SHS Web of Conferences,
Journal Year:
2024,
Volume and Issue:
207, P. 03015 - 03015
Published: Jan. 1, 2024
With
the
advancement
of
Industry
4.0,
manufacturing
industry
is
working
to
create
a
new
smart
industrial
world
through
computerization,
digitization
and
intelligence
enhancement.
Gen
AI
primarily
characterized
by
its
ability
generate
novel
data
patterns
solutions
rather
than
merely
analyzing
predefined
inputs.
This
paper
explores
transformative
impact
on
supply
chain
efficiency
in
engineering
logistics.
Key
applications
include
inventory
optimization,
predictive
maintenance,
fraud
detection,
risk
management,
logistics
demand
forecasting.
The
study
shows
that
significantly
improves
operational
reduces
stress
for
workers
providing
dynamic
data-driven
solutions.
Through
real-world
case
studies,
including
companies,
this
demonstrates
how
can
revolutionize
management
increase
productivity.
Despite
significant
benefits,
still
faces
several
challenges
due
cutting-edge
nature.
Further,
in-depth
research
needed
future
as
number
relevant
cases
literature
increases.
Research Square (Research Square),
Journal Year:
2024,
Volume and Issue:
unknown
Published: May 14, 2024
Abstract
Applying
Google
Gemini's
generative
AI
capabilities,
this
research
provided
a
novel
approach
to
developing
and
implementing
cybersecurity
policies
targeted
at
mitigating
spear
phishing
attacks
against
senior
corporate
managers.
The
study
demonstrated
significant
enhancements
in
the
detection,
prevention,
response
strategies
within
frameworks,
by
integrating
advanced
artificial
intelligence
with
traditional
security
protocols.
application
of
machine
learning
algorithms
not
only
improved
accuracy
speed
threat
detection
but
also
enabled
dynamic
policy
adjustments
based
on
real-time
data
analysis,
proving
crucial
evolving
landscape
digital
threats.
findings
underscore
potential
transform
practices,
offering
more
adaptable,
proactive,
robust
defenses
increasingly
sophisticated
techniques.
Further,
explores
implications
AI-driven
for
governance
compliance,
suggesting
new
paradigm
which
supports
actively
defines
strategic
decisions.
promising
results
invite
further
investigation
into
broader
applications
cybersecurity,
pointing
toward
future
where
integration
is
standard
defense
complex
cyber
Deleted Journal,
Journal Year:
2024,
Volume and Issue:
1(2), P. 41 - 41
Published: July 24, 2024
The
recent
emergence
of
large
language
models
(LLMs)
demonstrates
the
potential
for
artificial
general
intelligence,
revealing
new
opportunities
in
Industry
4.0
and
smart
manufacturing.
However,
a
notable
gap
exists
applying
these
LLMs
industry,
primarily
due
to
their
training
on
knowledge
rather
than
domain-specific
knowledge.
Such
specialized
domain
is
vital
effectively
addressing
complex
needs
industrial
applications.
To
bridge
this
gap,
paper
proposes
unified
model
(ILKM)
framework,
emphasizing
its
revolutionize
future
industries.
In
addition,
ILKMs
are
compared
from
eight
perspectives.
Finally,
“6S
Principle”
proposed
as
guideline
ILKM
development,
several
highlighted
deployment
Advances in business strategy and competitive advantage book series,
Journal Year:
2025,
Volume and Issue:
unknown, P. 435 - 462
Published: Jan. 23, 2025
This
chapter
examines
the
paradigm
shift
in
supply
chain
forecasting
brought
about
by
generative
AI
and
machine
learning
technologies.
Through
real-world
examples
case
studies,
proposed
explores
how
these
technologies
enhance
forecast
accuracy,
streamline
operations,
drive
cost
efficiency.
The
study
employed
systematic
analysis
of
literature,
drawing
upon
prominent
academic
databases
such
as
Google
Scholar,
Scopus,
Web
Science,
IEEE
Xplore.
Academic
publications,
reports,
related
materials
were
obtained
via
comprehensive
keyword
searches
to
serve
primary
sources
data,
with
a
focus
on
English-language
literature
ensure
consistency
accessibility.
synthesis
data
extracted
from
selected
this
provides
structured
overview
discussing
implications
for
theory,
practice,
future
research
forecasting.
IGI Global eBooks,
Journal Year:
2025,
Volume and Issue:
unknown, P. 273 - 306
Published: April 25, 2025
Artificial
intelligence
(AI)
is
transforming
the
technology
industry,
reshaping
business
operations,
competition,
and
growth.
This
chapter
explores
AI's
diverse
applications,
from
enhancing
operational
efficiency
to
revolutionizing
customer
service,
decision-making,
innovation.
AI
processes
vast
amounts
of
data,
uncovering
patterns
generating
actionable
insights.
Predictive
analytics
helps
businesses
anticipate
behavior,
market
trends,
challenges.
AI-driven
automation
reduces
costs
frees
human
resources
for
creative
roles,
boosting
productivity.
Customer
engagement
has
evolved
with
tools
like
chatbots,
virtual
assistants,
recommendation
engines,
enabling
personalized
interactions
marketing.
However,
challenges
such
as
ethics,
data
privacy,
workforce
upskilling
remain.
Businesses
must
balance
adoption
transparency
accountability
drive
sustainable
growth
competitiveness.
Ekonomika preduzeca,
Journal Year:
2024,
Volume and Issue:
72(5-6), P. 292 - 304
Published: Jan. 1, 2024
This
paper
analyzes
the
transformational
role
of
AI
in
logistics
within
context
Logistics
4.0.
Spectrum
artificial
intelligence
technologies
reinforces
both
operational
efficiencies
and
reduces
overall
cost.
The
integration
such
as
machine
learning,
predictive
analytics,
robotics
brings
a
new
revolution
to
process.
Also,
case
studies
will
be
elaborated
on
order
explain
how
leading
company,
DHL,
applies
technologies,
intelligence,
optimize
delivery
routes,
real-time
tracking,
inventory
management
while
bringing
great
improvement
customer
interaction.
It
further
discusses
number
challenges
opportunities
linked
AI,
thus
trying
present
wide
overview
its
influence
modern
future
trends.
Special
attention
is
paid
these
can
revolutionize
supply
chain
management.
Artificial
driving
innovation
setting
standards
for
efficiency
effectiveness
operations.
provides
analysis
highlighting
ways
which
make
practices
more
sustainable
international
chains
resilient
external
shocks,
therefore
cornerstone
any
strategy.
ends
by
underlining
strategic
importance
adopting
preserving
competitiveness
market.
The
demand
for
oil
and
gas
remains
high
despite
the
increasing
prominence
of
renewable
energy
sources,
highlighting
industry's
vital
role
in
global
economy.
Oil
projects,
requiring
significant
capital
facing
complexity
risk,
necessitate
effective
project
management
to
optimize
performance
through
agility,
accurate
forecasting,
risk
mitigation,
stakeholder
collaboration
(Redda,
Turner,
Milano
2018;
Yananto,
Putro,
Sunitiyoso
2022).
Leveraging
data-driven
approaches
enhance
operational
efficiency,
reduce
costs,
support
informed
decision-making,
which
is
crucial
given
lengthy
timelines
substantial
financial
commitments
this
sector
(Darusulistyo
et
al.
2022;
Urton
Murray
2021).
projects
span
upstream
exploration,
midstream
transportation,
downstream
refining
distribution.
Each
phase
presents
unique
challenges
due
technical
complexity,
stringent
regulatory
demands,
environmental
considerations,
market
volatility.
Projects
are
generally
managed
portfolios
enable
dynamic
prioritization
based
on
scope,
goals,
risks,
resource
availability,
alignment
with
organization's
strategy
governance
(Sirisomboonsuk
2018).
Portfolio
helps
prioritize
strategic
alignment,
improving
transparency
Wood
2016).
Major
typically
follow
a
stage-gate
process,
breaking
into
phases—Concept,
Feasibility,
Definition,
Execution,
Operation—each
marked
by
gate
ensuring
track
(Newman,
Begg,
Welsh
2020;
Akhtar
2020).
This
paper
focuses
critical
aspect
material
that
spans
across
all
phases
relevant
contribution
improve
capital-project
performance.
Globally,
64%
face
budget
overruns,
73%
experience
schedule
delays
equipment
issues
as
one
primary
contributors
(EY
Material
cost
component
overall
construction
constituting
25–40%
total
typical
(Mir
Effective
integral
throughout
entire
lifecycle,
from
initial
concept
feasibility
studies
final
operation
maintenance
phases.
By
emphasizing
comprehensive
integrated
approach
management,
study
aims
demonstrate
how
coordinated
efforts
can
lead
enhanced
performance,
reduced
improved
long-term
goals.
