or
and optimization throughout whole factories,
products
production networks or ecosystems.
§§ On the IT level when exchanging information or
sharing services
4.1 Technology challenges/needs §§ On the business level, where operations and
In applying technologies to the factory of the objectives have to be aligned.
future, consideration should be given to the fact
Figure 4-1 visualizes these levels of interoperability
that these technologies should contribute to the
fulfilment of various preconditions which apply When establishing interoperability in manufacturing
to factory of the future implementations. The environments, different dimensions of integration
following sub-sections give an overview of some have to be considered:
of these preconditions, i.e. challenges to be §§ Vertical integration, i.e. along the automation
addressed. pyramid as defined by IEC 62264/IEC 61512.
This includes factory-internal integration from
sensors and actuators within machines up to
4.1.1 Connectivity and interoperability
ERP systems.
To achieve increases in efficiency, quality and
§§ Horizontal integration, i.e. along the value
individualization, as promoted by the factory of
chain and throughout production networks.
the future, bidirectional digital information flows
This includes the integration of production
are to be implemented. These digital information
networks on the business level as achieved
flows require tighter integration and connectivity
by EDI-based supply chain integration,
between various components and participants in
but might include more in the future, when
manufacturing ecosystems.
close-to-real-time and product- or process-
Connectivity and interoperability are defined as the specific information is exchanged to increase
ability of a system to interact with other systems the level of detail and quality in distributed
without application of special effort for integration manufacturing optimization.
[15], for example customization of interfaces, etc.
§§ Integration towards engineering and prod-
In this context, systems involve various aspects,
uct/production life cycle applications (e.g.
from mechanical components and properties up
IEC 62890) in order to enable low-effort
to strategic objectives and business processes.
knowledge sharing and synchronization be-
Since low-effort integration of production systems tween product and service development and
is a major enabler of factories of the future, manufacturing environments. This is beneficial
26
Command & ctrl. Consulta+on

PoliDcal or Bbusiness O
objecDves
Harmonized strategy/doctrines
People &
process &
Aligned operaDons applica+ons
Aligned procedures
Knowledge/awareness of acDons

networks
SemanDc/informaDon interoperability Informa+on

services
Data
Data/object model interoperability

Comms.
ConnecDvity & network interoperability

Network
Physical interoperability transport
Figure 4-1 | NCOIC interoperability framework/layers of interoperability [16]
Product Produc.on Produc.on Produc.on

design design engineer execu.on Service
Order
management
Sta.c system integra.on Planning &

Transforma.on of
integra.on concepts scheduling
CAx ERP
Sourcing applica.on-‐specific integra.on flows

MES
SCADA Manufacturing
execu.on
Control
Delivery
Sensors & actuators
Figure 4-2 | Transformation towards factory of the future integration
for the establishment of manufacturing, when support product design, production planning,
information about the products to be created production engineering, production execution and
should be available for planning and manufac- services, of which each has its own data formats
turing configuration tasks, as well as during and models, making integration of them difficult.
product development, when knowledge about Interoperability will blur the boundaries between
the manufacturability of the respective product these systems and activities.
could be used for design optimization.
Rather than sequential and hierarchical system
The traditional industrial value chain consists of integration, there will be a network of connected
independently implemented systems, including things, processes and customers that will allow
hardware systems (PLC, DCS, CNC, etc.) and companies to interact with customers and
software systems (MES, ERP, QMS, etc.), which suppliers much more rapidly, accurately and
27
effectively. As a result, implementation of specific production lifecycles and locations. This not only
solutions and applications in the factory of the contributes to close-to-real time, application- and
future will not focus on system interfacing and user-specific visibility of relevant information from
customization, but rather on the application- any device or data source, but also might support
specific establishment of information access and fast and (semi-)automated decision making. So it
workflows. The full adoption of service-oriented is worth noting that not only technical issues and
architecture principles to production environments machine intelligence have to be addressed, but
could support that. also seamless interaction with human workers,
and that the utilization of their knowledge and
experience has to be guaranteed and deployed as
4.1.2 Seamless factory of the future a key to ensuring seamless system integration.
system integration
Besides connectivity and semantic interoperability,

4.1.3 Architecture for integrating existing
successful implementation and achievement of
systems
business value from distributed IoT-based systems
require more than a framework for connecting and Most manufacturing enterprises aiming to introduce
collecting data from devices. It requires the ability factory of the future concepts to their business
to map the business context in which such devices already operate production systems. In such
are applied to the management of their environment. (automated) production systems, some, most, or all
This is to be supported by operational visibility of devices and machines are connected with control
devices, as well as respective information model systems via various layers of automation pyramid,
analytics mechanisms which set device information such as PLC, MES and ERP systems.
to the application-specific context, for example the In order to introduce and integrate advanced
specific order, product and process. factory of the future technologies, i.e. to migrate
In mapping such contexts, it has to be considered production systems stepwise towards distributed
that not only singular business processes such and IoT technologies, interoperability and
as order execution are to be enabled throughout intelligence, it is necessary to establish appropriate
the factory of the future system, but that various (IT) system architectures which support the
business processes such as order management, stepwise implementation and extension of factory
material management, etc. have to be integrated of the future systems, i.e. the modular roll-out of
with one another. This requires the transformation respective solutions. For the implementation of
of pure system connectivity, which is achieved by such an architecture several needs have to be
appropriate interfaces, towards use case-specific, considered:
integrated workflows and related information §§ Device management and integration: In current
exchange, which seamlessly enable the utilization automated systems, every sensor, device
of knowledge and context information available in or machine has its own dialect for digital
other systems in order to exploit as yet inaccessible integration. A core feature of IoT solutions to
optimization opportunities. To achieve this, not be implemented in the factory of the future
only systems and devices of a particular domain, is connection and management of shop floor
such as customer and order management, have devices. Typically this requires a component
to be connected, but information sources and running on or close to the device or machine in
consumers have to be interlinked in application order to send and receive commands, events,
workflows across domains, product and and other data in a predefined and harmonized
28
format to implement interoperability. This incrementally with an increasing level of detail,

might be supported by device adapters which from conceptual ideas to detailed design. In this
enable protocol and content translation to context, conceptual design determines roughly
the respective integration standards. Related 80% of the total costs of a product, and detailed
computing activities should be pushed as design constitutes the critical path in terms of
close to the device as possible to enable real- time and resources during product development,
time response, data correlation across devices since domain experts create precise engineering
and machine-to-machine orchestration. Once specifications as part of the development, using
a device is connected to the network, to other domain-specific modelling and simulation tools.
devices or to the cloud, it has to be rendered Unfortunately, these models cannot be combined
utilizable by making it visible and providing easily – due to model, domain, and tool incompati-
appropriate management functionalities. bility – or effectively – due to performance reasons
§§ Persistence mechanisms: In order to prevent – to perform system-level analyses and simula-
data loss during migration processes, per- tions. Currently, only a few models and the infor-
sistence mechanisms are required that en- mation generated during product development are
sure the reliable transmission of information passed to the production development. The facto-
from existing systems to newly integrated ones ry of the future will be supported by inter-operable
which might replace them. Furthermore, data models and tools that provide a harmonized view
synchronization has to take place continuously of the product from multiple viewpoints during
among devices and factory of the future sys- product development – from domain-specific to
tems. To implement this bi-directional informa- system-level, and from concept design sketches
tion exchange reliably, embedded data stores to ultra-high-fidelity. Equally important will be the
or caching mechanisms need to be deployed capability of seamlessly propagating these mod-
on the devices which have small footprints and els and information to the production development
require little administration efforts. Such data modelling and simulation methods.
stores and caching mechanisms have to man- This interaction should be carried out as early
age device configuration and connectivity, and as possible in order to concurrently engineer the
preserve data during intermittent connection product and its production. While some tools al-
failures.
ready integrate these models and perform simu-
lations based on product-production information,
a disconnection still exists between the tools,
4.1.4 Modelling and simulation
with only basic information being exchanged.
Not only are flexible and seamless integration One promising way to solve this problem may be
of devices, machines and software systems through the creation of product-production se-
based on IoT technologies important, but also mantics that allow production modelling and sim-
business context integration is a key to achieving ulation tools to interact in higher-abstraction levels
optimization in the factory of the future. other than pure geometric information. Another
Product and production system development and challenge to overcome in production development
planning are becoming increasingly complex, as is the transition from virtual models to real produc-
the number of their components, frequency of tion. This requires that the information gathered
market demand changes and need for related during the virtual phase be translated into instruc-
innovation increase. To manage this complexity, tions, programmes, plans, and specifications,
product and production planning are executed and that it be distributed to the real production
29
systems to produce the product. This motivates paid to security and safety issues in factory of the
development of a cyber-physical operating system future implementations.
or middleware to provide a functional abstraction
of automation components, which other tools can
interoperate with in a simpler and more efficient 4.1.5.1 Security
manner. In the factory of the future, any physical space
Conversely, feedback of knowledge about connected to cyber space is exposed to the
actual production systems that might contribute potential threat of a cyber-attack, in addition
to the assessment and improvement of the to concerns regarding its physical security. To
manufacturability of the products to be designed prevent such attacks, which may result in damage
is to be provided to respective modelling tools. and liabilities, security measures are becoming
Currently, both product and production are increasingly important for the factory of the future.
modelled based on known and well-understood Typically, cyber security protection is defined as
assumptions, and thus fail to consider unknown following the path of confidentiality, integrity and
and unexpected situations. In the factory of the availability (C-I-A) which still applies for information
future, the models will be continuously calibrated, system networks. However, factory of the future
and herewith optimized, according to real systems which integrate both physical space
operating conditions. and cyber space require a protection priority
that follows the path of availability, integrity and
In doing so, increasing dispersion and real-time
confidentiality (A-I-C).
requirements have to be considered. Improved
software tools will be able to handle the real- To address system security designs, the
time distributed collaboration among people and IEC 62443, Industrial communication networks –
systems, within and beyond company boundaries, Network and system security series of International
and also integrate additional modelling and Standards for industrial control systems has been
simulation objectives such as resilience, reliability, developed. In order to strengthen the security
cyber-physical security and energy efficiency, in of the factory of the future, the notion of control
order to measure the impact of traditional design systems security needs to be broadened and
decisions in the overall lifecycle of the product and additional security requirements need to be
production system. developed, in order to also handle security issues
which might occur in factory of the future systems
that also include information system networks.
4.1.5 Security and safety
Unexpected threats will appear during the
System boundaries are extended when long-term operation of factories. Therefore, the
implementing factory of the future concepts, factory of the future should detect those threats
and the number of interfaces to remote systems responsively and react to them adaptively.
increases. So do access points for potential threats Furthermore, because the various control systems
from outside, which results in a need for appropriate of the factory of the future will rely on one other, it
IT security and safety measures. Moreover, system is important to prevent the spread of one security
complexity increases with the increasing number of accident to other systems.
system components and the connections between
Overall it can be asserted that every industrial
them, which might cause unintended back coupling
system functioning today is vulnerable, and that
effects or the accidental overlooking of risks. To
there is no single consistent approach to security.
address these issues, special attention has to be
Currently existing security standards addressing
30
TheG of
Cyber-‐agack electric power
or water
Terrorist Human
agack error
System layer
Ongoing
measures
Natural Cyberspace Fault
disaster
Adap+ve Physical world
OperaDon and
management
Energy Exercises
Manufacturing Defense, detecDon
Mobility
system
Countermeasures
Water (damage limitaDon)
Recovery
Responsive
Coordinated Coopera+ve Quick
measures
response
OrganizaDon Time
Figure 4-3 | Total concept for manufacturing system security
current requirements are not sufficient, so a security and privacy to a specified minimum level
continuous effort needs to be made to develop of compliance. Thus, the owner can objectively
security requirements for the factory of the future. measure and document the level of security and
privacy implemented.
To implement security consistently and reliably
in factory of the future systems, a framework
definition is required which is to be applied to the
4.1.5.2 Safety
technologies adopted there. This framework has to
ensure that the measures in place against possible In addition to security, the safety of workers
threats are sufficient to prevent both physical and equipment is also an important focus of
and cyber-attacks to local data residencies and attention when addressing accidental control
programmes, according to the needs of the level system failures or intentional cyber-attacks. Up to
of the information system on which they are now, actuating systems have been encapsulated
deployed, and that they incorporate consideration with regards to control systems, i.e. external ICT
of various aspects: from human-centered physical mechanisms were not able to impact the behaviour
access options to messaging systems and data of machines and other actuators in manufacturing
residencies. environments.
The mapping of appropriate security frameworks However, due to the increasing interlinkage of
to reference architectures and best practice industrial control systems and the automation of
solutions can help to recommend what steps information exchange, this protection is no longer
users have to undertake to increase the level of guaranteed. As a result, safety considerations
31
along system boundaries in the form in which they technologies used by the general public, for
have long been valid are not sufficient for factories example AM, leads to responsibility issues.
of the future. Examples of this include guarantees and
accountability for failures of crowd-designed
Besides issues related to system boundaries, in
products such as cars, but also the prevention of
networks of intelligent and potentially autonomous
easy manufacturing of dangerous goods such as
systems there can also occur intended or
guns.
unintended emergent behaviour, as such
networked systems usually result in functionalities
but also involve complexity and risks which 4.2 Enabling technologies
go beyond that of the sum of their singular
The technological challenges described above
components. This also includes feedback loops
need to be addressed by means of specific
that are created intentionally or by accident, and
technologies in order to implement factory of the
which may not only be established by interlinking
future concepts. In applying such technologies,
systems from an IT perspective but can also
it has to be considered that the maturity of
emerge as the result of physical connections
technologies in many cases does not correspond to
established, for example, by context-aware
the expectations placed on them, since their actual
systems that recognize their environment.
industrial application usually requires a significant
However, not only systems, their boundaries amount of time after promises have been made
and interlinkage play a role with regard to safety based on initial prototypes. Figure 4-4 illustrates the
issues. The introduction of new manufacturing maturity level and future direction of technologies
Internet of Things
Data science Big Data

In-‐memory database management systems
PrescripDve analyDcs
Content analyDcs
Neurobusiness
Hybrid cloud compuDng
Smart robots
Speech recogniDon
Machine-‐to-‐machine
communicaDon services
SoGware-‐defined
Enterprise 3D prinDng
anything
Cloud compuDng In-‐memory analyDcs
NFC
Smart workspace
BioacousDc sensing
Innova+on Peak of

Trough of Slope of Plateau of
trigger inflated
disillusionment enlightenment produc+vity
expecta+ons
+me
Plateau will be reached in:
less than 2 years 2 to 5 years 5 to 10 years More than 10 years
Figure 4-4 | Hype cycle for emerging technologies, 2014 [17]
32
which are currently regarded as emerging considerably, as both technologies contribute to

technologies, i.e. technologies that are observed the convergence of the classical manufacturing
with specific attention or that are believed will have space with internet technologies and the
a specific impact in the future. In the following sub- increasing intelligence of devices used to improve
sections, some examples of emerging technologies manufacturing environments. Five main tenets
thought to be relevant for the implementation of explain more explicitly the connection between the
factory of the future concepts are discussed. technological enablers and their direct impact on
manufacturing processes [18]:
4.2.1 Internet of Things and machine-to- 1) Smart devices (i.e. products, carriers,
machine communication machines, etc.) provide the raw data, analysis
IoT is used to link any type of objects in the and closed-loop feedback that are utilized
physical world having a virtual representation or to automate and manage process control
identity in the internet. Due to the decreased price systems at every stage of manufacturing.
of sensors, the small footprint of technology and 2) These devices are connected, embedded, and
ubiquitous connectivity, it is easier than ever to widely used.
capture and integrate data from an ever-growing
3) As an offshoot of the proliferation of smart
number of “things”.
devices, control systems will become far more
The term IoT mainly derives from end consumer flexible, complex and widely distributed.
areas, in which more and more intelligent things
4) Wireless technologies will tie these distributed
are changing the daily life of people throughout
control modules together to enable dynamic
the world, and use of the term is spreading to
reconfiguring of control system components.
the industrial area, where machines and devices
are also becoming increasingly intelligent and 5) Actionable intelligence will become increasing-
connected. Things that have a part or all of their ly important, because it will be impossible to
functionality represented as a service based on anticipate and account for all of the environ-
internet technology are also referred to as cyber- mental changes to which control systems will
physical systems (CPS) or, if particularly used in need to respond.
the production area, cyber-physical production As shown in Figure 4-5, an IoT solution requires
systems (CPPS), both of which will be core 3 main solution components made up of various
building blocks of the factory of the future. technologies. Cyber-physical integration occurs
Machine-to-machine (M2M) communication or at the edge of a network. There exists a natural
integration refers to the set of technologies and hierarchy of integration at the edge, from sensors
networks that provide connectivity and interoper- up to the cloud.
ability between machines in order to allow them to Sensors are becoming significantly more
interact. The concept of M2M integration in indus- performative and less expensive, enabling
trial applications overlaps with IoT to a large extent,
manufacturers to embed smart sensors in an
so that the terms are often used interchangeably,
increasing number of sophisticated devices and
as both relate to the impact that interconnected
machines. These machines and devices are
devices will have in both the industrial and con-
collecting and communicating more information
sumer worlds.
than ever before. In the past, automated data
IoT and M2M technologies and solutions will affect collection was rather the exception; now it is
the operational environment of manufacturers becoming the norm. To exploit the potentials
33
Edge Network Core
Cloud
Thing or cyber-‐physical enDty
Workflows | PredicDons
SIM based
Big Data or data science
Networked soluDons
Business processes
Operate and administrate
Equipment
| Visualize | Analyze
Internet protocols
Device integraDon |
Device ApplicaDon enablement
Wired or wireless
Sensor Store | Locate | Correlate
Figure 4-5 | Components of an IoT solution
which can be generated from analyzing these constant and stable communication channels
data, the network layer provides connectivity for are available but also with intermittent disruption.
all integrated devices, e.g. by means of wireless Cloud technology paired with mobile devices is
technologies, which contribute to the scalability providing transparency and visibility of information
of IoT solutions as they make it possible to at every location and time, even among various
increase the number of connected devices without partners in a network.
increasing hardware efforts proportionally. Energy
Data collected from the ever expanding network
harvesting technologies make sensors self-
and number of endpoints must be conveyed to
dependent by converting ambient energy from
processing systems that provide new business
various sources into usable electric power.
solutions and applications, whether it is through
the cloud or through an internal core infrastructure.
IoT solutions must have the ability to store and
4.2.2 Cloud-based application
process large volumes of historical and diverse
infrastructure and middleware
data and must be able to respond immediately to
Other key components of the IoT include incoming data streams, which makes cloud and
computing capabilities such as cloud and fog fog computing appropriate components of IoT
computing. Enterprises must make choices implementations.
about which information and processing can be
Accordingly, emerging cloud-based IoT solutions
delegated to the computing infrastructures at
and vendors are providing the capability to
the edge, and which should be delegated to the
integrate not only applications and processes
internal or external processing capabilities.
but also things and sensors. Such systems can
Data transfer from the edge of the IoT network serve as the IT backbone for factories of the future
to processing centres must take into account and for entire supply chains, especially when the
the variability of device communication, ranging systems enable seamless intra- and inter-factory
for example from high frequency pulses to batch integration and facilitate dynamic scaling of device
uploads. Methods of data transfer from device integration and computing power according to the
to cloud must function regardless of whether changing needs of the manufacturer. In addition,
34
cloud-based solutions will allow manufacturing allow business rules to be established governing
enterprises to reduce the required core computing how to search these patterns and gather the
infrastructure and will enable them to respond appropriate supporting information required to
flexibly to changing infrastructure needs that in analyze the situation. The point is to gather and
turn are caused by changing requirements in the store only the information required – the right
manufacturing environment. data – as opposed to all data generated from a
device, equipment or operation. These patterns
can then be used to derive insights about existing
4.2.3 Data analytics and future operations. The resulting models can
Both IoT and cloud-based technologies increase be incorporated into operational flows, so that
data generation and availability in manufacturing as device data is received, the models generate
environments. For instance, overall data generation projections, forecasts and recommendations for
is expected to grow by 40% per year, totalling improving the current operational situation.
35 zettabytes by 2020 [19], with an estimated 25 Given the amount of IoT information captured and
to 50 billion connected things generating trillions stored, the high performance offered by such an-
of gigabytes of data [20]. For the manufacturing alytics systems is important. The challenge here
domain, this data will allow enterprises to monitor is to know what subset of right data needs to be
and control processes at a much higher level of accessed to facilitate business process improve-
sophistication. Previously unknown sources of ment and optimization. Currently, IoT data can
incidents in shop floor processes will be identified, be analyzed deeply and broadly, but not quickly
anticipated and prevented. at the same time. With existing technologies, op-
The ad-hoc availability of such a large amount timization across all 5 dimensions in the spider
of data opens new opportunities for novel types diagram shown in Figure 4-6 is not possible.
of analysis and visual representation. Batch- Trade-offs need to be made.
generated static reports are no longer state-of- In-memory database computing helps to
the-art, as it becomes possible for users to view, address the challenges of IoT big data, as it
chart, drill into and explore data flexibly in close to removes the constraints of existing business
real-time, and as automated reasoning algorithms intelligence mechanisms and delivers information
can now be applied to provide decisions that have for making strategic as well as operational
in-process impact on manufacturing operation business decisions in real time, with little to no
and optimization. data preparation or staging effort and at high
However, not only manufacturing-related data speeds allowing deep analysis of broad IoT data.
gathered by respective IoT systems is relevant Thus it provides the ability to answer questions,
for analysis. In addition to common business i.e. execute analysis on as much IoT data as it
management systems, conditions on an inter- is relevant to the question, without boundaries
company level or from other ecosystems also have or restrictions and without limitations as to data
to be considered. volume or data types. This also includes the
consideration of the relevance of the data to be
The extraction of value from the vast amount of
analyzed, since, for example, recent IoT data can
available device data involves mining historical
be more valuable than old data.
data for specific patterns. This requires an
infrastructure that is capable of supporting the However, the business value of in-memory
very large data sets and applying machine learning computing is not only generated by the seamless
algorithms to the data. Event-driven analytics integration of various kinds of data, it also enables
35
Deep
Complex & interacDve quesDons on Deep
granular data Complex & interacDve quesDons on
granular data
OR
Broad High speed Broad High speed
Big data, Fast Big data, Fast
many response-‐Dme many response-‐Dme
data types interacDvity data types interacDvity
Real-‐+me Simple Simple

Recent data, preferably no data preparaDon,
Real-‐+me no data preparaDon,
Recent data, preferably no pre-‐aggregates,
real-‐Dme no pre-‐aggregates,
real-‐Dme no tuning
no tuning
Figure 4-6 | Trade-offs on data analytics
extraction of knowledge from this data without ESP requires IoT integration to stream the data from
prefabrication of information and requests. Efforts the edge to the ESP engine for processing. CEP is
which currently are necessary in order to create, a more sophisticated capability, which searches
aggregate, summarize, and transform requests for complex patterns in an ordered sequence
and data to the requested format step by step of events. It is ESP and CEP running on big data
will be eliminated, as questions regarding raw IoT enabled by in-memory capability that are providing
transactional data not prepared previously are the new type of analytics available from IoT.
enabled.
In order to utilize the information and knowledge
Additional recent data analytics capabilities which is gathered from such data analytics,
include event stream processing (ESP) and decision-making mechanisms have to be
complex event processing (CEP). Individual IoT implemented that allow IoT to drive business
data typically represents an event taking place in objectives (semi-)automatically. To do so, several
the manufacturing or operational environment. options have to be compared, with the best option
For example, a machine shutdown is an event;
being selected according to current business
the temperature change in a process is an event;
objectives. The available options can be obtained
the displacement of a product from one place to
from IoT data gathering as well as from the
another is an event. Multiple events can be related
execution of data analytics and simulation runs.
and correlated, for example, the temperature
The priorities of respective business objectives
of a process increased to such an extent that a
might be adjusted at runtime according to
machine failed. ESP makes it possible to stream,
changing manufacturing environment conditions.
process, filter and group all of the IoT data and
events collected. ESP business rules are created to The large volume of IoT data available from people,
determine which events are important, which data things and machines, along with the complexity
should be filtered out and which should be kept, of the processing of events and decision making,
and which event correlations or patterns should will drive the need for a unified IoT infrastructure
trigger a broader business event, alert or decision. architecture and interfaces. Such an infrastructure
36
can serve as the basis for industrial applications

which, for example, allow companies to access
Environment recogniDon
additional information on customer preferences
and market variations, product and service
creation and utilization, as well as for predictive
analysis functionalities that are applied, for
example, to optimize maintenance cycles.
4.2.4 Smart robotics
The emergence of IT in the manufacturing domain

not only introduces new solutions, such as IoT
technologies, to this field of application, but also
changes existing automation and control systems,
especially robotics.
For instance, human-robot collaboration,

which is enabled by integrating real-time context
awareness and safety mechanisms into robotic
systems, combines the flexibility of humans with Figure 4-7 | Human-robot collaboration
the precision, force and performance of robots. In
current production systems, cell or line production However, such collaboration presents safety
is common practice, in which single workers or issues, since failures of the involved active robot
small teams operate various tasks in a restricted might result in fatal injuries. Moreover, currently
area using well-formed jigs. However, recent no industry safety standards and regulations exist
market demands for simultaneous application of
covering this type of human-robot collaboration,
agility, efficiency and reliability are not satisfied by
so both innovation of system integration
such systems, which are operated solely by human
technology and creation of new safety standards
ability or on fully automated lines. Robot cells, in
and regulations are required.
which robots support humans in the execution
of production tasks, are being developed to The integration of sophisticated sensors and
overcome this issue. the application of artificial intelligence (AI)
enable machine vision, context awareness and
There exist 3 types of human-robot cooperation:
intelligence. This produces collaborative robots
synchronized cooperation, simultaneous coopera-
that not only interact with humans without
tion and assisted cooperation. Figure 4-7 shows
boundaries in a specific working area and for the
assisted cooperation as being the closest type
execution of a well-defined task, but also anticipate
of human-robot collaboration, in which the same
required assistance needs. On one hand, this will
component is operated by human operators and
make it possible to apply robotics to previously
robots together without physical separation. It
impossible use cases, and on the other hand it will
thus enables robots and operators to co-operate
lead to higher productivity due to the elimination of
closely, for instance to handle and process prod-
non-value adding activities for shop floor workers.
ucts jointly in order to incorporate both the agility
and reliability offered by robots and the flexibility This flexibility of collaboration can be
offered by human operators. implemented not only for human-robot interaction
37
but also for collaboration among robotics systems. 4.2.5 Integrated product-production
Advanced robots can enhance sensory perception, simulation
dexterity, mobility and intelligence in real time,
Not only innovations based on technologies on
using technologies such as M2M communication,
the shop floor, such as IoT technologies, data
machine vision and sensors. This makes such
analytics and smart robotics, will have an impact
robots capable of communicating or interacting
on the factory of the future. The digital factory,
much more easily with one another. The ability to
i.e. the representation of production systems in IT
connect flexibly with the surrounding environment
systems for planning and optimization purposes,
and the recognition of the related production
will also undergo considerable changes.
context make advanced robots easily adaptable to
new or changing production tasks, including those The digital factory concept refers to an integrated
which are to be executed collaboratively. approach to enhancing product and production
engineering processes and simulation. This vision
New robot programming paradigms also
attempts to improve product and production at
contribute to the low-effort implementation of new
all levels by using different types of simulation at
production tasks. The shift from programming
robots to training robots intuitively is enabled by various stages and levels throughout the value
new robot operation engines. Trajectory points chain. There exist several types of simulation
are traced manually and are then repeated by that create virtual models of the product and
the robots. Furthermore, the skills of robots and production, including discrete event simulation,
related tools are to be managed and mapped 3D motion simulation, mechatronic system-level
to production process requirements (semi-) simulation, supply chain simulation, robotics
automatically. As a result, the required time simulation and ergonomics simulation, among
for programming the robot and the necessary others. The ultimate objective is to create a fully
skill set of engineers will be significantly reduced. virtual product and production development,
This will lead to an increased adoption of robots, testing and optimization.
in particular in manufacturing enterprises that Traditionally, product and production design
previously did not apply robots due to lack of are separated. Product requirements have to
flexibility and the required programming effort. be specified completely before the production
Flexibility of robotic systems will also be planning and engineering phase can begin. This
increased by open robotic platforms that allow causes a sequential process, in which any changes
third parties to enrich robots (robot platforms) produce additional costs and delays. An integrated
with application-specific hardware and software. product and production simulation will decrease
Examples include special purpose grippers and time-to-market, as concurrent engineering can
associated control software. In this way, whole be performed on digital models. Visualization
ecosystems (comparable to smartphones) technologies will improve communications among
are about to emerge. The increased flexibility geographically dispersed teams in different time
afforded will lead to higher adoption of robotics zones. This integrated approach also promises a
in manufacturing enterprises, as robotics can be secure access to all relevant information within the
applied to a broader application area. Previously company and throughout partner organizations.
existing barriers, such as high prices, will be
Simulation tools for both products and production
significantly alleviated.
concentrate on various details, such as logistics
regarding material routes, cycle times or buffer
sizes; processes, such as assembly or machining;
38
or rigidity or thermal characteristics of materials. design to commissioning, it should be noted that

In integrated simulation applications, those feedback information loops exist that need to be
specific models are shared and integrated in put in place to take full advantage of simulation
order to transfer knowledge and synchronize tools. For example, calibrated simulation models
planning among specific lifecycle phases and with data from the field can provide more accurate
disciplines. For instance, robotic aspects such insights. Similarly, plant simulations can benefit
as robot placement and path planning can be from historical data from similar plants to produce
calculated by directly accessing the 3D computer- optimal operating conditions.
aided design (CAD) models of the products that
Figure 4-8 distinguishes virtual and real worlds.
are being manufactured. Using the results of
In the virtual world, the product, factory and plant
these calculations, the PLC programmes can be
design first exchange information to optimize both.
automatically generated for production. Similarly,
These designs are then turned into real world
PLC programmes can be directly validated virtually
production and process automation systems
using a plant-level simulation that is often referred
that interact in order to execute production jobs.
to as virtual commissioning.
Additionally, the real world provides information to
Although the trend is towards an integrated the simulated world to optimize current or future
product-production simulation capability, from designs of products and factories, and to get
Figure 4-8 | Virtual world vs. real world
39
feedback about potential improvements of the shapes which improve product characteristics
actual process automation and production systems. or enable the uses of safe materials. Another
possible consequence is a shift in the role of
The emerging concept of the digital thread extends
manufacturers from designing and producing
the integrated product-production simulation to
products to designing and selling the specification
the entire value chain via information feedback
and plans. The actual manufacturing can then be
loops that are used to optimize continuously both
done by others such as retailers or customers.
the product and production, but also service,
maintenance and disposal, i.e. the entire lifecycle.
4.2.6 Additive manufacturing/3D printing 4.2.7 Additional factory of the future

technologies
A major aspect of integrating digital and physical
worlds is the transfer of product specifications Besides these technologies, various other fields
to executable production processes. Moreover, of research and development exist which might
flexible manufacturing resources such as provide relevant solutions for the factory of the
machining equipment or 3D printers help to keep future, such as cognitive machines, augmented
associated configuration efforts low and thereby reality, wearable computing, exoskeletons, smart
support the production of small lot sizes or even materials, advanced and intuitive programming
individual products. techniques, or knowledge management systems.
The global market of additive manufacturing (AM)

products and services grew 29% (compounded
annual growth rate) in 2012 to over USD 2 billion
in 2013 [21]. The use of AM for the production of
parts for final products continues to grow. In 10
years it has gone from almost nothing to 28,3% of
the total product and services revenue from AM
worldwide [22]. Within AM for industry, there has
been a greater increase in direct part production,
as opposed to prototyping (AM’s traditional area
of dominance). Within direct part production, AM
serves a diverse list of products and sectors,
including consumer electronics, garments,
jewellery, musical instruments, medical and
aerospace products.
3D printing allows manufacturing to work

economically with a large variety of shapes and
geometries, including for small product quantities.
This has the potential to transform some parts
of the production industry from mass production
to individual production. The “batch size one”
will become more wide-spread. Furthermore,
the number of required steps for producing a
product will be reduced, which will lead to a more
environmentally friendly production and to new
40
Section 5
Market readiness
Implementation of factory of the future purposes, and respective feedback loops have to
concepts highly depends on the readiness of be implemented in order to best consider potential
involved stakeholders to adopt the appropriate interdependencies and enable the exploitation of
technologies. Several preconditions must be additional optimization potentials or even business
fulfilled to achieve this market readiness, as ideas.
explained in the following sub-sections.
5.2 Overcome “resistance

5.1 Implementation of a systems to change” in traditional
perspective production environments
The holistic implementation of factory of the future The interdisciplinary work not only enables more
concepts requires a partnership involving the efficient information exchange and execution of
traditionally strained organizational relationships work in product and production lifecycle phases.
between the engineering, information technology Widespread knowledge and awareness about
and operations groups. Moreover, this integration factory of the future technologies, concepts
of disciplines has to be implemented throughout and benefits also helps to overcome the lack
the entire lifecycle of products and production, i.e. of acceptance of new solutions. This lack of
during planning, construction and operation. acceptance is caused by concerns about potential
This not only requires the interoperability of job losses due to efficiency increases generated
systems on a technical level, as described in by automation and IT systems. Knowledge and
Sub-section 4.1.1, but also the realization of multi- awareness are important keys to overcoming such
disciplinary processes, in which personnel from concerns, since high levels of education reduce
the engineering, information technology and busi- the risk of job losses. Furthermore, the number
ness operations work closely together, under- of jobs might not be reduced, but instead their
stand one another or even have complementary content and style might change towards more
education. integrative and flexible working modes. This not
only concerns production jobs on the shop floor,
Such multidisciplinary work can be supported by
but also PLC or robot programming and other
appropriate IT systems, such as modelling and
tasks which are related to engineering.
simulation tools, or by configuration and integration
techniques for cyber-physical systems (CPS) Besides the fear of job losses, resistance to
and systems of systems (SoS). To make those change is often caused by uncertainties on the
solutions beneficial and to support the systems part of stakeholders and decision makers, who are
perspective during product and production insufficiently knowledgeable about the technical
planning, creation and operation, knowledge background, business models and benefits
from the different disciplines has to be integrated, involved, so that they remain restricted to well-
merged and utilized for related application known traditional concepts and solutions.
41
Market readiness
5.3 Financial issues 5.4 Migration strategies

Closely related to “resistance to change” is In existing factories, various legacy systems are
uncertainty about the actual benefits of factory usually in place, in which relevant historical data
of the future implementations. In order to make is stored and which are connected via customized
sure that new factory of the future applications interfaces. Furthermore, the slogan “never change
in manufacturing really fit the requirements of the a running system” is widely applied in industrial
production environment into which they should be production environments, in order to not jeopard-
integrated, it is necessary to assess their actual ize the robustness of existing production systems
performance as soon as possible, ideally before by integrating new features which might not nec-
integration decisions are made. Appropriate essarily be needed. To overcome these issues,
methods and tools, as well as best practice while introducing new methods, concepts and
examples, that make it possible to secure rapid technologies to factories, appropriate migration
and inexpensive statements about the efficiency strategies are necessary.
of certain technologies and production strategies
The implementation of a systems perspective and
in a company’s specific production environment
networked and flexible organization structures
would help to address this need and thus reduce
for factories of the future, plus specific project
the threshold for implementation of new factory of
management support tools designed for the needs
the future solutions in manufacturing. Knowledge-
of FoF implementation projects and appropriate
based systems using information from previous
rules and tools for decision making support in
analyses or simulation-based approval of certain
order to increase planning reliability, contribute
decisions and virtual try-out of specific system
to a smooth migration towards the factory of the
components can contribute to this. However, such
future. Further measures to reduce the complexity
technological measures must be complemented
and risks of migration projects include scalable
by integration of factory of the future activities
(CPS) architectures that enable continuous design,
into strategic company objectives and the set-up
configuration, monitoring and maintenance of
of harmonized controlling and measurement for
operational capability, quality and efficiency, and
system performance assessment.
the industrialization of software development,
Besides the introduction of new IT technologies i.e. modularization to enable rapid configuration,
to manufacturing, business models must be adaption and assembly of independently
evaluated with regard to their costs and benefits, in developed software components.
order to assess properly the potential of business
innovations and reduce related risks. While
transforming business through a combination
of existing and emerging business models, end-
to-end visibility of business value is required.
This requires a standardized and shared high
performance infrastructure for decision support.
However, even if the benefit of factory of the future

business models and technologies is proven by
respective assessments, the financial strategies of
companies have to allow related investments. In this
context, return on investment (ROI) predictions and
the rate of capital reinvestment must be considered.
42
Section 6
Predictions
Most of the key technologies for factories of The adoption of key technologies varies among
the future listed in Section 4 are still under industries and application cases. For instance,
development. Their maturity and applicability additive manufacturing is appraised as being
in different industries, as well as the readiness highly beneficial for personalized production
to adopt them in manufacturing industries, are and manufacturing of special parts, which, for
indicated in Figure 6-1. example, have complex geometries expensive
From this radar plot it can be seen that in particular or impossible to manufacture using common
non-technical challenges such as migration manufacturing technologies. On the other side,
strategies or the implementation of a system additive manufacturing probably will never reach
perspective are still at a premature stage. This is the degree of efficiency it already has for current
well in line with the observation that many of the mass production. Similarly, the maturity of
development activities in the context of factories modelling and simulation tools depends on the
of the future that are ongoing at the regional, area of application. They are already widely used
national and international levels are focusing on for product development and optimization, e.g.
technological issues. in the automotive and aerospace industry, while
Figure 6-1 | Market readiness and technology maturity/applicability of key technologies
43
Predictions
there is improvement potential for close-to-real-

time simulation applications for optimization of
manufacturing settings.
For other technologies such as IoT technologies,

M2M networks, smart robotics and cloud-based
AIM, singular solutions exist which are quite
mature in their specific application field. However,
further efforts have to be undertaken to implement
wide-spread applicability of such developments
by overcoming issues which are inhibiting their
market readiness, such as the “resistance to
change” or a lack of migration strategies.
Altogether, it can be said that the industry branch,

as well as the application context, i.e. the position
in the horizontal and vertical manufacturing
environment layers, impact the market and
deployment readiness of factory of the future
applications.
44
Section 7
Conclusions and recommendations
The factory of the future will deliver on-demand information between them. Manufacturers should
customized products with superior quality, while start to think of their facilities as constituting a
still benefiting from economies of scale and smart node in symbiotic ecosystem networks. This
offering human-centered jobs, with cyber-physical will allow them to anticipate the need for demand
systems enabling the future of manufacturing. management in a more proactive way.
New manufacturing processes will address the
challenges of sustainability, flexibility, innovation,
7.1.2 Agile manufacturing
and quality requirements in human-centric
manufacturing. Future infrastructures will support The adaptability of manufacturing systems to
access to information everywhere and at all times changing requirements such as market demands,
without the need for any specific installation of business models or product specifications is a core
parameterization. Production resources will be feature of the factory of the future. To implement
self-managing and will connect to one another this, various organizational and technological
(M2M), while products will know their own measures have to be undertaken. This includes
production systems. This is where the digital and the implementation of a systems perspective,
real worlds will merge. as well as solutions which enable configurability
A number of guiding principles and recommenda- of production systems such as interoperability
tions for the factory of the future emerge from the and connectivity, as well as their scalability.
considerations covered in the previous sections. Also advanced computing capabilities, which
The actions involved are either of a general char- for example enable first-time-right processing of
acter or are specifically focused on data, people, products, are recommended in this context.
technology and standards.
7.1.3 Maximize value chain and

7.1 General collaborative supply networks
7.1.1 Interaction with other ecosystems The extension of network infrastructures
The IEC recommends focusing on the interaction towards production network partners will help
of a factory, including all its components, such manufacturers gain a better understanding of
as IoT systems, with other ecosystems, such as supply chain information that can be delivered
the Smart Grid, and identifying the standards in real time. By connecting the production line to
needed to allow industrial facilities and the suppliers, all stakeholders can understand the
industrial automation systems within such facilities interdependencies, flow of material and process
to communicate with such ecosystems for the cycle times. Real time information access will help
purpose of planning, negotiating, managing and manufacturers identify potential issues as early as
optimizing the flow of electrical power, supply possible and thus prevent them, lower inventory
logistics, human resources, etc. and related costs and potentially reduce capital requirements.
45
7.1.4 Make use of independent potential benefits that related software components
manufacturing communities bear for a factory. This includes mechanisms for the
discovery, brokerage and execution of tasks.
The trend toward the “desktop factory” is not
new, but it is much more pronounced today
and is cheap, accessible and user-friendly. As
7.2.2 Cyber security
indicated in this White Paper, the requirements
posed by this trend suggest a need to make use Overall, with the expanded use of the internet
of new business models (e.g. crowdsourcing, for control functions in automation systems,
maker movement, product-service integrators it can be alleged that every industrial system
and robotic ecosystems) to decouple design and functioning today is vulnerable, and that there
manufacturing. is no one consistent approach to security. It is
therefore critical to take the requirements for
security standards seriously (i.e. corporate and
7.1.5 System safety throughout the personal data protection, actuating system safety,
lifecycle consideration of accidental feedback-loops,
etc.) and to focus on safeguarding against cyber
The prevention and avoidance of accidental system
terrorism, using an adaptive, responsive and
failures or intentional cyber-attacks has to take
cooperative model. The IEC has a key role to play
into account the increasing interconnectedness
in addressing this issue.
and complexity of systems. For this reason, it is
important to address system safety throughout the Appropriate security frameworks are to be
life cycle, from design to ramp-up and interlinkage, established that provide best practices and cost-
and to predict and evaluate the behaviour of efficient solutions according to the degrees or
(networked) systems in the future. layers the owner of a certain set of data is willing
to protect. Especially for the establishment of
such frameworks among production sites or
7.1.6 Sustainable security and network enterprises, it is also recommended to implement
solutions certification measures in order to establish trust
Security and networking solutions must be and accelerate the setup of production networks.
engineered to withstand harsh environmental
conditions inside manufacturing facilities and to
7.2.3 Interpretation of data
address the needs of industrial control systems,
which are not present in typical “white collar” For the large amounts of information being
office networks. generated to be useful, they must be harmonized,
consistent and up to date. To this end, the integration
of big data and semantic technologies and their
7.2 Data application to product lifecycle management and
7.2.1 Service-oriented architectures production systems will be necessary.
In a reconfigurable factory of the future, software

will play a major role in every aspect of the value 7.3 People
chain and on the shop floor. It is therefore important
7.3.1 Humans and machines
to create scalable service-oriented architectures
which are able to be adapted to the specific needs The idea of human-centered manufacturing is
of a company or factory in order to leverage all of the to put the focus in manufacturing back on the
46
employees, tailoring the workplace to their in- 7.4 Technology

dividual needs. A company can generate enor-
7.4.1 Digitalization of manufacturing
mous amounts of data but ultimately it must rely
on people in order to make decisions. HMI and Data is generated from numerous sources at all
human-centered design – the introduction of stages of the manufacturing cycle. Given that
augmented reality into the automation process – IoT and CPS produce even higher amounts of
allow people to visualize data in the context of data, real-time analytics (and feedback) for this
the real world in order to bridge the gap between data help with the self-organization of equipment
data and the physical world. Human-robot col- as well as with decision support. As a result,
laboration supports workers in complex or high- the IEC recommends that manufacturing machine
load tasks. designers develop their devices to be able to
communicate directly with various systems within
the internal and external supply chains. This will
7.3.2 Training allow them to gather the necessary information
about customers, suppliers, parts, tools, products,
The human operator will be supported by smart
calibration and maintenance schedules. The IoT
assistance systems that are interconnected with
will further enable realization of the common goal
the production equipment and IT systems to help
of manufacturing operations, which has been to
him/her make the right decisions and execute
increase the number of areas in the plant where
his/her tasks. This certainly will result in new skill
the manual data entry can be replaced with
profiles for workers, for which appropriate training
automated data collection.
will be needed. Such training is expected to occur
on the job – while workers perform their daily Interaction between humans and CPS is another
activities, they are simultaneously learning new significant factor, in which human knowhow
skills. should be transformed and digitalized as one
kind of data among the mass of other data. The
For the setup of factory of the future systems,
purpose here is to equip manufacturing with the
cross-sectorial education is essential in order to
capabilities of self-awareness, self-prediction, self-
implement, integrate and optimize the multiple
maintenance, self-reconfiguration, etc. throughout
components throughout all disciplines involved in
the manufacturing cycle.
product and production lifecycle phases.
7.4.2 Real time simulation

7.3.3 Worker mobility
Modelling and simulation will form an integral part
In face of the need to do more with less and
of the entire value chain, rather than being just an
the trend toward increased worker lifetimes, it is
R&D activity. Combining virtual simulation models
important to provide workers with an adequate
and data-driven models obtained directly from
workplace and continued mobility throughout the operation and making real-time simulation
their careers. As a result, the IEC emphasizes accessible to all activities in the factory of the
the importance of heightened development of future offers a great opportunity to enable new
wearables and exoskeletons that are comfortable, and better feedback control loops throughout the
affordable and enable functional activities at entire value chain, from design to disposal.
all times.
47
7.4.3 Promote cyber-physical systems 7.5.3 Standardize connection protocols
Digital information flows across company Every sensor and actuator is a participant in
boundaries, presenting a security challenge with the IoT. Each device has an IP address and is
regard to information-sensitive activities in the networked. In order for factories of the future to
value chain. Cyber security as well as physical come to fruition, a portfolio of connectors and
security will be a primary concern and key connection protocols must be available onboard
performance indicator in the factory of the future. any device and allow the unique dialect of each
Enabling technologies such as CPS and IoT will device and connector to be transformed without
play a fundamental role in the adoption of a more loss of information. The IEC should invite industry
flexible connectivity in the industrial value chain. to develop standardized protocols in this area.
As a result, the factory of the future will be highly
modular and connected.
7.5 Standards
7.5.1 Merge national concepts at
the international level
A highlight for the factory of the future is that

self-contained systems will communicate with
and control each other cooperatively. To make
this possible, international consensus-based
standards taking into account existing national
and regional standards for industrial automation
are required. A wider market with solid standards
will support the interoperability necessary for
the expansion of replicable and more affordable
technologies globally.
7.5.2 Systems level standardization
Keeping in line with previous IEC White Papers,

Coping with the Energy Challenge (2010) and
Orchestrating infrastructure for sustainable Smart
Cities (2014), the MSB recommends to the IEC to
ensure that standards giving preferred industrial
automation solutions go beyond a simple product
approach and consistently adopt a real application
perspective. This will involve keeping in mind the
global effects desired for the factory of the future,
smart manufacturing, Industrie 4.0, e-Factory,
Intelligent Manufacturing, et al.
48
Annex A
References
[1] www.marketsandmarkets.com/Market-Reports/smart-factory-market-1227.html [viewed 2015-09-15]
[2] KOREN, Y., The Global Manufacturing Revolution – Product-Process-Business Integration and
Reconfigurable Systems, Hoboken, NJ, Wiley, 2010
[3] Smile curve according to Stan Shih: chaitravi.wordpress.com/2010/02/10/the-smiling-curve-stan-

shih [viewed 2015-09-15]
[4] IEC e-tech, May 2015: iecetech.org/issue/2015-05 [viewed 2015-09-15]
[5] www.microlinks.org/good-practice-center/value-chain-wiki/types-value-chain-governance
[viewed 2015-09-15]
[6] The Modularization of the Value Chain, TheoryBiz.com: theorybiz.com/copycats/the-age-of-

imitation/3329-the-modularization-of-the-value-chain.html [viewed 2015-09-15]
[7] D’SOUZA, D. E.; WILLIAMS, F. P., Toward a taxonomy of manufacturing flexibility dimensions.
In: Journal of Operations Management 18(5), 2000, pp. 577-593
[8] ec.europa.eu/enterprise/policies/innovation/policy/business-innovation-observatory/case-studies/
index_en.htm [viewed 2015-09-15]
[9] ABRAHAMSON, S., RYDER, P., UNTERBERG, B., Crowdstorm: The Future of Innovation, Ideas,
and Problem Solving, John Wiley & Sons, Inc. 2013
[10] www.darpa.mil
[11] smartmanufacturingcoalition.org
[12] www.iiconsortium.org
[13] www.whitehouse.gov/sites/default/files/microsites/ostp/amp_final_report_annex_1_technology_
development_july_update.pdf [viewed 2015-09-15]
[14] German Engineering Association (www.vdma.org), German association of ICT industry

(www.bitkom.org), and German association of electrics and electronics industry (www.zvei.org)
[15] www.ieee.org/education_careers/education/standards/standards_glossary.html [viewed 2015-09-15]
[16] www.ncoic.org/images/technology/NIF_Pattern_Overview.pdf [viewed 2015-09-15]
[17] www.gartner.com/newsroom/id/2819918 [viewed 2015-09-15]
[18] www.forbes.com/sites/sap/2014/07/09/are-you-ready-for-the-internet-of-everything
[viewed 2015-09-15]
[19] Big Data: The Next Frontier for Innovation, Competition, and Productivity, McKinsey Global
Institute, May 2011
49
References
[20] scn.sap.com/community/internet-of-things/blog/2014/05 [viewed 2015-09-15]
[21] www.raeng.org.uk/publications/reports/additive-manufacturing [viewed 2015-09-15] p. 29
[22] www.raeng.org.uk/publications/reports/additive-manufacturing [viewed 2015-09-15] p. 5
50
ISBN 978-2-8322-2811-1
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Electrotechnical
® Commission ® CHF 50.-
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IEC WP Future Factory:2015-10(en)
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® Registered trademark of the International Electrotechnical Commission. Copyright © IEC, Geneva, Switzerland 2015

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White Paper

Factory of the future

Section 3 provides a brief background on

Section 7 concludes with a list of recommendations Mr. Mark Burhop, Siemens

Section 2 Current manufacturing environment 14

Section 3 Concepts of the factory of the future 17

Section 4 Driving technologies 26

4.2 Enabling technologies 32

Section 5 Market readiness 41

Section 7 Conclusions and recommendations 45

AVM adaptive vehicle make

BOM bill of materials

CAD computer-aided design

CAx computer-aided technologies

CEP complex event processing

CNC computer numerical control

CPPS cyber-physical production system

CPS cyber-physical system

DCS distributed control system

EDI electronic data interchange

ERP enterprise resource planning

ESP event stream processing

FoF factory of the future

HMI human-machine interface

ICT information and communication technology

IoT Internet of Things

M2M machine to machine

MEMS microelectromechanical system

MES manufacturing execution system

NFC near field communication

PLC programmable logic controller

QMS quality management software

R&D research and development

ROI return on investment

SCADA supervisory control and data acquisition

SIM subscriber identity module

SoS system of systems

WBS work breakdown structure

Organizations, AMO Advanced Manufacturing Office

IIC Industrial Internet Consortium

MSB Market Strategy Board (of the IEC)

NCOIC Network Centric Operations Industry Consortium

SMLC Smart Manufacturing Leadership Coalition

VDMA Verband Deutscher Maschinen- und Anlagenbau

ZVEI Zentralverband Elektrotechnik- und Elektronikindustrie

tradictory factors of market readiness versus

§§ To encourage the use of international stan-

Figure 2-1 | Evolution of production [2]

Concept/R&D Sales/aGer service

Figure 2-2 | Smile curve of value added in production industries [3]

throughout value chains, i.e. the full range of value

With today’s globalization explosion, it is clear that

While dealing with multiple value chains, it is

In modern production ecosystems, value chains

As a result, manufacturing processes, production

Kind of ﬂexibility Explana+on

Figure 3-1 | Kinds of manufacturing flexibility (excerpt) [7]

3.4 Business models a customer or factory operator announces

Method Materials assets

VDMA Verband Deutscher Maschinen- und Anlagenbau

IntroducDon of high-‐eﬃciency equipment, PromoDon of power-‐saving

Reduce frequency of troubles and pre-‐

Sourcing applica.on-‐speciﬁc integra.on ﬂows

Real-‐+me Simple Simple