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Digital Twin: Manufacturing Excellence through Virtual Factory Replication

  • Digital Twin Institute


This paper introduces the concept of a " Digital Twin " as a virtual representation of what has been produced. Compare a Digital Twin to its engineering design to better understand what was produced versus what was designed, tightening the loop between design and execution.
Digital Twin: Manufacturing
Excellence through Virtual
Factory Replication
A Whitepaper by Dr. Michael Grieves
This paper introduces the concept of a
“Digital Twin” as a virtual
representation of what has been
produced. Compare a Digital Twin to
its engineering design to better
understand what was produced versus
what was designed, tightening the loop
between design and execution.
Page 1 of 7 Digital Twin White Paper
Copyright © Michael W. Grieves, LLC 2014
The concept of a virtual, digital equivalent
to a physical product or the Digital Twin1
was introduced in 2003 at my University
of Michigan Executive Course on Product
Lifecycle Management (PLM). At the time
this concept was introduced, digital
representations of actual physical products
were relatively new and immature. In
addition, the information being collected
about the physical product as it was being
produced was limited, manually collected,
and mostly paper-based.
In the decade that has followed, the
information technology supporting both
the development and maintenance of the
virtual product and the design and
manufacture of the physical product has
Virtual products are rich representations of
products that are virtually
indistinguishable from their physical
counterparts. The rise of Manufacturing
Execution Systems on the factory floor has
resulted in a wealth of data collected and
maintained on the production and form of
physical products. In addition, this
collection has progressed from being
manually collected and paper based to
being digital and being collected by a wide
variety of physical non-destructive sensing
technologies, including sensors and gauges,
Coordinate Measuring Machines, lasers,
vision systems, and white light scanning.
1 I introduced the term “Digital Twin” in
Virtually Perfect: Driving Innovative and
Lean Products through Product Lifecycle
Management (pg. 133). I attributed it to
John Vickers of NASA whom I work with.
We have subsequently used this term in
current projects.
In light of these advances, it is timely to
explore how the Digital Twin can move
from an interesting and potentially useful
concept that aids in understanding the
relationship between a physical product
and its underlying information to a critical
component of an enterprise-wide closed-
loop product lifecycle. These tasks will
both reduce costs and foster innovation in
the manufacture of quality products.2
Digital Twin Concept
The Digital Twin concept model is shown
in Figure 1. It contains three main parts: a)
physical products in Real Space, b) virtual
products in Virtual Space, and c) the
connections of data and information that
ties the virtual and real products together.
In the decade since this model was
introduced, there have been tremendous
increases in the amount, richness, and
fidelity of information of both the physical
and virtual products.
On the virtual side, we have improved the
amount of information we have available.
We have added numerous behavioral
characteristics so that we can not only
visualize the product, but we can test it for
performance capabilities.
We have the ability to create lightweight
versions of the virtual model. This means
2 While the focus of this paper is on the
manufacturing phase, the use of the Digital
Twin extends throughout the product’s life
to provide value to its user and
information on how it actually performed
to its manufacturer. This larger use is
described in Virtually Perfect.
Page 2 of 7 Digital Twin White Paper
Copyright © Michael W. Grieves, LLC 2014
that we can select the geometry,
characteristics, and attributes that we
require without carrying around
unnecessary details. This dramatically
reduces the size of the models and allows
for faster processing.
These light-weight models allow today's
simulation products to visualize and
simulate complex systems and systems of
systems, including their physical behaviors,
in real-time and with acceptable compute
These lightweight models also mean that
the time and cost of communicating them
electronically is substantially less. They
now can be shared not only with the
organization but also throughout the
supplier network. This enhances
collaboration in both reducing time to
understand and enhancing both quality and
depth of understanding of product
information and changes.
As importantly, we can simulate the
manufacturing environment that creates
the product, including most operations,
both automated and manual, that constitute
the manufacturing process. These
operations include assembly, robotic
welding, forming, milling, and other
manufacturing floor operations.
On the physical side, we now collect more
and more information about the
characteristics of the physical product. We
can collect all types of physical
measurements from automated quality
control stations, such as Coordinate
Measuring Machines (CMMs). We can
collect the data from the machines that
perform operations on the physical part to
understand exactly what operations, at
what speeds and forces, were applied. For
example, we can collect the torque
readings of every bolt that attaches a fuel
pump to an engine in order to insure that
each engine/fuel pump attachment is
successfully performed.
Extending Model
Lifespans – A Matter of
Unifying the Virtual and
Real Worlds
The amount and quality of information
about the virtual and physical product
have progressed rapidly in the last decade.
The issue is that the two-way connection
between real and virtual space has been
lagging behind.
Global manufacturers today either work
with the physical product or with the
virtual product. We have not developed
the connection between the two products
so that we can work with both of them
The typical way we do this is to develop a
fully annotated 3-D model. We then
develop a manufacturing process that will
realize this model with a Bill of Process
(BOP) and Manufacturing Bill of
Materials (MBOM). The more
sophisticated and advanced manufacturers
then simulate the production process
Page 3 of 7 Digital Twin White Paper
Copyright © Michael W. Grieves, LLC 2014
However, at that stage, we then simply
turn over the BOP and MBOM to
manufacturing and leave the virtual
models behind. In many cases currently,
we even dramatically water down the
usefulness of the model by producing 2-D
blueprints for the factory floor.
There are manufacturers who are bringing
3-D models to the factory floor by way of
terminals stationed in the work cells.
However, even here there is not real
integration and connection between the
virtual model and the physical product
taking shape on the factory floor. The
terminal model merely serves as a
reference, and a human has to perform the
connection between the virtual and the
physical product on an ad hoc basis.
As shown in Figure 2, linking the physical
product with the virtual product could take
the form of the 3-D model not only
appearing on the screen but also
incorporating actual dimensions from the
physical product. The information of the
physical product would overlay the virtual
product and highlight differences that
would need to be addressed.
This simultaneous view and comparison of
the physical and virtual product will reap
major benefits, especially in the
manufacturing phase of the product.
Digital Twin Fulfillment
In order to deliver the substantial benefits
to be gained from this linkage between
virtual and physical products, one solution
is to have a Unified Repository (UR) that
will link the two products together.
Both virtual development tools and
physical collection tools would populate
the Unified Repository. This would enable
two-way connection between the virtual
and physical product.
On the virtual tool side, design and
engineering would identify characteristics,
such as dimensions, tolerances, torque
requirements, hardness measurements, etc.,
and place a unique tag in the virtual model
that would serve as a data placeholder for
the actual physical product. Included in the
tag would be the as-designed characteristic
When the design was released for
production, these tags would be collected
from the virtual product model and used to
create the UR. A lightweight model with
the tags and their characteristics and
geometrical location would also be created.
On the physical side, these tags would be
incorporated into the MES in the Bill of
Process creation at the process step where
they will be captured. As the processes
were completed on the factory floor, the
MES would output the captured
characteristic to the UR.
The final step would be to incorporate this
back into the factory simulation. This
would turn the factory simulation into a
factory replication application. Instead of
simulating what should be happening in
the factory, the application would be
Page 4 of 7 Digital Twin White Paper
Copyright © Michael W. Grieves, LLC 2014
replicating what actually was happening at
each step in the factory on each product.
The factory replication application would
be in constant communication with the UR,
picking up the latest data from actual
production and displaying it in the virtual
Users could see in near real-time or even
real time, what actually was occurring on
the factory floor and view the actual
product characteristics as they were going
through production cells.
There are a significant number of use
cases that can be envisioned from having
such a capability.
Digital Twin Model Use
The digital twin capability supports three
of the most powerful tools in the human
knowledge tool kit. These three tools are:
conceptualization, comparison, and
collaboration. Taken together, these
attributes form the foundation for the next
generation of problem solving and
Unlike computers, humans do not process
information, at least not in the sense of
sequential step-by-step processing that
computers do. Instead, humans look at a
situation and conceptualize the problem
and the context of the problem.
Humans take in all the data about the
situation there interested in. They then
conceptualize the situation, seeing in their
mind's eye its various aspects. While they
can do this looking at tables of numbers,
reports, and other symbolic information,
their most powerful and highest bandwidth
input device is their visual sight.
What currently happens is that humans
take visual information, reduce it to
symbols of numbers and letters, and then
re-conceptualize it visually. In the process,
we lose a great deal of information, and
we introduce inefficiencies in time.
The capability of the digital twin lets us
directly see the situation and eliminate the
inefficient and counterproductive mental
steps of decreasing the information and
translating it from visual information to
symbolic information and back to visually
conceptual information.
With the digital twin to build a common
perspective, we can directly see both the
physical product information and the
virtual product information,
simultaneously. Instead of looking at a
report of factory performance and re-
conceptualizing how the product is
moving through the individual stations,
looking at digital twin simulations allows
us to see the progress of the physical
product as it is moving and actually see
information about the characteristics of the
physical product.
Instead of looking at an array of numbers
on tolerance measurements, we can look at
the products lined up in the virtual factory
and see the actual trend lines that indicate
a problem is developing.
Because we have tagged the products with
the designed characteristics, we can select
those tags and see the designed parameters
and the actual parameters simultaneously.
The next tool that humans use in assessing
situations is the idea of a comparison. We
compare unconsciously and continuously
our desired result and our actual result in
order to determine a difference. We then
decide how to eliminate that difference.
Page 5 of 7 Digital Twin White Paper
Copyright © Michael W. Grieves, LLC 2014
Comparison is one of most powerful
intellectual tools that we possess.
When we have the virtual product
information and the physical product
information completely separate, we still
can do that comparison. However, it is
inefficient, as we have to look at the
physical product information, find the
corresponding virtual product information,
and then work out the differences.
With the digital twin model, we can view
the ideal characteristic, the tolerance
corridor around that ideal measurement,
and our actual trend line to determine for a
range of products whether we are where
we want to be. Tolerance corridors are the
positive and negative deviations we can
allow before we deem a result
Depending on how we implemented this
capability, we could see the differences in
terms of color, with colors progressing
from green, “there is no difference,” to
yellow, “we are in our tolerance corridor,”
to red, “we are beyond the tolerance
corridor.” We can then make
instantaneous decisions about the
We can do this with measurements, tensile
strength, torque readings, and pretty much
any characteristic where we can define the
desired characteristic in some sort of a
measurement, either quantitative or even
qualitative. We can enable this capability
for a single product or a range of products.
Using the example from above as to trend
lines, we could overlay the ideal trend on
the actual trend lines.
Having this capability, also allows us to do
the comparisons and adjust future
operations. For example, if we were seeing
tolerances on the plus side of our ideal
measurement, we could change parameters
in the operations of cells further down the
line to adjust them to err on the side of
negative tolerances. Instead of
degenerating into tolerance stacking, we
could ensure tolerances were distributed
around a mean.
The last tool we have is collaboration.
The most powerful things that humans do
is collaborate with each other in order to
bring more intelligence, more variability
of perspectives, and better problem
solving and innovation to situations. The
problem with conceptualization as it
occurs without the digital twin model is
that this conceptualization occurs only
within the individual. The digital twin
model allows a shared conceptualization
that can be visualized in exactly the same
way by an unlimited amount of individuals
and by individuals who do not need to
share the same location.
With the digital twin capability, we can
look at any physical product at any stage
on the factory floor and overlay the virtual,
product on top of it. This capability of
virtual products can be extended across
multiple factories. This means that
individuals across the world can not only
looking at the performance of their own
factory, but they can be monitoring how
they are doing against factories in other
parts of the world. A problem that arises in
one factory can be identified and
controlled not only in that factory, but the
solution immediately transferred and
implemented in all other factories across
the globe.
In the past, factory managers had their
office overlooking the factory so that they
could get a feel for what was happening on
the factory floor. With the digital twin, not
only the factory manager, but everyone
Page 6 of 7 Digital Twin White Paper
Copyright © Michael W. Grieves, LLC 2014
associated with factory production could
have that same virtual window to not only
a single factory, but to all the factories
across the globe.
Instead of simply viewing a factory
simulation of what should take place in the
factory, factory replication means that we
can see what is actually taking place on
the factory floor as parts move through the
various work cells and inspection stations.
However, as Figure 2 illustrates, it is
exponentially better than simply seeing the
progression and completion of products.
We can also see the key design
characteristics that we are most concerned
about, the actual characteristics we have
achieved, and the gap between the desired
and actual.
The digital twin capability with its
conceptualization, comparison, and
collaboration capability frees us from the
physical realm where humans operate
relatively inefficiently. We can now move
to virtual realm where physical location is
irrelevant, and humans from across the
globe can have common visualization,
engage in comparisons identifying the
difference between what is and what
should be, and collaborating together. This
is extremely powerful and only occurs if
we can match the physical product with
the virtual product.
Over the last decade, there have been
dramatic advances in the capabilities and
technologies of both the data collection of
the physical product and the creation and
representation of the virtual product, the
Digital Twin. The issue is that while the
data information of each of these areas has
increased dramatically, the connection
between the two data sources has lagged
This white paper has proposed that the
connection between the data about the
physical product and the information
contained on the virtual product be
synchronized. This will open up an entire
new set of use cases.
Specifically by merging the virtual product
information as to how the product is to be
manufactured and the information about
how the product is actually being
manufactured, we can have an
instantaneous and simultaneous
perspective on how the manufactured
product is meeting its design specification
By using this information, we can change
digital factory simulation, which attempts
to predict how the product is to be
manufactured, into a digital factory
replication, which shows how the product
is actually being manufactured. We can
then compare it against the design
specifications. This can occur in real time
or near real-time. This provides a window
onto the factory floor for anyone at any
time from any place.
Focusing on the connection between the
physical product and the virtual product
enables us to conceptualize, compare, and
collaborate. We can conceptualize visually
the actual manufacturing processes. We
can compare the formation of the physical
product to the virtual product in order to
ensure that what we are producing is what
we wanted to produce. Finally we can
collaborate with others in our organization
and even throughout the supply chain to
have up-to-the-minute knowledge of the
products that we are producing.
Page 7 of 7 Digital Twin White Paper
Copyright © Michael W. Grieves, LLC 2014
Focusing on this connection between the
physical product and the virtual product
will improve productivity, uniformity of
production, and ensure the highest quality
About Dr. Michael Grieves
Dr. Michael Grieves is a world-renowned
authority on Product Lifecycle
Management (PLM). Dr. Grieves has
written and lectured extensively on the
topic and is a frequent keynote speaker on
PLM. Dr. Grieves’ works include the
seminal work on PLM, Product Lifecycle
Management: Driving the Next Generation
of Lean Thinking (McGraw-Hill, 2006)
and Virtually Perfect: Driving Innovative
and Lean Products through Product
Lifecycle Management (SCP, 2010)
Dr. Grieves consults with a number of
leading international manufacturers and
governmental organizations such as
Dr. Grieves is the Co-Director of the
Center for Lifecycle and Innovation
Management (CLIM) at the Florida
Institute of Technology and is a Research
Professor in the College of Business and
the College of Engineering.
Dr. Grieves is Chairman Emeritus of
Oakland University’s School of Business
Board of Visitors. He has taught in the
United States, China, and Europe at the
university senior undergraduate, and
graduate school levels and has authored
and taught executive education courses. Dr.
Grieves is a Professor at CIMBA
University, Asolo, Italy with an
appointment at the University of Iowa.
Dr. Grieves has over forty-five years
experience in the computer and data
communications industry. He has been a
senior executive at both Fortune 1000
companies and entrepreneurial
organizations during his career. He
founded and took public a national
systems integration company and
subsequently served as its audit and
compensation committee chair. Dr.
Grieves has substantial board experience,
including serving on the board of public
companies in both China and Japan.
Dr. Grieves has a BSCE from Michigan
State University and an MBA from
Oakland University. He received his
doctorate from the Case Western Reserve
University Weatherhead School of
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... These applications have demonstrated the great advantages of DTs. Although the concept of DT was initially defined when it was first introduced in 2003 by Grieves in his course on product lifecycle management (Grieves, 2014), a preliminary form of DT was proposed to include three parts: physical part, virtual part, and their connections . With the enabling technologies of the Industrial Internet of Things (IIoT) and Industry 4.0 experiencing rapid growth, DTs become a popular research topic since then (Liu et al., 2021). ...
Conference Paper
Full-text available
Digital twin (DT) is an emerging and promising enabling technology for realizing smart manufacturing and Industry 4.0. DT is featured by the high-fidelity digital replica and seamless integration of the physical world. As a systematic embodiment of DT, the concept of an asset administration shell (AAS) is a virtual digital representation of the physical asset in Industry 4.0. Both DT and AAS could be used to monitor, control, and optimize physical entities through the interactions between virtual and physical worlds. To date, much research effort has been devoted to DT and AAS applications. However, the mapping between the DT model and the AAS model was hardly considered. This paper first conducted a model mapping of DT and AAS. Based on this, this paper also proposed a DT-based Industrial Internet of Things (IIoT) architecture and explored the feasibility of developing merged DT and AAS models for a virtual simulation system. Finally, a practical use case was developed to demonstrate the merged DT and AAS model as well as the physical-virtual data interaction.
In recent years, digital twins have become a more significant strategic trend in the construction industry. Stakeholders in the industry view it as a technology-driven innovation that has the potential to support the design, building, and operation of constructed assets, alongside advancements in other new-generation information technologies such as the Internet of Things (IoT), artificial intelligence (AI), big data, cloud computing, and edge computing. However, the construction project context generates various organizational and functional information through model-based domain-specific information models that require integration and analysis. Furthermore, commercial technologies enable the integration of real-time data sources with building information models (BIM), but these tools are often proprietary and incompatible with other applications. This lack of interoperability among heterogeneous data formats is a major obstacle to the reliable application of digital twins in the construction industry. To address this challenge, this study presents a multimodel framework developed using Information Container for Linked Document Delivery (ICDD) that can integrate multiple data models from autonomous and heterogeneous sources, including real-time data sources, in their original format at the system level. This framework enables stakeholders to analyze, exchange, and share linked information among the built asset stakeholders, relying on linked data and Semantic Web technologies.
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