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In recent years, German and US governments have established separate initiatives to accelerate the use of the Internet of Things (IoT) and smart analytics technologies in the manufacturing industries and, consequently, to improve the overall performance, 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.
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DOI 10.1007/s00287-015-0891-z
Smart Factory Systems
Jay Lee
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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
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
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
*Vorschläge an Prof. Dr. Frank Puppe
oder an Dr. Brigitte Bartsch-Spörl
Alle ,,Aktuellen Schlagwörter“ seit 1988 finden Sie unter:
Author's personal copy
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
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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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
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.
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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help companies improve their global competitive-
ness, restore their domestic manufacturing industry,
and break ground in new market opportunities.
1. Gill H (2008) From vision to reality: cyber-physical systems. Present, HCSS Natl.
2. European Commision Research and Innovation (2013) European Commision Re-
search and Innovation – Horizon 2020.
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
Author's personal copy
... Simultaneously, working in factories becomes smart if operators can interact and collaborate with technologies that enable remote monitoring and execution of production processes, facilitating execution and decision-making in both physical and cognitive processes (Romero, Stahre, Smart Manufacturing Vertical Integration This refers to the information technology (IT) infrastructure of the production systems, in which the information flows from the automation and control systems to enterprise resources planning and also enables feedback and corrective actions. Cimini, Pinto, and Cavalieri (2017), Kagermann, Wahlster, and Johannes (2013), Lee (2015) Virtualisation This refers to the creation of virtual models of the production via simulations that allow for virtual commissioning and digital twins. Gilchrist (2016), Hermann, Pentek, and Otto (2015) Automation This refers to implementation of intelligent machines, robots and logistics systems that can communicate through machine-to-machine communication and perform self-management. ...
... Gilchrist (2016), Hermann, Pentek, and Otto (2015) Automation This refers to implementation of intelligent machines, robots and logistics systems that can communicate through machine-to-machine communication and perform self-management. Gilchrist (2016), Lee (2015) Traceability This refers to the tracking of raw materials and goods through the supply chain. and Taisch 2020). ...
In modern manufacturing systems, operators’ role is evolving continuously in relation to the main production paradigms affecting how companies approach their manufacturing and logistics operations. If the introduction of Lean Manufacturing principles has affected workers’ well-being directly, the adoption of digital technologies is modifying how factory work is organised and performed. As a result, digitalised and Lean Manufacturing contexts require operators to enhance their skills increasingly to perform several tasks and functions. This paper examines the evolution of operators’ role in production by recalling two main work design paradigms that originated in the 1960s: Job Enlargement and Job Enrichment. Indeed, through a literature review and development of a causal loop diagram (CLD), this research points out how the main features of Industry 4.0 and Lean Manufacturing jointly affect the Job Enlargement and Job Enrichment concepts. The study significantly helps reshape work design in next-generation production systems, shedding light on the implications and relationships of the most widespread manufacturing paradigm, i.e. Lean Manufacturing and Industry 4.0, using Job Enrichment and Job Enlargement strategies. A CLD also can be a helpful tool for practitioners to understand what factors to leverage to increase efficiency and worker satisfaction.
... Prolonged performance of activities in the same (ergonomically inadequate) position (sitting or standing) adversely affects the health of workers. Workers performing activities on a nonergonomic workstation are particularly exposed to muscle strain and fatigue (Kim et al. 2017;Sun et al. 2018 (Lee 2015) contributes to the improvement of the working environment and performance of operators who perform monotonous and repetitive activities by improving safety and health of workers, providing greater autonomy to operators, enabling selfdevelopment, increasing flexibility and efficiency of production processes (Gorecky et al. 2014;Lasi et al. 2014). The main contribution of this paper is reflected in the presentation of an innovative prefabricated workstation for collaborative activities between operators and robots, where neuroergonomic research will be conducted in the coming period. ...
Conference Paper
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Modern organizations aim to improve key economic parameters (productivity, effectiveness) in order to be competitive in global market. Furthermore, contemporary organizations strive to improve the health and safety of workers. One of the possible solutions to achieve that goal is to modernise production processes through the integration of lean principles and innovative technologies of Industry 4.0. However, in many monotonous and repetitive assembly operations, it is not possible to implement full digitalization. The focus of this research paper is to propose a modular human-robot workstation where the operator and collaborative robot share activities to improve workplace safety and worker's performance. The proposed modular assembly workstation, integrated with a poka-yoke system, is designed in accordance with the individual characteristics of the operator. Authors plan in future periods to conduct researches on this workstation in the field of neuroergonomics using an innovative electroencephalogram system (EEG) during assembly tasks with collaborative robot to prove that it will improve the physical, cognitive and organizational ergonomics and, at the same time, increase productivity and effectiveness.
... Przemysł 4.0 łączy sfery cyberfizyczne, rewolucjonizuje produkcję oraz dostarczanie towarów i usług dzięki połączeniom między produktami, procesami i konsumentami (Lee, 2015;Teixeira & Tavares-Lehmann, 2022). Charakteryzuje się wykorzystaniem wielu nowych technologii (Zhang & Chen, 2020; Adamczyk & Gródek-Szostak, 2022), takich jak: Internet rzeczy (IoT), sztuczna inteligencja, przetwarzanie w chmurze, roboty autonomiczne, czujniki (Xu, 2020). ...
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Celem artykułu jest identyfikacja kluczowych obszarów regionalnego ekosystemu innowacji umożliwiających skuteczny rozwój sektora mikro, małych i średnich przedsiębiorstw (MŚP) w warunkach czwartej rewolucji przemysłowej. Praca ma charakter teoretyczno-empiryczny. Wykorzystano typowe dla tego typu opracowań metody badawcze: krytycznej analizy literatury przedmiotu, analizy dokumentów oraz publikowanych danych wtórnych.
... In terms of industries, a smart factory stands for completely automated and connected machines, which can operate without the presence of humans by acquiring data, processing, and performing necessary actions [7]. The author of [10] created an illustration based on [11][12][13][14][15][16][17] for smart factories. Furthermore, the I4.0 concept is not limited to industries, but is applicable in other aspects of our lives, such as healthcare. ...
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The prevalence of chronic diseases and the rapid rise in the aging population are some of the major challenges in our society. The utilization of the latest and unique technologies to provide fast, accurate, and economical ways to collect and process data is inevitable. Industry 4.0 (I4.0) is a trend toward automation and data exchange. The utilization of the same concept of I4.0 in healthcare is termed Healthcare 4.0 (H4.0). Digital Twin (DT) technology is an exciting and open research field in healthcare. DT can provide better healthcare in terms of improved patient monitoring, better disease diagnosis, the detection of falls in stroke patients, and the analysis of abnormalities in breathing patterns, and it is suitable for pre-and post-surgery routines to reduce surgery complications and improve recovery. Accurate data collection is not only important in medical diagnoses and procedures but also in the creation of healthcare DT models. Health-related data acquisition by unobtrusive microwave sensing is considered a cornerstone of health informatics. This paper presents the 3D modeling and analysis of unobtrusive microwave sensors in a digital care-home model. The sensor is studied for its performance and data-collection capability with regards to patients in care-home environments.
... The biggest challenge is being able to clearly define what the Future Factory is and to then transition those concepts effectively from theory to practice. For a start, the Future Factory is the nucleus of Industry 4.0 [127]. A proper articulation of the meaning of the Future Factory is heavily reliant on the contextual and comprehensive understanding of Industry 4.0 concepts, which is the reason why a Section 2.3 of this paper was dedicated to elaborating on Industry 4.0. ...
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Recent advances in manufacturing industry has paved way for a systematical deployment of Cyber-Physical Systems (CPS), within which information from all related perspectives is closely monitored and synchronized between the physical factory floor and the cyber computational space. Moreover, by utilizing advanced information analytics, networked machines will be able to perform more efficiently, collaboratively and resiliently. Such trend is transforming manufacturing industry to the next generation, namely Industry 4.0. At this early development phase, there is an urgent need for a clear definition of CPS. In this paper, a unified 5-level architecture is proposed as a guideline for implementation of CPS.
Manufacturing Systems and Technologies for the New Frontier
  • Erik Sundin
  • JÖ Erik Sundin
Erik Sundin JÖ (2008) Manufacturing Systems and Technologies for the New Frontier. Sfb 627:537-542
European Commision Research and Innovation -Horizon 2020
European Commision Research and Innovation (2013) European Commision Research and Innovation -Horizon 2020. horizon2020/en/, last access: 3. March 2015
From vision to reality: cyber-physical systems
  • H Gill