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 deﬁned as “a data intensive application of information

technology at the shop-ﬂoor level and above to enable intelli-

gent, efﬁcient 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 deﬁni-

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 speciﬁc 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 speciﬁc

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

FIGURE 1

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.

FIGURE 2

Two sides of the same medallion

The comparative properties of smart and advanced

manufacturing.

FIGURE 3

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

capabilities

• 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

manufactured

• 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 signiﬁcant 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-

cations.

• 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 efﬁcient 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-

dustry?

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. 