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Informatik-Spektrum
Organ der Gesellschaft für Informatik
e.V. und mit ihr assoziierter
Organisationen
ISSN 0170-6012
Informatik Spektrum
DOI 10.1007/s00287-015-0891-z
Smart Factory Systems
Jay Lee
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AKTUELLES SCHLAGWORT* / SMART FACTORY SYSTEMS }
Smart Factory Systems
Jay Lee
The manufacturing industry has been facing several
challenges, including sustainability and perform-
ance of production. These challenges are sourced
from numerous factors such as an aging workforce,
changes in the landscape of global manufactur-
ing and slow adaption of smart manufacturing by
implementing IT in manufacturing process.
In recent years, German and US governments
have establishedseparate initiatives to accelerate the
use of the Internet of Things (IoT) and smart ana-
lytics technologies in the manufacturing industries
and, consequently, to improve the overall perform-
ance, quality, and controllability of manufacturing
process. The smart factory is the integration of
all recent IoT technological advances in computer
networks, data integration, and analytics to bring
transparency to all manufacturing factories. In this
article, we review the most recent logistic decisions
for taking smart factories from idea to reality and
then describe the possible technologies for smart
factories.
From Traditional Factories
to Smart Factories
The manufacturing sector showed a tremendous
amount of interest in the new conception introduced
in 2013 at the Hannover Fair in Germany. A futuristic
plan developed under the auspices of the German
Federal Government’s High-Tech Strategy is out-
lined to be the framework of the fourth industrial
revolution. The first industrial revolution occurred
by the end of the 18th century with the mechaniza-
tion of manufacturing processes. Then towards the
start of the next century, electricity was utilized to
power mass production of goods based on the di-
vision of labor (station-oriented). In the 1970s, the
third industrial revolution was recognized with the
use of electronics and information technology (IT)
to achieve more automation of manufacturing oper-
ations. Based on the initiative, the fourth industrial
revolution is the integration of interconnected sys-
tems and IoT in manufacturing, which is called
Industry 4.0.
On the other hand, the US government as an-
other global pioneer in the manufacturing industry
defined the term cyber-physical systems (CPS). CPS
is a complex engineering system that integrates
physical, computation and networking, and com-
munication processes. CPS can be illustrated as
a physical device, object, equipment that is trans-
lated into cyberspace as a virtual model. With
networking capabilities, the virtual model can moni-
tor and control its physical aspect, while the physical
aspect sends data to update its virtual model. Con-
sidering the importance of this topic, cyber-physical
DOI 10.1007/s00287-015-0891-z
© Springer-Verlag Berlin Heidelberg 2015
Jay Lee
Ohio Eminent Scholar and L. W. Scott Alter Chair Professor
in Advanced Manufacturing, Univ. of Cincinnati,
Cincinnati, USA
E-Mail: jay.lee@uc.edu
Founding Director of NSF Industry/University Cooperative
Research Center on Intelligent Maintenance Systems (IMS),
Univ. of Cincinnati, Univ. of Michigan, Missouri Univ. of S&T,
Univ. of Texas at Austin,
Cincinnati, USA
www.imscenter.net
*Vorschläge an Prof. Dr. Frank Puppe
<puppe@informatik.uni-wuerzburg.de>
oder an Dr. Brigitte Bartsch-Spörl
<brigitte@bsr-consulting.de>
Alle ,,Aktuellen Schlagwörter“ seit 1988 finden Sie unter:
http://www.is.informatik.uni-wuerzburg.de/as
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{SMART FACTORY SYSTEMS
systems have been called a national research priority
of the United States [1] and the European research
council [2]. The US government recently established
four manufacturing hubs, including additional
manufacturing in Ohio, low-power semiconductor
manufacturing in North Carolina, digital manu-
facturing and design innovation (DMDI), and light
weight materials in Michigan. In addition, the White
House initiated Smart America Challenges based on
advanced cyber-physical systems in 2012.
The successful integration of Industry 4.0 and
cyber-physical systems provides significant benefits
for the entire manufacturing industry. These bene-
fits can be summarized in one term as the so-called:
smart factory [3]. The adoption of the smart factory
can be a game-changing event that can transform the
interaction of engineered systems just as the internet
transformed the way people interact with informa-
tion. To some extent, we are not only living in the
physical world, but also in internet (cyber) space.
For example, Facebook is our cyber-life that coex-
ists with our real life. Similar concepts and effects
also apply to the manufacturing system in a smart
factory. Each physical component and machine will
have a twin model in the cyberspace composed of
data generated from sensor networks and manual
inputs. Intelligent algorithms process the data in
cyberspace, so that information about the physical
components’ health conditions, performance, and
risks are calculated and synchronized in real time.
As smart factories leverage the web of informa-
tion from interconnected systems to perform highly
efficiently, agilely, and flexibly, the overall framework
can be divided into three major sections as defined
by Lee et al. [4]. These sections are components, ma-
chines, and production systems, where each of these
items brings different levels of understating and
transparency to the factory. Smart machines need
to use real-time data from their own components
and other machines to gain self-awareness and self-
comparison. Self-awareness enables machines to
assess their own performance and diagnose possible
malfunctioning components. Consequently, it can
predict and prevent potential failure and risk con-
tributions to the final product. Smart machines can
further share their information over the cyberspace
to compare their performance and productivity with
other similar machines. This self-comparison at-
tribute enables machines to adjust their settings
and performance properly through the knowledge
they gained from their working history. In this en-
vironment, the manufacturing system is also able
to schedule customized manufacturing criteria for
individual machines based on their performance.
Consequently, the production system can config-
ure itself to customize production of every single
product based on the current status of all machines
involved inthe manufacturing line to guarantee high
quality production with the optimum operation
costs. In such a smart factory, the manufacturer is
able to meet customer specifications at any produc-
tion rate with supporting last minute changes in the
production and other flexibilities that are far from
achievable in traditional factories.
Necessary Technologies
for the Smart Factory
The smar t factory defines a new approach in multi-
scale manufacturing by using the most recent IoT
and industrial internet technologies, which consist of
smart sensorsand sensing, computingand predictive
analytics, and resilient control technologies. These
technologies must be bonded together to acquire,
transfer, interpret, and analyze the information, and
to control the manufacturing process as intended. As
mentioned in the previous section, it is possible to
fulfill the requirements of the smart factory through
cyber-physical systems. Both Industry 4.0 and CPS
are in their infancy stages and require more in-depth
research for their practical usage to be established
in different sectors. The smart factory as the symbol
for using CPS in the manufacturing sector is not ex-
empted from the criteria. At the current stage,it is
required that applicable frameworks for establishing
CPS in the manufacturing industry be defined.
Recently, the author developed and proposed the
5C architecture as the general framework for imple-
menting CPS in manufacturing [5]. The proposed
5-level CPS structure shown in Fig. 1provides a step-
by-step guideline for developing and deploying
a cyber-physical system for the smart factory.
5C-level functions (Fig. 2) can be defined as
follows:
Level 1: Connection requires acquiring accurate
and reliable data from machines and their compo-
nents. Data source can be from IoT-based machine
controllers, add-on sensors, quality inspections,
maintenance logs, and enterprise management sys-
tems such as ERP, MES, and CMM. A seamless and
tether-free method for data management and com-
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Table 1
Comparison between today’s factories versus Industry 4.0-based smart factories
Data Today’s Factory Smart Factory (Industry 4.0-based)
Source Attributes Technologies Attributes Technologies
Component Sensor Precision Smart Sensors Self-Aware Degradation
and Fault Self-Predict Monitoring &
Detection Remaining Useful
Life Prediction
Machine Controller Producibility Condition-based Self-Aware Predictive Up Time
& Performance Monitoring & Self-Predict & Failure
Diagnostics Self-Compare Prevention
Production Networked Productivity Lean Self-Configure Worry-free
System System & OEE Operations: Self-Maintain Productivity with
Work and Waste Self-Organize Resilient Control
Reduction Systems
Fig. 1 5C architecture for Implementation of a cyber-physical system
munication, proper selection of sensors, and data
streaming are important considerations for this
level. At this level, a condition-based monitoring
system is normally used to monitor machine status.
Level 2: The conversion level is local machine
intelligence, where data are processed and con-
verted to meaningful information (such as machine
degradation information). Signal processing, fea-
ture extraction, and commonly used prognostics
and health management (PHM) algorithms (such as
self-organizing maps, logistics regression, support
vector machines, etc.) and predictive analytics are
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{SMART FACTORY SYSTEMS
Fig. 2 Applications and Techniques Associated with Each Level of the 5C Architecture
integrated in this level. The outputs of this level in-
clude but are not limited to machine health related
features, health value, and operation regime flags.
The goal for this level is to enable self-awarenessfor
the component and machine level.
Level 3: The cyber level is where al l information
confluences and is processed. Peer-to-peer compar-
isons, information sharing, collaborative modeling,
and time machine records of machine utilization and
health condition history are analyzed. These analyt-
ics provide machines with a self-comparison ability,
where the performance of a single machine can be
compared with and rated among the fleet and, on the
other hand, similarities between machine perform-
ance and previous assets (historical information) can
be measured to predict the future behavior of the ma-
chinery. Historical data can also be used to correlate
theinterfacialeffects ofmultiplefeatures.Atthislevel,
a c yber-physical system approach is normally used to
assess machine health in different cycles or regimes
and further compare it with its peers.
Level4:Thecognition level generates a thorough
knowledge of the system monitored and provides
reasoning information to correlate the effect of
different components within the system. Proper
organization and presentation of the knowledge
acquired for expert users will support proper de-
cisions. Infographic APPs can be used to integrate
with machine and user-friendly mobile devices such
as smart phones.
Level 5: The configuration level is feedback
from cyberspace to physical space, where actions
are taken as either human-in-the loop or a supervi-
sory control to make machines self-configure, and
be self-adaptive and self-maintained. This stage acts
as a resilience control system to apply corrective and
preventive decisions that were made in the cognition
level.
Design of the Dataless
and Information Rich Smart Factory
Another extreme advantage of cyber-physical sys-
tems for the smart factory is the capability of
managing and presenting data to different deci-
sion makers. The smart connection level makes all
the data digital, sorts the data according priority,
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Fig. 3 Cyber-physical systems-enabled dataless, information-rich worry-free factory
synchronizes the data in same time reference, and
organizes the data according to their correlations.
Hence a connected and paperless data management
environment is built. Moreover, since the data stream
is processed in real time, the value of information can
be secured with timely actions. Cloud computing
and storage capabilities of CPS will allow the user to
access information through mobile devices anytime
and anywhere. Info-graphs require a minimal data
volume to be accessed, and the meaning of the infor-
mation is only understandable by the users. Hence,
worries regarding data security will also be reduced.
Users can find useful information in cyberspace
at different levels of abstraction, ranging from the
condition of a machine component to the overall
throughput and quality risk of a manufacturing
line. The information retrieval and decision-making
process has become much easier due to effective
information abstraction and intuitive representa-
tions. Hence, users will no longer need to deal with
raw data and resolve the information by themselves.
Instead, useful information is mined from data con-
tinuously in real time to create an information-rich
decision environment, and most of the data are only
handled once in the whole processing cycle (Fig. 3).
This is what we call the only handle information once
(OHIO) philosophy for a worry-free factory.
Conclusion
Manufacturing has evolved through innovative
technologies and inventions for centuries and has
seen three major revolutions. These technological
advancements acted as catalysts for transforming
manufacturing into its current stage. In recent years,
the astounding advances in information technol-
ogy, cloud infrastructure, analytics, and even social
media have paved the way for the next major trans-
formation in this sector. In Industry 4.0, traditional
production facilities are converted into smart facto-
ries, which in turn make smart products. Therefore,
new business paradigms are being created for smart
factories. However, the journey towards smart fac-
tories is expected to be gradual and evolutionary.
Many of the basic underlying technologies need to
be researched and developed.
In this position paper, we have presented a gen-
eral technological framework of the smart factory.
In addition, we discussed the advantages of the smart
factoryinterms ofinterpretingdataintoinformation.
The full adoption of the framework presented will
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{SMART FACTORY SYSTEMS
help companies improve their global competitive-
ness, restore their domestic manufacturing industry,
and break ground in new market opportunities.
References
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2. European Commision Research and Innovation (2013) European Commision Re-
search and Innovation – Horizon 2020. http://ec.europa.eu/programmes/
horizon2020/en/, last access: 3. March 2015
3. Erik Sundin JÖ (2008) Manufacturing Systems and Technologies for the New Fron-
tier. Sfb 627:537–542
4. Lee J (2013) Industry 4.0 in Big Data Environment. German Harting Magazine
Technology Newsletter 26:8–10
5. Lee J, Bagheri B, Kao H-A (2014) A Cyber Physical Systems Architecture for Indus-
try 4.0-based Manufacturing Systems. Manuf Lett 3:18–23
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