Characterising the Digital Twin: A systematic literature review

Abstract

While there has been a recent growth of interest in the Digital Twin, a variety of definitions employed across industry and academia remain. There is a need to consolidate research such to maintain a common understanding of the topic and ensure future research efforts are to be based on solid foundations. Through a systematic literature review and a thematic analysis of 92 Digital Twin publications from the last ten years, this paper provides a characterisation of the Digital Twin, identification of gaps in knowledge, and required areas of future research. In characterising the Digital Twin, the state of the concept, key terminology, and associated processes are identified, discussed, and consolidated to produce 13 characteristics (Physical Entity/Twin; Virtual Entity/Twin; Physical Environment; Virtual Environment; State; Realisation; Metrology; Twinning; Twinning Rate; Physical-to-Virtual Connection/Twinning; Virtual-to-Physical Connection/Twinning; Physical Processes; and Virtual Processes) and a complete framework of the Digital Twin and its process of operation. Following this characterisation, seven knowledge gaps and topics for future research focus are identified: Perceived Benefits; Digital Twin across the Product Life-Cycle; Use-Cases; Technical Implementations; Levels of Fidelity; Data Ownership; and Integration between Virtual Entities; each of which are required to realise the Digital Twin.

Full text

Characterising
the
Digital
Twin:
A
systematic
literature
review
David
Jones*,
Chris
Snider,
Aydin
Nassehi,
Jason
Yon,
Ben
Hicks
Department
of
Mechanical
Engineering,
University
of
Bristol,
Queens
Building,
University
Walk,
Bristol
BS8
1TR,
United
Kingdom
A
R
T
I
C
L
E
I
N
F
O
Article
history:
Available
online
xxx
Keywords:
Digital
Twin
Virtual
Twin
A
B
S
T
R
A
C
T
While
there
has
been
a
recent
growth
of
interest
in
the
Digital
Twin,
a
variety
of
definitions
employed
across
industry
and
academia
remain.
There
is
a
need
to
consolidate
research
such
to
maintain
a
common
understanding
of
the
topic
and
ensure
future
research
efforts
are
to
be
based
on
solid
foundations.
Through
a
systematic
literature
review
and
a
thematic
analysis
of
92
Digital
Twin
publications
from
the
last
ten
years,
this
paper
provides
a
characterisation
of
the
Digital
Twin,
identification
of
gaps
in
knowledge,
and
required
areas
of
future
research.
In
characterising
the
Digital
Twin,
the
state
of
the
concept,
key
terminology,
and
associated
processes
are
identified,
discussed,
and
consolidated
to
produce
13
characteristics
(Physical
Entity/Twin;
Virtual
Entity/Twin;
Physical
Environment;
Virtual
Environment;
State;
Realisation;
Metrology;
Twinning;
Twinning
Rate;
Physical-to-Virtual
Connection/Twinning;
Virtual-
to-Physical
Connection/Twinning;
Physical
Processes;
and
Virtual
Processes)
and
a
complete
framework
of
the
Digital
Twin
and
its
process
of
operation.
Following
this
characterisation,
seven
knowledge
gaps
and
topics
for
future
research
focus
are
identified:
Perceived
Benefits;
Digital
Twin
across
the
Product
Life-Cycle;
Use-Cases;
Technical
Implementations;
Levels
of
Fidelity;
Data
Ownership;
and
Integration
between
Virtual
Entities;
each
of
which
are
required
to
realise
the
Digital
Twin.
©
2020
University
of
Bristol.
This
is
an
open
access
article
under
the
CC
BY
license
(http://
creativecommons.org/licenses/by/4.0/).
Introduction
Typically
described
as
consisting
of
a
physical
entity,
a
virtual
counterpart,
and
the
data
connections
in
between,
the
Digital
Twin
is
increasingly
being
explored
as
a
means
of
improving
the
performance
of
physical
entities
through
leveraging
computation-
al
techniques,
themselves
enabled
through
the
virtual
counterpart.
Interest
in
the
Digital
Twin
has
greatly
increased
in
the
past
five
years
across
both
academia
and
industry,
accompanied
by
a
growth
in
the
number
of
related
publications,
processes,
concepts,
and
envisaged
benefits
(see
Fig.
1).
Missing
from
literature,
however,
is
a
consolidated
and
consistent
view
on
what
the
Digital
Twin
is,
and
how
the
concept
is
evolving
to
meet
the
needs
of
the
many
use-
cases
to
which
it
is
being
tied.
This
lack
of
consistency
has
led
to
a
breadth
of
characterisations
and
definitions
for
digital
twins
and
the
digital
twinning
process
that,
due
to
the
breadth
of
frameworks
applied
across
industry,
leads
to
a
risk
of
diluting
the
concept
and
missing
the
benefits
that
the
Digital
Twin
was
originally
devised
to
deliver.
The
origin
of
the
Digital
Twin
The
origin
of
the
Digital
Twin
is
attributed
to
Michael
Grieves
and
his
work
with
John
Vickers
of
NASA,
with
Grieves
presenting
the
concept
in
a
lecture
on
product
life-cycle
management
in
2003
[33].
In
a
time
when
Grieves
describes
virtual
product
represen-
tations
as
“
.
.
.
relatively
new
and
immature”
and
data
collected
about
physical
products
as
“
.
.
.
limited,
manually
collected,
and
mostly
paper-based”,
Grieves
and
Vickers
saw
a
world
where
a
virtual
model
of
a
product
would
provide
the
foundations
for
product
life-cycle
management.
The
initial
description
defines
a
Digital
Twin
as
a
virtual
representation
of
a
physical
product
containing
information
about
said
product,
with
its
origins
in
the
field
of
product
life-cycle
management.
In
an
early
paper
[33]
Grieves
expands
on
this
definition
by
describing
the
Digital
Twin
as
consisting
of
three
components,
a
physical
product,
a
virtual
representation
of
that
product,
and
the
bi-directional
data
connections
that
feed
data
from
the
physical
to
the
virtual
representation,
and
information
and
processes
from
the
virtual
representation
to
the
physical.
Grieves
depicted
this
flow
as
a
cycle
between
the
physical
and
virtual
states
(mirroring
or
twinning);
of
data
from
the
physical
to
the
virtual,
and
of
information
and
processes
from
the
virtual
to
the
physical
(see
Fig.
2).
The
virtual
spaces
themselves
consisting
of
any
number
of
sub-spaces
that
enable
specific
virtual
operations:
modelling,
testing,
optimisation,
etc.
*
Corresponding
author.
E-mail
addresses:
david.jones@bristol.ac.uk
(D.
Jones),
chris.snider@bristol.ac.uk
(C.
Snider),
aydin.nassehi@bristol.ac.uk
(A.
Nassehi),
jason.yon.02@bristol.ac.uk
(J.
Yon),
ben.hicks@bristol.ac.uk
(B.
Hicks).
https://doi.org/10.1016/j.cirpj.2020.02.002
1755-5817/©
2020
University
of
Bristol.
This
is
an
open
access
article
under
the
CC
BY
license
(http://creativecommons.org/licenses/by/4.0/).
CIRP
Journal
of
Manufacturing
Science
and
Technology
xxx
(2019)
xxx–xxx
G
Model
CIRPJ
544
No.
of
Pages
17
Please
cite
this
article
in
press
as:
D.
Jones,
et
al.,
Characterising
the
Digital
Twin:
A
systematic
literature
review,
NULL
(2020),
https://doi.org/
10.1016/j.cirpj.2020.02.002
Contents
lists
available
at
ScienceDirect
CIRP
Journal
of
Manufacturing
Science
and
Technology
journa
l
home
page
:
www.e
lsevier.com/loca
te/cirpj

The
Digital
Twin
in
the
Product
Life-Cycle
In
a
later
paper
[34],
Grieves
further
aligned
the
Digital
Twin
to
the
product
life-cycle
through
the
expansion
of
the
concept
via
the
introduction
of
the
Digital
Twin
Prototype,
Digital
Twin
Instance,
Digital
Twin
Aggregate,
and
Digital
Twin
Environment
(defined
in
Table
1).
In
context
of
the
product
life-cycle
[82],
see
Fig.
3,
and
using
the
terms
within
Table
1,
the
Digital
Twin
starts
life
as
a
Digital
Twin
Prototype
(design
phase).
Digital
Twin
Instances
are
created
for
each
manufactured
product
during
the
realise
phase,
and
the
accumulation
of
the
Instances
form
the
Digital
Twin
Aggregate.
Both
the
Instances
and
Aggregate
exist
within
the
Digital
Twin
Environment
–
the
virtual
representation
of
the
environment
within
which
the
physical
product
exists
–
that
enables
virtual
techniques
such
as
simulation,
modelling,
and
evaluation.
The
Digital
Twin
Instances/Aggregates
and
Environ-
ment
persist
beyond
the
actual
life
of
the
physical
product,
which
ends
in
the
Retire/Dispose
phase.
This
core
concept
of
the
Digital
Twin
envisaged
a
system
that
couples
physical
entities
to
virtual
counterparts,
leveraging
the
benefits
of
both
the
virtual
and
physical
environments
to
the
benefit
of
the
entire
system.
Product
information
is
captured,
stored,
evaluated,
and
learning
applied
to
the
current
product,
as
well
as
future
products.
As
envisioned
by
Grieves,
this
process
in
essence
enables
the
application
of
a
knowledgeable,
data
driven
approach
to
the
monitoring,
management,
and
improvement
of
a
product
throughout
it's
life-cycle.
Since
the
inception
of
the
Digital
Twin
in
2003
the
concept
has
grown
in
interest,
and
is
now
listed
by
Gartner
as
a
key
strategic
technology
trend
for
2019.
1
This
growth
is
largely
driven
by
advances
in
related
technologies
and
initiatives
such
as
Internet-
of-Things,
big
data,
multi-physical
simulation,
and
Industry
4.0,
real-time
sensors
and
sensor
networks,
data
management,
data
processing,
and
a
drive
towards
a
data-driven
and
digital
manufacturing
future.
As
a
consequence
both
academia
and
industry
have
been
researching,
developing,
and
seeking
to
apply
Digital
Twins
or
the
principles
it
represents.
As
will
be
demonstrated
in
this
work,
however,
this
growth
has
led
to
inconsistent
application
and
divergence
beyond
the
original
descriptions
of
Greives,
leading
to
a
need
for
consolidation
of
the
concept
in
light
of
current
research
and
industry
application.
This
paper
initially
revisits
the
concept
of
the
Digital
Twin
and
through
a
systematic
literature
review
attempts
to
characterise
the
Digital
Twin
including
the
key
processes
and
associated
terminol-
ogy.
Through
this
process,
gaps
in
knowledge
within
the
wider
field
are
identified
and
discussed,
setting
directions
for
future
work
required
to
realise
the
Digital
Twin
and
its
envisaged
benefits.
Methodology
The
research
presented
in
this
paper
follows
a
systematic
approach
[93]
and
therefore
outlines
a
clear
aim
which
is
addressed
in
a
repeatable
and
thorough
manner.
With
the
aim
of
characterise
the
Digital
Twin
including
the
key
processes
and
associated
terminology,
Fig.
4
shows
the
methodology
used
to
gather
a
corpus
of
Digital
Twin
related
literature,
and
a
structured
technique
for
its
analysis.
The
results
described
in
the
following
sections
are
all
based
on
a
corpus
of
papers
relating
to
the
Digital
Twin.
This
corpus
was
collected
between
the
29th
of
September
2018
and
the
2nd
of
October
2018
using
Google
Scholar
and
the
search
query
“digital
twin”.
The
first
50
pages
of
results
(500
papers)
were
stored
for
review.
All
papers
that
cite
one
of
three
seminal
papers,
Grieves
[33,34]
or
Tao
et
al.
paper
[88]
where
also
reviewed.
At
the
time
of
writing
both
Grieves
and
Tao
et
al.
have
100+
citations
for
the
aforementioned
papers,
where
Grieves
describes
the
Digital
Twin
from
a
core
theory
perspective
and
Tao
et
al.
from
a
manufacturing
perspective
(manufacturing
being
a
the
core
Digital
Twin
research
area).
Once
collected
and
the
duplicate
papers
removed,
the
papers
were
filtered
to
eliminate
those
that
were
not
directly
related
to
the
Digital
Twin,
resulting
in
a
corpus
of
92
papers.
Fig.
1.
Number
of
Digital
Twin
related
publications
by
year
from
2009
to
October
2018
reviewed.
Fig.
2.
Mirroring
or
Twinning
between
the
physical
and
virtual
spaces.
Fig.
3.
The
scope
and
transitions/relationships
between
the
Digital
Twin
elements
and
physical
product.
1
Gartner
Top
10
Strategic
Technology
Trends
for
2019,
October
15
2018,
Kasey
Panetta,
https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-
technology-trends-for-2019/.
Last
visited:
2019-06-25.
Table
1
The
list
and
descriptions
of
key
concepts
surrounding
the
Digital
Twin.
Concept
Description
Digital
Twin
A
complete
virtual
description
of
a
physical
product
that
is
accurate
to
both
micro
and
macro
level.
Digital
Twin
Prototype
The
virtual
description
of
a
prototype
product,
containing
all
the
information
required
to
create
the
physical
twin.
Digital
Twin
Instance
A
specific
instance
of
a
physical
product
that
remains
linked
to
an
individual
product
throughout
that
products
life.
Digital
Twin
Aggregate
The
combination
of
all
the
Digital
Twin
Instance.
Digital
Twin
Environment
A
multiple
domain
physics
application
space
for
operating
on
Digital
twins.
These
operations
include
performance
prediction,
and
information
interrogation.
2
D.
Jones
et
al.
/
NULL
xxx
(2019)
xxx–xxx
G
Model
CIRPJ
544
No.
of
Pages
17
Please
cite
this
article
in
press
as:
D.
Jones,
et
al.,
Characterising
the
Digital
Twin:
A
systematic
literature
review,
NULL
(2020),
https://doi.org/
10.1016/j.cirpj.2020.02.002

Tables
2–4
show
a
breakdown
of
the
corpus
in
terms
of
number
of
papers
per
publication,
publication
type,
and
year
published.
Table
2
shows
the
top
six
journals
within
the
corpus,
and
highlights
the
manufacturing
domain
focus
of
Digital
Twin
research
to
date.
Table
3
highlights
that
approximately
two
thirds
of
the
corpus
are
journal
articles,
with
a
third
being
conference
papers,
and
the
remaining
3
being
book
sections.
Finally,
Table
4
shows
the
corpus
broken
down
by
publication
year,
evident
is
the
vast
increase
in
the
number
of
publications
in
2017
and
2018
(to
October
15th).
Combining
these,
the
corpus
and
research
area
appears
to
be
heavily
manufacturing/production
related,
and,
from
the
relatively
high
number
of
conference
papers
compared
to
journal
articles
and
the
breakdown
by
year,
booming.
A
thematic
analysis
[59]
was
then
performed
on
the
corpus.
This
form
of
analysis
utilises
a
structured
approach
to
identify
themes
within
published
work
and
involves
six
stages:
become
familiar
with
data;
generate
initial
codes;
search
for
themes;
review
themes;
define
themes;
write
up.
Following
this
process,
19
core
themes
where
identified.
These
themes
were
then
divided
into
those
that
related
to
the
characteristics
of
the
Digital
Twin
(see
“Characterising
the
Digital
Twin”
section),
and
those
that
relate
to
general
research
areas,
gaps
and
future
directions
(see
“Future
directions
and
gaps
in
research”
section).
Within
these
two
sections,
and
where
appropriate,
further
analysis
of
the
corpus
was
also
performed
to
further
understand
and
elicit
results.
These
include
the
identification
of
common
parameters
(see
“Param-
eters”
section),
a
mapping
of
the
corpus
against
the
product
life-
cycle
(see
“The
Digital
Twin
across
the
product
life-cycle”
section),
and
use-cases
(see
“Use-Cases”
section).
In
a
bid
to
further
Fig.
4.
Methodology
diagram.
Table
3
A
breakdown
of
publication
type
contained
within
the
corpus.
Publication
type
Number
of
papers
Journal
58
Conference
31
Book
(section)
3
Table
2
Top
six
publishers
of
papers
relating
to
the
Digital
Twin.
Publication
Number
of
papers
CIRP
(Annals:
4,
Procedia:
4)
8
IFAC-Papers
Online
7
Procedia
Manufacturing
7
International
Journal
of
Advanced
Manufacturing
Technology
4
Journal
of
Ambient
Intelligence
and
Humanoid
Computing
4
D.
Jones
et
al.
/
NULL
xxx
(2019)
xxx–xxx
3
G
Model
CIRPJ
544
No.
of
Pages
17
Please
cite
this
article
in
press
as:
D.
Jones,
et
al.,
Characterising
the
Digital
Twin:
A
systematic
literature
review,
NULL
(2020),
https://doi.org/
10.1016/j.cirpj.2020.02.002

underpin
the
12
characteristics
discovered,
the
“The
Digital
Twin
characteristics
within
the
context
of
related
literature”
section
then
selects
a
number
of
papers
from
similar
research
fields
to
show
how
the
characteristics
are
not
unique
to
the
Digital
Twin.
Results
As
detailed
in
the
“Methodology”
section,
the
first
part
of
the
review
involved
a
thematic
analysis.
Table
5
shows
the
identified
core
themes
relating
to
the
Digital
Twin
and
their
descriptions,
with
Table
6
showing
the
each
theme
mapped
to
related
papers.
Each
of
the
identified
themes
presents
a
key
concept
identified
across
literature
as
part
of
the
Digital
Twin.
Themes
1
to
12
form
the
basis
of
the
characteristics
of
the
Digital
Twin,
while
themes
13
to
19
form
the
basis
of
future
directions
and
gaps
in
research.
It
is
worth
noting
that
themes
18
and
19,
data
ownership
and
integration
between
virtual
entities,
are
an
exemplar
of
gaps
in
research
in
that
they
were
highlighted
as
important
within
literature,
and
no
papers
held
them
as
their
focus.
Characterising
the
Digital
Twin
Exploring
themes
1
to
12
from
Tables
5
and
6,
this
section
explores
each
themes
in
detail
before
formally
describing
and
characterising
the
Digital
Twin.
Physical
entity
In
discussing
the
physical
entity,
papers
are
typically
domain-
specific
in
their
terminology.
Examples
include:
‘vehicle’,
‘compo-
nent’,
‘product’,
‘system’,
‘models’,
and
‘artefact’.
The
commonality
in
these
entities
lies
in
their
‘real-world’
existence
and
that
they
are,
needless
to
say,
physical.
While
this
list
of
terms
all
refers
to
man-
made
entities,
as
interest
in
the
Digital
Twin
has
grown
the
Digital
Twins
of
children
[62],
farms
[98],
and
agricultural
supply
chains
[39]
have
also
been
considered.
To
encompass
all
types,
and
in
line
with
some
of
the
literature,
this
paper
proposes
the
use
of
the
term
‘physical
entity’
[112,76,90,35,3,89,57,28,88,68,22,66]
for
general
applicability
i.e.
where
a
physical
entity
exists
regardless
of
whether
it
has
been
twinned,
and
the
more
specific
‘physical
twin’
[37,2,48,55,18]
for
when
the
physical
entity
is
twinned.
Virtual
entity
As
with
the
physical
entity,
the
virtual
entity
is
also
referred
to
by
a
number
of
similar-yet-domain-specific
terms.
For
example,
‘product’,
‘world’,‘model’,
‘cyber’,
‘device’
and
‘object’.
For
symmetry
with
the
physical
entity
and
in
line
with
some
literature,
this
paper
proposes
the
use
of
the
term
‘virtual
entity’
[99,58]
in
the
general
case
and
‘virtual
twin’
[75,1,46,49,51]
when
the
virtual
entity
is
twinned.
In
line
with
Grieves’
concept,
there
are
multiple
Virtual
Entities
present
in
a
Digital
Twin,
each
with
a
specific
purpose,
i.e.
scheduling,
health
monitoring,
etc.
Yet
to
be
presented
in
literature
is
how
these
different
Virtual
Entities
interact,
cooperate,
and
are
aggregated.
Take,
for
example,
a
case
where
the
health
monitoring
Virtual
Entity
predicts
a
faulty
component
at
the
same
time
as
the
scheduling
Virtual
Entity
is
optimising
to
meet
a
deadline,
which
entity
is
prioritised?
and
how
is
that
decision
made?
Physical
environment
The
physical
environment
refers
to
the
‘real-world’
space
within
which
the
physical
entity
is
situated;
‘real-space’,
‘real-
world’,
and
‘factories’
all
being
examples
of
terms
used
in
literature.
Aspects
of
these
environments
are
measured
and
fed
into
to
the
Table
5
List
of
themes
identified
and
their
descriptions.
Theme
Description
1.
Physical
Entity
A
‘real-world’
artefact,
e.g.
a
vehicle,
component,
product,
system,
model.
2.
Virtual
Entity
A
computer
generated
representation
of
the
physical
artefact,
e.g.
a
vehicle,
component,
product,
system,
model.
3.
Physical
Environment
The
measurable
‘real-world’
environment
within
which
the
physical
entity
exists.
4.
Virtual
Environment
Any
number
of
virtual
‘worlds’
or
simulations
that
replicate
the
state
of
the
physical
environment
and
designed
for
specific
use-case
(s),
e.g.
heath
monitoring,
production
schedule
optimisation.
5.
Fidelity
The
number
of
parameters
transferred
between
the
physical
and
virtual
entities,
their
accuracy,
and
their
level
of
abstraction.
Examples
found
in
literature
include:
fully
comprehensive,
ultra-realistic,
high-fidelity,
data
from
multiple
sources,
micro-atomic
level
to
the
macro-geometrical
level.
6.
State
The
current
value
of
all
parameters
of
either
the
physical
or
virtual
entity/environment.
7.
Parameters
The
types
of
data,
information,
and
processes
transferred
between
entities,
e.g.
temperature,
production
scores,
processes.
8.
Physical-to-Virtual
Connection
The
connection
from
the
physical
to
the
virtual
environment.
Comprises
of
physical
metrology
and
virtual
realisation
stages.
9.
Virtual-to-Physical
Connection
The
connection
from
the
virtual
to
the
physical
environment.
Comprises
of
virtual
metrology
and
physical
realisation
stages.
10.
Twinning
and
Twinning
Rate
The
act
of
synchronisation
between
the
two
entities
and
the
rate
with
which
synchronisation
occurs.
11.
Physical
Processes
The
physical
purposes
and
process
within
which
the
physical
entity
engages,
e.g.
a
manufacturing
production
line.
12.
Virtual
Processes
The
computational
techniques
employed
within
the
virtual-world,
e.g.
optimisation,
prediction,
simulation,
analysis,
integrated
multi-physics,
multi-scale,
probabilistic
simulation.
13.
Perceived
Benefits
The
envisaged
advantages
achieved
in
realising
the
Digital
Twin,
e.g.
improved
design,
behaviour,
structure,
manufacturability,
conformance,
etc..
14.
Digital
Twin
across
the
Product
Life-Cycle
The
life-Cycle
of
the
Digital
Twin
–
(whole
life
cycle,
evolving
digital
profile,
historical
data)
15.
Use-Cases
The
applications
of
the
Digital
Twin,
e.g.
reducing
cost,
improving
service,
supporting
decision
making.
16.
Technical
Implementations
The
technology
used
in
realising
the
Digital
Twin,
e.g.
Internet-of-Things.
17.
Levels
of
Fidelity
The
number
of
parameters,
their
accuracy,
and
level
of
abstraction
that
are
transferred
between
the
virtual
and
physical
twin/
environment.
18.
Data
Ownership
The
legal
ownership
of
the
data
stored
within
the
Digital
Twin.
19.
Integration
between
Virtual
Entities
The
methods
required
to
enable
communication
between
different
virtual
entities.
Table
4
The
number
of
publications
per
year
contained
within
the
corpus.
Year
Number
of
papers
2018
(to
October
15th)
48
2017
31
2016
6
2015
2
2014
1
2012
2
2009
1
4
D.
Jones
et
al.
/
NULL
xxx
(2019)
xxx–xxx
G
Model
CIRPJ
544
No.
of
Pages
17
Please
cite
this
article
in
press
as:
D.
Jones,
et
al.,
Characterising
the
Digital
Twin:
A
systematic
literature
review,
NULL
(2020),
https://doi.org/
10.1016/j.cirpj.2020.02.002

virtual
twin
environment
to
ensure
an
accurate
virtual
environ-
ment,
upon
which
simulations,
optimisation,
and/or
decisions
will
be
made
(for
example)
and
achieving
this
requires
the
capture
of
all
relevant
parameters
(see
“Parameters”
section.
This
paper
proposes
the
term
‘physical
environment’
to
include
all
parameters
that
may
influence
the
physical
entity,
noting
that
these
need
not
be
limited
to
those
measured
as
part
of
the
Digital
Twin,
and
indeed
that
capture
of
all
parameters
may
not
be
viable.
Does
measurement
of
the
physical
environment
of
a
factory
include,
for
example,
the
weather,
regional
holidays,
or
the
schedule
of
a
local
sports
team's
home
games?
Arguably
each
of
these
factors
could
have
an
influence
on
the
production
output
of
said
factory
and
as
such
should
be
included
in
the
virtual
environment.
The
term
‘factory’
implies
not,
whereas
the
term
‘physical
environment’
implies
any
affecting
parameter
could
be
measured.
The
term
‘physical
environment’
is
also
widely
used
in
literature
[36,52,27,6,91,55,62,9].
Virtual
environment
The
virtual
environment
exists
within
the
digital
domain
and
is
a
mirrorof
the
physical
environment,
with
twinning
achieved
through
physical
metrology
(i.e.
sensors)
relaying
key
measures
from
the
physicaltothevirtual.Inlinewith termsused to describethe physical
environment,
there
are
many
similar
terms
used
in
place
of
the
virtual
environment,
e.g.
‘virtual-space’,
‘virtual-world’,
‘data-model’,
‘multi-domain
models’.
Unlike
the
physical
environment,
descrip-
tions
of
the
virtual
environment
are
sometimes
referred
to
by
the
underlying
technology,
such
as
‘database’,
‘data-warehouse’,
‘cloud-
platform’,
and
‘server’
and
‘API’.
In
an
ever-changing
technological
landscape
it
may
be
unwise
to
link
the
concept
to
a
particular
technology
outside
of
the
specific
use-cases
such
papers
present.
As
such,
this
paper
proposes
the
term
‘Virtual
Environment’;
a
popular
term
[78,33,46,7,21,27,6,91,55,49,51]
inclusive
of
existing
terminol-
ogy
and
with
parity
to
it's
physical
counterpart.
Parameters
Parameters
refer
to
the
types
of
data,
information,
and
processes
that
are
passed
between
the
physical
and
virtual
twins.
Table
7
shows
examples
of
parameters
mentioned
in
the
corpus
classified
into
overarching
themes.
These
themes
were
again
developed
through
a
thematic
analysis
of
the
corpus.
Table
7
shows
how
parameters
can
be
classified
into
just
ten
classes,
a
relatively
small
set
given
the
range
of
examined
literature.
Fidelity
The
term
fidelity
describes
the
number
of
parameters,
their
accuracy,
and
level
of
abstraction
that
are
transferred
between
the
virtual
and
physical
twin/environment.
Terms
such
as
‘comprehen-
sive
physical
and
functional
description’
[80],
and
‘fully
mirroring
its
(physical
twin)
characteristics
and
functionalities’
[85]
are
used
to
describe
the
fidelity,
with
the
term
fidelity
itself
used
in
[108,56,88,68,112,78,111,36,63,61,73,67,55,16,9,110,18,89].
Bar
a
small
number
of
occasions
where
an
appropriate,
use-case
specific
fidelity
is
called
for
[24,20,9],
the
fidelity
of
the
virtual
model
is
described
as
a
highly
accurate
replication
of
the
physical
entity.
Grieves
himself
describes
the
virtual
twin
as
accurate
“from
a
micro-atomic
level
to
the
macro-geometrical
level”.
Correspondingly,
this
paper
adopts
the
term
fidelity.
State
The
state
refers
to
the
current
condition
of
both
the
physical
and
virtual
twins,
or
the
current
values
for
each
of
the
measured
parameters.
Specific
examples
of
this
include
operational
and
health
[68],
processes
and
behaviour
[104],
mechanical
and
thermodynamic
[111],
as-built
[63,55],
and
even
the
state
of
a
disease
within
a
human
being
[92].
Considering
the
state
of
the
virtual
twin
on
par
with
the
physical
twin
achieves
functionality
such
as
real-time
state
estimation
[14],
and
the
presentation
and
prediction
of
past,
current,
and
future
states
[32,22,107,63,98].
Table
6
The
corpus
of
papers
mapped
to
the
identified
themes.
Theme
Citations
1.
Physical
Entity
[33,103,80,72,77,28,94,53,108,24,85,74,8,15,64,56,88,75,68,84,112,104,37,99,78,1,105,32,111,46,36,52,11,106,30,7,71,63,29,76,54,14,
109,21,61,73,2,40,67,86,27,90,58,35,22,70,6,17,91,4,48,55,49,95,16,92,98,62,87,107,39,3,9,66,110,18,89,5,51,12,57,101]
2.
Virtual
Entity
[33,103,80,72,77,108,85,74,8,15,64,23,56,88,75,68,84,47,112,104,37,99,78,1,105,32,111,46,36,52,11,106,30,71,63,29,76,54,14,109,21,61,
73,20,2,67,27,58,35,22,70,6,17,91,4,48,55,49,95,16,92,98,62,87,107,9,110,18,89,5,51,12,57]
3.
Physical
Environment
[33,103,80,94,108,85,15,56,88,68,84,112,104,99,78,105,111,46,36,52,106,30,7,63,76,54,14,109,61,73,20,2,40,67,27,90,58,6,17,91,4,48,
55,49,95,16,92,98,62,87,107,39,9,66,110,18,89,5,51,57]
4.
Virtual
Environment
[33,42,103,80,77,94,53,85,8,100,15,97,56,88,68,84,112,104,99,78,1,111,46,106,30,7,63,76,54,14,109,21,73,20,67,27,90,6,17,91,48,55,49,
16,92,98,62,87,110,89,5,51,57]
5.
Fidelity
[33,80,108,24,85,8,56,88,68,112,99,78,32,111,36,7,71,63,61,73,20,67,6,4,48,55,95,16,92,98,87,9,66,110,18,89,12]
6.
State
[80,108,85,68,84,47,104,37,78,105,32,111,71,63,14,21,61,40,67,35,22,6,91,4,48,55,95,92,98,87,107,110,18,89,5,12,57]
7.
Parameters
[33,103,80,72,77,94,53,108,24,85,15,97,84,47,112,104,99,78,1,32,111,46,36,52,11,106,7,71,63,54,21,61,73,2,40,67,35,6,4,55,49,95,16,92,
98,62,87,107,39,3,9,110,18,57]
8.
Physical-to-Virtual
Connection
[33,80,77,94,100,15,64,56,75,84,112,104,99,105,32,111,46,36,52,11,106,30,7,71,63,54,14,109,61,20,2,40,67,27,58,35,22,6,17,4,48,55,49,
95,16,92,98,87,107,39,9,110,89,5,51,12,57]
9.
Virtual-to-Physical
Connection
[33,80,15,64,75,68,112,104,99,105,111,46,36,52,7,71,2,67,27,22,6,48,55,49,95,16,92,87,107,9,66,110,5,12,57]
10.
Twinning/Twinning
Rate
[33,103,80,8,15,64,23,88,68,84,112,104,105,32,111,46,52,11,106,30,7,71,63,54,14,109,21,20,67,58,35,22,70,17,91,48,55,49,95,16,92,98,
62,87,107,9,66,110,18,89,5,12,57,101]
11.
Physical
Processes
[33,103,80,108,15,64,112,104,99,105,111,36,52,106,30,7,71,63,54,14,109,21,20,40,67,27,58,35,22,17,91,48,55,49,95,16,92,87,107,3,9,
110,18,89,5,12,57,101]
12.
Virtual
Processes
[33,103,80,94,53,108,85,74,15,64,56,75,68,84,112,104,37,99,105,32,111,36,52,106,30,7,71,54,14,109,21,61,73,20,2,40,67,27,58,35,22,
70,17,4,48,55,49,95,16,92,87,107,3,9,110,89,5,51,12,57,101]
13.
Perceived
Benefits
[33,80,72,85,78,111,11,2,40,67,58,22,6,17,4]
14.
Digital
Twin
across
the
Product
Life-Cycle
[103,80,77,85,100,56,84,112,37,99,78,1,111,36,52,11,106,30,71,63,76,54,109,21,61,73,20,2,67,58,22,70,17,91,4,48,55,49,92,107,9,66,110,
57,101]
15.
Use-Cases
[33,42,103,80,28,94,53,108,24,74,100,15,97,88,84,47,112,104,37,99,1,105,111,36,52,11,30,7,29,76,54,14,61,20,2,40,67,27,90,58,22,70,6,
17,91,48,55,49,95,16,98,87,107,39,3,9,110,51,12,57]
16.
Technical
Implementations
[16,98,87,51,12,57,77,100,112,105,11,109,22,17,94,111,107,106,110,5,75,104,71,2,27,6,55,46,80,8,88,84]
17.
Levels
of
Fidelity
[80,85,108,56,88,68,112,78,111,36,63,61,73,67,55,16,9,110,18,89,24,20]
18.
Data
Ownership
N/A
19.
Integration
between
Virtual
Entities
N/A
D.
Jones
et
al.
/
NULL
xxx
(2019)
xxx–xxx
5
G
Model
CIRPJ
544
No.
of
Pages
17
Please
cite
this
article
in
press
as:
D.
Jones,
et
al.,
Characterising
the
Digital
Twin:
A
systematic
literature
review,
NULL
(2020),
https://doi.org/
10.1016/j.cirpj.2020.02.002

The
term
state
is
then
both
appropriate
and
widely
used
and
is
thus
proposed
to
be
applicable
to
both
the
virtual
and
physical
entities.
Physical-to-virtual
connection
The
physical-to-virtual
connections
are
the
means
by
which
the
state
of
the
physical
entity
is
transferred
to,
and
realised
in,
the
virtual
environment
–
i.e.
the
updating
of
virtual
parameters
such
that
they
reflect
the
values
of
physical
parameters.
These
include
Internet-of-Things
sensors
[98,87,51,12,57,77,100,112,105,11,109,22,17,16],
web-services
[56,8,100],
5G
[16],
and
customer
requirements
[103].
All
descriptions
of
the
Digital
Twin
within
literature
contain
physical-to-virtual
connections.
The
connection
itself
consists
of
a
Metrology
phase,
in
which
the
state
of
the
physical
entity
is
captured,
and
a
Realisation
phase,
in
which
the
delta
between
the
physical
and
digital
entities
is
determined
and
the
virtual
entity
is
updated
accordingly.
Fig.
5a
shows
this
process.
As
an
example,
a
change
in
temperature
of
a
physical
motor
is
measured
using
an
Internet-of-Things
thermometer
(metrology
phase),
the
tempera-
ture
measurement
is
transferred
to
the
virtual
environment
via
a
web
service,
a
virtual
process
determines
the
difference
in
temperatures
between
the
physical
motor
and
the
virtual
motor,
and
then
updates
the
virtual
motor
such
that
both
measures
are
the
same
(realisation
phase).
There
is
no
widely
used
term
for
this
process
and
so
in
line
with
the
terms
presented
in
this
paper,
physical-to-virtual
connection
is
proposed.
This
continuous
connection
between
the
physical
and
virtual
is
one
differentiator
between
the
Digital
Twin
and
more
traditional
simulation
and
modelling
exercises,
where
analysis
is
frequently
performed
‘off-line’.
The
physical-to-virtual
connection
allows
for
the
monitoring
of
state
changes
that
occur
both
in
response
to
conditions
in
the
physical
environment,
as
well
as
to
changes
in
state
that
occur
in
response
to
interventions
enacted
by
the
Digital
Twin
itself,
i.e.
should
a
change
in
motor
speed
be
enacted
due
to
some
temperature
measurements,
the
physical-to-digital
connec-
tion
would
also
measure
the
effect
of
this
intervention.
Virtual-to-physical
connection
Grieves
describes
the
virtual-to-physical
connection
as
the
flow
of
information
and
processes
from
the
virtual
to
the
physical;
that
is,
the
Digital
Twin
contains
the
functionality
to
physically
realise
a
change
in
the
physical
state.
Examples
of
this
in
practice
include
changes
in
display
terminals
[111],
PLC's
[111,6],
process
control
[48],
machine
parameters
[55],
and
production
management
[112].
The
process
of
virtual-to-physical
connection
mirrors
that
of
the
physical-to-virtual,
in
that
it
contains
both
metrology
and
realisation
phases,
see
Fig.
5b.
Virtual
processes
and
metrology
methods
determine
and
measure
an
optimal
set
of
parameter
values
within
a
physical
entity
or
environment,
and
realisation
Table
7
List
of
parameter
type,
their
descriptions
and
example
usage.
Parameter
Description
Example
Form
The
entity's
geometric
structure
Geometry
[85,103],
dimensions
[77,15,33],
size
[103],
wear
[71,108],
tolerances
[35,33],
coordinate
system
[35],
work-piece
parameters
(strength,
hardness)
[108],
space
requirements
[85]
Functionality
The
entity's
movement
and/or
purpose
Functional
capability
[4],
control
[55],
machine
parameters
(spindle
speed,
feed
rate)
[108],
function
model
[85],
biochemical
[92],
general
[80,103]
Health
The
actual
state
of
the
entity
with
respect
to
its
ideal
state
Analysis
[98],
management
[98,32]
Location
The
entity's
geographic
position
With
respect
to
the
entity
[15],
with
respect
to
the
environment
[98],
layouts
[54],[106],
[94],
manufacturing
[16]
Process
The
activities
within
which
the
entity
is
engaged
Scheduling
parameters
(sequence,
idle
time)
[108],
models
[21],
logistics
[36],
general
[77,94,18,84]
Time
Both
the
time
taken
to
complete
an
activity
and
the
date/time
that
an
activity
takes
place.
Timeliness
[47],
idle
and
working
time
[108],
processing
and
production
[94],
exposure
[40]
State
The
current
measured
state
of
all
entity
and
environment
parameters
Entity
[108,97,16,6],
usage
[67],
environment
[4],
completeness
[47],
processes
[84],
human
stress
[92],
general
[73,40]
Performance
The
entity's
measured
operation
compared
to
its
optimal
operation
Part
[72],
general
[49,61,46]
Environment
The
physical
and
virtual
environment
within
which
the
entity
exists
General
[67,107,4,24,52]
Misc.
Qualitative
Information
that
is
qualitative
and
therefore
not
generally
measurable
by
traditional
Internet-of-Things
type
sensors.
Product
order
[103,16],
requirements
[85],
employee
qualifications
[94],
mission
[78],
diet
[92]
Fig.
5.
The
physical-to-virtual
twinning
(a)
and
virtual-to-physical
twinning
(b)
processes.
6
D.
Jones
et
al.
/
NULL
xxx
(2019)
xxx–xxx
G
Model
CIRPJ
544
No.
of
Pages
17
Please
cite
this
article
in
press
as:
D.
Jones,
et
al.,
Characterising
the
Digital
Twin:
A
systematic
literature
review,
NULL
(2020),
https://doi.org/
10.1016/j.cirpj.2020.02.002

methods
determine
the
delta
between
these
new
values
and
the
existing
state,
and
update
the
state
of
the
physical
entity
accordingly.
For
example,
in
response
to
an
increased
motor
temperature
that
exceeds
a
safety
threshold,
the
effect
of
changing
motor
speed
is
modelled,
a
speed
that
sufficiently
reduces
the
temperature
is
measured,
and
the
physical
motor
speed
is
adjusted.
In
comparison
to
the
physical-to-virtual
connection,
the
virtual-to-physical
connection
is
not
always
included
in
descrip-
tions
of
Digital
Twins,
even
though
it
is
included
in
Grieves’
original
definition.
The
reason
for
this
is
not
clear;
conceptually
it
is
possible
to
generate
a
‘Digital
Twin’
with
just
a
one
way
physical-
to-virtual
connection
–
the
state
of
the
virtual
entity
will
reflect
the
state
of
the
physical
hence
the
two
could
be
characterised
as
‘twinned’
–
although
it
is
challenging
to
understand
how
benefits
of
the
Digital
Twin
may
be
realised
without
a
virtual-to-physical
connection.
The
CIRP
Encyclopedia
of
Production
Engineering
definition
of
the
Digital
Twin
[83]
is
one
such
example
that
does
not
specifically
include
the
virtual-to-physical
connection.
A
potential
benefit
of
this
definition
is
that
it
is
more
universal
but
this
comes
at
the
expense
of
context
of
application
relating
to
the
fundamental
paradigm
of
twinning
and
its
origins,
i.e.
a
bi-
directional
relationship
between
the
virtual
and
the
physical.
The
value
of
the
virtual-to-physical
connection
is
that,
when
used
in
conjunction
with
a
physical-to-virtual
connection,
it
closes
the
loop
between
hypotheses
generated
in
the
virtual
environment
and
the
actual
consequences
realised
in
the
physical
environment.
Effectively,
the
Digital
Twin
with
both
physical-to-virtual
and
virtual-to-physical
connection
can
hypothesise,
and
subsequently
perform,
test,
and
adjust
that
hypothesis
in
a
continuous
adapting
and
improving
cycle.
It
is
this
continuous
loop
that
can
set
the
Digital
Twin
apart
from
more
traditional
modelling
methods,
where
hypothesis
testing
is
a
far
more
involved
and
labour
intensive
task.
An
aspect
of
this
which
is
again
frequently
not
discussed
in
literature
is
the
role
of
the
human-in-the-loop:
if
one
were
to,
for
example,
use
the
virtual
twin
to
determine
the
health
of
a
particular
component
using
a
predictive
model,
and
then
send
a
mechanic
to
replace
that
component,
the
mechanic
in
this
scenario
performs
the
realisation
process
of
virtual-to-physical
twinning.
If
one
did
not
have
the
virtual-to-physical
connection,
i.e.
the
information
generated
in
the
virtual
environment
is
not
acted
on
in
the
physical
environment,
then
is
becomes
difficult
to
separate
the
concept
from
those
of
more
traditional
multi-physics
simulation
and
modelling
approaches
that
can
be
considered
to
represent
an
instance
of
a
system
at
a
predefined
set
of
inputs/conditions.
Leaving
this
point
open
for
future
debate
and
returning
to
the
aim
of
this
review,
there
is
no
widely
used
term
for
this
process
and
so
in
line
with
the
terms
presented
in
this
paper
and
physical-to-
virtual
twinning,
virtual-to-physical
connection/twinning
is
pro-
posed
as
a
key
tenet
of
the
paradigm.
Twinning/twinning
rate
Twinning
is
‘simply’
the
act
of
synchronising
the
virtual
and
physical
states,
for
example,
the
act
of
measuring
the
state
of
the
physical
entity
and
realising
that
state
in
the
virtual
environment
such
that
the
virtual
and
physical
states
are
‘equal’,
in
that
all
of
the
virtual
parameters
are
the
same
value
as
physical
parameters.
The
process
is
depicted
in
Fig.
6
and
includes
the
process
of
physical-to-
virtual
and
virtual-to-physical
twinning.
A
change
that
takes
place
in
either
the
physical
or
virtual
entity
is
measured
before
being
realised
in
the
equivalent
virtual/physical
twin,
when
both
states
are
equal,
the
entities
are
‘twinned’.
The
combination
of
both
connections
allow
for
a
continuous
cycle
optimisation,
as
possible
physical
states
are
predicted
in
the
virtual
environment
and
optimised
for
a
specific
goal.
That
is,
a
virtual
optimisation
process
is
performed
using
the
current
state
of
the
Physical/Virtual
Entity,
once
determined
this
optimal
set
of
virtual
parameters
is
propagated
through
to
the
Physical
Twin.
The
Physical
Twin
then
responds
to
the
change,
the
loop
cycles
around
to
update
the
Virtual
Twin
with
the
measured
physical
state.
The
delta
between
the
actual
and
predicted
states
can
then
be
compared
and
the
optimisation
process
re-run
with
the
updated
information.
The
Twinning
Rate
is
then
the
frequency
with
which
twinning
occurs.
In
literature,
this
twinning
rate
is
only
described
as
being
in
‘real-time’;
that
is,
a
change
is
a
physical
state
will
near-instantly
be
reflected
by
the
same
change
in
the
virtual
state.
The
value
of
a
near
real-time
state
is
that
it
enables
the
Virtual
and
Physical
Twins
to
act
both
simultaneously
and
together,
and
theoretically
results
in
a
near
real-time
response
to
change.
For
example,
an
assembly
line
that
automatically
adjusts
scheduling
to
counter
production
losses
when
a
faulty
batch
of
components
is
detected.
Twinning
and
the
Twinning
Rate
are
in
effect
the
live
connection
between
the
Physical
Entity/Environment
and
the
Virtual
Entity/Environment.
A
key
aspect
of
Grieves’
Digital
Twin
is
the
collection
and
reuse
of
historical
data.
As
such,
all
these
Fig.
6.
The
physical-to-virtual
and
virtual-to-physical
twinning
process.
D.
Jones
et
al.
/
NULL
xxx
(2019)
xxx–xxx
7
G
Model
CIRPJ
544
No.
of
Pages
17
Please
cite
this
article
in
press
as:
D.
Jones,
et
al.,
Characterising
the
Digital
Twin:
A
systematic
literature
review,
NULL
(2020),
https://doi.org/
10.1016/j.cirpj.2020.02.002

interaction
are
stored
within
the
virtual
environment
and
made
accessible
to
future
Virtual
Processes.
This
effectively
means
the
Digital
Twin
can
learn
from
its
past,
both
in
terms
of
actual
historical
performance
and
in
terms
of
historical
virtual
processes.
Physical
processes
Physical
processes
refer
to
the
activities
being
performed
by
the
physical
entity
in
the
physical
environment.
Reported
examples
of
these
are
largely
manufacturing
related
[7,20,49,16,33,108,64,99,
105,52,106,30,71,54,14,109,27,35,17,91,48,55,101,15,63,104,21,112,-
95,9,80,36],
although
more
specific
examples
include:
smart
factories
[57,5],
iron
and
steel
manufacturing
process
(coking,
sintering,
blast
furnace
iron-making,
steel-making,
continuous
casting
and
rolling
production)
[103],
3D
printing
[40,18],
mobile
robot
control
[107,12],
engineering
design
[87,110],
and
medical
health,
disease
and
bio-mechanical
processes
[92].
It
is
during
physical
processes
that
changes
in
Physical
Twin
parameters
occur,
and
it
is
these
state
changes
that
are
captured
and
translated
to
the
Virtual
Twin.
Virtual
processes
Virtual
processes
refer
to
the
activities
performed
using
the
virtual
entity
within
the
virtual
environment.
The
vast
majority
of
these
processes
can
be
covered
by
the
activities
of
simulation,
modelling,
and
optimisation
[2–5,7,9,12,15,17,16,20,21,27,30,33,36,
35,40,48,49,52,54,55,10,56,58,61,64,70,71,73,75,74,85,87,89,92,94,
95,99,101,104,109,108,111,110],
and
health
monitoring,
diagnostics,
and
prediction
[15,32,48,53,67,80,89,95,107].
More
specific
exam-
ples
include
design
verification
[36],
welding
sequence
optimisa-
tion
[80]
and
‘what-if
scenario’
analyses
of
alternative
management
scenarios
[3].
These
processes
result
in
changes
in
Virtual
Twin
parameters,
the
state
of
which
can
then
be
analysed
and/or
realised
in
the
Physical
Twin.
The
Digital
Twin
and
twinning
process
Through
a
thematic
analysis
of
literature,
this
section
has
identified
a
range
of
themes
core
to
the
Digital
Twin
concept.
Here,
these
themes
are
consolidated
and
formalised
as
characteristics
of
the
Digital
Twin,
with
Table
8
presenting
these
characteristics
and
their
descriptions,
and
Fig.
7
presenting
the
Twinning
process
and
the
inter-relationship
of
terms
within
the
overall
Digital
Twin
concept.
Fig.
7
shows
how
physical/virtual
processes
act
on
the
corresponding
physical/virtual
entity,
where
these
processes
generate
a
change
in
the
state
of
that
entity
via
it's
parameters.
This
change
in
state
is
captured
using
metrology
methods,
transferred
via
physical-to-virtual
and
virtual-to-physical
connections,
and
realised
in
the
other
(virtual/physical)
environ-
ment
by
synchronisation
of
all
parameters.
Both
virtual
and
physical
environments
contain
the
means
to
measure
and
realise
state
changes.
The
process
of
change
→
metrology
→
realise
is
the
twinning
process,
and
runs
in
both
directions
from
virtual-to-
physical
and
physical-to-virtual.
The
twinning
rate
is
the
frequency
at
which
the
virtual
and
physical
twins
are
synchronised.
This
is
the
Digital
Twin.
Future
directions
and
gaps
in
research
This
subsection
examines
in
more
detail
the
literature
to
elicit
gaps
and
future
challenges
based
on
the
identified
themes
13
to
19
from
Table
6:
perceived
benefits;
Digital
Twins
across
the
product
life-cycle;
use-cases;
technical
implementation;
levels
of
fidelity;
data
ownership;
and
integration
between
virtual
entities.
While
the
previous
section
presents
a
description
of
what
the
Digital
Twin
is,
this
section
aims
to
identify
research
gaps
that
must
be
filled
to
fully
realise
the
Digital
Twin
and
its
envisaged
benefits.
Perceived
benefits
There
are
many
potential
and
perceived
benefits
highlighted
in
literature
and
industry
relating
to
the
digital
twin
concept.
These
include:
reducing
costs
[33,4,40,22],
risk
and
design
time
[22],
complexity
and
reconfiguration
time
[85];
improving
after-sales
service
[17,72],
efficiency
[2],
maintenance
decision
making
[58],
security
[6],
safety
and
reliability
[80],
manufacturing
manage-
ment
[67],
processes
and
tools
[11];
enhancing
flexibility
and
competitiveness
of
manufacturing
system
[111];
and
finally,
from
Grieves
himself,
the
fostering
of
innovation
[33].
There
are,
however,
very
few
examples
of
validation
and
quantification
of
such
perceived
benefits
against
existing
processes
and
systems,
with
very
few
papers
showing
tangible
improvement
over
current
norms.
Given
the
potential
costs
and
challenges
of
the
infrastruc-
ture
and
work-flow
changes
needed
to
effectively
implement
digital
twins
in
an
industrial
context,
a
lack
of
tangible
understanding
of
scale
and
nature
of
benefits
is
a
substantial
obstacle.
It
is
difficult
to
justify
substantial
change
without
clarity
in
return
on
investment,
and
similarly
difficult
to
identify
the
characteristics
and
nature
of
the
digital
twin
to
employ
in
order
to
realise
the
benefits
each
industry
context
requires.
Without
substantial
effort
to
describe
and
quantify
benefits,
it
is
challenging
even
to
suggest
that
the
digital
twin
concept
itself
may
be
the
most
appropriate
solution
to
the
challenges
faced
by
each
particular
industry.
Future
work
in
this
area
is
needed
to
evaluate
the
Digital
Twin
and
associated
processes
and
determine
where
quantifiable
improvement
may
be
achieved,
the
limits
of
this
improvement,
Table
8
The
characteristics
of
the
Digital
Twin
and
their
descriptions.
Characteristic
Description
Physical
Entity/Twin
The
physical
entity/twin
that
exists
in
the
physical
environment
Virtual
Entity/Twin
The
virtual
entity/twin
that
exists
in
the
virtual
environment
Physical
Environment
The
environment
within
which
the
physical
entity/twin
exists
Virtual
Environment
The
environment
within
which
the
virtual
entity/twin
exists
State
The
measured
values
for
all
parameters
corresponding
to
the
physical/virtual
entity/twin
and
its
environment
Metrology
The
act
of
measuring
the
state
of
the
physical/virtual
entity/twin
Realisation
The
act
of
changing
the
state
of
the
physical/virtual
entity/twin
Twinning
The
act
of
synchronising
the
states
of
the
physical
and
virtual
entity/twin
Twinning
Rate
The
rate
at
which
twinning
occurs
Physical-to-Virtual
Connection/
Twinning
The
data
connections/process
of
measuring
the
state
of
the
physical
entity/twin/environment
and
realising
that
state
in
the
virtual
entity/twin/environment
Virtual-to-Physical
Connection/
Twinning
The
data
connections/process
of
measuring
the
state
of
the
virtual
entity/twin/environment
and
realising
that
state
in
the
physical
entity/twin/environment
Physical
Processes
The
processes
within
which
the
physical
entity/twin
is
engaged,
and/or
the
processes
acting
with
or
upon
the
physical
entity/twin
Virtual
Processes
The
processes
within
which
the
virtual
entity/twin
is
engaged,
and/or
the
processes
acting
with
or
upon
the
virtual
entity/twin
8
D.
Jones
et
al.
/
NULL
xxx
(2019)
xxx–xxx
G
Model
CIRPJ
544
No.
of
Pages
17
Please
cite
this
article
in
press
as:
D.
Jones,
et
al.,
Characterising
the
Digital
Twin:
A
systematic
literature
review,
NULL
(2020),
https://doi.org/
10.1016/j.cirpj.2020.02.002

and
the
context/cases
in
which
it
may
be
operationalised.
Establishing
this
cost-benefit
is
essential
for
industrial
uptake.
The
Digital
Twin
across
the
Product
Life-Cycle
Grieves
depicts
the
life-cycle
of
the
Digital
Twin
as
starting
as
a
Digital
Twin
Prototype
in
the
concept
phase
of
the
product
life-
cycle,
and
continually
evolving
throughout
the
entire
life-cycle.
As
the
virtual
entity
may
be
stored
in-perpetuity
it
will
eventually
surpasses
the
physical
entity
itself,
with
continual
potential
value
for
future
analysis
and
insights
even
following
physical
entity
disposal.
For
the
papers
reviewed
in
which
the
life-cycle
is
described
authors
are
in
agreement
with
Grieves,
with
terms
such
as
over,
throughout,
and
entire
life-cycles
used
[77,30,71,9,101,52,107,
100,76,85,37,99,70,4,1,106,58,91,55,73,20,78,111,109,61,110,92,57,
103,36,63,54,67,17,48].
Post
et
al.
[66]
use
a
description
of
‘life
cycle
or
subset
of’,
indicating
a
Digital
Twin
that
exists
for
specific
use-
cases
along
the
life-cycle.
This
is
mirrored
in
a
small
number
of
papers
that
present
very
specific
use-cases
within
the
life-cycle,
and
as
such
terms
describing
the
production/manufacturing/
factory
life-cycle
[101,11,112,49],
and
operational/maintenance
phase
[84,22]
are
used.
If
this
is
where
the
Digital
Twin
is
envisaged,
Fig.
8
shows
where
research
effort
is
focused
through
the
classification
of
papers
against
Starks’
product
life-cycle
[82]
of:
Imagine,
Define,
Realise,
Support/Use,
Retire/Dispose.
Papers
are
classified
by
their
focus
on
digital
twins
as
a
concept,
the
methodology
of
their
implementa-
tion,
implementation
cases,
or
a
general
literature
review.
This
classification
shows
that
research
is
being
largely
focused
on
the
Realise
and
Support/Use
phases
of
the
life-cycle,
and
that
the
majority
of
papers
are
presenting
methodologies
followed
by
reports
on
implementations.
There
are
relatively
few
papers
that
place
focus
on
the
core
concept
of
the
Digital
Twin
or
consider
the
digital
twin
across
the
entire
life-cycle,
while
there
are
a
relatively
high
number
of
methodologies
and
implementations
that
present
an
interpretation
of
the
Digital
Twin
for
specific
use-cases.
Such
focus
highlights
several
areas
in
which
knowledge
gaps
exist,
in
particular
the
applicability
of
digital
twins
to
earlier
life-cycle
phases
and
disposal,
and
the
core
concepts
of
digital
twins
both
in
the
general
case
and
in
specific
life-cycle
phases.
As
a
means
for
generating
information
and
supporting
optimisation,
analysis,
and
understanding,
a
lack
of
detailed
study
of
digital
twins
across
the
life-cycle
implies
that
opportunities
for
benefit
may
to-date
have
been
missed.
Further
work
is
then
needed
to
understand
the
requirements
of
the
Digital
Twin
across
the
entire
life-cycle,
and
determine
whether
the
existing
methodologies
and
implementations
from
other
phases
are
applicable.
Performing
this
work
could
see
the
realisations
of
benefits
such
as
reduced
cost,
risks
and
design
time,
fostering
innovation,
general
reliability,
and
decision
making,
particularly
in
the
Imagine,
Define
and
Retire/Dispose
phases.
Fig.
7.
The
physical-to-virtual
and
virtual-to-physical
twinning
process.
Fig.
8.
The
number
of
research
papers
across
the
product
life-cycle.
D.
Jones
et
al.
/
NULL
xxx
(2019)
xxx–xxx
9
G
Model
CIRPJ
544
No.
of
Pages
17
Please
cite
this
article
in
press
as:
D.
Jones,
et
al.,
Characterising
the
Digital
Twin:
A
systematic
literature
review,
NULL
(2020),
https://doi.org/
10.1016/j.cirpj.2020.02.002

Use-cases
The
use-cases
presented
here
simply
refer
to
how
and
where
literature
is
applying
the
Digital
Twin.
The
vast
majority
of
identified
use-cases
are
manufacturing
related
[7,15,6,48,107,36,
103,11,52,55,1,105,111],
with
some
specific
examples
related
to
Industry
4.0
[104,99,30,79,67,27,95],
smart
factories/manufactur-
ing
[97,112,57,17,88],
and
learning
[91,94].
Other
use-cases
include:
product
design
(bicycle
[87],
pump
[29],
and
automotive
wiring
harness
[90]),
model-based
engineering
[61,20,74],
5G
communi-
cation
for
factories
[16],
air-frame
health
monitoring
[53],
composite
optimisation
[41],
smart
cars
[22],
farming
[98,28],
and
human
health
and
the
agriculture
supply
chain
[39].
Fig.
9b
shows
the
use-cases
mapped
against
the
twinning
cycle
with
the
mapping
based
on
the
most
appropriate
placement
for
each
use-case.
For
example,
Simulation,
Modelling,
and
Optimisation
are
all
virtual
processes
and
so
are
positioned
on
that
side
of
the
cycle,
Smart
Cars
and
Farms
are
both
physical
entities
and
so
are
placed
on
the
physical
side
of
the
cycle.
Those
use-cases
situated
in
the
centre
of
the
cycle
relate
to
the
entire
cycle.
Learning
for
example,
contains
both
physical
and
virtual
entities
with
the
connections
between.
Fig.
9.
Use-cases
(a)
and
technology
(b)
mapped
to
the
twinning
cycle.
10
D.
Jones
et
al.
/
NULL
xxx
(2019)
xxx–xxx
G
Model
CIRPJ
544
No.
of
Pages
17
Please
cite
this
article
in
press
as:
D.
Jones,
et
al.,
Characterising
the
Digital
Twin:
A
systematic
literature
review,
NULL
(2020),
https://doi.org/
10.1016/j.cirpj.2020.02.002

From
the
mapping
it
is
evident
that
the
majority
of
use-cases
involve
both
the
physical-to-virtual
and
virtual-to-physical
aspects
of
theDigital
Twin–
evenif
thevirtual-to-physical
involvesa human-
in-the-loop,
such
as
training
of
the
virtual
twin
before
engaging
with
the
physical
twin.
Two
use-cases
do
not
however
involve
both
forms
of
twinning,yet there
arespecificreasons for each
of
these.Geometry
assurance
is
a
stage
in
a
larger
process
–
manufacturing
and
this
larger
process
does
involve
both
forms
of
twinning.
Recycling
is
an
end-of-life
activity
and,
as
such,
there
is
no
longer
a
physical
entity
to
twin.
In
future
research
it
may
then
be
worth
an
increased
focus
on
the
virtual-to-physical
connection
if
benefits
of
reduced
times/costs
and
increased
safety
are
to
be
realised.
Examining
the
use-cases
from
a
product
life-cycle
perspective,
Table
9
shows
the
literature
classified
by
use-case
across
Stark's
product
life-cycle
[82].
These
results
show
that
largely,
research
is
concerned
with
data
management
and
data
usage
techniques
of
simulation,
modelling,
and
optimisation.
Outside
of
these,
there
are
some
more
specific
use-cases
such
as
geometry
assurance,
health
monitoring,
traceability,
etc.
The
‘Other’
category
covers
those
use-cases
that
are
either
very
specific
(the
design
of
an
automotive
wiring
harness
[90]
for
example)
or
at
too
high
a
level
to
fall
into
the
other
categories,
“Emergence
of
Digital
Twins”
[23]
for
example.
Through
the
sparseness
of
several
areas
of
research
across
many
life-cycle
phases,
Table
9
highlights
many
opportunities
for
research
and
implementation.
Aspects
studied
in
each
use-case
may
have
potential
for
application
across
life-cycle
phases,
with
concurrent
potential
benefit
and
opportunity
for
improvement.
For
example,
opportunities
for
learning
across
phases,
the
importance
of
design
traceability
throughout
earlier
phases
to
capture
rationale,
and
the
nature
of
effective
data
management
in
earlier
phases
or
disposal.
In
addition
to
the
spares
and
unpopulated
areas
of
Table
9,
there
is
a
lack
of
literature
studying
the
entire
life-cycle.
The
requirements
of
the
Digital
Twin
at
each
phase
of
the
life-cycle
are
not
yet
fully
understood.
The
required
fidelity
at
each
phase
for
example.
There
are
also
questions
over
how
many
Digital
Twins
exist,
is
one
Digital
Twin
across
the
entire
life-cycle
appropriate
or
is
a
new
one
implemented
at
each
phase?
And
either
way,
how
are
transitions
between
phases
managed?
Once
a
product
goes
into
production,
do
they
all
have
a
single
common
Digital
Twin
ancestor?
Or
is
that
ancestor
cloned
and
duplicated
across
all
instances?
If
this
is
the
case,
then
what
is
that
Digital
Twin
ancestor:
a
finished
design,
or
some
smaller
subset
of
the
finished
design?
There
are
then
many
interesting
gaps
in
this
area
that
require
future
work.
Technical
implementation
Research
into
technical
solutions
to
the
Digital
Twin
is
largely
focused
on
leveraging
existing
technologies.
These
include:
5G
[16],
Internet-of-Things
[98,87,51,12,57,77,100,112,105,11,109,22,17,
16],
Industrial
Internet-of-Things
[16],
wireless
[94,112,111,107],
RFID
[112,111]
[107],
Ethernet
[106,112],
actuators
[110,5,57,75,104,
71]
[2,27,6,55,16,87],
and
the
cloud
[46,80,8,100,88,84,112].
Fig.
9a
shows
the
technology
involved
in
enabling
the
Digital
Twin
presented
in
literature
mapped
to
the
twinning
cycle.
The
technology
is
placed
in
the
area
of
the
cycle
where
it
is
used.
Those
technologies
placed
in
the
centre
of
the
cycle
are
applicable
to
the
entire
cycle;
for
example,
5G
and
wireless
communication
technology
are
used
for
both
physical-to-virtual
and
virtual-to-
physical
connections.
Fig.
9b
shows
that
the
Digital
Twin
is
largely
dependent
on
(Industrial)
Internet-of-Things
for
twinning
for
both
physical-to-
virtual
and
virtual-to-physical
connection.
In
line
with
this,
sensors
(including
RFIDs)
are
being
used
for
data
capture,
and
actuators
are
being
used
to
realise
change
in
the
physical
environment.
In
terms
of
managing
the
virtual,
the
technologies
discussed
relate
to
the
Internet-of-Things
and
general
internet
technology.
Finally,
the
technology
relating
to
the
physical
entity
is
those
entities
themselves,
such
as
smart
factories.
The
Digital
Twin
is
being
constructed
on
existing,
state-of-the-art,
and
off-the-
shelf-technology
that
are
being
developed
independently
of
the
Digital
Twin.
While
this
has
benefits
in
terms
of
cost
and
availability
of
technology,
the
counter
to
this
is
whether
these
technologies
are
optimised
for
the
purpose
of
Digital
Twin
and
the
challenges
of
industrial
applications.
There
is
then
a
need
to
ensure
future
standards
are
suitable
for
Digital
Twin
purposes
and
if
this
is
not
possible,
to
develop
those
standards.
Levels
of
fidelity
In
the
earlier
discussion
on
fidelity,
it
was
shown
that
most
papers
that
discuss
fidelity
(including
Grieves)
advocate
the
highest
levels
feasible,
with
only
a
few
papers
(3)
presenting
fidelity
levels
specific
to
particular
use-cases.
Fidelity
is
important
as
it
governs
the
processes
that
can
be
performed
in
both
the
virtual
and
physical
environments,
i.e.
the
higher
the
fidelity,
the
closer
the
virtual
and
physical
twins
are
aligned
and,
for
example,
the
more
accurate
the
simulation,
modelling,
and
optimisation
will
be.
Placing
fidelity
on
a
scale
from
abstract
(low)
to
precise
(high)
with
medium
fidelity
in
the
centre,
those
cases
identified
in
the
corpus
are
typically
situated
around
the
centre.
That
is,
the
use-
cases
use
a
subset
of
parameters
(medium
fidelity)
and
not
the
full
set
(high
fidelity)
called
for
in
the
original
Digital
Twin
concept.
Literature
is
yet
to
present
an
exhaustive
high-fidelity
implemen-
tation,
where
parameters
for
every
aspect
of
the
physical
twin
are
captured.
The
reality
of
doing
so
may
see
challenges
in
elements
of
the
system
such
as
network
speeds
and
computational
processing
power
that
means
a
true
high-fidelity
Digital
Twin
is
not
actually
Table
9
The
list
of
themes
and
where
they
appear
across
the
product
life-cycle.
Use-case
Imagine
(3%)
Design
(20%)
Realise
(41%)
Support/use
(37%)
Retire/dispose
(3%)
Simulation
Modelling
and
Optimisation
(36%)
[63]
[42,63,103,74,36,54,29,20]
[108,21,74,68,56,101,109,
66,12,17,8,97,103]
[40,49,107,21,104,75,5,3,14,6]
Data
Driven
Design
(10%)
[110,103]
[47,4,9,111,67,86,87]
Data
Management
(33%)
[15,94,111,88,112,66,57,51,55,
64,99,52,56,76,68,16,85,11]
[87,86,9,105,61,27,40,73,76,103,11,68]
Geometry
Assurance
(4%)
[77,7,35,80]
Reconfiguration
(2%)
[85]
[1]
Health
Monitoring
(7%)
[90,32,58,53,95,103]
Learning
(2%)
[91]
[13]
Recycling
(2%)
[103,100]
Reconditioning
(1%)
[2]
Traceability
(1%)
[37]
Other
(7%)
[67,73,23]
[90]
[24,47]
D.
Jones
et
al.
/
NULL
xxx
(2019)
xxx–xxx
11
G
Model
CIRPJ
544
No.
of
Pages
17
Please
cite
this
article
in
press
as:
D.
Jones,
et
al.,
Characterising
the
Digital
Twin:
A
systematic
literature
review,
NULL
(2020),
https://doi.org/
10.1016/j.cirpj.2020.02.002

currently
achievable.
While
this
could
change
with
future
advances
in
technology,
research
should
also
be
exploring
techniques
to
mitigate
this.
Is
there
a
‘divide
and
conquer’
approach
to
twinning
complex
systems
for
example?
Or,
do
we
explore
the
importance
of
fidelity
further
and
determine
the
most
achievable
or
appropriate
level
of
level(s)
of
multi-scale
and
multi-fidelity
for
a
given
use-
case.
Equally,
literature
is
yet
to
visit
the
abstract
level
of
fidelity,
i.e.
a
spreadsheet
of
requirements
–
is
this
part
of
a
Digital
Twin
of
a
concept?
Both
these
levels
of
fidelity
raise
challenges.
Dependent
on
the
parameters
present
and
recorded,
it
is
questionable
whether
an
exhaustive
high-fidelity
Digital
Twin
is
an
achievable
goal.
If
it
is
not,
there
rises
a
question
of
what
level
of
fidelity
is
appropriate
and
realistic
for
a
given
case
in
order
to
maximise
benefit
while
minimising
expense
and
technical
difficulty
of
implementation.
The
abstract
level
of
fidelity
challenges
the
concept
of
the
Digital
Twin
itself,
i.e.
can
you
twin
prior
to
their
being
a
finalised
physical
and
virtual
design,
can
evolving
twins
of
design
prototypes
be
created
while
the
physical
entity
itself
varies
substantially
in
fidelity,
and
even
can
a
concept
or
idea
itself
be
twinned?
With
proposed
benefits
in
simulation
capability
and
information
generation,
and
earlier
process
stages
characterised
by
a
need
for
information,
and
up
to
70%
[96]
of
budget
dedicated
in
early
life-cycle
phases,
the
creation
of
such
abstract
and
early
stage
digital
twins
have
substantial
potential
benefits.
The
answers
to
both
these
will
likely
be
whether
benefits
are
realised
–
at
what
level
of
fidelity
does
one
maximise
the
improvement
in
decision
making
for
maintenance,
and
are
their
benefits
in
being
able
to
switch
between
physical
and
virtual
working
in
early
stage
product
design.
Data
ownership
In
a
world
where
the
ownership
of
data,
such
as
personal
data
(online
activity
for
example),
are
being
seen
as
increasingly
controversial.
This
is
also
true
within
the
field
of
engineering,
i.e.
car
airbag
‘black
box’
data
[19].
If
the
aim
of
the
Digital
Twin
is
the
exhaustive
capture
of
all
physical
environment
parameters,
then
there
is
a
high
possibility
that
those
parameters
can
in
someway
directly
or
indirectly
relate
to
aspects
of
people's
lives,
intellectual
property,
and
everything
in
between.
Determining
how
this
information
is
shared
between
organisations
and
individuals
poses
a
major
challenge.
For
example,
when
an
individual
purchases
a
car
they
own
that
physical
entity.
There
is
an
unanswered
question,
however,
as
to
whether
they
also
own
the
virtual
twin
and
associated
data.
This
is
particularly
relevant
if
the
individual
is
also
an
actor
in
the
virtual
environment
–
if
the
car
is
involved
in
a
collision,
there
are
a
number
of
parties
(insurance
company,
engineers,
accident
investigators)
who
may
want
access
to
data
on
how
the
car
was
being
driven.
The
question
of
ownership
is
pivotal
to
who
accesses
data
and
for
what
purpose.
There
are
then
social
and
cultural
implications
associated
with
the
large
scale
collection,
storage,
and
sharing
of
data
through
the
Digital
Twin
that
need
to
be
fully
addressed.
Integration
between
virtual
entities
Grieves
described
a
Digital
Twin
consisting
of
multiple
virtual
entities
and
environments,
each
with
it's
own
specific
use
case.
For
example,
a
production
line
virtual
entity
for
health
monitoring,
and
another
for
scheduling.
This
specificity
is
mirrored
in
the
corpus,
with
literature
discussing
virtual
entities
at
a
level
of
specific
use
cases
and
in
general,
singular
virtual
entities.
Literature
is
however
yet
to
step
back
to
the
higher
level
view
from
which
the
interaction
of
virtual
entities
can
be
addressed
–
for
example,
balancing
of
the
need
to
deliver
to
a
production
deadline
with
a
predicted
future
fault,
each
of
which
may
be
managed
by
separate
digital
twins.
As
with
the
integration
of
all
discrete
digital
systems,
automatically
taking
the
output
from
one
virtual
entity
(health
monitor)
and
using
it
to
trigger
a
re-run
of
another
virtual
entity's
analysis
(production
scheduler)
may
prove
a
non-trivial
challenge.
As
quantity
of
twins
increases,
and
hence
potential
complexity
of
the
management
of
disparate
twins,
there
may
prove
a
need
for
specific
research
into
twin
integration
and
control.
For
example,
there
is
potential
value
in
operation
of
digital
twins
as
agent-based
systems
that
cooperate
towards
specific
goals
with
emergent
benefits
for
the
wider
system.
This
detailed
problem
assumes
that
(1)
the
virtual
entities
are
on
the
same
platform
and
(2)
the
virtual
entities
have
a
common
means
of
interaction.
Standardisation
and
interoperability
such
that
virtual
entities
can
communicate
is
key
to
realising
this
aspect
of
the
Digital
twin
and,
again,
has
potential
to
be
a
complex,
non-
trivial
challenge.
Examples
of
this
can
be
seen
in
the
Building
Information
Modelling
field,
where
Stadler
et
al.
[81]
discuss
and
attempt
to
address
the
challenges
of
integrating
the
geographical
city
data
with
semantic
information
using
CityGML
[45],
itself
an
open
source
XML-based
data
structure
XML-based
designed
for
the
storage
and
sharing
of
virtual
cities.
Similar
discussions
and
research
must
be
performed
to
address
challenges
in
implemen-
tation
of
digital
twins,
and
realise
potential
benefits.
The
Digital
Twin
characteristics
within
the
context
of
related
literature
The
systematic
literature
review
presented
in
this
paper
was
deliberately
confined
to
those
papers
with
a
contribution
specific
to
the
Digital
Twin,
such
that
research
could
be
consolidated
and
a
common
understanding
developed.
There
are
however
a
number
of
related
fields
that
both
predate
the
Digital
Twin
(Virtual
Manufacturing
Systems
for
example),
and
are
developing
in
parallel
(Building
Information
Modelling
for
example).
To
provide
greater
underpinning
to
the
characteristics
developed,
this
paper
now
considers
the
developed
characteristics
within
the
wider
context
of
these
other
fields.
Unlike
the
systematic
approach
used
to
generate
the
characteristics
of
the
Digital
Twin
and
due
to
the
vast
number
of
publications
in
all
these
fields,
this
section
considers
only
seminal
works
from
the
related
fields.
Computer-integrated
manufacturing
Back
in
the
late
1980s,
the
CIM
Reference
Model
Committee
International
Purdue
Workshop
on
Industrial
Computer
Systems
published
a
reference
manual
for
Computer-Integrated
Manufacturing
[102].
With
the
advent
of
computers
with
processing
power
fit
for
the
‘real-time’
control
of
production
lines,
Computer-Integrated
Manufacturing
was
seen
as
the
means
of
developing
robust
and
dynamic
production
lines
with
the
ability
to
adapt
and
compensate
to
changes
caused
by
disruptions
such
as
breakdowns,
and
changes
in
customer
demands.
This
was
achieved
through
the
closing
of
information
loops,
i.e.
computers
could
both
monitor
and
enact
change
in
the
physical
entity.
In
2018
to
mark
the
30
year
anniversary
of
the
field
the
International
Journal
of
Computer
Integrated
Manufacturing,
Laengle
et
al.
[50]
produced
a
bibliometric
analysis
of
the
journal's
1687
papers.
Amongst
the
analysis
the
top
30
global
keywords
are
presented,
with
the
top
three
(and
their
position
in
brackets)
being
‘simulation’
(1),
‘scheduling’
(2),
and
‘process
planning’
(3).
While
not
taking
the
full
list
out
of
context,
it
is
effectively
made
up
from
virtual
techniques
(‘simulation’
(1),
‘modelling’
(6),
‘optimisation’
(22)),
the
means
of
realising
change
in
the
physical
entity
(‘Step-NC’
(5),
‘CNC’
(20)),
metrology
and
data
management
techniques
(‘interoperability’
(7),
‘RFID’
(24)),
and
specific
use-cases
‘supply
chain
management’
(10),
‘supplier
selection’
(25)).
Topics
that
all
12
D.
Jones
et
al.
/
NULL
xxx
(2019)
xxx–xxx
G
Model
CIRPJ
544
No.
of
Pages
17
Please
cite
this
article
in
press
as:
D.
Jones,
et
al.,
Characterising
the
Digital
Twin:
A
systematic
literature
review,
NULL
(2020),
https://doi.org/
10.1016/j.cirpj.2020.02.002

align
with
those
presented
in
the
characterisation
of
the
Digital
Twin
presented
in
this
paper.
Virtual
manufacturing
systems
In
the
1990s
Onosato
published
a
paper
on
the
development
of
a
Virtual
Manufacturing
System
[65],
a
system
aimed
at
generating
a
virtual
representation
of
a
physical
production
line
such
that
manufacturing
processes
could
be
modelled
without
the
need
for
the
physical
entity.
Specifically,
Onosato
presented
the
mean
to
model
the
factory,
product
life-cycle,
and
manufacturing
processes
over
time,
with
the
desired
use-cases
of
shop-floor
layout,
modelling,
testing
and
simulation
of
control
strategies,
programs
and
scheduling.
Later
that
decade,
Iwata
et
al.
[43,44]
built
on
Onosato's
work,
defining
architectures
and
information
infra-
structures
required
to
deliver
the
Virtual
Manufacturing
System.
While
communication
technology
and
processing
power
have
developed
and
are
capable
of
processing
more
and
faster,
changing
the
landscape
of
the
challenges,
Onosato's
Virtual
Manufacturing
System
was
aimed
at
being
an
“...manufacturing
systems
which
pursue
the
informational
equivalence
with
real
manufacturing
systems.”
Capable
of
replicating
the
physical
production
line
such
that
accurate
and
useful
models
could
be
created
and
evaluated.
In
comparison
with
the
characteristics
of
the
Digital
Twin,
Virtual
Manufacturing
Systems
are
discussed
in
terms
of
physical
and
virtual
entities,
with
the
virtual
entity
being
a
high-fidelity
representation
of
the
physical.
The
key
differences
are
the
lack
of
connection
between
physical
and
virtual
entities.
The
aim
of
the
Virtual
Manufacturing
System
was
to
be
useful
through
its
ability
to
replicate
‘real-world’
operations
through
high-fidelity
virtual
representations
of
the
physical.
So
the
concept
of
using
a
virtual
representation
of
a
physical
entity
is
one
that
has
existed
since
the
1990s,
albeit
one
that
relies
on
accurate
models,
rather
than
‘real-
world’
data.
Model-based
predictive
control
Originally
developed
as
a
mean
to
control
chemical
processes
in
the
oil
and
gas
industry,
Model
Predictive
Control
is
simply
the
means
of
controlling
a
process
based
on
some
form
of
model
(e.g.
linear,
non-linear)
[31].
Physical
processes
are
measured
and
compared
to
a
virtual
model
that
is
able
to
predict
the
future
states
of
the
process,
and
optimise/adapt/control
the
process
appropri-
ately.
As
a
means
of
control,
model-based
prediction
is
automated
and
robust,
and
as
such
is
now
widely
used
across
engineering
disciplines
and
has
evolved
through
a
number
of
generations
[69].
In
a
review
of
the
field
published
in
2014
Mayne
[60]
gives
a
good
general
overview
of
the
field,
highlighting
both
the
theoretical
and
mathematical
aspects
of
the
models,
as
well
as
the
more
physical
sensors,
actuators
and
the
practical
network
challenges
in
delivering
closed-loop
control
through
“sensor-to-controller”
and
“controller-to-actuator”
connections.
The
similarities
between
the
Digital
Twin
and
Model-Based
Predictive
Control
are
in
the
capture
and
interpretation
of
the
current
state
of
the
physical
entity
and
being
able
to
use
that
current
state
to
change
the
future
state.
Whether
that
is
to
optimise
or
to
react
to
problems
etc.
The
similarity
between
the
“sensor-to-controller”
and
“controller-to-actuator”
and
the
physi-
cal-to-virtual
and
virtual-to-physical
that
appear
in
the
character-
istics
of
the
Digital
Twin,
speak
to
the
benefits
of
the
closed-loop
approach
as
originally
conceived
by
Grieves
and
Vickers.
Advanced
control
systems
In
a
review
of
control
techniques
in
factory
automation,
Dotoli
et
al.
[25]
describe
a
survey
highlighting
both
Model-Based
Control
techniques
(such
as
Model
Prediction
Control),
techniques
based
on
Computational
Intelligence
(Adaptive
Control,
Discrete
Event
Systems
Based
Control,
and
Event-Triggered/Self-Triggered
Con-
trol).
Computational
Intelligence
techniques
have
grown
out
of
the
rapid
and
recent
growth
in
computer
science.
Dotoli
et
al.
show
how
approaches
such
as
Fuzzy
Logic,
Artificial
Neural
Networks,
and
Evolutionary
Algorithms
have
all
been
integrated
and
applied
to
the
control
of
industrial
machines.
Adaptive
Control
systems
simply
adapt
the
manner
in
which
they
control
based
on
input
parameters
from
the
system
that
they
control.
Discrete
Event
Systems
Based
Control
systems
are
based
on
the
occurrence
of
asynchronous
discrete
events,
i.e.
the
control
enters
particular
states
based
on
inputs
from
the
controlled
system.
Event-Triggered
Control
systems
respond
to
the
detection
of
particular
states
in
the
controlled
system.
Self-Triggered
Control
systems
respond
to
predicted
states
in
the
controlled
system,
i.e.
they
are
able
to
react
in
anticipation
of
the
controlled
system
entering
a
particular
state.
Similarly
to
the
description
of
Model-Based
Predictive
Control
systems
in
the
previous
subsection,
Advanced
Control
Systems
use
measurement
of
data
from
a
physical
entity,
preform
some
form
of
virtual
analysis
on
that
data,
and
use
it
to
realise
change
in
the
physical
entity/environment.
As
such,
the
similarity
between
Advanced
Control
Systems
and
the
Digital
Twin
mirror
that
of
Model
Predictive
Control.
In
addition
however,
the
advanced
techniques
speak
to
the
challenges
of
control
of
complex
systems,
with
the
need
for
intelligent
control
approaches.
Machine
health
monitoring/prognostics
In
their
paper
on
rotating
machinery
prognostics,
Heng
et
al.
[38]
describe
machine
prognostics
as
the
‘...forecast
of
the
remaining
operational
life,
future
condition,
or
probability
of
reliable
operation
of
an
equipment
based
on
the
acquired
condition
monitoring
data’
and
state
how
the
challenges
of
maintaining
the
health
of
machinery
has
moved
from
breakdown
maintenance
(fixing
a
broken
machine),
through
to
intelligent
predictive
maintenance
systems,
i.e.
the
automated
collection,
analysis
and
prediction
of
the
state
of
a
machine.
Heng
et
al.
describe
the
process
of
sensors
being
used
to
measure
the
state
of
a
machine
(vibration,
acoustics,
etc.),
and
these
measures
being
stored
and
analysed
using
physical-based
(mathematical
models)
and
data-driven
(artificial
neural
networks
on
historical
and
current
states)
prognostics
models.
With
a
review
of
the
state-of-the-art
in
the
field,
amongst
others,
Heng
at
al.
concluded
that
too
many
prognostics
models
were
based
on
data
collected
in
laboratory
environments,
rather
than
the
‘real-world’
operational
environment.
Within
the
context
of
the
characteristics
of
the
Digital
Twin,
the
techniques
clearly
map
to
metrology
methods,
physical-to-virtual
data
connections,
and
the
state
of
the
physical
entity.
The
Heng
et
al.
paper
is
used
as
an
example
here
as
it
is
both
a
widely
cited
publication,
and
also
speaks
to
the
importance
of
the
environment
within
which
the
physical
entity
is
situated.
Something
that
Grieves’
Digital
Twin
called
for
from
the
beginning.
Building
Information
Modelling
While
technically
both
an
engineering
field
and
one
that
manages
the
life-cycle
of
an
engineering
asset:
a
building,
Building
Information
Modelling
is
both
a
current
and
highly
related
research
field.
Being
largely
driven
by
government
legislation
requiring
the
capture
of
building
information
and
making
it
accessible
via
a
three-dimensional
representation
of
the
building,
Building
Information
Modelling
is
aimed
at
providing
a
single
source
from
which
all
stakeholders
can
operate,
across
the
building's
entire
life-cycle.
In
their
handbook
on
Building
Information
Modelling,
Eastman
et
al.
[26],
describe
an
overview
D.
Jones
et
al.
/
NULL
xxx
(2019)
xxx–xxx
13
G
Model
CIRPJ
544
No.
of
Pages
17
Please
cite
this
article
in
press
as:
D.
Jones,
et
al.,
Characterising
the
Digital
Twin:
A
systematic
literature
review,
NULL
(2020),
https://doi.org/
10.1016/j.cirpj.2020.02.002

of
the
field.
While
a
large
portion
of
the
book
is
dedicated
to
the
various
stakeholders
involved
in
building
projects,
the
delivery
of
systems
is
focused
on
interoperability
(standardised
file
formats),
parametric
modelling,
and
light-weight
representations
(CAD
models/visualisations).
So
from
its
foundations,
Building
Informa-
tion
Modelling
is
a
virtual
representation
of
a
physical
entity
albeit
with
a
greater
focus
on
the
users
of
the
system
than
the
Digital
Twin.
To
emphasise
the
similarity
with
the
Digital
Twin,
Table
10
shows
the
‘BIM
Levels’as
outlined
by
the
UK
Government's
Building
Information
Modelling
Industry
Task
Group
(2011).
2
BIM
Levels
1
to
3
map
across
to
the
Digital
Model,
Digital
Shadow,
and
Digital
Twin
as
described
by
Kritzinger
et
al.
[48].
The
Digital
Model
being
the
two
and
three-dimensional
CAD
model
(corresponding
to
BIM
Level
1),
the
Digital
Shadow
showing
the
three-dimensional
CAD
model
containing
data
from
the
actual
physical
construction
(corresponding
to
BIM
Level
2),
and
the
Digital
Twin
being
the
three-dimensional
model
with
two-way
data
connections
(corre-
sponding
to
BIM
Level
3).
Put
within
the
context
of
the
characteristics
of
the
Digital
Twin,
Building
Information
Modelling
aims
to
twin
a
building,
using
both
virtual-to-physical
and
physical-to-virtual
data
connections,
with
the
means
to
measure
and
realise
change
in
the
current
state
of
the
physical
building.
Summary
The
Digital
Twin
is
seen
as
a
relatively
new
research
field.
While
not
exhaustive,
this
section
frames
the
characteristics
of
the
Digital
Twin
with
respect
to
concepts
in
related
fields.
Many
of
which
predate
the
Digital
Twin.
The
purpose
of
this
section
is
to
highlight
and
further
underpin
the
characteristics
of
the
Digital
Twin:
Physical
Entity/Twin;
Virtual
Entity/Twin;
Physical
Environment;
Virtual
Environment;
State;
Realisation;
Metrology;
Twinning;
Twinning
Rate;
Physical-to-Virtual
Connection/Twinning;
Virtual-
to-Physical
Connection/Twinning;
Physical
Processes;
and
Virtual
Processes.
Whether
explicitly
stated
or
not,
each
of
these
characteristics
appear
elsewhere
in
literature.
One
could
also
argue
that
the
Digital
Twin
is
not
a
brand
new
concept,
it
can
also
be
seen
as
the
aggregation
or
evolution
of
a
number
of
existing
areas
of
research
and
industrial
techniques.
Conclusion
The
Digital
Twin
is
undergoing
an
increase
in
interest
from
both
an
academic
and
industrial
perspective.
In
response
to
this,
this
paper
presented
a
systematic
literature
review
in
a
bid
to
characterise
the
Digital
Twin,
identify
gaps
in
research,
and
highlight
directions
for
future
research.
The
review
methodology
comprised
a
collection
of
92
Digital
Twin
related
papers
from
two
sources:
a
Google
Scholar
search
with
the
query
‘digital
Twin’,
and
those
papers
that
cite
one
of
three
seminal
works.
A
thematic
analysis
was
then
performed
on
the
corpus
and
19
key
themes
were
extracted
(Tables
5
and
6).
These
themes
were
separated
into
those
relating
to
the
characteristics
of
the
Digital
Twin,
and
those
that
spoke
to
the
gaps
in
research
and
future
direction.
Starting
with
characterising
the
Digital
Twin,
the
main
contribution
of
this
section
of
work
were
13
characteristics
and
processes
where
generated
and
discussed
in
detail
(Table
8
and
Fig.
7).
These
characteristics
comprised:
Physical
Entity/Twin;
Virtual
Entity/Twin;
Physical
Environment;
Virtual
Environment;
State;
Realisation;
Metrology;
Twinning;
Twinning
Rate;
Physical-to-
Virtual
Connection/Twinning;
Virtual-to-Physical
Connection/Twin-
ning;
Physical
Processes;
and
Virtual
Processes.
These
characteristics
were
mapped
in
Fig.
7
to
generate
a
complete
description
of
the
digital
twin
and,
according
to
current
literature,
all
elements
and
concepts
it
contains.
The
second
part
of
this
paper
identified
gaps
in
and
future
directions
for
research
based
on
the
remaining
seven
identified
themes.
These
comprised:
Perceived
Benefits;
Digital
Twin
across
the
Product
Life-Cycle;
Use-Cases;
Technical
Implementations;
Levels
of
Fidelity;
Data
Ownership;
and
Integration
between
Virtual
Entities
(Tables
5
and
6).
Each
of
these
areas
were
discussed
in
detail
and
further
analysis
performed
where
required;
i.e.
mapping
of
the
corpus
to
the
product
life-cycle,
and
highlighting
the
lack
of
research
and
implementation
in
earlier
life-cycle
phases
or
system
disposal
(Fig.
3);
identification
of
11
use-cases
associated
to
the
Digital
Twin
(Table
9);
and
a
mapping
of
use-cases
and
technology
to
the
twinning
cycle
(Fig.
9b
and
a).
The
Digital
Twin
would
benefit
from
a
more
detailed
compari-
son
and
review
in
context
of
similar
and
connected
fields.
Building
Information
Modelling
shares
many
aspects
of
the
Digital
Twin,
is
arguably
a
more
advanced
field,
contains
both
physical
and
virtual
entities
with
data
connections
between,
and
is
yet
treated
as
a
separate
area
of
research.
Computer-Integrated
Manufacturing,
Virtual
Manufacturing
Systems,
Model-Based
Predictive
Control,
Advanced
Control
Systems,
and
Health
Monitoring/Prognostics
are
examples
of
well
established
research
areas
that
both
predate
the
Digital
Twin
and
underpin
the
characteristics
presented
in
this
paper.
Some
challenges
of
delivering
the
Digital
Twin
will
not
unique
and
may
have
been
addressed
in
these
related
fields.
For
example,
the
“Integration
between
virtual
entities”
section
showed
one
attempt
to
manage
integration
between
data
sources
in
Building
Information
Modelling.
Something
that
the
Digital
Twin
will
also
have
to
address.
This
paper
then
contributes
in
both
an
understanding
of
the
Digital
Twin
and
its
future
direction.
As
shown
by
both
the
2019
Gartner
Hype
Cycle
and
the
breakdown
of
number
of
papers
published
by
year
(Table
4),
the
field
is
appearing
to
undergo
a
Table
11
A
mapping
of
the
CIRP
Encyclopedia
8-Dimension
Model
for
Digital
Twin
against
the
findings
presented
in
this
paper.
CIRP
Encyclopedia
8-Dimension
Model
for
Digital
Twin
Findings
presented
in
this
paper
Integration
breadth
Physical
Entity/Twin,
Physical
Processes,
Virtual
Environment,
Physical
Entity/Twin,
State,
Metrology
Connectivity
mode
Physical-to-Virtual
Connection,
Virtual-to-
Physical
Connection,
Metrology
Update
frequency
Twinning
Rate
CPS
intelligence
Virtual
Processes
Simulation
capabilities
Virtual
Processes
Digital
model
richness
Fidelity
Human
interaction
Physical-to-Virtual
Connection,
Virtual-to-
Physical
Connection
Product
life-cycle
Product
life-cycle
Table
10
BIM
levels
as
described
by
the
BIM
Industry
Working
Group.
BIM
level
Description
0
Unmanaged
two-dimensional
CAD
shared
via
paper/electronic
paper
1
Managed
two/three-dimensional
CAD
adhering
to
BS1192:2007
and
within
a
Common
Data
Environment
that
allows
collaboration.
2
Level
1
within
a
three-dimensional
virtual
environment
with
attached
data.
Representations
for
Architectural,
Structural,
Facilities,
Building
Sources
and
Bridges.
3
Level
2
plus
interoperable
data.
2
https://www.cdbb.cam.ac.uk/Resources/ResoucePublications/BISBIMstrate-
gyReport.pdf.
Last
visited:
2019-12-02.
14
D.
Jones
et
al.
/
NULL
xxx
(2019)
xxx–xxx
G
Model
CIRPJ
544
No.
of
Pages
17
Please
cite
this
article
in
press
as:
D.
Jones,
et
al.,
Characterising
the
Digital
Twin:
A
systematic
literature
review,
NULL
(2020),
https://doi.org/
10.1016/j.cirpj.2020.02.002

large
increase
in
attention
from
both
academia
and
industry.
As
an
example
of
this,
the
CIRP
Encyclopedia
of
Production
Engineering
[83]
recently
launched
a
definition
of
the
Digital
Twin:
A
digital
twin
is
a
digital
representation
of
an
active
unique
product
(real
device,
object,
machine,
service,
or
intangible
asset)
or
unique
product-service
system
(a
system
consisting
of
a
product
and
a
related
service)
that
comprises
its
selected
characteristics,
properties,
conditions,
and
behaviors
by
means
of
models,
information,
and
data
within
a
single
or
even
across
multiple
life
cycle
phases.
alongside
a
“Digital
Twin
8-dimension
model”
for
planning
according
to
the
purpose
of
the
Digital
Twin.
The
8-dimension
model
reinforces
many
of
the
findings
presented
in
this
paper,
albeit
with
different
terminology:
the
model
talks
of
integration
breadth,
connectivity
mode,
update
frequency,
CPS
intelligence,
simulation
capabilities,
digital
model
richness,
human
interaction,
and
the
product
life-cycle.
Table
11
shows
the
CIRP
dimensions
mapped
to
the
findings
presented
in
this
paper.
The
CIRP
Encyclopedia
also
acknowledges
the
challenge
of
how
best
to
represent
the
Digital
Twin
that
future
research
efforts
will
need
to
address.
The
contribution
of
the
characterisation
of
the
Digital
Twin
is
a
step
forward
in
addressing
this.
Through
framing
future
Digital
Twin
use-cases
with
a
consolidated
common
understanding
and
terminology,
a
multitude
of
Digital
Twins
of
physical
entities
of
all
forms
can
be
realised
in
a
manner
that
holds
true
to
the
Digital
Twin
paradigm.
It
is
only
through
these
efforts
that
the
envisaged
benefits
afforded
by
the
Digital
Twin
can
be
fully
realised
and
shared
across
domains.
Declaration
of
Competing
Interest
The
authors
declare
that
they
have
no
known
competing
financial
interests
or
personal
relationships
that
could
have
appeared
to
influence
the
work
reported
in
this
paper.
Acknowledgements
The
work
reported
in
this
paper
has
been
undertaken
as
part
of
the
project:
Improving
the
product
development
process
through
integrated
revision
control
and
twinning
of
digital-physical
models
during
prototyping.
The
work
was
conducted
at
the
University
of
Bristol
in
the
Design
and
Manufacturing
Futures
Lab
(http://www.
dmf-lab.co.uk)
and
is
funded
by
the
Engineering
and
Physical
Sciences
Research
Council
(EPSRC),
Grant
reference
EP/R032696/1.
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D.
Jones,
et
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Characterising
the
Digital
Twin:
A
systematic
literature
review,
NULL
(2020),
https://doi.org/
10.1016/j.cirpj.2020.02.002

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