Journal of Process Management New Technologies,
Journal Year:
2024,
Volume and Issue:
12(3-4), P. 65 - 89
Published: Jan. 1, 2024
The
automotive
industry
is
undergoing
a
major
transformation
towards
sustainability,
driven
by
both
economic
and
environmental
concerns.
Traditional
manufacturing
processes
rely
heavily
on
non-renewable
resources
like
steel
plastics,
contributing
to
degradation
greenhouse
gas
emissions.
However,
with
increasing
regulatory
pressures
consumer
demand
for
eco-friendly
products,
automakers
are
adopting
sustainable
materials
such
as
bio-based
recycled
metals,
natural
fibers.
These
offer
benefits
reducing
carbon
emissions,
conserving
resources,
minimizing
waste,
while
also
providing
advantages
improved
fuel
efficiency,
lower
production
costs,
reduced
dependency
volatile
resource
markets.
Integrating
often
requires
changes
in
processes,
including
retooling
new
technologies,
but
these
adjustments
lead
long-term
benefits,
lighter
vehicles,
energy
consumption,
enhanced
recyclability.
Additionally,
innovations
3D
printing
have
facilitated
the
use
of
materials,
allowing
more
efficient
less
waste.
A
lifecycle
analysis
approach
reveals
that
can
significantly
reduce
impact
throughout
vehicle's
life,
from
disposal.
This
shift
has
opened
up
market
opportunities,
consumers
increasingly
favor
vehicles
align
their
values.
Overall,
practices,
address
ecological
priorities,
positioning
itself
future
growth
leading
way
demonstrating
how
sustainability
drive
innovation.
Eng—Advances in Engineering,
Journal Year:
2025,
Volume and Issue:
6(2), P. 22 - 22
Published: Jan. 22, 2025
The
rising
demand
for
housing
continues
to
outpace
traditional
construction
processes,
highlighting
the
need
innovative,
efficient,
and
sustainable
delivery
models.
Off-site
(OSC)
has
emerged
as
a
promising
alternative,
offering
faster
project
timelines
enhanced
cost
management.
However,
current
research
on
models
OSC,
particularly
in
automating
material
take-offs
optimising
performance,
remains
limited.
This
study
addresses
this
gap
by
proposing
new
model
integrating
Digital
Twin
(DT)
technology
AI-driven
decision
modular
UK.
explores
role
of
DTs
enhancing
estimation
decision-making
processes.
By
leveraging
AI,
proposed
evaluates
impact
emergent
technologies
efficiency,
sustainability
across
social,
environmental,
economic
dimensions.
As
proposed,
integrated
approach
enables
tailored
OSC
systems,
providing
data-driven
foundation
optimisation
take-offs.
study’s
findings
highlight
potential
combining
AI
enhance
modelling
construction,
capabilities
support
performance-driven
delivery.
paper
introduces
dynamic,
real-time
data
acquisition
through
AI-powered
predictive
analytics.
dynamic
enhances
accuracy,
reduces
lifecycle
variability,
supports
adaptive
throughout
lifecycle.
Sustainability,
Journal Year:
2024,
Volume and Issue:
16(17), P. 7873 - 7873
Published: Sept. 9, 2024
Nowadays,
there
is
a
constant
focus
on
implementing
the
net-zero
emission
(NZE)
concept
in
manufacturing
supply
chain
(MSC).
To
reduce
emissions
and
improve
organisational
efficiency,
adopting
prevalent
trend
today’s
highly
competitive
global
business
environment.
Governments
stakeholders
are
pressuring
sector
to
use
natural
resources
efficiently
environmental
impacts.
As
result,
industry
focusing
cleaner
production
using
practices.
This
study
aims
identify
analyse
interaction
among
drivers
of
adoption
MSC.
Through
systematic
literature
review
(SLR),
list
was
recognised.
validate
these
drivers,
we
conducted
an
empirical
with
173
respondents
from
Indian
industry.
Further,
employed
artificial
neural
network
(ANN)
weigh
nonlinear
effect
drivers.
Fuzzy
interpretive
structural
modelling
(F-ISM)
used
relationships
construct
hierarchical
structure
identified
The
fuzzy
matrix
cross-impact
multiplications
applied
classification
(F-MICMAC)
method
categorise
into
driving
dependent
categories.
outcomes
ANN
show
that
Environmental
predictors
(100%)
emerged
as
most
significant
followed
by
Economic
(60.38%)
Technological
(59.05%).
valuable
resource
for
academia
professionals,
providing
essential
insights
how
net
zero
facilitates
industry’s
ability
achieve
across
chain.
Information,
Journal Year:
2025,
Volume and Issue:
16(4), P. 283 - 283
Published: March 30, 2025
The
need
to
review
maritime
education
has
been
highlighted
in
the
relevant
literature.
Maritime
curricula
should
incorporate
recent
technological
advances,
as
well
address
needs
of
sector.
In
this
paper,
Fuzzy
Delphi
Method
(FDM)
and
Analytic
Hierarchy
Process
(FAHP)
are
utilized
order
propose
a
fuzzy
multicriteria
decision-making
(MCDM)
methodology
that
can
be
used
assess
importance
new
technologies
design
evaluation
model
assist
policy-making.
This
study
integrates
perspectives
main
stakeholders,
namely,
lecturers
sector
management.
We
selected
data
from
group
19
experienced
professors
business
managers.
results
indicate
such
artificial
intelligence
(AI),
augmented
virtual
reality
(AR/VR),
Internet
Things
(IoT),
digital
twins
(DTs),
cybersecurity,
eLearning
platforms,
constitute
set
requirements
policies
meet
by
designing
their
appropriately.
suggests
logic
MCDM
methods
human-centered
AI
approach
for
developing
explainable
policy-making
models
integrate
stakeholder
capture
subjectivity
is
often
inherited
perspectives.
Research Square (Research Square),
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 10, 2025
Abstract
Manufacturing
industries
across
the
globe
are
undergoing
a
digital
transformation
that
demands
both
efficiency
and
sustainability.
Industry
4.0
(I4.0)
Lean
(L4.0)
methodologies
have
become
focal
points
in
these
efforts.
Despite
widespread
recognition
of
benefits
integrating
L4.0
I4.0,
more
studies
need
to
address
practical
challenges
this
integration,
especially
key
factors
influence
its
successful
implementation.
Small
medium-sized
enterprises
(SMEs)
emerging
economies
often
face
significant
practices
due
resource
limitations
complex
operational
challenges.
This
study
bridges
critical
research
gap
by
proposing
an
integrated
framework
combines
Artificial
Neural
Networks
(ANN)
with
fuzzy
Interpretive
Structural
Modeling
(FISM)
identify
prioritise
success
(CSFs)
for
adoption.
A
survey
216
manufacturing
SMEs
was
used
validate
CSFs
through
Exploratory
Factor
Analysis
(EFA).
The
ANN
analysis
revealed
Process
Factors
highest
normalised
importance
(NI)
100%,
followed
Organizational
(NI
=
60.46%),
Human
58.93%),
Technological
43.21%),
External
42.13%),
Environmental
39.63%).
Complementary
FISM
Cross-Impact
Matrix
Multiplication
Applied
Classification
(MICMAC)
analyses
further
structured
relationships,
underscoring
roles
Change
Management,
Culture,
Waste
Reduction,
Regulatory
Compliance.
These
findings
offer
theoretical
advancement
understanding
CSF
interactions
guidance
striving
achieve
sustainable
practices.
Applied Mathematics and Nonlinear Sciences,
Journal Year:
2025,
Volume and Issue:
10(1)
Published: Jan. 1, 2025
Abstract
Accurate
cost
estimation
and
optimization
are
crucial
in
engineering
project
management,
as
budget
overruns
resource
misallocations
often
lead
to
financial
operational
inefficiencies.
Traditional
methods,
including
regression
models
heuristic
approaches,
struggle
adapt
the
complex
dynamic
nature
of
projects.
We
proposes
a
reinforcement
learning
(RL)-based
strategy
that
continuously
refines
predictions
allocations.
The
proposed
framework
integrates
deep
learning-based
model
with
an
RL-driven
strategy,
enabling
adaptive
from
historical
ongoing
data.
A
multi-objective
is
incorporated
balance
cost,
quality,
timeline
constraints
using
Pareto-front
analysis.
RL
agent
learns
optimal
allocation
policies
through
iterative
interactions
environment,
improving
decision-making
efficiency.
Experimental
evaluations
demonstrate
RL-based
outperforms
conventional
machine
achieving
lower
mean
absolute
error
root
square
estimation.
Additionally,
results
average
reduction
approximately
7%
across
different
categories.
integration
further
enhances
efficiency
while
maintaining
feasibility.
These
findings
validate
approach
effective
solution
for
accuracy
management.
