40 ISE Magazine | www.iise.org/ISEmagazine
Smart manufacturing builds
opportunities for ISEs
Engineering expertise is needed to manage challenges posed
by Industry 4.0 innovations
By Thorsten Wuest
April 2019 | ISE Magazine 41
As an industrial and systems engineer, it is almost
impossible to avoid conversations and discussions
hovering around smart manufacturing and Indus-
try 4.0. Whether one is active in an industrial or
academic context, smart manufacturing seems to
be omnipresent at every meeting, conference or
trade show.
While everyone has a certain understanding and opinion
about the topic and its impact, there is still some confusion
around what it truly means and how it relates to the job and
career outlook of an industrial and systems engineer. Here is
a concise overview of smart manufacturing, its opportunities
and challenges, and why ISEs are uniquely qualified to spear-
head the charge to master the fourth industrial revolution.
It is important to understand Industry 4.0 and smart manu-
facturing in a historic context. Both stem from the intelligent
manufacturing paradigm of the late 1990s. The term “fourth
industrial revolution” (aka Industry 4.0 or I4) was coined in
2011 by a German government initiative to prepare its manu-
facturing industry for the digital future. While the name is
certainly controversial – since when are revolutions pro-
claimed before they actually happen? – it rapidly caught trac-
tion and is now an established terminology across the globe
(see accompanying article on page 43).
The Industry 4.0 paradigm builds on the notion that after
the first three major industrial
revolutions – mechanization of
labor, division of labor and com-
puterization of the shop floor
– the convergence of the physi-
cal and virtual world, in form of
so-called cyberphysical systems
(CPS) is understood to be the
next major interruption of the
manufacturing industry and be-
yond (as shown in Figure 1).
The question remains: Was
manufacturing not smart, or
even “dumb” before? No, not at
all. The smartness was however
associated mainly with the hu-
man operators and process plan-
ners – the human experts – and
not inherited in the system itself.
We all know experienced op-
erators of complex machine tools
who seem to just “know” when a
machine is about to fail or is ex-
periencing problems. The goal in
a smart manufacturing system is
to reproduce this gut feeling by
collecting data and analyzing it to
draw conclusions, ultimately providing valuable insights for
decision support.
The term “smart manufacturing” originated in the U.S.
and is dened as “a data intensive application of information
technology at the shop-floor level and above to enable intelli-
gent, efcient and responsive operations” (Panel on Enabling
Smart Manufacturing,” Evan Wallace and Frank Riddick,
Advances in Production Management Systems conference,
2013). While there are several more comprehensive deni-
tions available, they all emphasize the use of information and
communication technology data and advanced data analytics
to improve manufacturing operations at all levels of the digital
supply network.
An important aspect that differentiates smart manufactur-
ing from many other initiatives is the specic emphasis on
human ingenuity within the framework. Humans are not to
be simply replaced by artificial intelligence and cognitive au-
tomation on the shop floor. Instead, their capabilities are to
be enhanced by smart, customized solutions for the specic
area. The importance of product and process information and
data, enabling technologies and human or machine inherent
knowledge is commonly accepted.
Smart manufacturing is often used in conjuncture with ad-
vanced manufacturing. At times, the terms are used as syn-
onyms. However, this is not accurate and we need to be clear
Four revolutions of manufacturing
A historic timeline showing how manufacturing has moved from each industrial revolution
to the next since the 19th century, leading to Industry 4.0.
42 ISE Magazine | www.iise.org/ISEmagazine
Smart manufacturing builds opportunities for ISEs
about the meaning of each term to avoid confusion. Smart
and advanced manufacturing describe distinct areas of the new
manufacturing realities and can be seen as two sides of the
same Industry 4.0 medallion (as seen in Figure 2). Smart man-
ufacturing at its core focuses on connectivity, virtualization
and data utilization, while advanced manufacturing focuses
on manufacturing process technologies such as automation,
robotics and additive manufacturing. Nevertheless, there is no
sharp dividing line between the two concepts, and in order to
be successful in the future, companies need to embrace both.
Smart manufacturing technologies,
characteristics, enabling factors
An important aspect of smart manufacturing for both research
and industrial application revolves around associated technolo-
gies. For instance, it is important for companies to understand
what technologies are relevant and might be worthwhile for
assessment or investment. Meanwhile, researchers seek to
identify areas where additional research is required to further
develop technologies that will address current needs and pre-
pare students for manufacturing careers.
The following is an introduction of the technologies, char-
acteristics and enabling factors are associated with smart manu-
facturing. This is a summary of our recent journal publication
regarding this topic (Smart Manufacturing: Characteristics,
Technologies and Enabling Factors,” Sameer Mittal, Muz-
toba Ahmad Khan, David Romero, Thor-
sten Wuest, 2017). If you are interested in a
more in-depth analysis, visit https://link.iise.
org/SmartManufacturing. In a comprehensive
study, we identified 38 different technologies,
27 characteristics and seven enabling factors
associated with smart manufacturing. The
technologies ranged from machine learning
to augmented reality; the characteristics from
agility to decentralized control; and enabling
factors from STEP standards to MTConnect.
We then clustered the list based on their se-
mantic similarity, as illustrated in Figure 3.
It has to be noted that additive manufac-
turing is included in this list despite the as-
sociation with advanced manufacturing based
on the methodology used. It showcases that
some authors use smart and advanced manu-
facturing interchangeably.
Research opportunities for
industrial and systems engineers
This section, discusses a selection of press-
ing research issues that requires the attention
of the ISE community to successfully sup-
port the manufacturing industry in its digital
transformation. This is not a comprehensive list, as there are
several more issues that deserve attention. However, the fol-
lowing challenges pose interesting and worthwhile problems
for industrial and systems engineers to tackle.
Given the width of the field, the research issues are pre-
sented in three categories: technical research issues, method-
ological research issues and business case research issues (fol-
lowing “Industrie 4.0 and Smart Manufacturing – A Review
of Research Issues and Application Examples,” Klaus-Dieter
Thoben, Stefan Wiesner and Wuest, 2017). This again reflects
the interdisciplinary nature and complexity of the field.
Two sides of the same medallion
The comparative properties of smart and advanced
Pillars of smart manufacturing
A look at technologies, characteristics and enabling factors.
April 2019 | ISE Magazine 43
Technical research issues include:
Standards and interfaces. To harvest the promise of
smart manufacturing on the shop floor and beyond, a strong
foundation that allows us to connect and communicate is
key. Well-developed and widely accepted standards and in-
terfaces are crucial to achieve this vision.
Sensors and actuators. With the dawn of the internet of
things, sensors and actuators are everywhere, more power-
ful and cheaper than ever. However, in an industrial setting,
the requirements for sensors and actuators are more rigor-
ous than in our homes. Therefore, we need to continue
to develop this field to provide the nodes for our growing
network of manufacturing things.
Data quality. Data are the lifeblood of all smart manufac-
turing initiatives. We need new ways to assess and ideally
guarantee a high quality of our data as an input for our
advanced algorithms and decision-making.
Data handling. Developing new and expanding existing
database systems and platforms is required. These systems
must be capable of storing, retrieving and manipulating vast
amount of data on premises, in the cloud and in-between
(fog/edge) as well as variating combinations of these three
approaches (hybrid systems), to facilitate collaboration and
truly enable cross-domain learning.
Data analytics and machine learning. Deriving valu-
able insights from a vast amount of data (big data) re-
quires continuous efforts in developing new and adapting
current algorithms to optimize predictions, computing ef-
ficiency and ease-of-use, to name a few.
Data security/cybersecurity. Manufacturing facilities
are among the most attacked entities, according to the U.S.
The 9 technologies of Industry 4.0
These are the nine technological advancements in manufacturing the
Boston Consulting Group analysts identify as the spark to the fourth
Industrial Revolution.
Big data and analytics. Collection and comprehensive evaluation
of data from multiple sources to support real-time decision-making
Autonomous robots. Not machines replacing humans but working
alongside them, and with each other, offering a greater range of
Simulation. Use of real-time data to mirror the physical world in a
virtual model, often including machines, products and people, to test
and optimize machine settings for products before they are actually
Horizontal and vertical system integration. The integration of
data networks between departments, functions and capabilities to form
cohesive cross-company networks
Industrial internet of things. Devices enriched with embedded
computing that can communicate and interact with each another and
with centralized controllers
Cybersecurity. The need to protect critical systems to ensure secure,
reliable communications
The cloud. Data sharing across sites and company boundaries
to speed reaction times and enable more data-driven services for
production systems.
Additive manufacturing. 3D printing that goes beyond prototypes
and individual components, now being used to produce small batches
of customized products with complex, lightweight designs.
Augmented reality. Systems to provide workers with real-time
information to improve decision-making and work procedures, such
as selecting parts or sending repair instructions over mobile devices
44 ISE Magazine | www.iise.org/ISEmagazine
Smart manufacturing builds opportunities for ISEs
government. With increasing connectivity, the threat and
potential damage of cyberattacks increases exponentially.
It’s therefore necessary to invest efforts in developing new
safeguards for industrial smart manufacturing systems that
improve security while minimizing the negative effect on
intended data exchange, sharing and flow.
Methodological research issues include:
Reference models. Smart manufacturing systems are in-
herently complex. This represents a signicant entry barrier
for many companies. Reference models providing a struc-
ture and guidelines to manage this complexity are needed,
as well as new adaptations and extensions of existing ones
for specific industries and special-use cases, such as small-
and medium-sized enterprises.
Visualization. Deriving insights from large amounts of
data are only one side of the medallion. Communicat-
ing these insights in an appropriate, efficient and effective
manner is equally important to create value and impact.
For example, the C-level executive requires a very differ-
ent visualization of the same data than the operator of a
certain machine tool or the maintenance team. Visualiza-
tion is strongly related to certain technologies such as digital
twins, dashboards and virtual and augmented reality appli-
Services and applications marketplaces. Given the
complexity of a smart manufacturing system, one common
approach is to address the different functionalities through
composable (micro-)services. Orchestrating those and cre-
ating efcient marketplaces to bring the various stakehold-
ers together is a challenge that has yet to be fully addressed.
Requirements engineering. This remains a continuous
issue for all engineering projects. With the dawn of smart
manufacturing and Industry 4.0, the possibility to collect
(through IoT) and analyze large amounts of usage data (big
data) opens up new opportunities to derive insights in the
real users’ needs and requirements directly from how they
interact with the products and systems. New methods and
ways to automate the translation of data and insights into
design requirements need to be developed.
Operator 4.0. While certain tasks in the manufacturing
environment will be increasingly automated, both physi-
cal and cognitive in nature, we believe the human operator
will remain a key part of a smart manufacturing system.
New ways to provide additional capabilities to the human
operator are referred to as the tech-augmented “Operator
4.0.” Case studies and innovative solutions to extend the
Operator 4.0 are in high demand.
Business case research issues include:
Privacy. Smart manufacturing revolves around data col-
lection, sharing and analysis. This introduces new challeng-
es in the data privacy area, which is different from the data
security aspect. The ethics behind sharing and analyzing
user data, for example, need to be critically assessed as well
as new rules and standards are needed.
Investment. Similar to most new developments that re-
quire new technologies, redesign of processes and training,
entering the smart manufacturing journey will require a
significant investment. Especially for small or medium en-
terprises, this initial investment might pose a barrier as they
are more likely to have limited resources, monetary and
in expertise. Identifying ways to reduce this initial invest-
ment through, for example, new open source and modular
solutions, will impact adoption in those cases. Collecting
lessons learned and best practices from recent implementa-
tions, as well as case studies, will further lower the bar to
engage in modernizing manufacturing operations.
Servitization and servitized business models. Serviti-
zation as a business strategy is a disruptive form of value
(co-)creation. The availability of connectivity and real-time
access to machine tool data enables the adoption of new
business models based on pay-per-use or pay-per-outcome
principles. For example, offering a complex machine tool
as a product service system (PSS) provides multiple benefits
to both the manufacturer of the machine tool as well as
to the user. The manufacturer has access to the usage data
as input for next generation designs, a continuous revenue
stream and a closer relationship with its customers. At the
same time, users benefit from reduced initial investments,
reduced maintenance efforts and regular upgrades. While
these theoretical benefits are very attractive, there are sev-
eral issues to be figured out regarding these new business
models, such as revenue sharing, data ownership, etc.
After covering the background of smart manufacturing and
Industry 4.0, discussing associated technologies and enabling
factors and identifying opportunities to advance the field, the
question remains: Why are ISEs uniquely qualified to address
the challenges put forth by the digital transformation of in-
The answer is simple: This brave new world requires inter-
disciplinary experts who are trained to think in systems, actu-
ally in systems of systems, and to deal with complexity both
efficiently and effectively. Who is better at that than industrial
and systems engineers?
Thorsten Wuest is assistant professor and J. Wayne & Kathy Richards
Faculty Fellow at West Virginia University in Morgantown, West
Virginia. He is an IISE member.